1. Overview of Strimzi

Strimzi simplifies the process of running Apache Kafka in a Kubernetes cluster.

1.1. Kafka capabilities

The underlying data stream-processing capabilities and component architecture of Kafka can deliver:

  • Microservices and other applications to share data with extremely high throughput and low latency

  • Message ordering guarantees

  • Message rewind/replay from data storage to reconstruct an application state

  • Message compaction to remove old records when using a key-value log

  • Horizontal scalability in a cluster configuration

  • Replication of data to control fault tolerance

  • Retention of high volumes of data for immediate access

1.2. Kafka use cases

Kafka’s capabilities make it suitable for:

  • Event-driven architectures

  • Event sourcing to capture changes to the state of an application as a log of events

  • Message brokering

  • Website activity tracking

  • Operational monitoring through metrics

  • Log collection and aggregation

  • Commit logs for distributed systems

  • Stream processing so that applications can respond to data in real time

1.3. How Strimzi supports Kafka

Strimzi provides container images and Operators for running Kafka on Kubernetes. Strimzi Operators are fundamental to the running of Strimzi. The Operators provided with Strimzi 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

1.4. Strimzi Operators

Strimzi supports Kafka using Operators to deploy and manage the components and dependencies of Kafka to Kubernetes.

Operators are a method of packaging, deploying, and managing a Kubernetes application. Strimzi Operators extend Kubernetes 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

Strimzi provides Operators for managing a Kafka cluster running within a Kubernetes 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 Strimzi architecture

Operators

1.4.1. Cluster Operator

Strimzi 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 Kubernetes 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

Cluster Operator

1.4.2. Topic Operator

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

Example architecture for the Topic Operator

Topic Operator

The role of the Topic Operator is to keep a set of KafkaTopic Kubernetes 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.

1.4.3. 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 Kubernetes 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.

1.5. Strimzi custom resources

A deployment of Kafka components to a Kubernetes cluster using Strimzi 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 Kubernetes resources.

CRDs act as configuration instructions to describe the custom resources in a Kubernetes cluster, and are provided with Strimzi 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 Strimzi distribution.

CRDs also allow Strimzi resources to benefit from native Kubernetes features like CLI accessibility and configuration validation.

Additional resources

1.5.1. Strimzi custom resource example

CRDs require a one-time installation in a cluster to define the schemas used to instantiate and manage Strimzi-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 Strimzi administrators.

A CRD defines a new kind of resource, such as kind:Kafka, within a Kubernetes 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 Kubernetes 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 Strimzi-specific custom resource conforms to the schema defined by the CRD for the resource’s kind. The custom resources for Strimzi 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, kubectl get kafkatopic my-topic or kubectl get kafkatopics.

  3. The shortname can be used in CLI commands. For example, kubectl get kt can be used as an abbreviation instead of kubectl 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 Strimzi 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 Strimzi.

1.6. Document Conventions

Replaceables

In this document, replaceable text is styled in monospace and italics.

For example, in the following code, you will want to replace my-namespace with the name of your namespace:

sed -i 's/namespace: .*/namespace: my-namespace/' install/cluster-operator/*RoleBinding*.yaml

2. Getting started with Strimzi

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

This section describes the procedures to deploy Strimzi on Kubernetes 1.11 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.

2.1. Preparing for your Strimzi deployment

This section shows how you prepare for a Strimzi 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.

2.1.1. Deployment prerequisites

To deploy Strimzi, make sure:

  • A Kubernetes 1.11 and later cluster is available

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

Note
 Strimzi supports some features that are specific to OpenShift, where such integration benefits OpenShift users and there is no equivalent implementation using standard Kubernetes.
Alternatives if a Kubernetes cluster is not available

If you do not have access to a Kubernetes cluster, as an alternative you can try installing Strimzi with:

2.1.2. Downloading Strimzi release artifacts

To install Strimzi, download the release artifacts from GitHub.

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

You deploy Strimzi to a Kubernetes cluster using the kubectl command-line tool.

Note
 Additionally, Strimzi container images are available through the Docker Hub. However, we recommend that you use the YAML files provided to deploy Strimzi.

2.1.3. Pushing container images to your own registry

Container images for Strimzi are available in the Docker Hub. The installation YAML files provided by Strimzi will pull the images directly from the Docker Hub.

If you do not have access to the Docker Hub 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 YAML files used in deployment

Note
 Each Kafka version supported for the release has a separate image.
Container image Namespace/Repository Description

Kafka

  • docker.io/strimzi/kafka:0.18.0-kafka-2.4.0

  • docker.io/strimzi/kafka:0.18.0-kafka-2.4.1

  • docker.io/strimzi/kafka:0.18.0-kafka-2.5.0

Strimzi image for running Kafka, including:

  • Kafka Broker

  • Kafka Connect / S2I

  • Kafka MirrorMaker

  • ZooKeeper

  • TLS Sidecars

Operator

  • docker.io/strimzi/operator:0.18.0

Strimzi image for running the operators:

  • Cluster Operator

  • Topic Operator

  • User Operator

  • Kafka Initializer

Kafka Bridge

  • docker.io/strimzi/kafka-bridge:0.16.0

Strimzi image for running the Strimzi kafka Bridge

JmxTrans

  • docker.io/strimzi/jmxtrans:0.18.0

Strimzi image for running the Strimzi JmxTrans

2.1.4. Designating Strimzi administrators

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

  • strimzi-view allows users to view and list Strimzi resources.

  • strimzi-admin allows users to also create, edit or delete Strimzi resources.

When you install these roles, they will automatically aggregate (add) these rights to the default Kubernetes 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 Strimzi resources.

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

Prerequisites
Procedure

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

    kubectl apply -f install/strimzi-admin

  2. If needed, assign the roles that provide access rights to users that require them.

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

2.1.5. Alternative cluster deployment options

This section suggests alternatives to using a Kubernetes cluster.

If a Kubernetes cluster is unavailable, you can use:

  • Minikube to create a local cluster

  • Minishift to create a local OpenShift cluster and use OpenShift-specific features

Installing a local Kubernetes cluster

The easiest way to get started with Kubernetes is using Minikube. This section provides basic guidance on how to use it. For more information on the tools, refer to the documentation available online.

In order to interact with a Kubernetes cluster the kubectl utility needs to be installed.

You can download and install Minikube from the Kubernetes website. Depending on the number of brokers you want to deploy inside the cluster, and if you need Kafka Connect running as well, try running Minikube with at least with 4 GB of RAM instead of the default 2 GB.

Once installed, start Minikube using:

minikube start --memory 4096
Installing a local OpenShift cluster

The easiest way to get started with OpenShift is using Minishift or oc cluster up. This section provides basic guidance on how to use them. For more information on the tools, refer to the documentation available online.

oc cluster up

The oc utility is one of the main tools for interacting with OpenShift. It provides a simple way of starting a local cluster using the command:

oc cluster up

This command requires Docker to be installed. You can find more inforation on here.

Minishift

Minishift is an OpenShift installation within a VM. It can be downloaded and installed from the Minishift website. Depending on the number of brokers you want to deploy inside the cluster, and if you need Kafka Connect running as well, try running Minishift with at least 4 GB of RAM instead of the default 2 GB.

Once installed, start Minishift using:

minishift start --memory 4GB

If you want to use kubectl with either an oc cluster up or minishift cluster, you will need to configure it, as unlike with Minikube this won’t be done automatically.

oc and kubectl commands

The oc command functions as an alternative to kubectl. In almost all cases the example kubectl commands given in this guide can be done using oc simply by replacing the command name (options and arguments remain the same).

In other words, instead of using:

kubectl apply -f your-file

when using OpenShift you can use

oc apply -f your-file
Note
 As an exception to this general rule, oc uses oc adm subcommands for cluster management, while kubectl does not make such a distinction. For example, the oc equivalent of kubectl taint is oc adm taint.

2.2. 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 configure the deployment to deploy the Topic Operator and User Operator at the same time. Alternatively, if you are using a non-Strimzi 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 Strimzi.

  1. Deploy the Cluster Operator

  2. Use the Cluster Operator to deploy the:

    1. Kafka cluster

    2. Topic Operator

    3. User Operator

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 Strimzi.

  1. Deploy the standalone Topic Operator

  2. Deploy the standalone User Operator

2.2.1. Deploying the Cluster Operator

The Cluster Operator is responsible for deploying and managing Apache Kafka clusters within a Kubernetes cluster.

The procedures in this section show:

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
 Strimzi 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 Kubernetes cluster, the operator gets the cluster description from the resource and starts creating a new cluster for the resource by creating the necessary Kubernetes resources, such as StatefulSets, Services and ConfigMaps.

Each time a Kafka resource is updated, the operator performs corresponding updates on the Kubernetes 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 Kubernetes resources.

Deploying the Cluster Operator to watch a single namespace

This procedure shows how to deploy the Cluster Operator to watch Strimzi resources in a single namespace in your Kubernetes cluster.

Prerequisites

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

Procedure

  1. Edit the Strimzi installation files to use the namespace the Cluster Operator is going to be installed to.

    For example, in this procedure the Cluster Operator is installed to 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:

    kubectl apply -f install/cluster-operator -n my-cluster-operator-namespace

  3. Verify that the Cluster Operator was successfully deployed:

    kubectl get deployments
Deploying the Cluster Operator to watch multiple namespaces

This procedure shows how to deploy the Cluster Operator to watch Strimzi resources across multiple namespaces in your Kubernetes cluster.

Prerequisites

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

Procedure

  1. Edit the Strimzi installation files to use the namespace the Cluster Operator is going to be installed to.

    For example, in this procedure the Cluster Operator is installed to 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/050-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: strimzi/operator:0.18.0
        imagePullPolicy: IfNotPresent
        env:
        - name: STRIMZI_NAMESPACE
          value: watched-namespace-1,watched-namespace-2,watched-namespace-3

  3. For each namespace listed, install the RoleBindings.

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

    kubectl apply -f install/cluster-operator/020-RoleBinding-strimzi-cluster-operator.yaml -n watched-namespace
kubectl apply -f install/cluster-operator/031-RoleBinding-strimzi-cluster-operator-entity-operator-delegation.yaml -n watched-namespace
kubectl apply -f install/cluster-operator/032-RoleBinding-strimzi-cluster-operator-topic-operator-delegation.yaml -n watched-namespace

  4. Deploy the Cluster Operator

    kubectl apply -f install/cluster-operator -n my-cluster-operator-namespace

  5. Verify that the Cluster Operator was successfully deployed:

    kubectl get deployments
Deploying the Cluster Operator to watch all namespaces

This procedure shows how to deploy the Cluster Operator to watch Strimzi resources across all namespaces in your Kubernetes 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 a Kubernetes user account which is able to create CustomResourceDefinitions, ClusterRoles and ClusterRoleBindings. Use of Role Base Access Control (RBAC) in the Kubernetes cluster usually means that permission to create, edit, and delete these resources is limited to Kubernetes cluster administrators, such as system:admin.

Procedure

  1. Edit the install/cluster-operator/050-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: strimzi/operator:0.18.0
        imagePullPolicy: IfNotPresent
        env:
        - name: STRIMZI_NAMESPACE
          value: "*"
        # ...

  2. Create ClusterRoleBindings that grant cluster-wide access for all namespaces to the Cluster Operator.

    kubectl create clusterrolebinding strimzi-cluster-operator-namespaced --clusterrole=strimzi-cluster-operator-namespaced --serviceaccount my-cluster-operator-namespace:strimzi-cluster-operator
kubectl create clusterrolebinding strimzi-cluster-operator-entity-operator-delegation --clusterrole=strimzi-entity-operator --serviceaccount my-cluster-operator-namespace:strimzi-cluster-operator
kubectl 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 to.

  3. Deploy the Cluster Operator to your Kubernetes cluster.

    kubectl apply -f install/cluster-operator -n my-cluster-operator-namespace

  4. Verify that the Cluster Operator was successfully deployed:

    kubectl get deployments
Deploying the Cluster Operator using a Helm Chart

As an alternative to using the YAML deployment files, this procedure shows how to deploy the the Cluster Operator using a Helm chart provided with Strimzi.

Prerequisites

  • The Helm client must be installed on a local machine.

  • Helm must be installed to the Kubernetes cluster.

For more information about Helm, see the Helm website.

Procedure

  1. Add the Strimzi Helm Chart repository:

    helm repo add strimzi https://strimzi.io/charts/

  2. Deploy the Cluster Operator using the Helm command line tool:

    helm install strimzi/strimzi-kafka-operator

  3. Verify that the Cluster Operator has been deployed successfully using the Helm command line tool:

    helm ls
Deploying the Cluster Operator from OperatorHub.io

OperatorHub.io is a catalog of Kubernetes Operators sourced from multiple providers. It offers you an alternative way to install stable versions of Strimzi using the Strimzi Kafka Operator.

The Operator Lifecycle Manager is used for the installation and management of all Operators published on OperatorHub.io.

To install Strimzi from OperatorHub.io, locate the Strimzi Kafka Operator and follow the instructions provided.

2.2.2. 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, Strimzi also installs a ZooKeeper cluster and adds the necessary configuration to connect Kafka with ZooKeeper.

Deploying the Kafka cluster

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

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

Strimzi 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

Prerequisites
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:

      kubectl apply -f examples/kafka/kafka-ephemeral.yaml

    • To create and deploy a persistent cluster:

      kubectl apply -f examples/kafka/kafka-persistent.yaml

  2. Verify that the Kafka cluster was successfully deployed:

    kubectl get deployments
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 Strimzi, you must deploy the Topic Operator as a standalone component.

For more information about configuring the entityOperator and topicOperator properties, see Entity Operator.

Prerequisites
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 kubectl apply:

    kubectl apply -f <your-file>
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 Strimzi, you must deploy the User Operator as a standalone component.

For more information about configuring the entityOperator and userOperator properties, see Entity Operator.

Prerequisites
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.

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

  3. Create or update the resource:

    kubectl apply -f <your-file>

2.2.3. Alternative standalone deployment options for Strimzi 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 Strimzi.

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.

For more information about the environment variables used to configure the Topic Operator, see Topic Operator environment.

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 Kubernetes 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:

    kubectl apply -f install/topic-operator

  3. Verify that the Topic Operator has been deployed successfully:

    kubectl 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 Kubernetes cluster and the images have not been downloaded before.
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 Kubernetes 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 a Kubernetes 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 a Kubernetes 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 a Kubernetes 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 a Kubernetes 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:

    kubectl apply -f install/user-operator

  3. Verify that the User Operator has been deployed successfully:

    kubectl 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 Kubernetes cluster and the images have not been downloaded before.

2.3. Deploy Kafka Connect

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

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 interchangably 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.

2.3.1. Deploying Kafka Connect to your Kubernetes cluster

This procedure shows how to deploy a Kafka Connect cluster to your Kubernetes 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 Strimzi:

  • examples/kafka-connect/kafka-connect.yaml

For more information about configuring the KafkaConnect resource, see:

Prerequisites
Procedure

  1. Deploy Kafka Connect to your Kubernetes cluster.

    kubectl apply -f examples/kafka-connect/kafka-connect.yaml

  2. Verify that Kafka Connect was successfully deployed:

    kubectl get deployments

2.3.2. Extending Kafka Connect with connector plug-ins

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

File Connector Description

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 Kubernetes cluster.

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

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 Docker Hub as a base image for creating your own custom image with additional connector plug-ins.

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

Prerequisites
Procedure

  1. Create a new Dockerfile using strimzi/kafka:0.18.0-kafka-2.5.0 as the base image:

    FROM strimzi/kafka:0.18.0-kafka-2.5.0
USER root:root
COPY ./my-plugins/ /opt/kafka/plugins/
USER 1001

  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:
  #...
  image: my-new-container-image

      or

    • In the install/cluster-operator/050-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
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 Docker Hub as part of the strimzi/kafka:0.18.0-kafka-2.5.0 image. This S2I image takes your binaries (with plug-ins and connectors) and stores them in the /tmp/kafka-plugins/s2i directory. It creates a new Kafka Connect image from this directory, which can then be used with the Kafka Connect deployment. When started using the enhanced image, Kafka Connect loads any third-party plug-ins from the /tmp/kafka-plugins/s2i directory.

Procedure

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

    oc apply -f examples/kafka-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.

2.3.3. 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.

Strimzi 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

KafkaConnector resources

KafkaConnectors allow you to create and manage connector instances for Kafka Connect in a Kubernetes-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 Kubernetes 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 Strimzi, KafkaConnectors are disabled by default. To enable them for a Kafka Connect cluster, you must use annotations on the KafkaConnect resource. For instructions, see Enabling KafkaConnector resources.

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.

Strimzi includes an example KafkaConnector, named examples/connector/source-connector.yaml. You can use this example to create and manage a FileStreamSourceConnector.

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.

2.3.4. 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/connector/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 a Kubernetes 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 Kubernetes cluster:

    kubectl apply -f examples/connector/source-connector.yaml

  3. Check that the resource was created:

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

2.4. 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.

2.4.1. Deploying Kafka MirrorMaker to your Kubernetes cluster

This procedure shows how to deploy a Kafka MirrorMaker cluster to your Kubernetes 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 Strimzi:

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

  • examples/kafka-connect/kafka-mirror-maker2.yaml

For information about configuring KafkaMirrorMaker or KafkaMirrorMaker2 resources, see Kafka MirrorMaker configuration.

Prerequisites
Procedure

  1. Deploy Kafka MirrorMaker to your Kubernetes cluster:

    For MirrorMaker:

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

    For MirrorMaker 2.0:

    kubectl apply -f examples/kafka-mirror-maker-2/kafka-mirror-maker-2.yaml

  2. Verify that MirrorMaker was successfully deployed:

    kubectl get deployments

2.5. Deploy Kafka Bridge

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

2.5.1. Deploying Kafka Bridge to your Kubernetes cluster

This procedure shows how to deploy a Kafka Bridge cluster to your Kubernetes 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 Strimzi:

  • examples/kafka-bridge/kafka-bridge.yaml

For information about configuring the KafkaBridge resource, see Kafka Bridge configuration.

Prerequisites
Procedure

  1. Deploy Kafka Bridge to your Kubernetes cluster:

    kubectl apply -f examples/kafka-bridge/kafka-bridge.yaml

  2. Verify that Kafka Bridge was successfully deployed:

    kubectl get deployments

2.6. 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.

    kubectl run kafka-producer -ti --image=strimzi/kafka:0.18.0-kafka-2.5.0 --rm=true --restart=Never -- bin/kafka-console-producer.sh --broker-list cluster-name-kafka-bootstrap:9092 --topic my-topic

  2. Type a message into the console where the producer is running.

  3. Press Enter to send the message.

  4. Deploy a Kafka consumer.

    kubectl run kafka-consumer -ti --image=strimzi/kafka:0.18.0-kafka-2.5.0 --rm=true --restart=Never -- bin/kafka-console-consumer.sh --bootstrap-server cluster-name-kafka-bootstrap:9092 --topic my-topic --from-beginning

  5. Confirm that you see the incoming messages in the consumer console.

3. Deployment configuration

This chapter describes how to configure different aspects of the supported deployments:

  • Kafka clusters

  • Kafka Connect clusters

  • Kafka Connect clusters with Source2Image support

  • Kafka MirrorMaker

  • Kafka Bridge

  • OAuth 2.0 token-based authentication

  • OAuth 2.0 token-based authorization

  • Cruise Control

3.1. Kafka cluster configuration

The full schema of the Kafka resource is described in the Kafka schema reference. All labels that are applied to the desired Kafka resource will also be applied to the Kubernetes resources making up the Kafka cluster. This provides a convenient mechanism for resources to be labeled as required.

3.1.1. Sample Kafka YAML configuration

For help in understanding the configuration options available for your Kafka deployment, refer to sample YAML file provided here.

The sample shows only some of the possible configuration options, but those that are particularly important include:

  • Resource requests (CPU / Memory)

  • JVM options for maximum and minimum memory allocation

  • Listeners (and authentication)

  • Authentication

  • Storage

  • Rack awareness

  • Metrics

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    replicas: 3 (1)
    version: 0.18.0 (2)
    resources: (3)
      requests:
        memory: 64Gi
        cpu: "8"
      limits: (4)
        memory: 64Gi
        cpu: "12"
    jvmOptions: (5)
      -Xms: 8192m
      -Xmx: 8192m
    listeners: (6)
      tls:
        authentication:(7)
          type: tls
      external: (8)
        type: route
        authentication:
          type: tls
        configuration:
          brokerCertChainAndKey: (9)
            secretName: my-secret
            certificate: my-certificate.crt
            key: my-key.key
    authorization: (10)
      type: simple
    config: (11)
      auto.create.topics.enable: "false"
      offsets.topic.replication.factor: 3
      transaction.state.log.replication.factor: 3
      transaction.state.log.min.isr: 2
    storage: (12)
      type: persistent-claim (13)
      size: 10000Gi (14)
    rack: (15)
      topologyKey: failure-domain.beta.kubernetes.io/zone
    metrics: (16)
      lowercaseOutputName: true
      rules: (17)
      # Special cases and very specific rules
      - pattern : kafka.server<type=(.+), name=(.+), clientId=(.+), topic=(.+), partition=(.*)><>Value
        name: kafka_server_$1_$2
        type: GAUGE
        labels:
          clientId: "$3"
          topic: "$4"
          partition: "$5"
        # ...
  zookeeper: (18)
    replicas: 3
    resources:
      requests:
        memory: 8Gi
        cpu: "2"
      limits:
        memory: 8Gi
        cpu: "2"
    jvmOptions:
      -Xms: 4096m
      -Xmx: 4096m
    storage:
      type: persistent-claim
      size: 1000Gi
    metrics:
      # ...
  entityOperator: (19)
    topicOperator:
      resources:
        requests:
          memory: 512Mi
          cpu: "1"
        limits:
          memory: 512Mi
          cpu: "1"
    userOperator:
      resources:
        requests:
          memory: 512Mi
          cpu: "1"
        limits:
          memory: 512Mi
          cpu: "1"
  kafkaExporter: (20)
    # ...
  cruiseControl: (21)
    # ...

  1. Replicas specifies the number of broker nodes.

  2. Kafka version, which can be changed by following the upgrade procedure.

  3. Resource requests specify the resources to reserve for a given container.

  4. Resource limits specify the maximum resources that can be consumed by a container.

  5. JVM options can specify the minimum (-Xms) and maximum (-Xmx) memory allocation for JVM.

  6. Listeners configure how clients connect to the Kafka cluster via bootstrap addresses. Listeners are configured as plain (without encryption), tls or external.

  7. Listener authentication mechanisms may be configured for each listener, and specified as mutual TLS or SCRAM-SHA.

  8. External listener configuration specifies how the Kafka cluster is exposed outside Kubernetes, such as through a route, loadbalancer or nodeport.

  9. Optional configuration for a Kafka listener certificate managed by an external Certificate Authority. The brokerCertChainAndKey property specifies a Secret that holds a server certificate and a private key. Kafka listener certificates can also be configured for TLS listeners.

  10. Authorization enables simple authorization on the Kafka broker using the SimpleAclAuthorizer Kafka plugin.

  11. Config specifies the broker configuration. Standard Apache Kafka configuration may be provided, restricted to those properties not managed directly by Strimzi.

  12. Storage is configured as ephemeral, persistent-claim or jbod.

  13. Storage size for persistent volumes may be increased and additional volumes may be added to JBOD storage.

  14. Persistent storage has additional configuration options, such as a storage id and class for dynamic volume provisioning.

  15. Rack awareness is configured to spread replicas across different racks. A topology key must match the label of a cluster node.

  16. Kafka metrics configuration for use with Prometheus.

  17. Kafka rules for exporting metrics to a Grafana dashboard through the JMX Exporter. A set of rules provided with Strimzi may be copied to your Kafka resource configuration.

  18. ZooKeeper-specific configuration, which contains properties similar to the Kafka configuration.

  19. Entity Operator configuration, which specifies the configuration for the Topic Operator and User Operator.

  20. Kafka Exporter configuration, which is used to expose data as Prometheus metrics.

  21. Cruise Control configuration, which is used to monitor and balance data across the Kafka cluster.

3.1.2. Data storage considerations

An efficient data storage infrastructure is essential to the optimal performance of Strimzi.

Block storage is required. File storage, such as NFS, does not work with Kafka.

For your block storage, you can choose, for example:

Note
 Strimzi does not require Kubernetes raw block volumes.
File systems

It is recommended that you configure your storage system to use the XFS file system. Strimzi is also compatible with the ext4 file system, but this might require additional configuration for best results.

Apache Kafka and ZooKeeper storage

Use separate disks for Apache Kafka and ZooKeeper.

Three types of data storage are supported:

  • Ephemeral (Recommended for development only)

  • Persistent

  • JBOD (Just a Bunch of Disks, suitable for Kafka only)

For more information, see Kafka and ZooKeeper storage.

Solid-state drives (SSDs), though not essential, can improve the performance of Kafka in large clusters where data is sent to and received from multiple topics asynchronously. SSDs are particularly effective with ZooKeeper, which requires fast, low latency data access.

Note
 You do not need to provision replicated storage because Kafka and ZooKeeper both have built-in data replication.

3.1.3. Kafka and ZooKeeper storage types

As stateful applications, Kafka and ZooKeeper need to store data on disk. Strimzi supports three storage types for this data:

  • Ephemeral

  • Persistent

  • JBOD storage

Note
 JBOD storage is supported only for Kafka, not for ZooKeeper.

When configuring a Kafka resource, you can specify the type of storage used by the Kafka broker and its corresponding ZooKeeper node. You configure the storage type using the storage property in the following resources:

  • Kafka.spec.kafka

  • Kafka.spec.zookeeper

The storage type is configured in the type field.

Warning
 The storage type cannot be changed after a Kafka cluster is deployed.
Additional resources
Ephemeral storage

Ephemeral storage uses the emptyDir volumes to store data. To use ephemeral storage, the type field should be set to ephemeral.

Important
 emptyDir volumes are not persistent and the data stored in them will be lost when the Pod is restarted. After the new pod is started, it has to recover all data from other nodes of the cluster. Ephemeral storage is not suitable for use with single node ZooKeeper clusters and for Kafka topics with replication factor 1, because it will lead to data loss.
An example of Ephemeral storage
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    storage:
      type: ephemeral
    # ...
  zookeeper:
    # ...
    storage:
      type: ephemeral
    # ...
Log directories

The ephemeral volume will be used by the Kafka brokers as log directories mounted into the following path:

/var/lib/kafka/data/kafka-log_idx_

Where idx is the Kafka broker pod index. For example /var/lib/kafka/data/kafka-log0.

Persistent storage

Persistent storage uses Persistent Volume Claims to provision persistent volumes for storing data. Persistent Volume Claims can be used to provision volumes of many different types, depending on the Storage Class which will provision the volume. The data types which can be used with persistent volume claims include many types of SAN storage as well as Local persistent volumes.

To use persistent storage, the type has to be set to persistent-claim. Persistent storage supports additional configuration options:

id (optional)

Storage identification number. This option is mandatory for storage volumes defined in a JBOD storage declaration. Default is 0.

size (required)

Defines the size of the persistent volume claim, for example, "1000Gi".

class (optional)

The Kubernetes Storage Class to use for dynamic volume provisioning.

selector (optional)

Allows selecting a specific persistent volume to use. It contains key:value pairs representing labels for selecting such a volume.

deleteClaim (optional)

Boolean value which specifies if the Persistent Volume Claim has to be deleted when the cluster is undeployed. Default is false.

Warning
 Increasing the size of persistent volumes in an existing Strimzi cluster is only supported in Kubernetes versions that support persistent volume resizing. The persistent volume to be resized must use a storage class that supports volume expansion. For other versions of Kubernetes and storage classes which do not support volume expansion, you must decide the necessary storage size before deploying the cluster. Decreasing the size of existing persistent volumes is not possible.
Example fragment of persistent storage configuration with 1000Gi size
# ...
storage:
  type: persistent-claim
  size: 1000Gi
# ...

The following example demonstrates the use of a storage class.

Example fragment of persistent storage configuration with specific Storage Class
# ...
storage:
  type: persistent-claim
  size: 1Gi
  class: my-storage-class
# ...

Finally, a selector can be used to select a specific labeled persistent volume to provide needed features such as an SSD.

Example fragment of persistent storage configuration with selector
# ...
storage:
  type: persistent-claim
  size: 1Gi
  selector:
    hdd-type: ssd
  deleteClaim: true
# ...
Storage class overrides

You can specify a different storage class for one or more Kafka brokers, instead of using the default storage class. This is useful if, for example, storage classes are restricted to different availability zones or data centers. You can use the overrides field for this purpose.

In this example, the default storage class is named my-storage-class:

Example Strimzi cluster using storage class overrides
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  labels:
    app: my-cluster
  name: my-cluster
  namespace: myproject
spec:
  # ...
  kafka:
    replicas: 3
    storage:
      deleteClaim: true
      size: 100Gi
      type: persistent-claim
      class: my-storage-class
      overrides:
        - broker: 0
          class: my-storage-class-zone-1a
        - broker: 1
          class: my-storage-class-zone-1b
        - broker: 2
          class: my-storage-class-zone-1c
  # ...

As a result of the configured overrides property, the broker volumes use the following storage classes:

  • The persistent volumes of broker 0 will use my-storage-class-zone-1a.

  • The persistent volumes of broker 1 will use my-storage-class-zone-1b.

  • The persistent volumes of broker 2 will use my-storage-class-zone-1c.

The overrides property is currently used only to override storage class configurations. Overriding other storage configuration fields is not currently supported. Other fields from the storage configuration are currently not supported.

Persistent Volume Claim naming

When persistent storage is used, it creates Persistent Volume Claims with the following names:

data-cluster-name-kafka-idx

Persistent Volume Claim for the volume used for storing data for the Kafka broker pod idx.

data-cluster-name-zookeeper-idx

Persistent Volume Claim for the volume used for storing data for the ZooKeeper node pod idx.

Log directories

The persistent volume will be used by the Kafka brokers as log directories mounted into the following path:

/var/lib/kafka/data/kafka-log_idx_

Where idx is the Kafka broker pod index. For example /var/lib/kafka/data/kafka-log0.

Resizing persistent volumes

You can provision increased storage capacity by increasing the size of the persistent volumes used by an existing Strimzi cluster. Resizing persistent volumes is supported in clusters that use either a single persistent volume or multiple persistent volumes in a JBOD storage configuration.

Note
 You can increase but not decrease the size of persistent volumes. Decreasing the size of persistent volumes is not currently supported in Kubernetes.
Prerequisites

  • A Kubernetes cluster with support for volume resizing.

  • The Cluster Operator is running.

  • A Kafka cluster using persistent volumes created using a storage class that supports volume expansion.

Procedure

  1. In a Kafka resource, increase the size of the persistent volume allocated to the Kafka cluster, the ZooKeeper cluster, or both.

    • To increase the volume size allocated to the Kafka cluster, edit the spec.kafka.storage property.

    • To increase the volume size allocated to the ZooKeeper cluster, edit the spec.zookeeper.storage property.

      For example, to increase the volume size from 1000Gi to 2000Gi:

      apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    storage:
      type: persistent-claim
      size: 2000Gi
      class: my-storage-class
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    Use kubectl apply:

    kubectl apply -f your-file

    Kubernetes increases the capacity of the selected persistent volumes in response to a request from the Cluster Operator. When the resizing is complete, the Cluster Operator restarts all pods that use the resized persistent volumes. This happens automatically.

Additional resources

For more information about resizing persistent volumes in Kubernetes, see Resizing Persistent Volumes using Kubernetes.

JBOD storage overview

You can configure Strimzi to use JBOD, a data storage configuration of multiple disks or volumes. JBOD is one approach to providing increased data storage for Kafka brokers. It can also improve performance.

A JBOD configuration is described by one or more volumes, each of which can be either ephemeral or persistent. The rules and constraints for JBOD volume declarations are the same as those for ephemeral and persistent storage. For example, you cannot change the size of a persistent storage volume after it has been provisioned.

JBOD configuration

To use JBOD with Strimzi, the storage type must be set to jbod. The volumes property allows you to describe the disks that make up your JBOD storage array or configuration. The following fragment shows an example JBOD configuration:

# ...
storage:
  type: jbod
  volumes:
  - id: 0
    type: persistent-claim
    size: 100Gi
    deleteClaim: false
  - id: 1
    type: persistent-claim
    size: 100Gi
    deleteClaim: false
# ...

The ids cannot be changed once the JBOD volumes are created.

Users can add or remove volumes from the JBOD configuration.

JBOD and Persistent Volume Claims

When persistent storage is used to declare JBOD volumes, the naming scheme of the resulting Persistent Volume Claims is as follows:

data-id-cluster-name-kafka-idx

Where id is the ID of the volume used for storing data for Kafka broker pod idx.

Log directories

The JBOD volumes will be used by the Kafka brokers as log directories mounted into the following path:

/var/lib/kafka/data-id/kafka-log_idx_

Where id is the ID of the volume used for storing data for Kafka broker pod idx. For example /var/lib/kafka/data-0/kafka-log0.

Adding volumes to JBOD storage

This procedure describes how to add volumes to a Kafka cluster configured to use JBOD storage. It cannot be applied to Kafka clusters configured to use any other storage type.

Note
 When adding a new volume under an id which was already used in the past and removed, you have to make sure that the previously used PersistentVolumeClaims have been deleted.
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

  • A Kafka cluster with JBOD storage

Procedure

  1. Edit the spec.kafka.storage.volumes property in the Kafka resource. Add the new volumes to the volumes array. For example, add the new volume with id 2:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    storage:
      type: jbod
      volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
      - id: 1
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
      - id: 2
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

  3. Create new topics or reassign existing partitions to the new disks.

Additional resources

For more information about reassigning topics, see Partition reassignment.

Removing volumes from JBOD storage

This procedure describes how to remove volumes from Kafka cluster configured to use JBOD storage. It cannot be applied to Kafka clusters configured to use any other storage type. The JBOD storage always has to contain at least one volume.

Important
 To avoid data loss, you have to move all partitions before removing the volumes.
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

  • A Kafka cluster with JBOD storage with two or more volumes

Procedure

  1. Reassign all partitions from the disks which are you going to remove. Any data in partitions still assigned to the disks which are going to be removed might be lost.

  2. Edit the spec.kafka.storage.volumes property in the Kafka resource. Remove one or more volumes from the volumes array. For example, remove the volumes with ids 1 and 2:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    storage:
      type: jbod
      volumes:
      - id: 0
        type: persistent-claim
        size: 100Gi
        deleteClaim: false
    # ...
  zookeeper:
    # ...

  3. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Additional resources

For more information about reassigning topics, see Partition reassignment.

3.1.4. Kafka broker replicas

A Kafka cluster can run with many brokers. You can configure the number of brokers used for the Kafka cluster in Kafka.spec.kafka.replicas. The best number of brokers for your cluster has to be determined based on your specific use case.

Configuring the number of broker nodes

This procedure describes how to configure the number of Kafka broker nodes in a new cluster. It only applies to new clusters with no partitions. If your cluster already has topics defined, see Scaling clusters.

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

  • A Kafka cluster with no topics defined yet

Procedure

  1. Edit the replicas property in the Kafka resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    replicas: 3
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Additional resources

If your cluster already has topics defined, see Scaling clusters.

3.1.5. Kafka broker configuration

Strimzi allows you to customize the configuration of the Kafka brokers in your Kafka cluster. You can specify and configure most of the options listed in the "Broker Configs" section of the Apache Kafka documentation. You cannot configure options that are related to the following areas:

  • Security (Encryption, Authentication, and Authorization)

  • Listener configuration

  • Broker ID configuration

  • Configuration of log data directories

  • Inter-broker communication

  • ZooKeeper connectivity

These options are automatically configured by Strimzi.

Kafka broker configuration

The config property in Kafka.spec.kafka contains Kafka broker configuration options as keys with values in one of the following JSON types:

  • String

  • Number

  • Boolean

You can specify and configure all of the options in the "Broker Configs" section of the Apache Kafka documentation apart from those managed directly by Strimzi. Specifically, you are prevented from modifying all configuration options with keys equal to or starting with one of the following strings:

  • listeners

  • advertised.

  • broker.

  • listener.

  • host.name

  • port

  • inter.broker.listener.name

  • sasl.

  • ssl.

  • security.

  • password.

  • principal.builder.class

  • log.dir

  • zookeeper.connect

  • zookeeper.set.acl

  • authorizer.

  • super.user

If the config property specifies a restricted option, it is ignored and a warning message is printed to the Cluster Operator log file. All other supported options are passed to Kafka.

An example Kafka broker configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    config:
      num.partitions: 1
      num.recovery.threads.per.data.dir: 1
      default.replication.factor: 3
      offsets.topic.replication.factor: 3
      transaction.state.log.replication.factor: 3
      transaction.state.log.min.isr: 1
      log.retention.hours: 168
      log.segment.bytes: 1073741824
      log.retention.check.interval.ms: 300000
      num.network.threads: 3
      num.io.threads: 8
      socket.send.buffer.bytes: 102400
      socket.receive.buffer.bytes: 102400
      socket.request.max.bytes: 104857600
      group.initial.rebalance.delay.ms: 0
    # ...
Configuring Kafka brokers

You can configure an existing Kafka broker, or create a new Kafka broker with a specified configuration.

Prerequisites

  • A Kubernetes cluster is available.

  • The Cluster Operator is running.

Procedure

  1. Open the YAML configuration file that contains the Kafka resource specifying the cluster deployment.

  2. In the spec.kafka.config property in the Kafka resource, enter one or more Kafka configuration settings. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    config:
      default.replication.factor: 3
      offsets.topic.replication.factor: 3
      transaction.state.log.replication.factor: 3
      transaction.state.log.min.isr: 1
    # ...
  zookeeper:
    # ...

  3. Apply the new configuration to create or update the resource.

    Use kubectl apply:

    kubectl apply -f kafka.yaml

    where kafka.yaml is the YAML configuration file for the resource that you want to configure; for example, kafka-persistent.yaml.

3.1.6. Kafka broker listeners

You can configure the listeners enabled in Kafka brokers. The following types of listeners are supported:

  • Plain listener on port 9092 (without TLS encryption)

  • TLS listener on port 9093 (with TLS encryption)

  • External listener on port 9094 for access from outside of Kubernetes

OAuth 2.0

If you are using OAuth 2.0 token-based authentication, you can configure the listeners to connect to your authorization server. For more information, see Using OAuth 2.0 token-based authentication.

Listener certificates

You can provide your own server certificates, called Kafka listener certificates, for TLS listeners or external listeners which have TLS encryption enabled. For more information, see Kafka listener certificates.

Kafka listeners

You can configure Kafka broker listeners using the listeners property in the Kafka.spec.kafka resource. The listeners property contains three sub-properties:

  • plain

  • tls

  • external

Each listener will only be defined when the listeners object has the given property.

An example of listeners property with all listeners enabled
# ...
listeners:
  plain: {}
  tls: {}
  external:
    type: loadbalancer
# ...
An example of listeners property with only the plain listener enabled
# ...
listeners:
  plain: {}
# ...
Configuring Kafka listeners
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the listeners property in the Kafka.spec.kafka resource.

    An example configuration of the plain (unencrypted) listener without authentication:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    listeners:
      plain: {}
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Additional resources
Listener authentication

The listener authentication property is used to specify an authentication mechanism specific to that listener:

  • Mutual TLS authentication (only on the listeners with TLS encryption)

  • SCRAM-SHA authentication

If no authentication property is specified then the listener does not authenticate clients which connect through that listener.

Authentication must be configured when using the User Operator to manage KafkaUsers.

Authentication configuration for a listener

The following example shows:

  • A plain listener configured for SCRAM-SHA authentication

  • A tls listener with mutual TLS authentication

  • An external listener with mutual TLS authentication

An example showing listener authentication configuration
# ...
listeners:
  plain:
    authentication:
      type: scram-sha-512
  tls:
    authentication:
      type: tls
  external:
    type: loadbalancer
    tls: true
    authentication:
      type: tls
# ...
Mutual TLS authentication

Mutual TLS authentication is always used for the communication between Kafka brokers and ZooKeeper pods.

Mutual authentication or two-way authentication is when both the server and the client present certificates. Strimzi can configure Kafka to use TLS (Transport Layer Security) to provide encrypted communication between Kafka brokers and clients either with or without mutual authentication. When you configure mutual authentication, the broker authenticates the client and the client authenticates the broker.

Note
 TLS authentication is more commonly one-way, with one party authenticating the identity of another. For example, when HTTPS is used between a web browser and a web server, the server obtains proof of the identity of the browser.
When to use mutual TLS authentication for clients

Mutual TLS authentication is recommended for authenticating Kafka clients when:

  • The client supports authentication using mutual TLS authentication

  • It is necessary to use the TLS certificates rather than passwords

  • You can reconfigure and restart client applications periodically so that they do not use expired certificates.

SCRAM-SHA authentication

SCRAM (Salted Challenge Response Authentication Mechanism) is an authentication protocol that can establish mutual authentication using passwords. Strimzi can configure Kafka to use SASL (Simple Authentication and Security Layer) SCRAM-SHA-512 to provide authentication on both unencrypted and TLS-encrypted client connections. TLS authentication is always used internally between Kafka brokers and ZooKeeper nodes. When used with a TLS client connection, the TLS protocol provides encryption, but is not used for authentication.

The following properties of SCRAM make it safe to use SCRAM-SHA even on unencrypted connections:

  • The passwords are not sent in the clear over the communication channel. Instead the client and the server are each challenged by the other to offer proof that they know the password of the authenticating user.

  • The server and client each generate a new challenge for each authentication exchange. This means that the exchange is resilient against replay attacks.

Supported SCRAM credentials

Strimzi supports SCRAM-SHA-512 only. When a KafkaUser.spec.authentication.type is configured with scram-sha-512 the User Operator will generate a random 12 character password consisting of upper and lowercase ASCII letters and numbers.

When to use SCRAM-SHA authentication for clients

SCRAM-SHA is recommended for authenticating Kafka clients when:

  • The client supports authentication using SCRAM-SHA-512

  • It is necessary to use passwords rather than the TLS certificates

  • Authentication for unencrypted communication is required

External listeners

Use an external listener to expose your Strimzi Kafka cluster to a client outside a Kubernetes environment.

Additional resources
Customizing advertised addresses on external listeners

By default, Strimzi tries to automatically determine the hostnames and ports that your Kafka cluster advertises to its clients. This is not sufficient in all situations, because the infrastructure on which Strimzi is running might not provide the right hostname or port through which Kafka can be accessed. You can customize the advertised hostname and port in the overrides property of the external listener. Strimzi will then automatically configure the advertised address in the Kafka brokers and add it to the broker certificates so it can be used for TLS hostname verification. Overriding the advertised host and ports is available for all types of external listeners.

Example of an external listener configured with overrides for advertised addresses
# ...
listeners:
  external:
    type: route
    authentication:
      type: tls
    overrides:
      brokers:
      - broker: 0
        advertisedHost: example.hostname.0
        advertisedPort: 12340
      - broker: 1
        advertisedHost: example.hostname.1
        advertisedPort: 12341
      - broker: 2
        advertisedHost: example.hostname.2
        advertisedPort: 12342
# ...

Additionally, you can specify the name of the bootstrap service. This name will be added to the broker certificates and can be used for TLS hostname verification. Adding the additional bootstrap address is available for all types of external listeners.

Example of an external listener configured with an additional bootstrap address
# ...
listeners:
  external:
    type: route
    authentication:
      type: tls
    overrides:
      bootstrap:
        address: example.hostname
# ...
Route external listeners

An external listener of type route exposes Kafka using OpenShift Routes and the HAProxy router.

Note
 route is only supported on OpenShift
Exposing Kafka using OpenShift Routes

When exposing Kafka using OpenShift Routes and the HAProxy router, a dedicated Route is created for every Kafka broker pod. An additional Route is created to serve as a Kafka bootstrap address. Kafka clients can use these Routes to connect to Kafka on port 443.

TLS encryption is always used with Routes.

By default, the route hosts are automatically assigned by OpenShift. However, you can override the assigned route hosts by specifying the requested hosts in the overrides property. Strimzi will not perform any validation that the requested hosts are available; you must ensure that they are free and can be used.

Example of an external listener of type routes configured with overrides for OpenShift route hosts
# ...
listeners:
  external:
    type: route
    authentication:
      type: tls
    overrides:
      bootstrap:
        host: bootstrap.myrouter.com
      brokers:
      - broker: 0
        host: broker-0.myrouter.com
      - broker: 1
        host: broker-1.myrouter.com
      - broker: 2
        host: broker-2.myrouter.com
# ...

For more information on using Routes to access Kafka, see Accessing Kafka using OpenShift routes.

Accessing Kafka using OpenShift routes
Prerequisites

  • An OpenShift cluster

  • A running Cluster Operator

Procedure

  1. Deploy Kafka cluster with an external listener enabled and configured to the type route.

    An example configuration with an external listener configured to use Routes:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    listeners:
      external:
        type: route
        # ...
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    oc apply -f your-file

  3. Find the address of the bootstrap Route.

    oc get routes _cluster-name_-kafka-bootstrap -o=jsonpath='{.status.ingress[0].host}{"\n"}'

    Use the address together with port 443 in your Kafka client as the bootstrap address.

  4. Extract the public certificate of the broker certification authority

    kubectl get secret _<cluster-name>_-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt

    Use the extracted certificate in your Kafka client to configure TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication.

Additional resources
Loadbalancer external listeners

External listeners of type loadbalancer expose Kafka by using Loadbalancer type Services.

Exposing Kafka using loadbalancers

When exposing Kafka using Loadbalancer type Services, a new loadbalancer service is created for every Kafka broker pod. An additional loadbalancer is created to serve as a Kafka bootstrap address. Loadbalancers listen to connections on port 9094.

By default, TLS encryption is enabled. To disable it, set the tls field to false.

Example of an external listener of type loadbalancer
# ...
listeners:
  external:
    type: loadbalancer
    authentication:
      type: tls
# ...

For more information on using loadbalancers to access Kafka, see Accessing Kafka using loadbalancers.

Customizing the DNS names of external loadbalancer listeners

On loadbalancer listeners, you can use the dnsAnnotations property to add additional annotations to the loadbalancer services. You can use these annotations to instrument DNS tooling such as External DNS, which automatically assigns DNS names to the loadbalancer services.

Example of an external listener of type loadbalancer using dnsAnnotations
# ...
listeners:
  external:
    type: loadbalancer
    authentication:
      type: tls
    overrides:
      bootstrap:
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: kafka-bootstrap.mydomain.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
      brokers:
      - broker: 0
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: kafka-broker-0.mydomain.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
      - broker: 1
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: kafka-broker-1.mydomain.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
      - broker: 2
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: kafka-broker-2.mydomain.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
# ...
Customizing the loadbalancer IP addresses

On loadbalancer listeners, you can use the loadBalancerIP property to request a specific IP address when creating a loadbalancer. Use this property when you need to use a loadbalancer with a specific IP address. The loadBalancerIP field is ignored if the cloud provider does not support the feature.

Example of an external listener of type loadbalancer with specific loadbalancer IP address requests
# ...
listeners:
  external:
    type: loadbalancer
    authentication:
      type: tls
    overrides:
      bootstrap:
        loadBalancerIP: 172.29.3.10
      brokers:
      - broker: 0
        loadBalancerIP: 172.29.3.1
      - broker: 1
        loadBalancerIP: 172.29.3.2
      - broker: 2
        loadBalancerIP: 172.29.3.3
# ...
Accessing Kafka using loadbalancers
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Deploy Kafka cluster with an external listener enabled and configured to the type loadbalancer.

    An example configuration with an external listener configured to use loadbalancers:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    listeners:
      external:
        type: loadbalancer
        authentication:
          type: tls
        # ...
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

  3. Find the hostname of the bootstrap loadbalancer.

    This can be done using kubectl get:

    kubectl get service cluster-name-kafka-external-bootstrap -o=jsonpath='{.status.loadBalancer.ingress[0].hostname}{"\n"}'

    If no hostname was found (nothing was returned by the command), use the loadbalancer IP address.

    This can be done using kubectl get:

    kubectl get service cluster-name-kafka-external-bootstrap -o=jsonpath='{.status.loadBalancer.ingress[0].ip}{"\n"}'

    Use the hostname or IP address together with port 9094 in your Kafka client as the bootstrap address.

  4. Unless TLS encryption was disabled, extract the public certificate of the broker certification authority.

    This can be done using kubectl get:

    kubectl get secret cluster-name-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt

    Use the extracted certificate in your Kafka client to configure TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication.

Additional resources
Node Port external listeners

External listeners of type nodeport expose Kafka by using NodePort type Services.

Exposing Kafka using node ports

When exposing Kafka using NodePort type Services, Kafka clients connect directly to the nodes of Kubernetes. You must enable access to the ports on the Kubernetes nodes for each client (for example, in firewalls or security groups). Each Kafka broker pod is then accessible on a separate port.

An additional NodePort type of service is created to serve as a Kafka bootstrap address.

When configuring the advertised addresses for the Kafka broker pods, Strimzi uses the address of the node on which the given pod is running. Nodes often have multiple addresses. The address type used is based on the first type found in the following order of priority:

  1. ExternalDNS

  2. ExternalIP

  3. Hostname

  4. InternalDNS

  5. InternalIP

You can use the preferredAddressType property in your listener configuration to specify the first address type checked as the node address. This property is useful, for example, if your deployment does not have DNS support, or you only want to expose a broker internally through an internal DNS or IP address. If an address of this type is found, it is used. If the preferred address type is not found, Strimzi proceeds through the types in the standard order of priority.

Example of an external listener configured with a preferred address type
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    listeners:
      external:
        type: nodeport
        tls: true
        authentication:
          type: tls
        configuration:
          preferredAddressType: InternalDNS
    # ...
  zookeeper:
    # ...

By default, TLS encryption is enabled. To disable it, set the tls field to false.

Note
 TLS hostname verification is not currently supported when exposing Kafka clusters using node ports.

By default, the port numbers used for the bootstrap and broker services are automatically assigned by Kubernetes. However, you can override the assigned node ports by specifying the requested port numbers in the overrides property. Strimzi does not perform any validation on the requested ports; you must ensure that they are free and available for use.

Example of an external listener configured with overrides for node ports
# ...
listeners:
  external:
    type: nodeport
    tls: true
    authentication:
      type: tls
    overrides:
      bootstrap:
        nodePort: 32100
      brokers:
      - broker: 0
        nodePort: 32000
      - broker: 1
        nodePort: 32001
      - broker: 2
        nodePort: 32002
# ...

For more information on using node ports to access Kafka, see Accessing Kafka using node ports.

Customizing the DNS names of external node port listeners

On nodeport listeners, you can use the dnsAnnotations property to add additional annotations to the nodeport services. You can use these annotations to instrument DNS tooling such as External DNS, which automatically assigns DNS names to the cluster nodes.

Example of an external listener of type nodeport using dnsAnnotations
# ...
listeners:
  external:
    type: nodeport
    tls: true
    authentication:
      type: tls
    overrides:
      bootstrap:
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: kafka-bootstrap.mydomain.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
      brokers:
      - broker: 0
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: kafka-broker-0.mydomain.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
      - broker: 1
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: kafka-broker-1.mydomain.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
      - broker: 2
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: kafka-broker-2.mydomain.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
# ...
Accessing Kafka using node ports

This procedure describes how to access a Strimzi Kafka cluster from an external client using node ports.

To connect to a broker, you need the hostname (advertised address) and port number for the Kafka bootstrap address, as well as the certificate used for authentication.

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Deploy the Kafka cluster with an external listener enabled and configured to the type nodeport.

    For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    listeners:
      external:
        type: nodeport
        tls: true
        authentication:
          type: tls
        configuration:
          brokerCertChainAndKey: (1)
            secretName: my-secret
            certificate: my-certificate.crt
            key: my-key.key
          preferredAddressType: InternalDNS (2)
    # ...
  zookeeper:
    # ...

    1. Optional configuration for a Kafka listener certificate managed by an external Certificate Authority. The brokerCertChainAndKey property specifies a Secret that holds a server certificate and a private key. Kafka listener certificates can also be configured for TLS listeners.

    2. Optional configuration to specify a preference for the first address type used by Strimzi as the node address.

  2. Create or update the resource.

    kubectl apply -f your-file

  3. Find the port number of the bootstrap service.

    kubectl get service cluster-name-kafka-external-bootstrap -o=jsonpath='{.spec.ports[0].nodePort}{"\n"}'

    The port is used in the Kafka bootstrap address.

  4. Find the address of the Kubernetes node.

    kubectl get node node-name -o=jsonpath='{range .status.addresses[*]}{.type}{"\t"}{.address}{"\n"}'

    If several different addresses are returned, select the address type you want based on the following order:

    1. ExternalDNS

    2. ExternalIP

    3. Hostname

    4. InternalDNS

    5. InternalIP

    Use the address with the port found in the previous step in the Kafka bootstrap address.

  5. Unless TLS encryption was disabled, extract the public certificate of the broker certification authority.

    kubectl get secret cluster-name-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt

    Use the extracted certificate in your Kafka client to configure TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication.

Additional resources
Kubernetes Ingress external listeners

External listeners of type ingress exposes Kafka by using Kubernetes Ingress and the NGINX Ingress Controller for Kubernetes.

Exposing Kafka using Kubernetes Ingress

When exposing Kafka using using Kubernetes Ingress and the NGINX Ingress Controller for Kubernetes, a dedicated Ingress resource is created for every Kafka broker pod. An additional Ingress resource is created to serve as a Kafka bootstrap address. Kafka clients can use these Ingress resources to connect to Kafka on port 443.

Note
 External listeners using Ingress have been currently tested only with the NGINX Ingress Controller for Kubernetes.

Strimzi uses the TLS passthrough feature of the NGINX Ingress Controller for Kubernetes. Make sure TLS passthrough is enabled in your NGINX Ingress Controller for Kubernetes deployment. For more information about enabling TLS passthrough see TLS passthrough documentation. Because it is using the TLS passthrough functionality, TLS encryption cannot be disabled when exposing Kafka using Ingress.

The Ingress controller does not assign any hostnames automatically. You have to specify the hostnames which should be used by the bootstrap and per-broker services in the spec.kafka.listeners.external.configuration section. You also have to make sure that the hostnames resolve to the Ingress endpoints. Strimzi will not perform any validation that the requested hosts are available and properly routed to the Ingress endpoints.

Example of an external listener of type ingress
# ...
listeners:
  external:
    type: ingress
    authentication:
      type: tls
    configuration:
      bootstrap:
        host: bootstrap.myingress.com
      brokers:
      - broker: 0
        host: broker-0.myingress.com
      - broker: 1
        host: broker-1.myingress.com
      - broker: 2
        host: broker-2.myingress.com
# ...

For more information on using Ingress to access Kafka, see Accessing Kafka using ingress.

Configuring the Ingress class

By default, the Ingress class is set to nginx. You can change the Ingress class using the class property.

Example of an external listener of type ingress using Ingress class nginx-internal
# ...
listeners:
  external:
    type: ingress
    class: nginx-internal
    # ...
# ...
Customizing the DNS names of external ingress listeners

On ingress listeners, you can use the dnsAnnotations property to add additional annotations to the ingress resources. You can use these annotations to instrument DNS tooling such as External DNS, which automatically assigns DNS names to the ingress resources.

Example of an external listener of type ingress using dnsAnnotations
# ...
listeners:
  external:
    type: ingress
    authentication:
      type: tls
    configuration:
      bootstrap:
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: bootstrap.myingress.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
        host: bootstrap.myingress.com
      brokers:
      - broker: 0
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: broker-0.myingress.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
        host: broker-0.myingress.com
      - broker: 1
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: broker-1.myingress.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
        host: broker-1.myingress.com
      - broker: 2
        dnsAnnotations:
          external-dns.alpha.kubernetes.io/hostname: broker-2.myingress.com.
          external-dns.alpha.kubernetes.io/ttl: "60"
        host: broker-2.myingress.com
# ...
Accessing Kafka using ingress

This procedure shows how to access Strimzi Kafka clusters from outside of Kubernetes using Ingress.

Prerequisites
Procedure

  1. Deploy Kafka cluster with an external listener enabled and configured to the type ingress.

    An example configuration with an external listener configured to use Ingress:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    listeners:
      external:
        type: ingress
        authentication:
          type: tls
        configuration:
          bootstrap:
            host: bootstrap.myingress.com
          brokers:
          - broker: 0
            host: broker-0.myingress.com
          - broker: 1
            host: broker-1.myingress.com
          - broker: 2
            host: broker-2.myingress.com
    # ...
  zookeeper:
    # ...

  2. Make sure the hosts in the configuration section properly resolve to the Ingress endpoints.

  3. Create or update the resource.

    kubectl apply -f your-file

  4. Extract the public certificate of the broker certificate authority

    kubectl get secret cluster-name-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt

  5. Use the extracted certificate in your Kafka client to configure the TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication. Connect with your client to the host you specified in the configuration on port 443.

Additional resources
Network policies

Strimzi automatically creates a NetworkPolicy resource for every listener that is enabled on a Kafka broker. By default, a NetworkPolicy grants access to a listener to all applications and namespaces.

If you want to restrict access to a listener at the network level to only selected applications or namespaces, use the networkPolicyPeers field.

Use network policies in conjunction with authentication and authorization.

Each listener can have a different networkPolicyPeers configuration.

Network policy configuration for a listener

The following example shows a networkPolicyPeers configuration for a plain and a tls listener:

# ...
listeners:
  plain:
    authentication:
      type: scram-sha-512
    networkPolicyPeers:
      - podSelector:
          matchLabels:
            app: kafka-sasl-consumer
      - podSelector:
          matchLabels:
            app: kafka-sasl-producer
  tls:
    authentication:
      type: tls
    networkPolicyPeers:
      - namespaceSelector:
          matchLabels:
            project: myproject
      - namespaceSelector:
          matchLabels:
            project: myproject2
# ...

In the example:

  • Only application pods matching the labels app: kafka-sasl-consumer and app: kafka-sasl-producer can connect to the plain listener. The application pods must be running in the same namespace as the Kafka broker.

  • Only application pods running in namespaces matching the labels project: myproject and project: myproject2 can connect to the tls listener.

The syntax of the networkPolicyPeers field is the same as the from field in NetworkPolicy resources. For more information about the schema, see NetworkPolicyPeer API reference and the KafkaListeners schema reference.

Note
 Your configuration of Kubernetes must support ingress NetworkPolicies in order to use network policies in Strimzi.
Restricting access to Kafka listeners using networkPolicyPeers

You can restrict access to a listener to only selected applications by using the networkPolicyPeers field.

Prerequisites

  • A Kubernetes cluster with support for Ingress NetworkPolicies.

  • The Cluster Operator is running.

Procedure

  1. Open the Kafka resource.

  2. In the networkPolicyPeers field, define the application pods or namespaces that will be allowed to access the Kafka cluster.

    For example, to configure a tls listener to allow connections only from application pods with the label app set to kafka-client:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    listeners:
      tls:
        networkPolicyPeers:
          - podSelector:
              matchLabels:
                app: kafka-client
    # ...
  zookeeper:
    # ...

  3. Create or update the resource.

    Use kubectl apply:

    kubectl apply -f your-file
Additional resources

3.1.7. Authentication and Authorization

Strimzi supports authentication and authorization. Authentication can be configured independently for each listener. Authorization is always configured for the whole Kafka cluster.

Authentication

Authentication is configured as part of the listener configuration in the authentication property. The authentication mechanism is defined by the type field.

When the authentication property is missing, no authentication is enabled on a given listener. The listener will accept all connections without authentication.

Supported authentication mechanisms:

TLS client authentication

TLS Client authentication is enabled by specifying the type as tls. The TLS client authentication is supported only on the tls listener.

An example of authentication with type tls
# ...
authentication:
  type: tls
# ...
Configuring authentication in Kafka brokers
Prerequisites

  • A Kubernetes cluster is available.

  • The Cluster Operator is running.

Procedure

  1. Open the YAML configuration file that contains the Kafka resource specifying the cluster deployment.

  2. In the spec.kafka.listeners property in the Kafka resource, add the authentication field to the listeners for which you want to enable authentication. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    listeners:
      tls:
        authentication:
          type: tls
    # ...
  zookeeper:
    # ...

  3. Apply the new configuration to create or update the resource.

    Use kubectl apply:

    kubectl apply -f kafka.yaml

    where kafka.yaml is the YAML configuration file for the resource that you want to configure; for example, kafka-persistent.yaml.

Additional resources
Authorization

You can configure authorization for Kafka brokers using the authorization property in the Kafka.spec.kafka resource. If the authorization property is missing, no authorization is enabled. When enabled, authorization is applied to all enabled listeners. The authorization method is defined in the type field.

You can configure:

Simple authorization

Simple authorization in Strimzi uses the SimpleAclAuthorizer plugin, the default Access Control Lists (ACLs) authorization plugin provided with Apache Kafka. ACLs allow you to define which users have access to which resources at a granular level. To enable simple authorization, set the type field to simple.

An example of Simple authorization
# ...
authorization:
  type: simple
# ...

Access rules for users are defined using Access Control Lists (ACLs). You can optionally designate a list of super users in the superUsers field.

Super users

Super users can access all resources in your Kafka cluster regardless of any access restrictions defined in ACLs. To designate super users for a Kafka cluster, enter a list of user principles in the superUsers field. If a user uses TLS Client Authentication, the username will be the common name from their certificate subject prefixed with CN=.

An example of designating super users
# ...
authorization:
  type: simple
  superUsers:
    - CN=fred
    - sam
    - CN=edward
# ...
Note
 The super.user configuration option in the config property in Kafka.spec.kafka is ignored. Designate super users in the authorization property instead. For more information, see Kafka broker configuration.
Configuring authorization in Kafka brokers

Configure authorization and designate super users for a particular Kafka broker.

Prerequisites

  • A Kubernetes cluster

  • The Cluster Operator is running

Procedure

  1. Add or edit the authorization property in the Kafka.spec.kafka resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    authorization:
      type: simple
      superUsers:
        - CN=fred
        - sam
        - CN=edward
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Additional resources

3.1.8. ZooKeeper replicas

ZooKeeper clusters or ensembles usually run with an odd number of nodes, typically three, five, or seven.

The majority of nodes must be available in order to maintain an effective quorum. If the ZooKeeper cluster loses its quorum, it will stop responding to clients and the Kafka brokers will stop working. Having a stable and highly available ZooKeeper cluster is crucial for Strimzi.

Three-node cluster

A three-node ZooKeeper cluster requires at least two nodes to be up and running in order to maintain the quorum. It can tolerate only one node being unavailable.

Five-node cluster

A five-node ZooKeeper cluster requires at least three nodes to be up and running in order to maintain the quorum. It can tolerate two nodes being unavailable.

Seven-node cluster

A seven-node ZooKeeper cluster requires at least four nodes to be up and running in order to maintain the quorum. It can tolerate three nodes being unavailable.

Note
 For development purposes, it is also possible to run ZooKeeper with a single node.

Having more nodes does not necessarily mean better performance, as the costs to maintain the quorum will rise with the number of nodes in the cluster. Depending on your availability requirements, you can decide for the number of nodes to use.

Number of ZooKeeper nodes

The number of ZooKeeper nodes can be configured using the replicas property in Kafka.spec.zookeeper.

An example showing replicas configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
    replicas: 3
    # ...
Changing the number of ZooKeeper replicas
Prerequisites

  • A Kubernetes cluster is available.

  • The Cluster Operator is running.

Procedure

  1. Open the YAML configuration file that contains the Kafka resource specifying the cluster deployment.

  2. In the spec.zookeeper.replicas property in the Kafka resource, enter the number of replicated ZooKeeper servers. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
    replicas: 3
    # ...

  3. Apply the new configuration to create or update the resource.

    Use kubectl apply:

    kubectl apply -f kafka.yaml

    where kafka.yaml is the YAML configuration file for the resource that you want to configure; for example, kafka-persistent.yaml.

3.1.9. ZooKeeper configuration

Strimzi allows you to customize the configuration of Apache ZooKeeper nodes. You can specify and configure most of the options listed in the ZooKeeper documentation.

Options which cannot be configured are those related to the following areas:

  • Security (Encryption, Authentication, and Authorization)

  • Listener configuration

  • Configuration of data directories

  • ZooKeeper cluster composition

These options are automatically configured by Strimzi.

ZooKeeper configuration

ZooKeeper nodes are configured using the config property in Kafka.spec.zookeeper. This property contains the ZooKeeper configuration options as keys. The values can be described using one of the following JSON types:

  • String

  • Number

  • Boolean

Users can specify and configure the options listed in ZooKeeper documentation with the exception of those options which are managed directly by Strimzi. Specifically, all configuration options with keys equal to or starting with one of the following strings are forbidden:

  • server.

  • dataDir

  • dataLogDir

  • clientPort

  • authProvider

  • quorum.auth

  • requireClientAuthScheme

When one of the forbidden options is present in the config property, it is ignored and a warning message is printed to the Custer Operator log file. All other options are passed to ZooKeeper.

Important
 The Cluster Operator does not validate keys or values in the provided config object. When invalid configuration is provided, the ZooKeeper cluster might not start or might become unstable. In such cases, the configuration in the Kafka.spec.zookeeper.config object should be fixed and the Cluster Operator will roll out the new configuration to all ZooKeeper nodes.

Selected options have default values:

  • timeTick with default value 2000

  • initLimit with default value 5

  • syncLimit with default value 2

  • autopurge.purgeInterval with default value 1

These options will be automatically configured when they are not present in the Kafka.spec.zookeeper.config property.

An example showing ZooKeeper configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
  zookeeper:
    # ...
    config:
      autopurge.snapRetainCount: 3
      autopurge.purgeInterval: 1
    # ...
Configuring ZooKeeper
Prerequisites

  • A Kubernetes cluster is available.

  • The Cluster Operator is running.

Procedure

  1. Open the YAML configuration file that contains the Kafka resource specifying the cluster deployment.

  2. In the spec.zookeeper.config property in the Kafka resource, enter one or more ZooKeeper configuration settings. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
  zookeeper:
    # ...
    config:
      autopurge.snapRetainCount: 3
      autopurge.purgeInterval: 1
    # ...

  3. Apply the new configuration to create or update the resource.

    Use kubectl apply:

    kubectl apply -f kafka.yaml

    where kafka.yaml is the YAML configuration file for the resource that you want to configure; for example, kafka-persistent.yaml.

3.1.10. ZooKeeper connection

ZooKeeper services are secured with encryption and authentication and are not intended to be used by external applications that are not part of Strimzi.

However, if you want to use Kafka CLI tools that require a connection to ZooKeeper, such as the kafka-topics tool, you can use a terminal inside a Kafka container and connect to the local end of the TLS tunnel to ZooKeeper by using localhost:2181 as the ZooKeeper address.

Connecting to ZooKeeper from a terminal

Open a terminal inside a Kafka container to use Kafka CLI tools that require a ZooKeeper connection.

Prerequisites

  • A Kubernetes cluster is available.

  • A kafka cluster is running.

  • The Cluster Operator is running.

Procedure

  1. Open the terminal using the Kubernetes console or run the exec command from your CLI.

    For example:

    kubectl exec -it my-cluster-kafka-0 -- bin/kafka-topics.sh --list --zookeeper localhost:2181

    Be sure to use localhost:2181.

    You can now run Kafka commands to ZooKeeper.

3.1.11. Entity Operator

The Entity Operator is responsible for managing Kafka-related entities in a running Kafka cluster.

The Entity Operator comprises the:

Through Kafka resource configuration, the Cluster Operator can deploy the Entity Operator, including one or both operators, when deploying a Kafka cluster.

Note
 When deployed, the Entity Operator contains the operators according to the deployment configuration.

The operators are automatically configured to manage the topics and users of the Kafka cluster.

Entity Operator configuration properties

Use the entityOperator property in Kafka.spec to configure the Entity Operator.

The entityOperator property supports several sub-properties:

  • tlsSidecar

  • topicOperator

  • userOperator

  • template

The tlsSidecar property contains the configuration of the TLS sidecar container, which is used to communicate with ZooKeeper. For more information on configuring the TLS sidecar, see TLS sidecar.

The template property contains the configuration of the Entity Operator pod, such as labels, annotations, affinity, and tolerations. For more information on configuring templates, see Template properties.

The topicOperator property contains the configuration of the Topic Operator. When this option is missing, the Entity Operator is deployed without the Topic Operator.

The userOperator property contains the configuration of the User Operator. When this option is missing, the Entity Operator is deployed without the User Operator.

For more information on the properties to configure the Entity Operator, see the EntityUserOperatorSpec schema reference.

Example of basic configuration enabling both operators
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    topicOperator: {}
    userOperator: {}

If an empty object ({}) is used for the topicOperator and userOperator, all properties use their default values.

When both topicOperator and userOperator properties are missing, the Entity Operator is not deployed.

Topic Operator configuration properties

Topic Operator deployment can be configured using additional options inside the topicOperator object. The following properties are supported:

watchedNamespace

The Kubernetes namespace in which the topic operator watches for KafkaTopics. Default is the namespace where the Kafka cluster is deployed.

reconciliationIntervalSeconds

The interval between periodic reconciliations in seconds. Default 90.

zookeeperSessionTimeoutSeconds

The ZooKeeper session timeout in seconds. Default 20.

topicMetadataMaxAttempts

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.

image

The image property can be used to configure the container image which will be used. For more details about configuring custom container images, see Container images.

resources

The resources property configures the amount of resources allocated to the Topic Operator. For more details about resource request and limit configuration, see CPU and memory resources.

logging

The logging property configures the logging of the Topic Operator. For more details, see Operator loggers.

Example of Topic Operator configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    # ...
    topicOperator:
      watchedNamespace: my-topic-namespace
      reconciliationIntervalSeconds: 60
    # ...
User Operator configuration properties

User Operator deployment can be configured using additional options inside the userOperator object. The following properties are supported:

watchedNamespace

The Kubernetes namespace in which the user operator watches for KafkaUsers. Default is the namespace where the Kafka cluster is deployed.

reconciliationIntervalSeconds

The interval between periodic reconciliations in seconds. Default 120.

zookeeperSessionTimeoutSeconds

The ZooKeeper session timeout in seconds. Default 6.

image

The image property can be used to configure the container image which will be used. For more details about configuring custom container images, see Container images.

resources

The resources property configures the amount of resources allocated to the User Operator. For more details about resource request and limit configuration, see CPU and memory resources.

logging

The logging property configures the logging of the User Operator. For more details, see Operator loggers.

Example of User Operator configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    # ...
    userOperator:
      watchedNamespace: my-user-namespace
      reconciliationIntervalSeconds: 60
    # ...
Operator loggers

The Topic Operator and User Operator have a configurable logger:

  • rootLogger.level

The operators use the Apache log4j2 logger implementation.

Use the logging property in the Kafka resource to configure loggers and logger levels.

You can set the log levels by specifying the logger and level directly (inline) or use a custom (external) ConfigMap. If a ConfigMap is used, you set logging.name property to the name of the ConfigMap containing the external logging configuration. Inside the ConfigMap, the logging configuration is described using log4j2.properties.

Here we see examples of inline and external logging.

Inline logging
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    # ...
    topicOperator:
      watchedNamespace: my-topic-namespace
      reconciliationIntervalSeconds: 60
      logging:
        type: inline
        loggers:
          rootLogger.level: INFO
    # ...
    userOperator:
      watchedNamespace: my-topic-namespace
      reconciliationIntervalSeconds: 60
      logging:
        type: inline
        loggers:
          rootLogger.level: INFO
# ...
External logging
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    # ...
    topicOperator:
      watchedNamespace: my-topic-namespace
      reconciliationIntervalSeconds: 60
      logging:
        type: external
        name: customConfigMap
# ...
Additional resources
Configuring the Entity Operator
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the entityOperator property in the Kafka resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    topicOperator:
      watchedNamespace: my-topic-namespace
      reconciliationIntervalSeconds: 60
    userOperator:
      watchedNamespace: my-user-namespace
      reconciliationIntervalSeconds: 60

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.1.12. CPU and memory resources

For every deployed container, Strimzi allows you to request specific resources and define the maximum consumption of those resources.

Strimzi supports two types of resources:

  • CPU

  • Memory

Strimzi uses the Kubernetes syntax for specifying CPU and memory resources.

Resource limits and requests

Resource limits and requests are configured using the resources property in the following resources:

  • Kafka.spec.kafka

  • Kafka.spec.kafka.tlsSidecar

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.entityOperator.tlsSidecar

  • Kafka.spec.KafkaExporter

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaBridge.spec

Additional resources
Resource requests

Requests specify the resources to reserve for a given container. Reserving the resources ensures that they are always available.

Important
 If the resource request is for more than the available free resources in the Kubernetes cluster, the pod is not scheduled.

Resources requests are specified in the requests property. Resources requests currently supported by Strimzi:

  • cpu

  • memory

A request may be configured for one or more supported resources.

Example resource request configuration with all resources
# ...
resources:
  requests:
    cpu: 12
    memory: 64Gi
# ...
Resource limits

Limits specify the maximum resources that can be consumed by a given container. The limit is not reserved and might not always be available. A container can use the resources up to the limit only when they are available. Resource limits should be always higher than the resource requests.

Resource limits are specified in the limits property. Resource limits currently supported by Strimzi:

  • cpu

  • memory

A resource may be configured for one or more supported limits.

Example resource limits configuration
# ...
resources:
  limits:
    cpu: 12
    memory: 64Gi
# ...
Supported CPU formats

CPU requests and limits are supported in the following formats:

  • Number of CPU cores as integer (5 CPU core) or decimal (2.5 CPU core).

  • Number or millicpus / millicores (100m) where 1000 millicores is the same 1 CPU core.

Example CPU units
# ...
resources:
  requests:
    cpu: 500m
  limits:
    cpu: 2.5
# ...
Note
 The computing power of 1 CPU core may differ depending on the platform where Kubernetes is deployed.
Additional resources
Supported memory formats

Memory requests and limits are specified in megabytes, gigabytes, mebibytes, and gibibytes.

  • To specify memory in megabytes, use the M suffix. For example 1000M.

  • To specify memory in gigabytes, use the G suffix. For example 1G.

  • To specify memory in mebibytes, use the Mi suffix. For example 1000Mi.

  • To specify memory in gibibytes, use the Gi suffix. For example 1Gi.

An example of using different memory units
# ...
resources:
  requests:
    memory: 512Mi
  limits:
    memory: 2Gi
# ...
Additional resources

  • For more details about memory specification and additional supported units, see Meaning of memory.

Configuring resource requests and limits
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the resources property in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    resources:
      requests:
        cpu: "8"
        memory: 64Gi
      limits:
        cpu: "12"
        memory: 128Gi
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Additional resources

3.1.13. Kafka loggers

Kafka has its own configurable loggers:

  • kafka.root.logger.level

  • log4j.logger.org.I0Itec.zkclient.ZkClient

  • log4j.logger.org.apache.zookeeper

  • log4j.logger.kafka

  • log4j.logger.org.apache.kafka

  • log4j.logger.kafka.request.logger

  • log4j.logger.kafka.network.Processor

  • log4j.logger.kafka.server.KafkaApis

  • log4j.logger.kafka.network.RequestChannel$

  • log4j.logger.kafka.controller

  • log4j.logger.kafka.log.LogCleaner

  • log4j.logger.state.change.logger

  • log4j.logger.kafka.authorizer.logger

ZooKeeper also has a configurable logger:

  • zookeeper.root.logger

Kafka and ZooKeeper use the Apache log4j logger implementation.

Use the logging property to configure loggers and logger levels.

You can set the log levels by specifying the logger and level directly (inline) or use a custom (external) ConfigMap. If a ConfigMap is used, you set logging.name property to the name of the ConfigMap containing the external logging configuration. Inside the ConfigMap, the logging configuration is described using log4j.properties.

Here we see examples of inline and external logging.

Inline logging
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  # ...
  logging:
    type: inline
    loggers:
      kafka.root.logger.level: "INFO"
  # ...
  zookeeper:
    # ...
    logging:
      type: inline
      loggers:
        zookeeper.root.logger: "INFO"
  # ...
  entityOperator:
    # ...
    topicOperator:
      # ...
      logging:
        type: inline
        loggers:
          rootLogger.level: INFO
    # ...
    userOperator:
      # ...
      logging:
        type: inline
        loggers:
          rootLogger.level: INFO
    # ...
External logging
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  # ...
  logging:
    type: external
    name: customConfigMap
  # ...

Operators use the Apache log4j2 logger implementation, so the logging configuration is described inside the ConfigMap using log4j2.properties. For more information, see Operator loggers.

Additional resources

  • Garbage collector (GC) logging can also be enabled (or disabled). For more information on garbage collection, see JVM configuration

  • For more information about log levels, see Apache logging services.

3.1.14. Kafka rack awareness

The rack awareness feature in Strimzi helps to spread the Kafka broker pods and Kafka topic replicas across different racks. Enabling rack awareness helps to improve availability of Kafka brokers and the topics they are hosting.

Note
 "Rack" might represent an availability zone, data center, or an actual rack in your data center.
Configuring rack awareness in Kafka brokers

Kafka rack awareness can be configured in the rack property of Kafka.spec.kafka. The rack object has one mandatory field named topologyKey. This key needs to match one of the labels assigned to the Kubernetes cluster nodes. The label is used by Kubernetes when scheduling the Kafka broker pods to nodes. If the Kubernetes cluster is running on a cloud provider platform, that label should represent the availability zone where the node is running. Usually, the nodes are labeled with failure-domain.beta.kubernetes.io/zone that can be easily used as the topologyKey value. This has the effect of spreading the broker pods across zones, and also setting the brokers' broker.rack configuration parameter inside Kafka broker.

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Consult your Kubernetes administrator regarding the node label that represents the zone / rack into which the node is deployed.

  2. Edit the rack property in the Kafka resource using the label as the topology key.

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    rack:
      topologyKey: failure-domain.beta.kubernetes.io/zone
    # ...

  3. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Additional resources

  • For information about Configuring init container image for Kafka rack awareness, see Container images.

3.1.15. Healthchecks

Healthchecks are periodical tests which verify the health of an application. When a Healthcheck probe fails, Kubernetes assumes that the application is not healthy and attempts to fix it.

Kubernetes supports two types of Healthcheck probes:

  • Liveness probes

  • Readiness probes

For more details about the probes, see Configure Liveness and Readiness Probes. Both types of probes are used in Strimzi components.

Users can configure selected options for liveness and readiness probes.

Healthcheck configurations

Liveness and readiness probes can be configured using the livenessProbe and readinessProbe properties in following resources:

  • Kafka.spec.kafka

  • Kafka.spec.kafka.tlsSidecar

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator.tlsSidecar

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.KafkaExporter

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaMirrorMaker.spec

  • KafkaBridge.spec

Both livenessProbe and readinessProbe support the following options:

  • initialDelaySeconds

  • timeoutSeconds

  • periodSeconds

  • successThreshold

  • failureThreshold

For more information about the livenessProbe and readinessProbe options, see Probe schema reference.

An example of liveness and readiness probe configuration
# ...
readinessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
livenessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
# ...
Configuring healthchecks
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the livenessProbe or readinessProbe property in the Kafka, KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    readinessProbe:
      initialDelaySeconds: 15
      timeoutSeconds: 5
    livenessProbe:
      initialDelaySeconds: 15
      timeoutSeconds: 5
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.1.16. Prometheus metrics

Strimzi supports Prometheus metrics using Prometheus JMX exporter to convert the JMX metrics supported by Apache Kafka and ZooKeeper to Prometheus metrics. When metrics are enabled, they are exposed on port 9404.

For more information about setting up and deploying Prometheus and Grafana, see Introducing Metrics to Kafka.

Metrics configuration

Prometheus metrics are enabled by configuring the metrics property in following resources:

  • Kafka.spec.kafka

  • Kafka.spec.zookeeper

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

When the metrics property is not defined in the resource, the Prometheus metrics will be disabled. To enable Prometheus metrics export without any further configuration, you can set it to an empty object ({}).

Example of enabling metrics without any further configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    metrics: {}
    # ...
  zookeeper:
    # ...

The metrics property might contain additional configuration for the Prometheus JMX exporter.

Example of enabling metrics with additional Prometheus JMX Exporter configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    metrics:
      lowercaseOutputName: true
      rules:
        - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*><>Count"
          name: "kafka_server_$1_$2_total"
        - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*, topic=(.+)><>Count"
          name: "kafka_server_$1_$2_total"
          labels:
            topic: "$3"
    # ...
  zookeeper:
    # ...
Configuring Prometheus metrics
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the metrics property in the Kafka, KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
    metrics:
      lowercaseOutputName: true
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.1.17. JMX Options

Strimzi supports obtaining JMX metrics from the Kafka brokers by opening a JMX port on 9999. You can obtain various metrics about each Kafka broker, for example, usage data such as the BytesPerSecond value or the request rate of the network of the broker. Strimzi supports opening a password and username protected JMX port or a non-protected JMX port.

Configuring JMX options

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

You can configure JMX options by using the jmxOptions property in the following resources:

  • Kafka.spec.kafka

You can configure username and password protection for the JMX port that is opened on the Kafka brokers.

Securing the JMX Port

You can secure the JMX port to prevent unauthorized pods from accessing the port. Currently the JMX port can only be secured using a username and password. To enable security for the JMX port, set the type parameter in the authentication field to password.:

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    jmxOptions:
      authentication:
        type: "password"
    # ...
  zookeeper:
    # ...

This allows you to deploy a pod internally into a cluster and obtain JMX metrics by using the headless service and specifying which broker you want to address. To get JMX metrics from broker 0 we address the headless service appending broker 0 in front of the headless service:

"<cluster-name>-kafka-0-<cluster-name>-<headless-service-name>"

If the JMX port is secured, you can get the username and password by referencing them from the JMX secret in the deployment of your pod.

Using an open JMX port

To disable security for the JMX port, do not fill in the authentication field

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    jmxOptions: {}
    # ...
  zookeeper:
    # ...

This will just open the JMX Port on the headless service and you can follow a similar approach as described above to deploy a pod into the cluster. The only difference is that any pod will be able to read from the JMX port.

3.1.18. Retrieving JMX metrics with JMXTrans

JmxTrans is a way of retrieving JMX metrics data from Java processes and pushing that data in various formats to remote sinks inside or outside of the cluster. JmxTrans can communicate with a secure JMX port. Strimzi supports using JmxTrans to read JMX data from Kafka brokers.

Jmxtrans

JmxTrans reads JMX metrics data from secure or insecure Kafka brokers and pushes the data to remote sinks in various data formats. An example use case of the Jmxtrans would be to obtain JMX metrics about the request rate of each Kafka broker’s network and push it to a Logstash database outside of the Kubernetes cluster.

Configuring a JMXTrans deployment
Prerequisites

  • A running Kubernetes cluster

You can configure a JmxTrans deployment by using the Kafka.spec.jmxTrans property. A JmxTrans deployment can read from a secure or insecure Kafka broker. To configure a JmxTrans deployment, define the following properties:

  • Kafka.spec.jmxTrans.outputDefinitions

  • Kafka.spec.jmxTrans.kafkaQueries

For more information on these properties see, JmxTransSpec schema reference.

Note
 JmxTrans will not come up enable you specify that JmxOptions on the Kafka broker. For more information see, Kafka Jmx Options .Configuring JmxTrans output definitions

Output definitions specify where JMX metrics are pushed to, and in which data format. For information about supported data formats, see Data formats. How many seconds JmxTrans agent waits for before pushing new data can be configured through the flushDelay property. The host and port properties define the target host address and target port the data is pushed to. The name property is a required property that is referenced by the Kafka.spec.kafka.jmxOptions.jmxTrans.queries property.

Here is an example configuration pushing JMX data in the Graphite format every 5 seconds to a Logstash database on http://myLogstash:9999, and another pushing to standardOut (standard output):

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  jmxTrans:
    outputDefinitions:
      - outputType: "com.googlecode.jmxtrans.model.output.GraphiteWriter"
        host: "http://myLogstash"
        port: 9999
        flushDelay: 5
        name: "logstash"
      - outputType: "com.googlecode.jmxtrans.model.output.StdOutWriter"
        name: "standardOut"
        # ...
    # ...
  zookeeper:
    # ...
Configuring JmxTrans queries

JmxTrans queries specify what JMX metrics are read from the Kafka brokers. Currently JmxTrans queries can only be sent to the Kafka Brokers. Configure the targetMBean property to specify which target MBean on the Kafka broker is addressed. Configuring the attributes property specifies which MBean attribute is read as JMX metrics from the target MBean. JmxTrans supports wildcards to read from target MBeans, and filter by specifying the typenames. The outputs property defines where the metrics are pushed to by specifying the name of the output definitions.

The following JmxTrans deployment reads from all MBeans that match the pattern kafka.server:type=BrokerTopicMetrics,name=* and have name in the target MBean’s name. From those Mbeans, it obtains JMX metrics about the Count attribute and pushes the metrics to standard output as defined by outputs.

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  # ...
  jmxTrans:
    kafkaQueries:
      - targetMBean: "kafka.server:type=BrokerTopicMetrics,*"
        typeNames: ["name"]
        attributes:  ["Count"]
        outputs: ["standardOut"]
  zookeeper:
    # ...
Additional resources

For more information about the Jmxtrans see Jmxtrans github

3.1.19. JVM Options

The following components of Strimzi run inside a Virtual Machine (VM):

  • Apache Kafka

  • Apache ZooKeeper

  • Apache Kafka Connect

  • Apache Kafka MirrorMaker

  • Strimzi Kafka Bridge

JVM configuration options optimize the performance for different platforms and architectures. Strimzi allows you to configure some of these options.

JVM configuration

JVM options can be configured using the jvmOptions property in following resources:

  • Kafka.spec.kafka

  • Kafka.spec.zookeeper

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaMirrorMaker.spec

  • KafkaBridge.spec

Only a selected subset of available JVM options can be configured. The following options are supported:

-Xms and -Xmx

-Xms configures the minimum initial allocation heap size when the JVM starts. -Xmx configures the maximum heap size.

Note
 The units accepted by JVM settings such as -Xmx and -Xms are those accepted by the JDK java binary in the corresponding image. Accordingly, 1g or 1G means 1,073,741,824 bytes, and Gi is not a valid unit suffix. This is in contrast to the units used for memory requests and limits, which follow the Kubernetes convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes

The default values used for -Xms and -Xmx depends on whether there is a memory request limit configured for the container:

  • If there is a memory limit then the JVM’s minimum and maximum memory will be set to a value corresponding to the limit.

  • If there is no memory limit then the JVM’s minimum memory will be set to 128M and the JVM’s maximum memory will not be defined. This allows for the JVM’s memory to grow as-needed, which is ideal for single node environments in test and development.

Important

Setting -Xmx explicitly requires some care:

  • The JVM’s overall memory usage will be approximately 4 × the maximum heap, as configured by -Xmx.

  • If -Xmx is set without also setting an appropriate Kubernetes memory limit, it is possible that the container will be killed should the Kubernetes node experience memory pressure (from other Pods running on it).

  • If -Xmx is set without also setting an appropriate Kubernetes memory request, it is possible that the container will be scheduled to a node with insufficient memory. In this case, the container will not start but crash (immediately if -Xms is set to -Xmx, or some later time if not).

When setting -Xmx explicitly, it is recommended to:

  • set the memory request and the memory limit to the same value,

  • use a memory request that is at least 4.5 × the -Xmx,

  • consider setting -Xms to the same value as -Xmx.

Important
 Containers doing lots of disk I/O (such as Kafka broker containers) will need to leave some memory available for use as operating system page cache. On such containers, the requested memory should be significantly higher than the memory used by the JVM.
Example fragment configuring -Xmx and -Xms
# ...
jvmOptions:
  "-Xmx": "2g"
  "-Xms": "2g"
# ...

In the above example, the JVM will use 2 GiB (=2,147,483,648 bytes) for its heap. Its total memory usage will be approximately 8GiB.

Setting the same value for initial (-Xms) and maximum (-Xmx) heap sizes avoids the JVM having to allocate memory after startup, at the cost of possibly allocating more heap than is really needed. For Kafka and ZooKeeper pods such allocation could cause unwanted latency. For Kafka Connect avoiding over allocation may be the most important concern, especially in distributed mode where the effects of over-allocation will be multiplied by the number of consumers.

-server

-server enables the server JVM. This option can be set to true or false.

Example fragment configuring -server
# ...
jvmOptions:
  "-server": true
# ...
Note
 When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.
-XX

-XX object can be used for configuring advanced runtime options of a JVM. The -server and -XX options are used to configure the KAFKA_JVM_PERFORMANCE_OPTS option of Apache Kafka.

Example showing the use of the -XX object
jvmOptions:
  "-XX":
    "UseG1GC": true
    "MaxGCPauseMillis": 20
    "InitiatingHeapOccupancyPercent": 35
    "ExplicitGCInvokesConcurrent": true
    "UseParNewGC": false

The example configuration above will result in the following JVM options:

-XX:+UseG1GC -XX:MaxGCPauseMillis=20 -XX:InitiatingHeapOccupancyPercent=35 -XX:+ExplicitGCInvokesConcurrent -XX:-UseParNewGC
Note
 When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.
Garbage collector logging

The jvmOptions section also allows you to enable and disable garbage collector (GC) logging. GC logging is disabled by default. To enable it, set the gcLoggingEnabled property as follows:

Example of enabling GC logging
# ...
jvmOptions:
  gcLoggingEnabled: true
# ...
Configuring JVM options
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the jvmOptions property in the Kafka, KafkaConnect, KafkaConnectS2I, KafkaMirrorMaker, or KafkaBridge resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    jvmOptions:
      "-Xmx": "8g"
      "-Xms": "8g"
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.1.20. Container images

Strimzi allows you to configure container images which will be used for its components. Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by Strimzi. In such a case, you should either copy the Strimzi images or build them from the source. If the configured image is not compatible with Strimzi images, it might not work properly.

Container image configurations

You can specify which container image to use for each component using the image property in the following resources:

  • Kafka.spec.kafka

  • Kafka.spec.kafka.tlsSidecar

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.entityOperator.tlsSidecar

  • Kafka.spec.jmxTrans

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaBridge.spec

Configuring the image property for Kafka, Kafka Connect, and Kafka MirrorMaker

Kafka, Kafka Connect (including Kafka Connect with S2I support), and Kafka MirrorMaker support multiple versions of Kafka. Each component requires its own image. The default images for the different Kafka versions are configured in the following environment variables:

  • STRIMZI_KAFKA_IMAGES

  • STRIMZI_KAFKA_CONNECT_IMAGES

  • STRIMZI_KAFKA_CONNECT_S2I_IMAGES

  • STRIMZI_KAFKA_MIRROR_MAKER_IMAGES

These environment variables contain mappings between the Kafka versions and their corresponding images. The mappings are used together with the image and version properties:

  • If neither image nor version are given in the custom resource then the version will default to the Cluster Operator’s default Kafka version, and the image will be the one corresponding to this version in the environment variable.

  • If image is given but version is not, then the given image is used and the version is assumed to be the Cluster Operator’s default Kafka version.

  • If version is given but image is not, then the image that corresponds to the given version in the environment variable is used.

  • If both version and image are given, then the given image is used. The image is assumed to contain a Kafka image with the given version.

The image and version for the different components can be configured in the following properties:

  • For Kafka in spec.kafka.image and spec.kafka.version.

  • For Kafka Connect, Kafka Connect S2I, and Kafka MirrorMaker in spec.image and spec.version.

Warning
 It is recommended to provide only the version and leave the image property unspecified. This reduces the chance of making a mistake when configuring the custom resource. If you need to change the images used for different versions of Kafka, it is preferable to configure the Cluster Operator’s environment variables.
Configuring the image property in other resources

For the image property in the other custom resources, the given value will be used during deployment. If the image property is missing, the image specified in the Cluster Operator configuration will be used. If the image name is not defined in the Cluster Operator configuration, then the default value will be used.

  • For Kafka broker TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_KAFKA_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.18.0-kafka-2.5.0 container image.

  • For ZooKeeper nodes:

  • For ZooKeeper node TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_ZOOKEEPER_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.18.0-kafka-2.5.0 container image.

  • For Topic Operator:

    1. Container image specified in the STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/operator:0.18.0 container image.

  • For User Operator:

    1. Container image specified in the STRIMZI_DEFAULT_USER_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/operator:0.18.0 container image.

  • For Entity Operator TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.18.0-kafka-2.5.0 container image.

  • For Kafka Exporter:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_EXPORTER_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.18.0-kafka-2.5.0 container image.

  • For Kafka Bridge:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_BRIDGE_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka-bridge:0.16.0 container image.

  • For Kafka broker initializer:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_INIT_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/operator:0.18.0 container image.

  • For Kafka broker initializer:

    1. Container image specified in the STRIMZI_DEFAULT_JMXTRANS_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/operator:0.18.0 container image.

Warning
 Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by Strimzi. In such case, you should either copy the Strimzi images or build them from source. In case the configured image is not compatible with Strimzi images, it might not work properly.
Example of container image configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    image: my-org/my-image:latest
    # ...
  zookeeper:
    # ...
Configuring container images
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the image property in the Kafka, KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    image: my-org/my-image:latest
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.1.21. TLS sidecar

A sidecar is a container that runs in a pod but serves a supporting purpose. In Strimzi, the TLS sidecar uses TLS to encrypt and decrypt all communication between the various components and ZooKeeper. ZooKeeper does not have native TLS support.

The TLS sidecar is used in:

  • Kafka brokers

  • ZooKeeper nodes

  • Entity Operator

TLS sidecar configuration

The TLS sidecar can be configured using the tlsSidecar property in:

  • Kafka.spec.kafka

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator

The TLS sidecar supports the following additional options:

  • image

  • resources

  • logLevel

  • readinessProbe

  • livenessProbe

The resources property can be used to specify the memory and CPU resources allocated for the TLS sidecar.

The image property can be used to configure the container image which will be used. For more details about configuring custom container images, see Container images.

The logLevel property is used to specify the logging level. Following logging levels are supported:

  • emerg

  • alert

  • crit

  • err

  • warning

  • notice

  • info

  • debug

The default value is notice.

For more information about configuring the readinessProbe and livenessProbe properties for the healthchecks, see Healthcheck configurations.

Example of TLS sidecar configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    tlsSidecar:
      image: my-org/my-image:latest
      resources:
        requests:
          cpu: 200m
          memory: 64Mi
        limits:
          cpu: 500m
          memory: 128Mi
      logLevel: debug
      readinessProbe:
        initialDelaySeconds: 15
        timeoutSeconds: 5
      livenessProbe:
        initialDelaySeconds: 15
        timeoutSeconds: 5
    # ...
  zookeeper:
    # ...
Configuring TLS sidecar
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the tlsSidecar property in the Kafka resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    tlsSidecar:
      resources:
        requests:
          cpu: 200m
          memory: 64Mi
        limits:
          cpu: 500m
          memory: 128Mi
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.1.22. Configuring pod scheduling

Important
 When two applications are scheduled to the same Kubernetes node, both applications might use the same resources like disk I/O and impact performance. That can lead to performance degradation. Scheduling Kafka pods in a way that avoids sharing nodes with other critical workloads, using the right nodes or dedicated a set of nodes only for Kafka are the best ways how to avoid such problems.
Scheduling pods based on other applications
Avoid critical applications to share the node

Pod anti-affinity can be used to ensure that critical applications are never scheduled on the same disk. When running Kafka cluster, it is recommended to use pod anti-affinity to ensure that the Kafka brokers do not share the nodes with other workloads like databases.

Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod

  • Kafka.spec.zookeeper.template.pod

  • Kafka.spec.entityOperator.template.pod

  • KafkaConnect.spec.template.pod

  • KafkaConnectS2I.spec.template.pod

  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

The format of the affinity property follows the Kubernetes specification. For more details, see the Kubernetes node and pod affinity documentation.

Configuring pod anti-affinity in Kafka components
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the affinity property in the resource specifying the cluster deployment. Use labels to specify the pods which should not be scheduled on the same nodes. The topologyKey should be set to kubernetes.io/hostname to specify that the selected pods should not be scheduled on nodes with the same hostname. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    template:
      pod:
        affinity:
          podAntiAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              - labelSelector:
                  matchExpressions:
                    - key: application
                      operator: In
                      values:
                        - postgresql
                        - mongodb
                topologyKey: "kubernetes.io/hostname"
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Scheduling pods to specific nodes
Node scheduling

The Kubernetes cluster usually consists of many different types of worker nodes. Some are optimized for CPU heavy workloads, some for memory, while other might be optimized for storage (fast local SSDs) or network. Using different nodes helps to optimize both costs and performance. To achieve the best possible performance, it is important to allow scheduling of Strimzi components to use the right nodes.

Kubernetes uses node affinity to schedule workloads onto specific nodes. Node affinity allows you to create a scheduling constraint for the node on which the pod will be scheduled. The constraint is specified as a label selector. You can specify the label using either the built-in node label like beta.kubernetes.io/instance-type or custom labels to select the right node.

Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod

  • Kafka.spec.zookeeper.template.pod

  • Kafka.spec.entityOperator.template.pod

  • KafkaConnect.spec.template.pod

  • KafkaConnectS2I.spec.template.pod

  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

The format of the affinity property follows the Kubernetes specification. For more details, see the Kubernetes node and pod affinity documentation.

Configuring node affinity in Kafka components
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Label the nodes where Strimzi components should be scheduled.

    This can be done using kubectl label:

    kubectl label node your-node node-type=fast-network

    Alternatively, some of the existing labels might be reused.

  2. Edit the affinity property in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    template:
      pod:
        affinity:
          nodeAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
                - matchExpressions:
                  - key: node-type
                    operator: In
                    values:
                    - fast-network
    # ...
  zookeeper:
    # ...

  3. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Using dedicated nodes
Dedicated nodes

Cluster administrators can mark selected Kubernetes nodes as tainted. Nodes with taints are excluded from regular scheduling and normal pods will not be scheduled to run on them. Only services which can tolerate the taint set on the node can be scheduled on it. The only other services running on such nodes will be system services such as log collectors or software defined networks.

Taints can be used to create dedicated nodes. Running Kafka and its components on dedicated nodes can have many advantages. There will be no other applications running on the same nodes which could cause disturbance or consume the resources needed for Kafka. That can lead to improved performance and stability.

To schedule Kafka pods on the dedicated nodes, configure node affinity and tolerations.

Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod

  • Kafka.spec.zookeeper.template.pod

  • Kafka.spec.entityOperator.template.pod

  • KafkaConnect.spec.template.pod

  • KafkaConnectS2I.spec.template.pod

  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

The format of the affinity property follows the Kubernetes specification. For more details, see the Kubernetes node and pod affinity documentation.

Tolerations

Tolerations can be configured using the tolerations property in following resources:

  • Kafka.spec.kafka.template.pod

  • Kafka.spec.zookeeper.template.pod

  • Kafka.spec.entityOperator.template.pod

  • KafkaConnect.spec.template.pod

  • KafkaConnectS2I.spec.template.pod

  • KafkaBridge.spec.template.pod

The format of the tolerations property follows the Kubernetes specification. For more details, see the Kubernetes taints and tolerations.

Setting up dedicated nodes and scheduling pods on them
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Select the nodes which should be used as dedicated.

  2. Make sure there are no workloads scheduled on these nodes.

  3. Set the taints on the selected nodes:

    This can be done using kubectl taint:

    kubectl taint node your-node dedicated=Kafka:NoSchedule

  4. Additionally, add a label to the selected nodes as well.

    This can be done using kubectl label:

    kubectl label node your-node dedicated=Kafka

  5. Edit the affinity and tolerations properties in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    template:
      pod:
        tolerations:
          - key: "dedicated"
            operator: "Equal"
            value: "Kafka"
            effect: "NoSchedule"
        affinity:
          nodeAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
              - matchExpressions:
                - key: dedicated
                  operator: In
                  values:
                  - Kafka
    # ...
  zookeeper:
    # ...

  6. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.1.23. Kafka Exporter

You can configure the Kafka resource to automatically deploy Kafka Exporter in your cluster.

Kafka Exporter extracts data for analysis as Prometheus metrics, primarily data relating to offsets, consumer groups, consumer lag and topics.

For information on Kafka Exporter and why it is important to monitor consumer lag for performance, see Kafka Exporter.

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.

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

  • A Kubernetes 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. CPU and memory resources to reserve.

    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. Customization of deployment templates and pods.

    8. Healthcheck readiness probes.

    9. Healthcheck liveness probes.

  2. Create or update the resource:

    kubectl apply -f kafka.yaml
What to do next

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

Additional resources

./using.html#type-KafkaExporterTemplate-reference.

3.1.24. Performing a rolling update of a Kafka cluster

This procedure describes how to manually trigger a rolling update of an existing Kafka cluster by using a Kubernetes annotation.

Prerequisites

  • A running Kafka cluster.

  • A running Cluster Operator.

Procedure

  1. Find the name of the StatefulSet that controls the Kafka pods you want to manually update.

    For example, if your Kafka cluster is named my-cluster, the corresponding StatefulSet is named my-cluster-kafka.

  2. Annotate the StatefulSet resource in Kubernetes. For example, using kubectl annotate:

    kubectl annotate statefulset cluster-name-kafka strimzi.io/manual-rolling-update=true

  3. Wait for the next reconciliation to occur (every two minutes by default). A rolling update of all pods within the annotated StatefulSet is triggered, as long as the annotation was detected by the reconciliation process. When the rolling update of all the pods is complete, the annotation is removed from the StatefulSet.

Additional resources

3.1.25. Performing a rolling update of a ZooKeeper cluster

This procedure describes how to manually trigger a rolling update of an existing ZooKeeper cluster by using a Kubernetes annotation.

Prerequisites

  • A running ZooKeeper cluster.

  • A running Cluster Operator.

Procedure

  1. Find the name of the StatefulSet that controls the ZooKeeper pods you want to manually update.

    For example, if your Kafka cluster is named my-cluster, the corresponding StatefulSet is named my-cluster-zookeeper.

  2. Annotate the StatefulSet resource in Kubernetes. For example, using kubectl annotate:

    kubectl annotate statefulset cluster-name-zookeeper strimzi.io/manual-rolling-update=true

  3. Wait for the next reconciliation to occur (every two minutes by default). A rolling update of all pods within the annotated StatefulSet is triggered, as long as the annotation was detected by the reconciliation process. When the rolling update of all the pods is complete, the annotation is removed from the StatefulSet.

Additional resources

3.1.26. Scaling clusters

Scaling Kafka clusters
Adding brokers to a cluster

The primary way of increasing throughput for a topic is to increase the number of partitions for that topic. That works because the extra partitions allow the load of the topic to be shared between the different brokers in the cluster. However, in situations where every broker is constrained by a particular resource (typically I/O) using more partitions will not result in increased throughput. Instead, you need to add brokers to the cluster.

When you add an extra broker to the cluster, Kafka does not assign any partitions to it automatically. You must decide which partitions to move from the existing brokers to the new broker.

Once the partitions have been redistributed between all the brokers, the resource utilization of each broker should be reduced.

Removing brokers from a cluster

Because Strimzi uses StatefulSets to manage broker pods, you cannot remove any pod from the cluster. You can only remove one or more of the highest numbered pods from the cluster. For example, in a cluster of 12 brokers the pods are named cluster-name-kafka-0 up to cluster-name-kafka-11. If you decide to scale down by one broker, the cluster-name-kafka-11 will be removed.

Before you remove a broker from a cluster, ensure that it is not assigned to any partitions. You should also decide which of the remaining brokers will be responsible for each of the partitions on the broker being decommissioned. Once the broker has no assigned partitions, you can scale the cluster down safely.

Partition reassignment

The Topic Operator does not currently support reassigning replicas to different brokers, so it is necessary to connect directly to broker pods to reassign replicas to brokers.

Within a broker pod, the kafka-reassign-partitions.sh utility allows you to reassign partitions to different brokers.

It has three different modes:

--generate

Takes a set of topics and brokers and generates a reassignment JSON file which will result in the partitions of those topics being assigned to those brokers. Because this operates on whole topics, it cannot be used when you just need to reassign some of the partitions of some topics.

--execute

Takes a reassignment JSON file and applies it to the partitions and brokers in the cluster. Brokers that gain partitions as a result become followers of the partition leader. For a given partition, once the new broker has caught up and joined the ISR (in-sync replicas) the old broker will stop being a follower and will delete its replica.

--verify

Using the same reassignment JSON file as the --execute step, --verify checks whether all of the partitions in the file have been moved to their intended brokers. If the reassignment is complete, --verify also removes any throttles that are in effect. Unless removed, throttles will continue to affect the cluster even after the reassignment has finished.

It is only possible to have one reassignment running in a cluster at any given time, and it is not possible to cancel a running reassignment. If you need to cancel a reassignment, wait for it to complete and then perform another reassignment to revert the effects of the first reassignment. The kafka-reassign-partitions.sh will print the reassignment JSON for this reversion as part of its output. Very large reassignments should be broken down into a number of smaller reassignments in case there is a need to stop in-progress reassignment.

Reassignment JSON file

The reassignment JSON file has a specific structure:

{
  "version": 1,
  "partitions": [
    <PartitionObjects>
  ]
}

Where <PartitionObjects> is a comma-separated list of objects like:

{
  "topic": <TopicName>,
  "partition": <Partition>,
  "replicas": [ <AssignedBrokerIds> ]
}
Note
 Although Kafka also supports a "log_dirs" property this should not be used in Strimzi.

The following is an example reassignment JSON file that assigns topic topic-a, partition 4 to brokers 2, 4 and 7, and topic topic-b partition 2 to brokers 1, 5 and 7:

{
  "version": 1,
  "partitions": [
    {
      "topic": "topic-a",
      "partition": 4,
      "replicas": [2,4,7]
    },
    {
      "topic": "topic-b",
      "partition": 2,
      "replicas": [1,5,7]
    }
  ]
}

Partitions not included in the JSON are not changed.

Reassigning partitions between JBOD volumes

When using JBOD storage in your Kafka cluster, you can choose to reassign the partitions between specific volumes and their log directories (each volume has a single log directory). To reassign a partition to a specific volume, add the log_dirs option to <PartitionObjects> in the reassignment JSON file.

{
  "topic": <TopicName>,
  "partition": <Partition>,
  "replicas": [ <AssignedBrokerIds> ],
  "log_dirs": [ <AssignedLogDirs> ]
}

The log_dirs object should contain the same number of log directories as the number of replicas specified in the replicas object. The value should be either an absolute path to the log directory, or the any keyword.

For example:

{
      "topic": "topic-a",
      "partition": 4,
      "replicas": [2,4,7].
      "log_dirs": [ "/var/lib/kafka/data-0/kafka-log2", "/var/lib/kafka/data-0/kafka-log4", "/var/lib/kafka/data-0/kafka-log7" ]
}
Generating reassignment JSON files

This procedure describes how to generate a reassignment JSON file that reassigns all the partitions for a given set of topics using the kafka-reassign-partitions.sh tool.

Prerequisites

  • A running Cluster Operator

  • A Kafka resource

  • A set of topics to reassign the partitions of

Procedure

  1. Prepare a JSON file named topics.json that lists the topics to move. It must have the following structure:

    {
  "version": 1,
  "topics": [
    <TopicObjects>
  ]
}

    where <TopicObjects> is a comma-separated list of objects like:

    {
  "topic": <TopicName>
}

    For example if you want to reassign all the partitions of topic-a and topic-b, you would need to prepare a topics.json file like this:

    {
  "version": 1,
  "topics": [
    { "topic": "topic-a"},
    { "topic": "topic-b"}
  ]
}

  2. Copy the topics.json file to one of the broker pods:

    cat topics.json | kubectl exec -c kafka <BrokerPod> -i -- \
  /bin/bash -c \
  'cat > /tmp/topics.json'

  3. Use the kafka-reassign-partitions.sh command to generate the reassignment JSON.

    kubectl exec <BrokerPod> -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
  --topics-to-move-json-file /tmp/topics.json \
  --broker-list <BrokerList> \
  --generate

    For example, to move all the partitions of topic-a and topic-b to brokers 4 and 7

    kubectl exec <BrokerPod> -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
  --topics-to-move-json-file /tmp/topics.json \
  --broker-list 4,7 \
  --generate
Creating reassignment JSON files manually

You can manually create the reassignment JSON file if you want to move specific partitions.

Reassignment throttles

Partition reassignment can be a slow process because it involves transferring large amounts of data between brokers. To avoid a detrimental impact on clients, you can throttle the reassignment process. This might cause the reassignment to take longer to complete.

  • If the throttle is too low then the newly assigned brokers will not be able to keep up with records being published and the reassignment will never complete.

  • If the throttle is too high then clients will be impacted.

For example, for producers, this could manifest as higher than normal latency waiting for acknowledgement. For consumers, this could manifest as a drop in throughput caused by higher latency between polls.

Scaling up a Kafka cluster

This procedure describes how to increase the number of brokers in a Kafka cluster.

Prerequisites

  • An existing Kafka cluster.

  • A reassignment JSON file named reassignment.json that describes how partitions should be reassigned to brokers in the enlarged cluster.

Procedure

  1. Add as many new brokers as you need by increasing the Kafka.spec.kafka.replicas configuration option.

  2. Verify that the new broker pods have started.

  3. Copy the reassignment.json file to the broker pod on which you will later execute the commands:

    cat reassignment.json | \
  kubectl exec broker-pod -c kafka -i -- /bin/bash -c \
  'cat > /tmp/reassignment.json'

    For example:

    cat reassignment.json | \
  kubectl exec my-cluster-kafka-0 -c kafka -i -- /bin/bash -c \
  'cat > /tmp/reassignment.json'

  4. Execute the partition reassignment using the kafka-reassign-partitions.sh command line tool from the same broker pod.

    kubectl exec broker-pod -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
  --reassignment-json-file /tmp/reassignment.json \
  --execute

    If you are going to throttle replication you can also pass the --throttle option with an inter-broker throttled rate in bytes per second. For example:

    kubectl exec my-cluster-kafka-0 -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
  --reassignment-json-file /tmp/reassignment.json \
  --throttle 5000000 \
  --execute

    This command will print out two reassignment JSON objects. The first records the current assignment for the partitions being moved. You should save this to a local file (not a file in the pod) in case you need to revert the reassignment later on. The second JSON object is the target reassignment you have passed in your reassignment JSON file.

  5. If you need to change the throttle during reassignment you can use the same command line with a different throttled rate. For example:

    kubectl exec my-cluster-kafka-0 -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
  --reassignment-json-file /tmp/reassignment.json \
  --throttle 10000000 \
  --execute

  6. Periodically verify whether the reassignment has completed using the kafka-reassign-partitions.sh command line tool from any of the broker pods. This is the same command as the previous step but with the --verify option instead of the --execute option.

    kubectl exec broker-pod -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
  --reassignment-json-file /tmp/reassignment.json \
  --verify

    For example,

    kubectl exec my-cluster-kafka-0 -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
  --reassignment-json-file /tmp/reassignment.json \
  --verify

  7. The reassignment has finished when the --verify command reports each of the partitions being moved as completed successfully. This final --verify will also have the effect of removing any reassignment throttles. You can now delete the revert file if you saved the JSON for reverting the assignment to their original brokers.

Scaling down a Kafka cluster
Additional resources

This procedure describes how to decrease the number of brokers in a Kafka cluster.

Prerequisites

  • An existing Kafka cluster.

  • A reassignment JSON file named reassignment.json describing how partitions should be reassigned to brokers in the cluster once the broker(s) in the highest numbered Pod(s) have been removed.

Procedure

  1. Copy the reassignment.json file to the broker pod on which you will later execute the commands:

    cat reassignment.json | \
  kubectl exec broker-pod -c kafka -i -- /bin/bash -c \
  'cat > /tmp/reassignment.json'

    For example:

    cat reassignment.json | \
  kubectl exec my-cluster-kafka-0 -c kafka -i -- /bin/bash -c \
  'cat > /tmp/reassignment.json'

  2. Execute the partition reassignment using the kafka-reassign-partitions.sh command line tool from the same broker pod.

    kubectl exec broker-pod -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
  --reassignment-json-file /tmp/reassignment.json \
  --execute

    If you are going to throttle replication you can also pass the --throttle option with an inter-broker throttled rate in bytes per second. For example:

    kubectl exec my-cluster-kafka-0 -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
  --reassignment-json-file /tmp/reassignment.json \
  --throttle 5000000 \
  --execute

    This command will print out two reassignment JSON objects. The first records the current assignment for the partitions being moved. You should save this to a local file (not a file in the pod) in case you need to revert the reassignment later on. The second JSON object is the target reassignment you have passed in your reassignment JSON file.

  3. If you need to change the throttle during reassignment you can use the same command line with a different throttled rate. For example:

    kubectl exec my-cluster-kafka-0 -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
  --reassignment-json-file /tmp/reassignment.json \
  --throttle 10000000 \
  --execute

  4. Periodically verify whether the reassignment has completed using the kafka-reassign-partitions.sh command line tool from any of the broker pods. This is the same command as the previous step but with the --verify option instead of the --execute option.

    kubectl exec broker-pod -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
  --reassignment-json-file /tmp/reassignment.json \
  --verify

    For example,

    kubectl exec my-cluster-kafka-0 -c kafka -it -- \
  bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \
  --reassignment-json-file /tmp/reassignment.json \
  --verify

  5. The reassignment has finished when the --verify command reports each of the partitions being moved as completed successfully. This final --verify will also have the effect of removing any reassignment throttles. You can now delete the revert file if you saved the JSON for reverting the assignment to their original brokers.

  6. Once all the partition reassignments have finished, the broker(s) being removed should not have responsibility for any of the partitions in the cluster. You can verify this by checking that the broker’s data log directory does not contain any live partition logs. If the log directory on the broker contains a directory that does not match the extended regular expression [a-zA-Z0-9.-]+\.[a-z0-9]+-delete$ then the broker still has live partitions and it should not be stopped.

    You can check this by executing the command:

    kubectl exec my-cluster-kafka-0 -c kafka -it -- \
  /bin/bash -c \
  "ls -l /var/lib/kafka/kafka-log_<N>_ | grep -E '^d' | grep -vE '[a-zA-Z0-9.-]+\.[a-z0-9]+-delete$'"

    where N is the number of the Pod(s) being deleted.

    If the above command prints any output then the broker still has live partitions. In this case, either the reassignment has not finished, or the reassignment JSON file was incorrect.

  7. Once you have confirmed that the broker has no live partitions you can edit the Kafka.spec.kafka.replicas of your Kafka resource, which will scale down the StatefulSet, deleting the highest numbered broker Pod(s).

3.1.27. Deleting Kafka nodes manually

Additional resources

This procedure describes how to delete an existing Kafka node by using a Kubernetes annotation. Deleting a Kafka node consists of deleting both the Pod on which the Kafka broker is running and the related PersistentVolumeClaim (if the cluster was deployed with persistent storage). After deletion, the Pod and its related PersistentVolumeClaim are recreated automatically.

Warning
 Deleting a PersistentVolumeClaim can cause permanent data loss. The following procedure should only be performed if you have encountered storage issues.
Prerequisites

  • A running Kafka cluster.

  • A running Cluster Operator.

Procedure

  1. Find the name of the Pod that you want to delete.

    For example, if the cluster is named cluster-name, the pods are named cluster-name-kafka-index, where index starts at zero and ends at the total number of replicas.

  2. Annotate the Pod resource in Kubernetes.

    Use kubectl annotate:

    kubectl annotate pod cluster-name-kafka-index strimzi.io/delete-pod-and-pvc=true

  3. Wait for the next reconciliation, when the annotated pod with the underlying persistent volume claim will be deleted and then recreated.

Additional resources

3.1.28. Deleting ZooKeeper nodes manually

This procedure describes how to delete an existing ZooKeeper node by using a Kubernetes annotation. Deleting a ZooKeeper node consists of deleting both the Pod on which ZooKeeper is running and the related PersistentVolumeClaim (if the cluster was deployed with persistent storage). After deletion, the Pod and its related PersistentVolumeClaim are recreated automatically.

Warning
 Deleting a PersistentVolumeClaim can cause permanent data loss. The following procedure should only be performed if you have encountered storage issues.
Prerequisites

  • A running ZooKeeper cluster.

  • A running Cluster Operator.

Procedure

  1. Find the name of the Pod that you want to delete.

    For example, if the cluster is named cluster-name, the pods are named cluster-name-zookeeper-index, where index starts at zero and ends at the total number of replicas.

  2. Annotate the Pod resource in Kubernetes.

    Use kubectl annotate:

    kubectl annotate pod cluster-name-zookeeper-index strimzi.io/delete-pod-and-pvc=true

  3. Wait for the next reconciliation, when the annotated pod with the underlying persistent volume claim will be deleted and then recreated.

Additional resources

3.1.29. Maintenance time windows for rolling updates

Maintenance time windows allow you to schedule certain rolling updates of your Kafka and ZooKeeper clusters to start at a convenient time.

Maintenance time windows overview

In most cases, the Cluster Operator only updates your Kafka or ZooKeeper clusters in response to changes to the corresponding Kafka resource. This enables you to plan when to apply changes to a Kafka resource to minimize the impact on Kafka client applications.

However, some updates to your Kafka and ZooKeeper clusters can happen without any corresponding change to the Kafka resource. For example, the Cluster Operator will need to perform a rolling restart if a CA (Certificate Authority) certificate that it manages is close to expiry.

While a rolling restart of the pods should not affect availability of the service (assuming correct broker and topic configurations), it could affect performance of the Kafka client applications. Maintenance time windows allow you to schedule such spontaneous rolling updates of your Kafka and ZooKeeper clusters to start at a convenient time. If maintenance time windows are not configured for a cluster then it is possible that such spontaneous rolling updates will happen at an inconvenient time, such as during a predictable period of high load.

Maintenance time window definition

You configure maintenance time windows by entering an array of strings in the Kafka.spec.maintenanceTimeWindows property. Each string is a cron expression interpreted as being in UTC (Coordinated Universal Time, which for practical purposes is the same as Greenwich Mean Time).

The following example configures a single maintenance time window that starts at midnight and ends at 01:59am (UTC), on Sundays, Mondays, Tuesdays, Wednesdays, and Thursdays:

# ...
maintenanceTimeWindows:
  - "* * 0-1 ? * SUN,MON,TUE,WED,THU *"
# ...

In practice, maintenance windows should be set in conjunction with the Kafka.spec.clusterCa.renewalDays and Kafka.spec.clientsCa.renewalDays properties of the Kafka resource, to ensure that the necessary CA certificate renewal can be completed in the configured maintenance time windows.

Note
 Strimzi does not schedule maintenance operations exactly according to the given windows. Instead, for each reconciliation, it checks whether a maintenance window is currently "open". This means that the start of maintenance operations within a given time window can be delayed by up to the Cluster Operator reconciliation interval. Maintenance time windows must therefore be at least this long.
Additional resources
Configuring a maintenance time window

You can configure a maintenance time window for rolling updates triggered by supported processes.

Prerequisites

  • A Kubernetes cluster.

  • The Cluster Operator is running.

Procedure

  1. Add or edit the maintenanceTimeWindows property in the Kafka resource. For example to allow maintenance between 0800 and 1059 and between 1400 and 1559 you would set the maintenanceTimeWindows as shown below:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  maintenanceTimeWindows:
    - "* * 8-10 * * ?"
    - "* * 14-15 * * ?"

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Additional resources

3.1.30. Renewing CA certificates manually

Unless the Kafka.spec.clusterCa.generateCertificateAuthority and Kafka.spec.clientsCa.generateCertificateAuthority objects are set to false, the cluster and clients CA certificates will auto-renew at the start of their respective certificate renewal periods. You can manually renew one or both of these certificates before the certificate renewal period starts, if required for security reasons. A renewed certificate uses the same private key as the old certificate.

Prerequisites

  • The Cluster Operator is running.

  • A Kafka cluster in which CA certificates and private keys are installed.

Procedure

  • Apply the strimzi.io/force-renew annotation to the Secret that contains the CA certificate that you want to renew.

    Certificate Secret Annotate command

    Cluster CA

    <cluster-name>-cluster-ca-cert

    kubectl annotate secret <cluster-name>-cluster-ca-cert strimzi.io/force-renew=true

    Clients CA

    <cluster-name>-clients-ca-cert

    kubectl annotate secret <cluster-name>-clients-ca-cert strimzi.io/force-renew=true

At the next reconciliation the Cluster Operator will generate a new CA certificate for the Secret that you annotated. If maintenance time windows are configured, the Cluster Operator will generate the new CA certificate at the first reconciliation within the next maintenance time window.

Client applications must reload the cluster and clients CA certificates that were renewed by the Cluster Operator.

Additional resources

3.1.31. Replacing private keys

You can replace the private keys used by the cluster CA and clients CA certificates. When a private key is replaced, the Cluster Operator generates a new CA certificate for the new private key.

Prerequisites

  • The Cluster Operator is running.

  • A Kafka cluster in which CA certificates and private keys are installed.

Procedure

  • Apply the strimzi.io/force-replace annotation to the Secret that contains the private key that you want to renew.

    Private key for Secret Annotate command

    Cluster CA

    <cluster-name>-cluster-ca

    kubectl annotate secret <cluster-name>-cluster-ca strimzi.io/force-replace=true

    Clients CA

    <cluster-name>-clients-ca

    kubectl annotate secret <cluster-name>-clients-ca strimzi.io/force-replace=true

At the next reconciliation the Cluster Operator will:

  • Generate a new private key for the Secret that you annotated

  • Generate a new CA certificate

If maintenance time windows are configured, the Cluster Operator will generate the new private key and CA certificate at the first reconciliation within the next maintenance time window.

Client applications must reload the cluster and clients CA certificates that were renewed by the Cluster Operator.

Additional resources

3.1.32. List of resources created as part of Kafka cluster

The following resources will created by the Cluster Operator in the Kubernetes cluster:

cluster-name-kafka

StatefulSet which is in charge of managing the Kafka broker pods.

cluster-name-kafka-brokers

Service needed to have DNS resolve the Kafka broker pods IP addresses directly.

cluster-name-kafka-bootstrap

Service can be used as bootstrap servers for Kafka clients.

cluster-name-kafka-external-bootstrap

Bootstrap service for clients connecting from outside of the Kubernetes cluster. This resource will be created only when external listener is enabled.

cluster-name-kafka-pod-id

Service used to route traffic from outside of the Kubernetes cluster to individual pods. This resource will be created only when external listener is enabled.

cluster-name-kafka-external-bootstrap

Bootstrap route for clients connecting from outside of the Kubernetes cluster. This resource will be created only when external listener is enabled and set to type route.

cluster-name-kafka-pod-id

Route for traffic from outside of the Kubernetes cluster to individual pods. This resource will be created only when external listener is enabled and set to type route.

cluster-name-kafka-config

ConfigMap which contains the Kafka ancillary configuration and is mounted as a volume by the Kafka broker pods.

cluster-name-kafka-brokers

Secret with Kafka broker keys.

cluster-name-kafka

Service account used by the Kafka brokers.

cluster-name-kafka

Pod Disruption Budget configured for the Kafka brokers.

strimzi-namespace-name-cluster-name-kafka-init

Cluster role binding used by the Kafka brokers.

cluster-name-zookeeper

StatefulSet which is in charge of managing the ZooKeeper node pods.

cluster-name-zookeeper-nodes

Service needed to have DNS resolve the ZooKeeper pods IP addresses directly.

cluster-name-zookeeper-client

Service used by Kafka brokers to connect to ZooKeeper nodes as clients.

cluster-name-zookeeper-config

ConfigMap which contains the ZooKeeper ancillary configuration and is mounted as a volume by the ZooKeeper node pods.

cluster-name-zookeeper-nodes

Secret with ZooKeeper node keys.

cluster-name-zookeeper

Pod Disruption Budget configured for the ZooKeeper nodes.

cluster-name-entity-operator

Deployment with Topic and User Operators. This resource will be created only if Cluster Operator deployed Entity Operator.

cluster-name-entity-topic-operator-config

Configmap with ancillary configuration for Topic Operators. This resource will be created only if Cluster Operator deployed Entity Operator.

cluster-name-entity-user-operator-config

Configmap with ancillary configuration for User Operators. This resource will be created only if Cluster Operator deployed Entity Operator.

cluster-name-entity-operator-certs

Secret with Entity operators keys for communication with Kafka and ZooKeeper. This resource will be created only if Cluster Operator deployed Entity Operator.

cluster-name-entity-operator

Service account used by the Entity Operator.

strimzi-cluster-name-topic-operator

Role binding used by the Entity Operator.

strimzi-cluster-name-user-operator

Role binding used by the Entity Operator.

cluster-name-cluster-ca

Secret with the Cluster CA used to encrypt the cluster communication.

cluster-name-cluster-ca-cert

Secret with the Cluster CA public key. This key can be used to verify the identity of the Kafka brokers.

cluster-name-clients-ca

Secret with the Clients CA used to encrypt the communication between Kafka brokers and Kafka clients.

cluster-name-clients-ca-cert

Secret with the Clients CA public key. This key can be used to verify the identity of the Kafka brokers.

cluster-name-cluster-operator-certs

Secret with Cluster operators keys for communication with Kafka and ZooKeeper.

data-cluster-name-kafka-idx

Persistent Volume Claim for the volume used for storing data for the Kafka broker pod idx. This resource will be created only if persistent storage is selected for provisioning persistent volumes to store data.

data-id-cluster-name-kafka-idx

Persistent Volume Claim for the volume id used for storing data for the Kafka broker pod idx. This resource is only created if persistent storage is selected for JBOD volumes when provisioning persistent volumes to store data.

data-cluster-name-zookeeper-idx

Persistent Volume Claim for the volume used for storing data for the ZooKeeper node pod idx. This resource will be created only if persistent storage is selected for provisioning persistent volumes to store data.

cluster-name-jmx

Secret with JMX username and password used to secure the Kafka broker port.

3.2. Kafka Connect cluster configuration

The full schema of the KafkaConnect resource is described in the KafkaConnect schema reference. All labels that are applied to the desired KafkaConnect resource will also be applied to the Kubernetes resources making up the Kafka Connect cluster. This provides a convenient mechanism for resources to be labeled as required.

3.2.1. Replicas

Kafka Connect clusters can consist of one or more nodes. The number of nodes is defined in the KafkaConnect and KafkaConnectS2I resources. Running a Kafka Connect cluster with multiple nodes can provide better availability and scalability. However, when running Kafka Connect on Kubernetes it is not necessary to run multiple nodes of Kafka Connect for high availability. If a node where Kafka Connect is deployed to crashes, Kubernetes will automatically reschedule the Kafka Connect pod to a different node. However, running Kafka Connect with multiple nodes can provide faster failover times, because the other nodes will be up and running already.

Configuring the number of nodes

The number of Kafka Connect nodes is configured using the replicas property in KafkaConnect.spec and KafkaConnectS2I.spec.

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the replicas property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnectS2I
metadata:
  name: my-cluster
spec:
  # ...
  replicas: 3
  # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.2.2. Bootstrap servers

A Kafka Connect cluster always works in combination with a Kafka cluster. A Kafka cluster is specified as a list of bootstrap servers. On Kubernetes, the list must ideally contain the Kafka cluster bootstrap service named cluster-name-kafka-bootstrap, and a port of 9092 for plain traffic or 9093 for encrypted traffic.

The list of bootstrap servers is configured in the bootstrapServers property in KafkaConnect.spec and KafkaConnectS2I.spec. The servers must be defined as a comma-separated list specifying one or more Kafka brokers, or a service pointing to Kafka brokers specified as a hostname:_port_ pairs.

When using Kafka Connect with a Kafka cluster not managed by Strimzi, you can specify the bootstrap servers list according to the configuration of the cluster.

Configuring bootstrap servers
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the bootstrapServers property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  bootstrapServers: my-cluster-kafka-bootstrap:9092
  # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.2.3. Connecting to Kafka brokers using TLS

By default, Kafka Connect tries to connect to Kafka brokers using a plain text connection. If you prefer to use TLS, additional configuration is required.

TLS support in Kafka Connect

TLS support is configured in the tls property in KafkaConnect.spec and KafkaConnectS2I.spec. The tls property contains a list of secrets with key names under which the certificates are stored. The certificates must be stored in X509 format.

An example showing TLS configuration with multiple certificates
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  tls:
    trustedCertificates:
      - secretName: my-secret
        certificate: ca.crt
      - secretName: my-other-secret
        certificate: certificate.crt
  # ...

When multiple certificates are stored in the same secret, it can be listed multiple times.

An example showing TLS configuration with multiple certificates from the same secret
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnectS2I
metadata:
  name: my-cluster
spec:
  # ...
  tls:
    trustedCertificates:
      - secretName: my-secret
        certificate: ca.crt
      - secretName: my-secret
        certificate: ca2.crt
  # ...
Configuring TLS in Kafka Connect
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

  • If they exist, the name of the Secret for the certificate used for TLS Server Authentication, and the key under which the certificate is stored in the Secret

Procedure

  1. (Optional) If they do not already exist, prepare the TLS certificate used in authentication in a file and create a Secret.

    Note
    		 The secrets created by the Cluster Operator for Kafka cluster may be used directly.

    This can be done using kubectl create:

    kubectl create secret generic my-secret --from-file=my-file.crt

  2. Edit the tls property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  tls:
    trustedCertificates:
      - secretName: my-cluster-cluster-cert
        certificate: ca.crt
  # ...

  3. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.2.4. Connecting to Kafka brokers with Authentication

By default, Kafka Connect will try to connect to Kafka brokers without authentication. Authentication is enabled through the KafkaConnect and KafkaConnectS2I resources.

Authentication support in Kafka Connect

Authentication is configured through the authentication property in KafkaConnect.spec and KafkaConnectS2I.spec. The authentication property specifies the type of the authentication mechanisms which should be used and additional configuration details depending on the mechanism. The supported authentication types are:

TLS Client Authentication

To use TLS client authentication, set the type property to the value tls. TLS client authentication uses a TLS certificate to authenticate. The certificate is specified in the certificateAndKey property and is always loaded from a Kubernetes secret. In the secret, the certificate must be stored in X509 format under two different keys: public and private.

Note
 TLS client authentication can be used only with TLS connections. For more details about TLS configuration in Kafka Connect see Connecting to Kafka brokers using TLS.
An example TLS client authentication configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  authentication:
    type: tls
    certificateAndKey:
      secretName: my-secret
      certificate: public.crt
      key: private.key
  # ...
SASL based SCRAM-SHA-512 authentication

To configure Kafka Connect to use SASL-based SCRAM-SHA-512 authentication, set the type property to scram-sha-512. This authentication mechanism requires a username and password.

  • Specify the username in the username property.

  • In the passwordSecret property, specify a link to a Secret containing the password. The secretName property contains the name of the Secret and the password property contains the name of the key under which the password is stored inside the Secret.

Important
 Do not specify the actual password in the password field.
An example SASL based SCRAM-SHA-512 client authentication configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  authentication:
    type: scram-sha-512
    username: my-connect-user
    passwordSecret:
      secretName: my-connect-user
      password: my-connect-password-key
  # ...
SASL based PLAIN authentication

To configure Kafka Connect to use SASL-based PLAIN authentication, set the type property to plain. This authentication mechanism requires a username and password.

Warning
 The SASL PLAIN mechanism will transfer the username and password across the network in cleartext. Only use SASL PLAIN authentication if TLS encryption is enabled.

  • Specify the username in the username property.

  • In the passwordSecret property, specify a link to a Secret containing the password. The secretName property contains the name of such a Secret and the password property contains the name of the key under which the password is stored inside the Secret.

Important
 Do not specify the actual password in the password field.
An example showing SASL based PLAIN client authentication configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  authentication:
    type: plain
    username: my-connect-user
    passwordSecret:
      secretName: my-connect-user
      password: my-connect-password-key
  # ...
Configuring TLS client authentication in Kafka Connect
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

  • If they exist, the name of the Secret with the public and private keys used for TLS Client Authentication, and the keys under which they are stored in the Secret

Procedure

  1. (Optional) If they do not already exist, prepare the keys used for authentication in a file and create the Secret.

    Note
    		 Secrets created by the User Operator may be used.

    This can be done using kubectl create:

    kubectl create secret generic my-secret --from-file=my-public.crt --from-file=my-private.key

  2. Edit the authentication property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  authentication:
    type: tls
    certificateAndKey:
      secretName: my-secret
      certificate: my-public.crt
      key: my-private.key
  # ...

  3. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Configuring SCRAM-SHA-512 authentication in Kafka Connect
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

  • Username of the user which should be used for authentication

  • If they exist, the name of the Secret with the password used for authentication and the key under which the password is stored in the Secret

Procedure

  1. (Optional) If they do not already exist, prepare a file with the password used in authentication and create the Secret.

    Note
    		 Secrets created by the User Operator may be used.

    This can be done using kubectl create:

    echo -n '<password>' > <my-password.txt>
kubectl create secret generic <my-secret> --from-file=<my-password.txt>

  2. Edit the authentication property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  authentication:
    type: scram-sha-512
    username: _<my-username>_
    passwordSecret:
      secretName: _<my-secret>_
      password: _<my-password.txt>_
  # ...

  3. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f your-file

3.2.5. Kafka Connect configuration

Strimzi allows you to customize the configuration of Apache Kafka Connect nodes by editing certain options listed in Apache Kafka documentation.

Configuration options that cannot be configured relate to:

  • Kafka cluster bootstrap address

  • Security (Encryption, Authentication, and Authorization)

  • Listener / REST interface configuration

  • Plugin path configuration

These options are automatically configured by Strimzi.

Kafka Connect configuration

Kafka Connect is configured using the config property in KafkaConnect.spec and KafkaConnectS2I.spec. This property contains the Kafka Connect configuration options as keys. The values can be one of the following JSON types:

  • String

  • Number

  • Boolean

You can specify and configure the options listed in the Apache Kafka documentation with the exception of those options that are managed directly by Strimzi. Specifically, configuration options with keys equal to or starting with one of the following strings are forbidden:

  • ssl.

  • sasl.

  • security.

  • listeners

  • plugin.path

  • rest.

  • bootstrap.servers

When a forbidden option is present in the config property, it is ignored and a warning message is printed to the Custer Operator log file. All other options are passed to Kafka Connect.

Important
 The Cluster Operator does not validate keys or values in the config object provided. When an invalid configuration is provided, the Kafka Connect cluster might not start or might become unstable. In this circumstance, fix the configuration in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config object, then the Cluster Operator can roll out the new configuration to all Kafka Connect nodes.

Certain options have default values:

  • group.id with default value connect-cluster

  • offset.storage.topic with default value connect-cluster-offsets

  • config.storage.topic with default value connect-cluster-configs

  • status.storage.topic with default value connect-cluster-status

  • key.converter with default value org.apache.kafka.connect.json.JsonConverter

  • value.converter with default value org.apache.kafka.connect.json.JsonConverter

These options are automatically configured in case they are not present in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config properties.

Example Kafka Connect configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  config:
    group.id: my-connect-cluster
    offset.storage.topic: my-connect-cluster-offsets
    config.storage.topic: my-connect-cluster-configs
    status.storage.topic: my-connect-cluster-status
    key.converter: org.apache.kafka.connect.json.JsonConverter
    value.converter: org.apache.kafka.connect.json.JsonConverter
    key.converter.schemas.enable: true
    value.converter.schemas.enable: true
    config.storage.replication.factor: 3
    offset.storage.replication.factor: 3
    status.storage.replication.factor: 3
  # ...
Kafka Connect configuration for multiple instances

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

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  config:
    group.id: connect-cluster (1)
    offset.storage.topic: connect-cluster-offsets (2)
    config.storage.topic: connect-cluster-configs (3)
    status.storage.topic: connect-cluster-status  (4)
    # ...
# ...

  1. Kafka Connect cluster group that the instance belongs to.

  2. Kafka topic that stores connector offsets.

  3. Kafka topic that stores connector and task status configurations.

  4. Kafka topic that stores connector and task status updates.

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

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

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

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

Configuring Kafka Connect
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the config property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  config:
    group.id: my-connect-cluster
    offset.storage.topic: my-connect-cluster-offsets
    config.storage.topic: my-connect-cluster-configs
    status.storage.topic: my-connect-cluster-status
    key.converter: org.apache.kafka.connect.json.JsonConverter
    value.converter: org.apache.kafka.connect.json.JsonConverter
    key.converter.schemas.enable: true
    value.converter.schemas.enable: true
    config.storage.replication.factor: 3
    offset.storage.replication.factor: 3
    status.storage.replication.factor: 3
  # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

  3. If authorization is enabled for Kafka Connect, configure the Kafka Connect user to enable access to the Kafka Connect consumer group and topics.

3.2.6. Kafka Connect user authorization

If authorization is enabled for Kafka Connect, the Kafka Connect user must be configured to provide read/write access rights to the Kafka Connect consumer group and internal topics.

The properties for the consumer group and internal topics are automatically configured by Strimzi, or they can be specified explicitly in the spec for the KafkaConnect or KafkaConnectS2I configuration.

The following example shows the configuration of the properties in the KafkaConnect resource, which need to be represented in the Kafka Connect user configuration.

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  config:
    group.id: my-connect-cluster (1)
    offset.storage.topic: my-connect-cluster-offsets (2)
    config.storage.topic: my-connect-cluster-configs (3)
    status.storage.topic: my-connect-cluster-status (4)
    # ...
  # ...

  1. Kafka Connect cluster group that the instance belongs to.

  2. Kafka topic that stores connector offsets.

  3. Kafka topic that stores connector and task status configurations.

  4. Kafka topic that stores connector and task status updates.

Configuring Kafka Connect user authorization

This procedure describes how to authorize user access to Kafka Connect.

When any type of authorization is being used in Kafka, a Kafka Connect user requires access rights to the consumer group and the internal topics of Kafka Connect.

This procedure shows how access is provided when simple authorization is being used.

Simple authorization uses ACL rules, handled by the Kafka SimpleAclAuthorizer plugin, to provide the right level of access. For more information on configuring a KafkaUser resource to use simple authorization, see Kafka User resource.

Note
 The default values for the consumer group and topics will differ when running multiple instances.
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the authorization property in the KafkaUser resource to provide access rights to the user.

    In the following example, access rights are configured for the Kafka Connect topics and consumer group using literal name values:

    Property Name

    offset.storage.topic

    connect-cluster-offsets

    status.storage.topic

    connect-cluster-status

    config.storage.topic

    connect-cluster-configs

    group

    connect-cluster

    		 
    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster
spec:
  # ...
  authorization:
    type: simple
    acls:
      # access to offset.storage.topic
      - resource:
          type: topic
          name: connect-cluster-offsets
          patternType: literal
        operation: Write
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-offsets
          patternType: literal
        operation: Create
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-offsets
          patternType: literal
        operation: Describe
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-offsets
          patternType: literal
        operation: Read
        host: "*"
      # access to status.storage.topic
      - resource:
          type: topic
          name: connect-cluster-status
          patternType: literal
        operation: Write
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-status
          patternType: literal
        operation: Create
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-status
          patternType: literal
        operation: Describe
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-status
          patternType: literal
        operation: Read
        host: "*"
      # access to config.storage.topic
      - resource:
          type: topic
          name: connect-cluster-configs
          patternType: literal
        operation: Write
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-configs
          patternType: literal
        operation: Create
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-configs
          patternType: literal
        operation: Describe
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-configs
          patternType: literal
        operation: Read
        host: "*"
      # consumer group
      - resource:
          type: group
          name: connect-cluster
          patternType: literal
        operation: Read
        host: "*"

  2. Create or update the resource.

    kubectl apply -f your-file

3.2.7. CPU and memory resources

For every deployed container, Strimzi allows you to request specific resources and define the maximum consumption of those resources.

Strimzi supports two types of resources:

  • CPU

  • Memory

Strimzi uses the Kubernetes syntax for specifying CPU and memory resources.

Resource limits and requests

Resource limits and requests are configured using the resources property in the following resources:

  • Kafka.spec.kafka

  • Kafka.spec.kafka.tlsSidecar

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.entityOperator.tlsSidecar

  • Kafka.spec.KafkaExporter

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaBridge.spec

Additional resources
Resource requests

Requests specify the resources to reserve for a given container. Reserving the resources ensures that they are always available.

Important
 If the resource request is for more than the available free resources in the Kubernetes cluster, the pod is not scheduled.

Resources requests are specified in the requests property. Resources requests currently supported by Strimzi:

  • cpu

  • memory

A request may be configured for one or more supported resources.

Example resource request configuration with all resources
# ...
resources:
  requests:
    cpu: 12
    memory: 64Gi
# ...
Resource limits

Limits specify the maximum resources that can be consumed by a given container. The limit is not reserved and might not always be available. A container can use the resources up to the limit only when they are available. Resource limits should be always higher than the resource requests.

Resource limits are specified in the limits property. Resource limits currently supported by Strimzi:

  • cpu

  • memory

A resource may be configured for one or more supported limits.

Example resource limits configuration
# ...
resources:
  limits:
    cpu: 12
    memory: 64Gi
# ...
Supported CPU formats

CPU requests and limits are supported in the following formats:

  • Number of CPU cores as integer (5 CPU core) or decimal (2.5 CPU core).

  • Number or millicpus / millicores (100m) where 1000 millicores is the same 1 CPU core.

Example CPU units
# ...
resources:
  requests:
    cpu: 500m
  limits:
    cpu: 2.5
# ...
Note
 The computing power of 1 CPU core may differ depending on the platform where Kubernetes is deployed.
Additional resources
Supported memory formats

Memory requests and limits are specified in megabytes, gigabytes, mebibytes, and gibibytes.

  • To specify memory in megabytes, use the M suffix. For example 1000M.

  • To specify memory in gigabytes, use the G suffix. For example 1G.

  • To specify memory in mebibytes, use the Mi suffix. For example 1000Mi.

  • To specify memory in gibibytes, use the Gi suffix. For example 1Gi.

An example of using different memory units
# ...
resources:
  requests:
    memory: 512Mi
  limits:
    memory: 2Gi
# ...
Additional resources

  • For more details about memory specification and additional supported units, see Meaning of memory.

Configuring resource requests and limits
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the resources property in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    resources:
      requests:
        cpu: "8"
        memory: 64Gi
      limits:
        cpu: "12"
        memory: 128Gi
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Additional resources

3.2.8. Kafka Connect loggers

Kafka Connect has its own configurable loggers:

  • connect.root.logger.level

  • log4j.logger.org.reflections

Kafka Connect uses the Apache log4j logger implementation.

Use the logging property to configure loggers and logger levels.

You can set the log levels by specifying the logger and level directly (inline) or use a custom (external) ConfigMap. If a ConfigMap is used, you set logging.name property to the name of the ConfigMap containing the external logging configuration. Inside the ConfigMap, the logging configuration is described using log4j.properties.

Here we see examples of inline and external logging.

Inline logging
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
spec:
  # ...
  logging:
    type: inline
    loggers:
      connect.root.logger.level: "INFO"
  # ...
External logging
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
spec:
  # ...
  logging:
    type: external
    name: customConfigMap
  # ...
Additional resources

3.2.9. Healthchecks

Healthchecks are periodical tests which verify the health of an application. When a Healthcheck probe fails, Kubernetes assumes that the application is not healthy and attempts to fix it.

Kubernetes supports two types of Healthcheck probes:

  • Liveness probes

  • Readiness probes

For more details about the probes, see Configure Liveness and Readiness Probes. Both types of probes are used in Strimzi components.

Users can configure selected options for liveness and readiness probes.

Healthcheck configurations

Liveness and readiness probes can be configured using the livenessProbe and readinessProbe properties in following resources:

  • Kafka.spec.kafka

  • Kafka.spec.kafka.tlsSidecar

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator.tlsSidecar

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.KafkaExporter

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaMirrorMaker.spec

  • KafkaBridge.spec

Both livenessProbe and readinessProbe support the following options:

  • initialDelaySeconds

  • timeoutSeconds

  • periodSeconds

  • successThreshold

  • failureThreshold

For more information about the livenessProbe and readinessProbe options, see Probe schema reference.

An example of liveness and readiness probe configuration
# ...
readinessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
livenessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
# ...
Configuring healthchecks
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the livenessProbe or readinessProbe property in the Kafka, KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    readinessProbe:
      initialDelaySeconds: 15
      timeoutSeconds: 5
    livenessProbe:
      initialDelaySeconds: 15
      timeoutSeconds: 5
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.2.10. Prometheus metrics

Strimzi supports Prometheus metrics using Prometheus JMX exporter to convert the JMX metrics supported by Apache Kafka and ZooKeeper to Prometheus metrics. When metrics are enabled, they are exposed on port 9404.

For more information about setting up and deploying Prometheus and Grafana, see Introducing Metrics to Kafka.

Metrics configuration

Prometheus metrics are enabled by configuring the metrics property in following resources:

  • Kafka.spec.kafka

  • Kafka.spec.zookeeper

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

When the metrics property is not defined in the resource, the Prometheus metrics will be disabled. To enable Prometheus metrics export without any further configuration, you can set it to an empty object ({}).

Example of enabling metrics without any further configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    metrics: {}
    # ...
  zookeeper:
    # ...

The metrics property might contain additional configuration for the Prometheus JMX exporter.

Example of enabling metrics with additional Prometheus JMX Exporter configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    metrics:
      lowercaseOutputName: true
      rules:
        - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*><>Count"
          name: "kafka_server_$1_$2_total"
        - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*, topic=(.+)><>Count"
          name: "kafka_server_$1_$2_total"
          labels:
            topic: "$3"
    # ...
  zookeeper:
    # ...
Configuring Prometheus metrics
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the metrics property in the Kafka, KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
    metrics:
      lowercaseOutputName: true
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.2.11. JVM Options

The following components of Strimzi run inside a Virtual Machine (VM):

  • Apache Kafka

  • Apache ZooKeeper

  • Apache Kafka Connect

  • Apache Kafka MirrorMaker

  • Strimzi Kafka Bridge

JVM configuration options optimize the performance for different platforms and architectures. Strimzi allows you to configure some of these options.

JVM configuration

JVM options can be configured using the jvmOptions property in following resources:

  • Kafka.spec.kafka

  • Kafka.spec.zookeeper

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaMirrorMaker.spec

  • KafkaBridge.spec

Only a selected subset of available JVM options can be configured. The following options are supported:

-Xms and -Xmx

-Xms configures the minimum initial allocation heap size when the JVM starts. -Xmx configures the maximum heap size.

Note
 The units accepted by JVM settings such as -Xmx and -Xms are those accepted by the JDK java binary in the corresponding image. Accordingly, 1g or 1G means 1,073,741,824 bytes, and Gi is not a valid unit suffix. This is in contrast to the units used for memory requests and limits, which follow the Kubernetes convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes

The default values used for -Xms and -Xmx depends on whether there is a memory request limit configured for the container:

  • If there is a memory limit then the JVM’s minimum and maximum memory will be set to a value corresponding to the limit.

  • If there is no memory limit then the JVM’s minimum memory will be set to 128M and the JVM’s maximum memory will not be defined. This allows for the JVM’s memory to grow as-needed, which is ideal for single node environments in test and development.

Important

Setting -Xmx explicitly requires some care:

  • The JVM’s overall memory usage will be approximately 4 × the maximum heap, as configured by -Xmx.

  • If -Xmx is set without also setting an appropriate Kubernetes memory limit, it is possible that the container will be killed should the Kubernetes node experience memory pressure (from other Pods running on it).

  • If -Xmx is set without also setting an appropriate Kubernetes memory request, it is possible that the container will be scheduled to a node with insufficient memory. In this case, the container will not start but crash (immediately if -Xms is set to -Xmx, or some later time if not).

When setting -Xmx explicitly, it is recommended to:

  • set the memory request and the memory limit to the same value,

  • use a memory request that is at least 4.5 × the -Xmx,

  • consider setting -Xms to the same value as -Xmx.

Important
 Containers doing lots of disk I/O (such as Kafka broker containers) will need to leave some memory available for use as operating system page cache. On such containers, the requested memory should be significantly higher than the memory used by the JVM.
Example fragment configuring -Xmx and -Xms
# ...
jvmOptions:
  "-Xmx": "2g"
  "-Xms": "2g"
# ...

In the above example, the JVM will use 2 GiB (=2,147,483,648 bytes) for its heap. Its total memory usage will be approximately 8GiB.

Setting the same value for initial (-Xms) and maximum (-Xmx) heap sizes avoids the JVM having to allocate memory after startup, at the cost of possibly allocating more heap than is really needed. For Kafka and ZooKeeper pods such allocation could cause unwanted latency. For Kafka Connect avoiding over allocation may be the most important concern, especially in distributed mode where the effects of over-allocation will be multiplied by the number of consumers.

-server

-server enables the server JVM. This option can be set to true or false.

Example fragment configuring -server
# ...
jvmOptions:
  "-server": true
# ...
Note
 When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.
-XX

-XX object can be used for configuring advanced runtime options of a JVM. The -server and -XX options are used to configure the KAFKA_JVM_PERFORMANCE_OPTS option of Apache Kafka.

Example showing the use of the -XX object
jvmOptions:
  "-XX":
    "UseG1GC": true
    "MaxGCPauseMillis": 20
    "InitiatingHeapOccupancyPercent": 35
    "ExplicitGCInvokesConcurrent": true
    "UseParNewGC": false

The example configuration above will result in the following JVM options:

-XX:+UseG1GC -XX:MaxGCPauseMillis=20 -XX:InitiatingHeapOccupancyPercent=35 -XX:+ExplicitGCInvokesConcurrent -XX:-UseParNewGC
Note
 When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.
Garbage collector logging

The jvmOptions section also allows you to enable and disable garbage collector (GC) logging. GC logging is disabled by default. To enable it, set the gcLoggingEnabled property as follows:

Example of enabling GC logging
# ...
jvmOptions:
  gcLoggingEnabled: true
# ...
Configuring JVM options
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the jvmOptions property in the Kafka, KafkaConnect, KafkaConnectS2I, KafkaMirrorMaker, or KafkaBridge resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    jvmOptions:
      "-Xmx": "8g"
      "-Xms": "8g"
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.2.12. Container images

Strimzi allows you to configure container images which will be used for its components. Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by Strimzi. In such a case, you should either copy the Strimzi images or build them from the source. If the configured image is not compatible with Strimzi images, it might not work properly.

Container image configurations

You can specify which container image to use for each component using the image property in the following resources:

  • Kafka.spec.kafka

  • Kafka.spec.kafka.tlsSidecar

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.entityOperator.tlsSidecar

  • Kafka.spec.jmxTrans

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaBridge.spec

Configuring the image property for Kafka, Kafka Connect, and Kafka MirrorMaker

Kafka, Kafka Connect (including Kafka Connect with S2I support), and Kafka MirrorMaker support multiple versions of Kafka. Each component requires its own image. The default images for the different Kafka versions are configured in the following environment variables:

  • STRIMZI_KAFKA_IMAGES

  • STRIMZI_KAFKA_CONNECT_IMAGES

  • STRIMZI_KAFKA_CONNECT_S2I_IMAGES

  • STRIMZI_KAFKA_MIRROR_MAKER_IMAGES

These environment variables contain mappings between the Kafka versions and their corresponding images. The mappings are used together with the image and version properties:

  • If neither image nor version are given in the custom resource then the version will default to the Cluster Operator’s default Kafka version, and the image will be the one corresponding to this version in the environment variable.

  • If image is given but version is not, then the given image is used and the version is assumed to be the Cluster Operator’s default Kafka version.

  • If version is given but image is not, then the image that corresponds to the given version in the environment variable is used.

  • If both version and image are given, then the given image is used. The image is assumed to contain a Kafka image with the given version.

The image and version for the different components can be configured in the following properties:

  • For Kafka in spec.kafka.image and spec.kafka.version.

  • For Kafka Connect, Kafka Connect S2I, and Kafka MirrorMaker in spec.image and spec.version.

Warning
 It is recommended to provide only the version and leave the image property unspecified. This reduces the chance of making a mistake when configuring the custom resource. If you need to change the images used for different versions of Kafka, it is preferable to configure the Cluster Operator’s environment variables.
Configuring the image property in other resources

For the image property in the other custom resources, the given value will be used during deployment. If the image property is missing, the image specified in the Cluster Operator configuration will be used. If the image name is not defined in the Cluster Operator configuration, then the default value will be used.

  • For Kafka broker TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_KAFKA_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.18.0-kafka-2.5.0 container image.

  • For ZooKeeper nodes:

  • For ZooKeeper node TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_ZOOKEEPER_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.18.0-kafka-2.5.0 container image.

  • For Topic Operator:

    1. Container image specified in the STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/operator:0.18.0 container image.

  • For User Operator:

    1. Container image specified in the STRIMZI_DEFAULT_USER_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/operator:0.18.0 container image.

  • For Entity Operator TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.18.0-kafka-2.5.0 container image.

  • For Kafka Exporter:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_EXPORTER_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.18.0-kafka-2.5.0 container image.

  • For Kafka Bridge:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_BRIDGE_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka-bridge:0.16.0 container image.

  • For Kafka broker initializer:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_INIT_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/operator:0.18.0 container image.

  • For Kafka broker initializer:

    1. Container image specified in the STRIMZI_DEFAULT_JMXTRANS_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/operator:0.18.0 container image.

Warning
 Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by Strimzi. In such case, you should either copy the Strimzi images or build them from source. In case the configured image is not compatible with Strimzi images, it might not work properly.
Example of container image configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    image: my-org/my-image:latest
    # ...
  zookeeper:
    # ...
Configuring container images
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the image property in the Kafka, KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    image: my-org/my-image:latest
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.2.13. Configuring pod scheduling

Important
 When two applications are scheduled to the same Kubernetes node, both applications might use the same resources like disk I/O and impact performance. That can lead to performance degradation. Scheduling Kafka pods in a way that avoids sharing nodes with other critical workloads, using the right nodes or dedicated a set of nodes only for Kafka are the best ways how to avoid such problems.
Scheduling pods based on other applications
Avoid critical applications to share the node

Pod anti-affinity can be used to ensure that critical applications are never scheduled on the same disk. When running Kafka cluster, it is recommended to use pod anti-affinity to ensure that the Kafka brokers do not share the nodes with other workloads like databases.

Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod

  • Kafka.spec.zookeeper.template.pod

  • Kafka.spec.entityOperator.template.pod

  • KafkaConnect.spec.template.pod

  • KafkaConnectS2I.spec.template.pod

  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

The format of the affinity property follows the Kubernetes specification. For more details, see the Kubernetes node and pod affinity documentation.

Configuring pod anti-affinity in Kafka components
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the affinity property in the resource specifying the cluster deployment. Use labels to specify the pods which should not be scheduled on the same nodes. The topologyKey should be set to kubernetes.io/hostname to specify that the selected pods should not be scheduled on nodes with the same hostname. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    template:
      pod:
        affinity:
          podAntiAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              - labelSelector:
                  matchExpressions:
                    - key: application
                      operator: In
                      values:
                        - postgresql
                        - mongodb
                topologyKey: "kubernetes.io/hostname"
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Scheduling pods to specific nodes
Node scheduling

The Kubernetes cluster usually consists of many different types of worker nodes. Some are optimized for CPU heavy workloads, some for memory, while other might be optimized for storage (fast local SSDs) or network. Using different nodes helps to optimize both costs and performance. To achieve the best possible performance, it is important to allow scheduling of Strimzi components to use the right nodes.

Kubernetes uses node affinity to schedule workloads onto specific nodes. Node affinity allows you to create a scheduling constraint for the node on which the pod will be scheduled. The constraint is specified as a label selector. You can specify the label using either the built-in node label like beta.kubernetes.io/instance-type or custom labels to select the right node.

Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod

  • Kafka.spec.zookeeper.template.pod

  • Kafka.spec.entityOperator.template.pod

  • KafkaConnect.spec.template.pod

  • KafkaConnectS2I.spec.template.pod

  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

The format of the affinity property follows the Kubernetes specification. For more details, see the Kubernetes node and pod affinity documentation.

Configuring node affinity in Kafka components
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Label the nodes where Strimzi components should be scheduled.

    This can be done using kubectl label:

    kubectl label node your-node node-type=fast-network

    Alternatively, some of the existing labels might be reused.

  2. Edit the affinity property in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    template:
      pod:
        affinity:
          nodeAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
                - matchExpressions:
                  - key: node-type
                    operator: In
                    values:
                    - fast-network
    # ...
  zookeeper:
    # ...

  3. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Using dedicated nodes
Dedicated nodes

Cluster administrators can mark selected Kubernetes nodes as tainted. Nodes with taints are excluded from regular scheduling and normal pods will not be scheduled to run on them. Only services which can tolerate the taint set on the node can be scheduled on it. The only other services running on such nodes will be system services such as log collectors or software defined networks.

Taints can be used to create dedicated nodes. Running Kafka and its components on dedicated nodes can have many advantages. There will be no other applications running on the same nodes which could cause disturbance or consume the resources needed for Kafka. That can lead to improved performance and stability.

To schedule Kafka pods on the dedicated nodes, configure node affinity and tolerations.

Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod

  • Kafka.spec.zookeeper.template.pod

  • Kafka.spec.entityOperator.template.pod

  • KafkaConnect.spec.template.pod

  • KafkaConnectS2I.spec.template.pod

  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

The format of the affinity property follows the Kubernetes specification. For more details, see the Kubernetes node and pod affinity documentation.

Tolerations

Tolerations can be configured using the tolerations property in following resources:

  • Kafka.spec.kafka.template.pod

  • Kafka.spec.zookeeper.template.pod

  • Kafka.spec.entityOperator.template.pod

  • KafkaConnect.spec.template.pod

  • KafkaConnectS2I.spec.template.pod

  • KafkaBridge.spec.template.pod

The format of the tolerations property follows the Kubernetes specification. For more details, see the Kubernetes taints and tolerations.

Setting up dedicated nodes and scheduling pods on them
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Select the nodes which should be used as dedicated.

  2. Make sure there are no workloads scheduled on these nodes.

  3. Set the taints on the selected nodes:

    This can be done using kubectl taint:

    kubectl taint node your-node dedicated=Kafka:NoSchedule

  4. Additionally, add a label to the selected nodes as well.

    This can be done using kubectl label:

    kubectl label node your-node dedicated=Kafka

  5. Edit the affinity and tolerations properties in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    template:
      pod:
        tolerations:
          - key: "dedicated"
            operator: "Equal"
            value: "Kafka"
            effect: "NoSchedule"
        affinity:
          nodeAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
              - matchExpressions:
                - key: dedicated
                  operator: In
                  values:
                  - Kafka
    # ...
  zookeeper:
    # ...

  6. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.2.14. Using external configuration and secrets

Connectors are created, reconfigured, and deleted using the Kafka Connect HTTP REST interface, or by using KafkaConnectors. For more information on these methods, see Creating and managing connectors. The connector configuration is passed to Kafka Connect as part of an HTTP request and stored within Kafka itself.

ConfigMaps and Secrets are standard Kubernetes resources used for storing configurations and confidential data. Whichever method you use to manage connectors, you can use ConfigMaps and Secrets to configure certain elements of a connector. You can then reference the configuration values in HTTP REST commands (this keeps the configuration separate and more secure, if needed). This method applies especially to confidential data, such as usernames, passwords, or certificates.

Storing connector configurations externally

You can mount ConfigMaps or Secrets into a Kafka Connect pod as volumes or environment variables. Volumes and environment variables are configured in the externalConfiguration property in KafkaConnect.spec and KafkaConnectS2I.spec.

External configuration as environment variables

The env property is used to specify one or more environment variables. These variables can contain a value from either a ConfigMap or a Secret.

Note
 The names of user-defined environment variables cannot start with KAFKA_ or STRIMZI_.

To mount a value from a Secret to an environment variable, use the valueFrom property and the secretKeyRef as shown in the following example.

Example of an environment variable set to a value from a Secret
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  externalConfiguration:
    env:
      - name: MY_ENVIRONMENT_VARIABLE
        valueFrom:
          secretKeyRef:
            name: my-secret
            key: my-key

A common use case for mounting Secrets to environment variables is when your connector needs to communicate with Amazon AWS and needs to read the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables with credentials.

To mount a value from a ConfigMap to an environment variable, use configMapKeyRef in the valueFrom property as shown in the following example.

Example of an environment variable set to a value from a ConfigMap
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  externalConfiguration:
    env:
      - name: MY_ENVIRONMENT_VARIABLE
        valueFrom:
          configMapKeyRef:
            name: my-config-map
            key: my-key
External configuration as volumes

You can also mount ConfigMaps or Secrets to a Kafka Connect pod as volumes. Using volumes instead of environment variables is useful in the following scenarios:

  • Mounting truststores or keystores with TLS certificates

  • Mounting a properties file that is used to configure Kafka Connect connectors

In the volumes property of the externalConfiguration resource, list the ConfigMaps or Secrets that will be mounted as volumes. Each volume must specify a name in the name property and a reference to ConfigMap or Secret.

Example of volumes with external configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  externalConfiguration:
    volumes:
      - name: connector1
        configMap:
          name: connector1-configuration
      - name: connector1-certificates
        secret:
          secretName: connector1-certificates

The volumes will be mounted inside the Kafka Connect containers in the path /opt/kafka/external-configuration/<volume-name>. For example, the files from a volume named connector1 would appear in the directory /opt/kafka/external-configuration/connector1.

The FileConfigProvider has to be used to read the values from the mounted properties files in connector configurations.

Mounting Secrets as environment variables

You can create a Kubernetes Secret and mount it to Kafka Connect as an environment variable.

Prerequisites

  • A running Cluster Operator.

Procedure

  1. Create a secret containing the information that will be mounted as an environment variable. For example:

    apiVersion: v1
kind: Secret
metadata:
  name: aws-creds
type: Opaque
data:
  awsAccessKey: QUtJQVhYWFhYWFhYWFhYWFg=
  awsSecretAccessKey: Ylhsd1lYTnpkMjl5WkE=

  2. Create or edit the Kafka Connect resource. Configure the externalConfiguration section of the KafkaConnect or KafkaConnectS2I custom resource to reference the secret. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  externalConfiguration:
    env:
      - name: AWS_ACCESS_KEY_ID
        valueFrom:
          secretKeyRef:
            name: aws-creds
            key: awsAccessKey
      - name: AWS_SECRET_ACCESS_KEY
        valueFrom:
          secretKeyRef:
            name: aws-creds
            key: awsSecretAccessKey

  3. Apply the changes to your Kafka Connect deployment.

    Use kubectl apply:

    kubectl apply -f your-file

The environment variables are now available for use when developing your connectors.

Additional resources
Mounting Secrets as volumes

You can create a Kubernetes Secret, mount it as a volume to Kafka Connect, and then use it to configure a Kafka Connect connector.

Prerequisites

  • A running Cluster Operator.

Procedure

  1. Create a secret containing a properties file that defines the configuration options for your connector configuration. For example:

    apiVersion: v1
kind: Secret
metadata:
  name: mysecret
type: Opaque
stringData:
  connector.properties: |-
    dbUsername: my-user
    dbPassword: my-password

  2. Create or edit the Kafka Connect resource. Configure the FileConfigProvider in the config section and the externalConfiguration section of the KafkaConnect or KafkaConnectS2I custom resource to reference the secret. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  config:
    config.providers: file
    config.providers.file.class: org.apache.kafka.common.config.provider.FileConfigProvider
  #...
  externalConfiguration:
    volumes:
      - name: connector-config
        secret:
          secretName: mysecret

  3. Apply the changes to your Kafka Connect deployment.

    Use kubectl apply:

    kubectl apply -f your-file

  4. Configure your connector

    • If you are using the Kafka Connect HTTP REST interface, use the values from the mounted properties file in your JSON payload with connector configuration. For example:

      {
   "name":"my-connector",
   "config":{
      "connector.class":"MyDbConnector",
      "tasks.max":"3",
      "database": "my-postgresql:5432",
      "username":"${file:/opt/kafka/external-configuration/connector-config/connector.properties:dbUsername}",
      "password":"${file:/opt/kafka/external-configuration/connector-config/connector.properties:dbPassword}",
      # ...
   }
}

    • If you are using a KafkaConnector resource, use the values from the mounted properties file in the spec.config section of your custom resource. For example:

      apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnector
metadata:
  name: my-connector
  # ...
spec:
  class: "MyDbConnector"
  tasksMax: 3
  config:
    database: "my-postgresql:5432"
    username: "${file:/opt/kafka/external-configuration/connector-config/connector.properties:dbUsername}"
    password: "${file:/opt/kafka/external-configuration/connector-config/connector.properties:dbPassword}"
Additional resources

3.2.15. Enabling KafkaConnector resources

To enable KafkaConnectors for a Kafka Connect cluster, add the strimzi.io/use-connector-resources annotation to the KafkaConnect or KafkaConnectS2I custom resource.

Prerequisites

  • A running Cluster Operator

Procedure

  1. Edit the KafkaConnect or KafkaConnectS2I resource. Add the strimzi.io/use-connector-resources annotation. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect-cluster
  annotations:
    strimzi.io/use-connector-resources: "true"
spec:
  # ...

  2. Create or update the resource using kubectl apply:

    kubectl apply -f kafka-connect.yaml
Additional resources

3.2.16. List of resources created as part of Kafka Connect cluster

The following resources will created by the Cluster Operator in the Kubernetes cluster:

connect-cluster-name-connect

Deployment which is in charge to create the Kafka Connect worker node pods.

connect-cluster-name-connect-api

Service which exposes the REST interface for managing the Kafka Connect cluster.

connect-cluster-name-config

ConfigMap which contains the Kafka Connect ancillary configuration and is mounted as a volume by the Kafka broker pods.

connect-cluster-name-connect

Pod Disruption Budget configured for the Kafka Connect worker nodes.

3.3. Kafka Connect cluster configuration with Source2Image support

The full schema of the KafkaConnectS2I resource is described in the KafkaConnectS2I schema reference. All labels that are applied to the desired KafkaConnectS2I resource will also be applied to the Kubernetes resources making up the Kafka Connect cluster with Source2Image support. This provides a convenient mechanism for resources to be labeled as required.

3.3.1. Replicas

Kafka Connect clusters can consist of one or more nodes. The number of nodes is defined in the KafkaConnect and KafkaConnectS2I resources. Running a Kafka Connect cluster with multiple nodes can provide better availability and scalability. However, when running Kafka Connect on Kubernetes it is not necessary to run multiple nodes of Kafka Connect for high availability. If a node where Kafka Connect is deployed to crashes, Kubernetes will automatically reschedule the Kafka Connect pod to a different node. However, running Kafka Connect with multiple nodes can provide faster failover times, because the other nodes will be up and running already.

Configuring the number of nodes

The number of Kafka Connect nodes is configured using the replicas property in KafkaConnect.spec and KafkaConnectS2I.spec.

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the replicas property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnectS2I
metadata:
  name: my-cluster
spec:
  # ...
  replicas: 3
  # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.3.2. Bootstrap servers

A Kafka Connect cluster always works in combination with a Kafka cluster. A Kafka cluster is specified as a list of bootstrap servers. On Kubernetes, the list must ideally contain the Kafka cluster bootstrap service named cluster-name-kafka-bootstrap, and a port of 9092 for plain traffic or 9093 for encrypted traffic.

The list of bootstrap servers is configured in the bootstrapServers property in KafkaConnect.spec and KafkaConnectS2I.spec. The servers must be defined as a comma-separated list specifying one or more Kafka brokers, or a service pointing to Kafka brokers specified as a hostname:_port_ pairs.

When using Kafka Connect with a Kafka cluster not managed by Strimzi, you can specify the bootstrap servers list according to the configuration of the cluster.

Configuring bootstrap servers
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the bootstrapServers property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  bootstrapServers: my-cluster-kafka-bootstrap:9092
  # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.3.3. Connecting to Kafka brokers using TLS

By default, Kafka Connect tries to connect to Kafka brokers using a plain text connection. If you prefer to use TLS, additional configuration is required.

TLS support in Kafka Connect

TLS support is configured in the tls property in KafkaConnect.spec and KafkaConnectS2I.spec. The tls property contains a list of secrets with key names under which the certificates are stored. The certificates must be stored in X509 format.

An example showing TLS configuration with multiple certificates
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  tls:
    trustedCertificates:
      - secretName: my-secret
        certificate: ca.crt
      - secretName: my-other-secret
        certificate: certificate.crt
  # ...

When multiple certificates are stored in the same secret, it can be listed multiple times.

An example showing TLS configuration with multiple certificates from the same secret
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnectS2I
metadata:
  name: my-cluster
spec:
  # ...
  tls:
    trustedCertificates:
      - secretName: my-secret
        certificate: ca.crt
      - secretName: my-secret
        certificate: ca2.crt
  # ...
Configuring TLS in Kafka Connect
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

  • If they exist, the name of the Secret for the certificate used for TLS Server Authentication, and the key under which the certificate is stored in the Secret

Procedure

  1. (Optional) If they do not already exist, prepare the TLS certificate used in authentication in a file and create a Secret.

    Note
    		 The secrets created by the Cluster Operator for Kafka cluster may be used directly.

    This can be done using kubectl create:

    kubectl create secret generic my-secret --from-file=my-file.crt

  2. Edit the tls property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  tls:
    trustedCertificates:
      - secretName: my-cluster-cluster-cert
        certificate: ca.crt
  # ...

  3. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.3.4. Connecting to Kafka brokers with Authentication

By default, Kafka Connect will try to connect to Kafka brokers without authentication. Authentication is enabled through the KafkaConnect and KafkaConnectS2I resources.

Authentication support in Kafka Connect

Authentication is configured through the authentication property in KafkaConnect.spec and KafkaConnectS2I.spec. The authentication property specifies the type of the authentication mechanisms which should be used and additional configuration details depending on the mechanism. The supported authentication types are:

TLS Client Authentication

To use TLS client authentication, set the type property to the value tls. TLS client authentication uses a TLS certificate to authenticate. The certificate is specified in the certificateAndKey property and is always loaded from a Kubernetes secret. In the secret, the certificate must be stored in X509 format under two different keys: public and private.

Note
 TLS client authentication can be used only with TLS connections. For more details about TLS configuration in Kafka Connect see Connecting to Kafka brokers using TLS.
An example TLS client authentication configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  authentication:
    type: tls
    certificateAndKey:
      secretName: my-secret
      certificate: public.crt
      key: private.key
  # ...
SASL based SCRAM-SHA-512 authentication

To configure Kafka Connect to use SASL-based SCRAM-SHA-512 authentication, set the type property to scram-sha-512. This authentication mechanism requires a username and password.

  • Specify the username in the username property.

  • In the passwordSecret property, specify a link to a Secret containing the password. The secretName property contains the name of the Secret and the password property contains the name of the key under which the password is stored inside the Secret.

Important
 Do not specify the actual password in the password field.
An example SASL based SCRAM-SHA-512 client authentication configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  authentication:
    type: scram-sha-512
    username: my-connect-user
    passwordSecret:
      secretName: my-connect-user
      password: my-connect-password-key
  # ...
SASL based PLAIN authentication

To configure Kafka Connect to use SASL-based PLAIN authentication, set the type property to plain. This authentication mechanism requires a username and password.

Warning
 The SASL PLAIN mechanism will transfer the username and password across the network in cleartext. Only use SASL PLAIN authentication if TLS encryption is enabled.

  • Specify the username in the username property.

  • In the passwordSecret property, specify a link to a Secret containing the password. The secretName property contains the name of such a Secret and the password property contains the name of the key under which the password is stored inside the Secret.

Important
 Do not specify the actual password in the password field.
An example showing SASL based PLAIN client authentication configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  authentication:
    type: plain
    username: my-connect-user
    passwordSecret:
      secretName: my-connect-user
      password: my-connect-password-key
  # ...
Configuring TLS client authentication in Kafka Connect
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

  • If they exist, the name of the Secret with the public and private keys used for TLS Client Authentication, and the keys under which they are stored in the Secret

Procedure

  1. (Optional) If they do not already exist, prepare the keys used for authentication in a file and create the Secret.

    Note
    		 Secrets created by the User Operator may be used.

    This can be done using kubectl create:

    kubectl create secret generic my-secret --from-file=my-public.crt --from-file=my-private.key

  2. Edit the authentication property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  authentication:
    type: tls
    certificateAndKey:
      secretName: my-secret
      certificate: my-public.crt
      key: my-private.key
  # ...

  3. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Configuring SCRAM-SHA-512 authentication in Kafka Connect
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

  • Username of the user which should be used for authentication

  • If they exist, the name of the Secret with the password used for authentication and the key under which the password is stored in the Secret

Procedure

  1. (Optional) If they do not already exist, prepare a file with the password used in authentication and create the Secret.

    Note
    		 Secrets created by the User Operator may be used.

    This can be done using kubectl create:

    echo -n '<password>' > <my-password.txt>
kubectl create secret generic <my-secret> --from-file=<my-password.txt>

  2. Edit the authentication property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  authentication:
    type: scram-sha-512
    username: _<my-username>_
    passwordSecret:
      secretName: _<my-secret>_
      password: _<my-password.txt>_
  # ...

  3. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f your-file

3.3.5. Kafka Connect configuration

Strimzi allows you to customize the configuration of Apache Kafka Connect nodes by editing certain options listed in Apache Kafka documentation.

Configuration options that cannot be configured relate to:

  • Kafka cluster bootstrap address

  • Security (Encryption, Authentication, and Authorization)

  • Listener / REST interface configuration

  • Plugin path configuration

These options are automatically configured by Strimzi.

Kafka Connect configuration

Kafka Connect is configured using the config property in KafkaConnect.spec and KafkaConnectS2I.spec. This property contains the Kafka Connect configuration options as keys. The values can be one of the following JSON types:

  • String

  • Number

  • Boolean

You can specify and configure the options listed in the Apache Kafka documentation with the exception of those options that are managed directly by Strimzi. Specifically, configuration options with keys equal to or starting with one of the following strings are forbidden:

  • ssl.

  • sasl.

  • security.

  • listeners

  • plugin.path

  • rest.

  • bootstrap.servers

When a forbidden option is present in the config property, it is ignored and a warning message is printed to the Custer Operator log file. All other options are passed to Kafka Connect.

Important
 The Cluster Operator does not validate keys or values in the config object provided. When an invalid configuration is provided, the Kafka Connect cluster might not start or might become unstable. In this circumstance, fix the configuration in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config object, then the Cluster Operator can roll out the new configuration to all Kafka Connect nodes.

Certain options have default values:

  • group.id with default value connect-cluster

  • offset.storage.topic with default value connect-cluster-offsets

  • config.storage.topic with default value connect-cluster-configs

  • status.storage.topic with default value connect-cluster-status

  • key.converter with default value org.apache.kafka.connect.json.JsonConverter

  • value.converter with default value org.apache.kafka.connect.json.JsonConverter

These options are automatically configured in case they are not present in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config properties.

Example Kafka Connect configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  config:
    group.id: my-connect-cluster
    offset.storage.topic: my-connect-cluster-offsets
    config.storage.topic: my-connect-cluster-configs
    status.storage.topic: my-connect-cluster-status
    key.converter: org.apache.kafka.connect.json.JsonConverter
    value.converter: org.apache.kafka.connect.json.JsonConverter
    key.converter.schemas.enable: true
    value.converter.schemas.enable: true
    config.storage.replication.factor: 3
    offset.storage.replication.factor: 3
    status.storage.replication.factor: 3
  # ...
Kafka Connect configuration for multiple instances

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

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  config:
    group.id: connect-cluster (1)
    offset.storage.topic: connect-cluster-offsets (2)
    config.storage.topic: connect-cluster-configs (3)
    status.storage.topic: connect-cluster-status  (4)
    # ...
# ...

  1. Kafka Connect cluster group that the instance belongs to.

  2. Kafka topic that stores connector offsets.

  3. Kafka topic that stores connector and task status configurations.

  4. Kafka topic that stores connector and task status updates.

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

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

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

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

Configuring Kafka Connect
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the config property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  config:
    group.id: my-connect-cluster
    offset.storage.topic: my-connect-cluster-offsets
    config.storage.topic: my-connect-cluster-configs
    status.storage.topic: my-connect-cluster-status
    key.converter: org.apache.kafka.connect.json.JsonConverter
    value.converter: org.apache.kafka.connect.json.JsonConverter
    key.converter.schemas.enable: true
    value.converter.schemas.enable: true
    config.storage.replication.factor: 3
    offset.storage.replication.factor: 3
    status.storage.replication.factor: 3
  # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

  3. If authorization is enabled for Kafka Connect, configure the Kafka Connect user to enable access to the Kafka Connect consumer group and topics.

3.3.6. Kafka Connect user authorization

If authorization is enabled for Kafka Connect, the Kafka Connect user must be configured to provide read/write access rights to the Kafka Connect consumer group and internal topics.

The properties for the consumer group and internal topics are automatically configured by Strimzi, or they can be specified explicitly in the spec for the KafkaConnect or KafkaConnectS2I configuration.

The following example shows the configuration of the properties in the KafkaConnect resource, which need to be represented in the Kafka Connect user configuration.

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  config:
    group.id: my-connect-cluster (1)
    offset.storage.topic: my-connect-cluster-offsets (2)
    config.storage.topic: my-connect-cluster-configs (3)
    status.storage.topic: my-connect-cluster-status (4)
    # ...
  # ...

  1. Kafka Connect cluster group that the instance belongs to.

  2. Kafka topic that stores connector offsets.

  3. Kafka topic that stores connector and task status configurations.

  4. Kafka topic that stores connector and task status updates.

Configuring Kafka Connect user authorization

This procedure describes how to authorize user access to Kafka Connect.

When any type of authorization is being used in Kafka, a Kafka Connect user requires access rights to the consumer group and the internal topics of Kafka Connect.

This procedure shows how access is provided when simple authorization is being used.

Simple authorization uses ACL rules, handled by the Kafka SimpleAclAuthorizer plugin, to provide the right level of access. For more information on configuring a KafkaUser resource to use simple authorization, see Kafka User resource.

Note
 The default values for the consumer group and topics will differ when running multiple instances.
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the authorization property in the KafkaUser resource to provide access rights to the user.

    In the following example, access rights are configured for the Kafka Connect topics and consumer group using literal name values:

    Property Name

    offset.storage.topic

    connect-cluster-offsets

    status.storage.topic

    connect-cluster-status

    config.storage.topic

    connect-cluster-configs

    group

    connect-cluster

    		 
    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster
spec:
  # ...
  authorization:
    type: simple
    acls:
      # access to offset.storage.topic
      - resource:
          type: topic
          name: connect-cluster-offsets
          patternType: literal
        operation: Write
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-offsets
          patternType: literal
        operation: Create
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-offsets
          patternType: literal
        operation: Describe
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-offsets
          patternType: literal
        operation: Read
        host: "*"
      # access to status.storage.topic
      - resource:
          type: topic
          name: connect-cluster-status
          patternType: literal
        operation: Write
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-status
          patternType: literal
        operation: Create
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-status
          patternType: literal
        operation: Describe
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-status
          patternType: literal
        operation: Read
        host: "*"
      # access to config.storage.topic
      - resource:
          type: topic
          name: connect-cluster-configs
          patternType: literal
        operation: Write
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-configs
          patternType: literal
        operation: Create
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-configs
          patternType: literal
        operation: Describe
        host: "*"
      - resource:
          type: topic
          name: connect-cluster-configs
          patternType: literal
        operation: Read
        host: "*"
      # consumer group
      - resource:
          type: group
          name: connect-cluster
          patternType: literal
        operation: Read
        host: "*"

  2. Create or update the resource.

    kubectl apply -f your-file

3.3.7. CPU and memory resources

For every deployed container, Strimzi allows you to request specific resources and define the maximum consumption of those resources.

Strimzi supports two types of resources:

  • CPU

  • Memory

Strimzi uses the Kubernetes syntax for specifying CPU and memory resources.

Resource limits and requests

Resource limits and requests are configured using the resources property in the following resources:

  • Kafka.spec.kafka

  • Kafka.spec.kafka.tlsSidecar

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.entityOperator.tlsSidecar

  • Kafka.spec.KafkaExporter

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaBridge.spec

Additional resources
Resource requests

Requests specify the resources to reserve for a given container. Reserving the resources ensures that they are always available.

Important
 If the resource request is for more than the available free resources in the Kubernetes cluster, the pod is not scheduled.

Resources requests are specified in the requests property. Resources requests currently supported by Strimzi:

  • cpu

  • memory

A request may be configured for one or more supported resources.

Example resource request configuration with all resources
# ...
resources:
  requests:
    cpu: 12
    memory: 64Gi
# ...
Resource limits

Limits specify the maximum resources that can be consumed by a given container. The limit is not reserved and might not always be available. A container can use the resources up to the limit only when they are available. Resource limits should be always higher than the resource requests.

Resource limits are specified in the limits property. Resource limits currently supported by Strimzi:

  • cpu

  • memory

A resource may be configured for one or more supported limits.

Example resource limits configuration
# ...
resources:
  limits:
    cpu: 12
    memory: 64Gi
# ...
Supported CPU formats

CPU requests and limits are supported in the following formats:

  • Number of CPU cores as integer (5 CPU core) or decimal (2.5 CPU core).

  • Number or millicpus / millicores (100m) where 1000 millicores is the same 1 CPU core.

Example CPU units
# ...
resources:
  requests:
    cpu: 500m
  limits:
    cpu: 2.5
# ...
Note
 The computing power of 1 CPU core may differ depending on the platform where Kubernetes is deployed.
Additional resources
Supported memory formats

Memory requests and limits are specified in megabytes, gigabytes, mebibytes, and gibibytes.

  • To specify memory in megabytes, use the M suffix. For example 1000M.

  • To specify memory in gigabytes, use the G suffix. For example 1G.

  • To specify memory in mebibytes, use the Mi suffix. For example 1000Mi.

  • To specify memory in gibibytes, use the Gi suffix. For example 1Gi.

An example of using different memory units
# ...
resources:
  requests:
    memory: 512Mi
  limits:
    memory: 2Gi
# ...
Additional resources

  • For more details about memory specification and additional supported units, see Meaning of memory.

Configuring resource requests and limits
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the resources property in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    resources:
      requests:
        cpu: "8"
        memory: 64Gi
      limits:
        cpu: "12"
        memory: 128Gi
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Additional resources

3.3.8. Kafka Connect with S2I loggers

Kafka Connect with Source2Image support has its own configurable loggers:

  • connect.root.logger.level

  • log4j.logger.org.reflections

Kafka Connect uses the Apache log4j logger implementation.

Use the logging property to configure loggers and logger levels.

You can set the log levels by specifying the logger and level directly (inline) or use a custom (external) ConfigMap. If a ConfigMap is used, you set logging.name property to the name of the ConfigMap containing the external logging configuration. Inside the ConfigMap, the logging configuration is described using log4j.properties.

Here we see examples of inline and external logging.

Inline logging
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnectS2I
spec:
  # ...
  logging:
    type: inline
    loggers:
      connect.root.logger.level: "INFO"
  # ...
External logging
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnectS2I
spec:
  # ...
  logging:
    type: external
    name: customConfigMap
  # ...
Additional resources

3.3.9. Healthchecks

Healthchecks are periodical tests which verify the health of an application. When a Healthcheck probe fails, Kubernetes assumes that the application is not healthy and attempts to fix it.

Kubernetes supports two types of Healthcheck probes:

  • Liveness probes

  • Readiness probes

For more details about the probes, see Configure Liveness and Readiness Probes. Both types of probes are used in Strimzi components.

Users can configure selected options for liveness and readiness probes.

Healthcheck configurations

Liveness and readiness probes can be configured using the livenessProbe and readinessProbe properties in following resources:

  • Kafka.spec.kafka

  • Kafka.spec.kafka.tlsSidecar

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator.tlsSidecar

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.KafkaExporter

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaMirrorMaker.spec

  • KafkaBridge.spec

Both livenessProbe and readinessProbe support the following options:

  • initialDelaySeconds

  • timeoutSeconds

  • periodSeconds

  • successThreshold

  • failureThreshold

For more information about the livenessProbe and readinessProbe options, see Probe schema reference.

An example of liveness and readiness probe configuration
# ...
readinessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
livenessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
# ...
Configuring healthchecks
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the livenessProbe or readinessProbe property in the Kafka, KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    readinessProbe:
      initialDelaySeconds: 15
      timeoutSeconds: 5
    livenessProbe:
      initialDelaySeconds: 15
      timeoutSeconds: 5
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.3.10. Prometheus metrics

Strimzi supports Prometheus metrics using Prometheus JMX exporter to convert the JMX metrics supported by Apache Kafka and ZooKeeper to Prometheus metrics. When metrics are enabled, they are exposed on port 9404.

For more information about setting up and deploying Prometheus and Grafana, see Introducing Metrics to Kafka.

Metrics configuration

Prometheus metrics are enabled by configuring the metrics property in following resources:

  • Kafka.spec.kafka

  • Kafka.spec.zookeeper

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

When the metrics property is not defined in the resource, the Prometheus metrics will be disabled. To enable Prometheus metrics export without any further configuration, you can set it to an empty object ({}).

Example of enabling metrics without any further configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    metrics: {}
    # ...
  zookeeper:
    # ...

The metrics property might contain additional configuration for the Prometheus JMX exporter.

Example of enabling metrics with additional Prometheus JMX Exporter configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    metrics:
      lowercaseOutputName: true
      rules:
        - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*><>Count"
          name: "kafka_server_$1_$2_total"
        - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*, topic=(.+)><>Count"
          name: "kafka_server_$1_$2_total"
          labels:
            topic: "$3"
    # ...
  zookeeper:
    # ...
Configuring Prometheus metrics
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the metrics property in the Kafka, KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
    metrics:
      lowercaseOutputName: true
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.3.11. JVM Options

The following components of Strimzi run inside a Virtual Machine (VM):

  • Apache Kafka

  • Apache ZooKeeper

  • Apache Kafka Connect

  • Apache Kafka MirrorMaker

  • Strimzi Kafka Bridge

JVM configuration options optimize the performance for different platforms and architectures. Strimzi allows you to configure some of these options.

JVM configuration

JVM options can be configured using the jvmOptions property in following resources:

  • Kafka.spec.kafka

  • Kafka.spec.zookeeper

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaMirrorMaker.spec

  • KafkaBridge.spec

Only a selected subset of available JVM options can be configured. The following options are supported:

-Xms and -Xmx

-Xms configures the minimum initial allocation heap size when the JVM starts. -Xmx configures the maximum heap size.

Note
 The units accepted by JVM settings such as -Xmx and -Xms are those accepted by the JDK java binary in the corresponding image. Accordingly, 1g or 1G means 1,073,741,824 bytes, and Gi is not a valid unit suffix. This is in contrast to the units used for memory requests and limits, which follow the Kubernetes convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes

The default values used for -Xms and -Xmx depends on whether there is a memory request limit configured for the container:

  • If there is a memory limit then the JVM’s minimum and maximum memory will be set to a value corresponding to the limit.

  • If there is no memory limit then the JVM’s minimum memory will be set to 128M and the JVM’s maximum memory will not be defined. This allows for the JVM’s memory to grow as-needed, which is ideal for single node environments in test and development.

Important

Setting -Xmx explicitly requires some care:

  • The JVM’s overall memory usage will be approximately 4 × the maximum heap, as configured by -Xmx.

  • If -Xmx is set without also setting an appropriate Kubernetes memory limit, it is possible that the container will be killed should the Kubernetes node experience memory pressure (from other Pods running on it).

  • If -Xmx is set without also setting an appropriate Kubernetes memory request, it is possible that the container will be scheduled to a node with insufficient memory. In this case, the container will not start but crash (immediately if -Xms is set to -Xmx, or some later time if not).

When setting -Xmx explicitly, it is recommended to:

  • set the memory request and the memory limit to the same value,

  • use a memory request that is at least 4.5 × the -Xmx,

  • consider setting -Xms to the same value as -Xmx.

Important
 Containers doing lots of disk I/O (such as Kafka broker containers) will need to leave some memory available for use as operating system page cache. On such containers, the requested memory should be significantly higher than the memory used by the JVM.
Example fragment configuring -Xmx and -Xms
# ...
jvmOptions:
  "-Xmx": "2g"
  "-Xms": "2g"
# ...

In the above example, the JVM will use 2 GiB (=2,147,483,648 bytes) for its heap. Its total memory usage will be approximately 8GiB.

Setting the same value for initial (-Xms) and maximum (-Xmx) heap sizes avoids the JVM having to allocate memory after startup, at the cost of possibly allocating more heap than is really needed. For Kafka and ZooKeeper pods such allocation could cause unwanted latency. For Kafka Connect avoiding over allocation may be the most important concern, especially in distributed mode where the effects of over-allocation will be multiplied by the number of consumers.

-server

-server enables the server JVM. This option can be set to true or false.

Example fragment configuring -server
# ...
jvmOptions:
  "-server": true
# ...
Note
 When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.
-XX

-XX object can be used for configuring advanced runtime options of a JVM. The -server and -XX options are used to configure the KAFKA_JVM_PERFORMANCE_OPTS option of Apache Kafka.

Example showing the use of the -XX object
jvmOptions:
  "-XX":
    "UseG1GC": true
    "MaxGCPauseMillis": 20
    "InitiatingHeapOccupancyPercent": 35
    "ExplicitGCInvokesConcurrent": true
    "UseParNewGC": false

The example configuration above will result in the following JVM options:

-XX:+UseG1GC -XX:MaxGCPauseMillis=20 -XX:InitiatingHeapOccupancyPercent=35 -XX:+ExplicitGCInvokesConcurrent -XX:-UseParNewGC
Note
 When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.
Garbage collector logging

The jvmOptions section also allows you to enable and disable garbage collector (GC) logging. GC logging is disabled by default. To enable it, set the gcLoggingEnabled property as follows:

Example of enabling GC logging
# ...
jvmOptions:
  gcLoggingEnabled: true
# ...
Configuring JVM options
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the jvmOptions property in the Kafka, KafkaConnect, KafkaConnectS2I, KafkaMirrorMaker, or KafkaBridge resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    jvmOptions:
      "-Xmx": "8g"
      "-Xms": "8g"
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.3.12. Container images

Strimzi allows you to configure container images which will be used for its components. Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by Strimzi. In such a case, you should either copy the Strimzi images or build them from the source. If the configured image is not compatible with Strimzi images, it might not work properly.

Container image configurations

You can specify which container image to use for each component using the image property in the following resources:

  • Kafka.spec.kafka

  • Kafka.spec.kafka.tlsSidecar

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.entityOperator.tlsSidecar

  • Kafka.spec.jmxTrans

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaBridge.spec

Configuring the image property for Kafka, Kafka Connect, and Kafka MirrorMaker

Kafka, Kafka Connect (including Kafka Connect with S2I support), and Kafka MirrorMaker support multiple versions of Kafka. Each component requires its own image. The default images for the different Kafka versions are configured in the following environment variables:

  • STRIMZI_KAFKA_IMAGES

  • STRIMZI_KAFKA_CONNECT_IMAGES

  • STRIMZI_KAFKA_CONNECT_S2I_IMAGES

  • STRIMZI_KAFKA_MIRROR_MAKER_IMAGES

These environment variables contain mappings between the Kafka versions and their corresponding images. The mappings are used together with the image and version properties:

  • If neither image nor version are given in the custom resource then the version will default to the Cluster Operator’s default Kafka version, and the image will be the one corresponding to this version in the environment variable.

  • If image is given but version is not, then the given image is used and the version is assumed to be the Cluster Operator’s default Kafka version.

  • If version is given but image is not, then the image that corresponds to the given version in the environment variable is used.

  • If both version and image are given, then the given image is used. The image is assumed to contain a Kafka image with the given version.

The image and version for the different components can be configured in the following properties:

  • For Kafka in spec.kafka.image and spec.kafka.version.

  • For Kafka Connect, Kafka Connect S2I, and Kafka MirrorMaker in spec.image and spec.version.

Warning
 It is recommended to provide only the version and leave the image property unspecified. This reduces the chance of making a mistake when configuring the custom resource. If you need to change the images used for different versions of Kafka, it is preferable to configure the Cluster Operator’s environment variables.
Configuring the image property in other resources

For the image property in the other custom resources, the given value will be used during deployment. If the image property is missing, the image specified in the Cluster Operator configuration will be used. If the image name is not defined in the Cluster Operator configuration, then the default value will be used.

  • For Kafka broker TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_KAFKA_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.18.0-kafka-2.5.0 container image.

  • For ZooKeeper nodes:

  • For ZooKeeper node TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_ZOOKEEPER_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.18.0-kafka-2.5.0 container image.

  • For Topic Operator:

    1. Container image specified in the STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/operator:0.18.0 container image.

  • For User Operator:

    1. Container image specified in the STRIMZI_DEFAULT_USER_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/operator:0.18.0 container image.

  • For Entity Operator TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.18.0-kafka-2.5.0 container image.

  • For Kafka Exporter:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_EXPORTER_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.18.0-kafka-2.5.0 container image.

  • For Kafka Bridge:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_BRIDGE_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka-bridge:0.16.0 container image.

  • For Kafka broker initializer:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_INIT_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/operator:0.18.0 container image.

  • For Kafka broker initializer:

    1. Container image specified in the STRIMZI_DEFAULT_JMXTRANS_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/operator:0.18.0 container image.

Warning
 Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by Strimzi. In such case, you should either copy the Strimzi images or build them from source. In case the configured image is not compatible with Strimzi images, it might not work properly.
Example of container image configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    image: my-org/my-image:latest
    # ...
  zookeeper:
    # ...
Configuring container images
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the image property in the Kafka, KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    image: my-org/my-image:latest
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.3.13. Configuring pod scheduling

Important
 When two applications are scheduled to the same Kubernetes node, both applications might use the same resources like disk I/O and impact performance. That can lead to performance degradation. Scheduling Kafka pods in a way that avoids sharing nodes with other critical workloads, using the right nodes or dedicated a set of nodes only for Kafka are the best ways how to avoid such problems.
Scheduling pods based on other applications
Avoid critical applications to share the node

Pod anti-affinity can be used to ensure that critical applications are never scheduled on the same disk. When running Kafka cluster, it is recommended to use pod anti-affinity to ensure that the Kafka brokers do not share the nodes with other workloads like databases.

Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod

  • Kafka.spec.zookeeper.template.pod

  • Kafka.spec.entityOperator.template.pod

  • KafkaConnect.spec.template.pod

  • KafkaConnectS2I.spec.template.pod

  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

The format of the affinity property follows the Kubernetes specification. For more details, see the Kubernetes node and pod affinity documentation.

Configuring pod anti-affinity in Kafka components
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the affinity property in the resource specifying the cluster deployment. Use labels to specify the pods which should not be scheduled on the same nodes. The topologyKey should be set to kubernetes.io/hostname to specify that the selected pods should not be scheduled on nodes with the same hostname. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    template:
      pod:
        affinity:
          podAntiAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              - labelSelector:
                  matchExpressions:
                    - key: application
                      operator: In
                      values:
                        - postgresql
                        - mongodb
                topologyKey: "kubernetes.io/hostname"
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Scheduling pods to specific nodes
Node scheduling

The Kubernetes cluster usually consists of many different types of worker nodes. Some are optimized for CPU heavy workloads, some for memory, while other might be optimized for storage (fast local SSDs) or network. Using different nodes helps to optimize both costs and performance. To achieve the best possible performance, it is important to allow scheduling of Strimzi components to use the right nodes.

Kubernetes uses node affinity to schedule workloads onto specific nodes. Node affinity allows you to create a scheduling constraint for the node on which the pod will be scheduled. The constraint is specified as a label selector. You can specify the label using either the built-in node label like beta.kubernetes.io/instance-type or custom labels to select the right node.

Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod

  • Kafka.spec.zookeeper.template.pod

  • Kafka.spec.entityOperator.template.pod

  • KafkaConnect.spec.template.pod

  • KafkaConnectS2I.spec.template.pod

  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

The format of the affinity property follows the Kubernetes specification. For more details, see the Kubernetes node and pod affinity documentation.

Configuring node affinity in Kafka components
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Label the nodes where Strimzi components should be scheduled.

    This can be done using kubectl label:

    kubectl label node your-node node-type=fast-network

    Alternatively, some of the existing labels might be reused.

  2. Edit the affinity property in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    template:
      pod:
        affinity:
          nodeAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
                - matchExpressions:
                  - key: node-type
                    operator: In
                    values:
                    - fast-network
    # ...
  zookeeper:
    # ...

  3. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Using dedicated nodes
Dedicated nodes

Cluster administrators can mark selected Kubernetes nodes as tainted. Nodes with taints are excluded from regular scheduling and normal pods will not be scheduled to run on them. Only services which can tolerate the taint set on the node can be scheduled on it. The only other services running on such nodes will be system services such as log collectors or software defined networks.

Taints can be used to create dedicated nodes. Running Kafka and its components on dedicated nodes can have many advantages. There will be no other applications running on the same nodes which could cause disturbance or consume the resources needed for Kafka. That can lead to improved performance and stability.

To schedule Kafka pods on the dedicated nodes, configure node affinity and tolerations.

Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod

  • Kafka.spec.zookeeper.template.pod

  • Kafka.spec.entityOperator.template.pod

  • KafkaConnect.spec.template.pod

  • KafkaConnectS2I.spec.template.pod

  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

The format of the affinity property follows the Kubernetes specification. For more details, see the Kubernetes node and pod affinity documentation.

Tolerations

Tolerations can be configured using the tolerations property in following resources:

  • Kafka.spec.kafka.template.pod

  • Kafka.spec.zookeeper.template.pod

  • Kafka.spec.entityOperator.template.pod

  • KafkaConnect.spec.template.pod

  • KafkaConnectS2I.spec.template.pod

  • KafkaBridge.spec.template.pod

The format of the tolerations property follows the Kubernetes specification. For more details, see the Kubernetes taints and tolerations.

Setting up dedicated nodes and scheduling pods on them
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Select the nodes which should be used as dedicated.

  2. Make sure there are no workloads scheduled on these nodes.

  3. Set the taints on the selected nodes:

    This can be done using kubectl taint:

    kubectl taint node your-node dedicated=Kafka:NoSchedule

  4. Additionally, add a label to the selected nodes as well.

    This can be done using kubectl label:

    kubectl label node your-node dedicated=Kafka

  5. Edit the affinity and tolerations properties in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    template:
      pod:
        tolerations:
          - key: "dedicated"
            operator: "Equal"
            value: "Kafka"
            effect: "NoSchedule"
        affinity:
          nodeAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
              - matchExpressions:
                - key: dedicated
                  operator: In
                  values:
                  - Kafka
    # ...
  zookeeper:
    # ...

  6. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.3.14. Using external configuration and secrets

Connectors are created, reconfigured, and deleted using the Kafka Connect HTTP REST interface, or by using KafkaConnectors. For more information on these methods, see Creating and managing connectors. The connector configuration is passed to Kafka Connect as part of an HTTP request and stored within Kafka itself.

ConfigMaps and Secrets are standard Kubernetes resources used for storing configurations and confidential data. Whichever method you use to manage connectors, you can use ConfigMaps and Secrets to configure certain elements of a connector. You can then reference the configuration values in HTTP REST commands (this keeps the configuration separate and more secure, if needed). This method applies especially to confidential data, such as usernames, passwords, or certificates.

Storing connector configurations externally

You can mount ConfigMaps or Secrets into a Kafka Connect pod as volumes or environment variables. Volumes and environment variables are configured in the externalConfiguration property in KafkaConnect.spec and KafkaConnectS2I.spec.

External configuration as environment variables

The env property is used to specify one or more environment variables. These variables can contain a value from either a ConfigMap or a Secret.

Note
 The names of user-defined environment variables cannot start with KAFKA_ or STRIMZI_.

To mount a value from a Secret to an environment variable, use the valueFrom property and the secretKeyRef as shown in the following example.

Example of an environment variable set to a value from a Secret
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  externalConfiguration:
    env:
      - name: MY_ENVIRONMENT_VARIABLE
        valueFrom:
          secretKeyRef:
            name: my-secret
            key: my-key

A common use case for mounting Secrets to environment variables is when your connector needs to communicate with Amazon AWS and needs to read the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables with credentials.

To mount a value from a ConfigMap to an environment variable, use configMapKeyRef in the valueFrom property as shown in the following example.

Example of an environment variable set to a value from a ConfigMap
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  externalConfiguration:
    env:
      - name: MY_ENVIRONMENT_VARIABLE
        valueFrom:
          configMapKeyRef:
            name: my-config-map
            key: my-key
External configuration as volumes

You can also mount ConfigMaps or Secrets to a Kafka Connect pod as volumes. Using volumes instead of environment variables is useful in the following scenarios:

  • Mounting truststores or keystores with TLS certificates

  • Mounting a properties file that is used to configure Kafka Connect connectors

In the volumes property of the externalConfiguration resource, list the ConfigMaps or Secrets that will be mounted as volumes. Each volume must specify a name in the name property and a reference to ConfigMap or Secret.

Example of volumes with external configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  externalConfiguration:
    volumes:
      - name: connector1
        configMap:
          name: connector1-configuration
      - name: connector1-certificates
        secret:
          secretName: connector1-certificates

The volumes will be mounted inside the Kafka Connect containers in the path /opt/kafka/external-configuration/<volume-name>. For example, the files from a volume named connector1 would appear in the directory /opt/kafka/external-configuration/connector1.

The FileConfigProvider has to be used to read the values from the mounted properties files in connector configurations.

Mounting Secrets as environment variables

You can create a Kubernetes Secret and mount it to Kafka Connect as an environment variable.

Prerequisites

  • A running Cluster Operator.

Procedure

  1. Create a secret containing the information that will be mounted as an environment variable. For example:

    apiVersion: v1
kind: Secret
metadata:
  name: aws-creds
type: Opaque
data:
  awsAccessKey: QUtJQVhYWFhYWFhYWFhYWFg=
  awsSecretAccessKey: Ylhsd1lYTnpkMjl5WkE=

  2. Create or edit the Kafka Connect resource. Configure the externalConfiguration section of the KafkaConnect or KafkaConnectS2I custom resource to reference the secret. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  externalConfiguration:
    env:
      - name: AWS_ACCESS_KEY_ID
        valueFrom:
          secretKeyRef:
            name: aws-creds
            key: awsAccessKey
      - name: AWS_SECRET_ACCESS_KEY
        valueFrom:
          secretKeyRef:
            name: aws-creds
            key: awsSecretAccessKey

  3. Apply the changes to your Kafka Connect deployment.

    Use kubectl apply:

    kubectl apply -f your-file

The environment variables are now available for use when developing your connectors.

Additional resources
Mounting Secrets as volumes

You can create a Kubernetes Secret, mount it as a volume to Kafka Connect, and then use it to configure a Kafka Connect connector.

Prerequisites

  • A running Cluster Operator.

Procedure

  1. Create a secret containing a properties file that defines the configuration options for your connector configuration. For example:

    apiVersion: v1
kind: Secret
metadata:
  name: mysecret
type: Opaque
stringData:
  connector.properties: |-
    dbUsername: my-user
    dbPassword: my-password

  2. Create or edit the Kafka Connect resource. Configure the FileConfigProvider in the config section and the externalConfiguration section of the KafkaConnect or KafkaConnectS2I custom resource to reference the secret. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect
spec:
  # ...
  config:
    config.providers: file
    config.providers.file.class: org.apache.kafka.common.config.provider.FileConfigProvider
  #...
  externalConfiguration:
    volumes:
      - name: connector-config
        secret:
          secretName: mysecret

  3. Apply the changes to your Kafka Connect deployment.

    Use kubectl apply:

    kubectl apply -f your-file

  4. Configure your connector

    • If you are using the Kafka Connect HTTP REST interface, use the values from the mounted properties file in your JSON payload with connector configuration. For example:

      {
   "name":"my-connector",
   "config":{
      "connector.class":"MyDbConnector",
      "tasks.max":"3",
      "database": "my-postgresql:5432",
      "username":"${file:/opt/kafka/external-configuration/connector-config/connector.properties:dbUsername}",
      "password":"${file:/opt/kafka/external-configuration/connector-config/connector.properties:dbPassword}",
      # ...
   }
}

    • If you are using a KafkaConnector resource, use the values from the mounted properties file in the spec.config section of your custom resource. For example:

      apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnector
metadata:
  name: my-connector
  # ...
spec:
  class: "MyDbConnector"
  tasksMax: 3
  config:
    database: "my-postgresql:5432"
    username: "${file:/opt/kafka/external-configuration/connector-config/connector.properties:dbUsername}"
    password: "${file:/opt/kafka/external-configuration/connector-config/connector.properties:dbPassword}"
Additional resources

3.3.15. Enabling KafkaConnector resources

To enable KafkaConnectors for a Kafka Connect cluster, add the strimzi.io/use-connector-resources annotation to the KafkaConnect or KafkaConnectS2I custom resource.

Prerequisites

  • A running Cluster Operator

Procedure

  1. Edit the KafkaConnect or KafkaConnectS2I resource. Add the strimzi.io/use-connector-resources annotation. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect-cluster
  annotations:
    strimzi.io/use-connector-resources: "true"
spec:
  # ...

  2. Create or update the resource using kubectl apply:

    kubectl apply -f kafka-connect.yaml
Additional resources

3.3.16. List of resources created as part of Kafka Connect cluster with Source2Image support

The following resources will created by the Cluster Operator in the Kubernetes cluster:

connect-cluster-name-connect-source

ImageStream which is used as the base image for the newly-built Docker images.

connect-cluster-name-connect

BuildConfig which is responsible for building the new Kafka Connect Docker images.

connect-cluster-name-connect

ImageStream where the newly built Docker images will be pushed.

connect-cluster-name-connect

DeploymentConfig which is in charge of creating the Kafka Connect worker node pods.

connect-cluster-name-connect-api

Service which exposes the REST interface for managing the Kafka Connect cluster.

connect-cluster-name-config

ConfigMap which contains the Kafka Connect ancillary configuration and is mounted as a volume by the Kafka broker pods.

connect-cluster-name-connect

Pod Disruption Budget configured for the Kafka Connect worker nodes.

3.4. Kafka MirrorMaker configuration

This chapter describes how to configure a Kafka MirrorMaker deployment in your Strimzi cluster to replicate data between Kafka clusters.

You can use Strimzi with MirrorMaker or MirrorMaker 2.0. MirrorMaker 2.0 is the latest version, and offers a more efficient way to mirror data between Kafka clusters.

MirrorMaker

If you are using MirrorMaker, you configure the KafkaMirrorMaker resource.

The following procedure shows how the resource is configured:

Supported properties are also described in more detail for your reference:

The full schema of the KafkaMirrorMaker resource is described in the KafkaMirrorMaker schema reference.

Note
 Labels applied to a KafkaMirrorMaker resource are also applied to the Kubernetes resources comprising Kafka MirrorMaker. This provides a convenient mechanism for resources to be labeled as required.

MirrorMaker 2.0

If you are using MirrorMaker 2.0, you configure the KafkaMirrorMaker2 resource.

MirrorMaker 2.0 introduces an entirely new way of replicating data between clusters.

As a result, the resource configuration differs from the previous version of MirrorMaker. If you choose to use MirrorMaker 2.0, there is currently no legacy support, so any resources must be manually converted into the new format.

How MirrorMaker 2.0 replicates data is described here:

The following procedure shows how the resource is configured for MirrorMaker 2.0:

The full schema of the KafkaMirrorMaker2 resource is described in the KafkaMirrorMaker2 schema reference.

3.4.1. Configuring Kafka MirrorMaker

Use the properties of the KafkaMirrorMaker resource to configure your Kafka MirrorMaker deployment.

You can configure access control for producers and consumers using TLS or SASL authentication. This procedure shows a configuration that uses TLS encryption and authentication on the consumer and producer side.

Prerequisites
Procedure

  1. Edit the spec properties for the KafkaMirrorMaker resource.

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

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  replicas: 3 (1)
  consumer:
    bootstrapServers: my-source-cluster-kafka-bootstrap:9092 (2)
    groupId: "my-group" (3)
    numStreams: 2 (4)
    offsetCommitInterval: 120000 (5)
    tls: (6)
      trustedCertificates:
      - secretName: my-source-cluster-ca-cert
        certificate: ca.crt
    authentication: (7)
      type: tls
      certificateAndKey:
        secretName: my-source-secret
        certificate: public.crt
        key: private.key
    config: (8)
      max.poll.records: 100
      receive.buffer.bytes: 32768
  producer:
    bootstrapServers: my-target-cluster-kafka-bootstrap:9092
    abortOnSendFailure: false (9)
    tls:
      trustedCertificates:
      - secretName: my-target-cluster-ca-cert
        certificate: ca.crt
    authentication:
      type: tls
      certificateAndKey:
        secretName: my-target-secret
        certificate: public.crt
        key: private.key
    config:
      compression.type: gzip
      batch.size: 8192
  whitelist: "my-topic|other-topic" (10)
  resources: (11)
    requests:
      cpu: "1"
      memory: 2Gi
    limits:
      cpu: "2"
      memory: 2Gi
  logging: (12)
    type: inline
    loggers:
      mirrormaker.root.logger: "INFO"
  readinessProbe: (13)
    initialDelaySeconds: 15
    timeoutSeconds: 5
  livenessProbe:
    initialDelaySeconds: 15
    timeoutSeconds: 5
  metrics: (14)
    lowercaseOutputName: true
    rules:
      - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*><>Count"
        name: "kafka_server_$1_$2_total"
      - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*,
        topic=(.+)><>Count"
        name: "kafka_server_$1_$2_total"
        labels:
          topic: "$3"
  jvmOptions: (15)
    "-Xmx": "1g"
    "-Xms": "1g"
  image: my-org/my-image:latest (16)
  template: (17)
      pod:
        affinity:
          podAntiAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              - labelSelector:
                  matchExpressions:
                    - key: application
                      operator: In
                      values:
                        - postgresql
                        - mongodb
                topologyKey: "kubernetes.io/hostname"

    1. The number of replica nodes.

    2. Bootstrap servers for consumer and producer.

    3. Group ID for the consumer.

    4. The number of consumer streams.

    5. The offset auto-commit interval in milliseconds.

    6. TLS encryption with key names under which TLS certificates are stored in X.509 format for consumer or producer. For more details see KafkaMirrorMakerTls schema reference.

    7. Authentication for consumer or producer, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-512 or PLAIN mechanism.

    8. Kafka configuration options for consumer and producer.

    9. If set to true, Kafka MirrorMaker will exit and the container will restart following a send failure for a message.

    10. Topics mirrored from source to target Kafka cluster.

    11. Requests for reservation of supported resources, currently cpu and memory, and limits to specify the maximum resources that can be consumed.

    12. Specified loggers and log levels added directly (inline) or indirectly (external) through a ConfigMap. A custom ConfigMap must be placed under the log4j.properties or log4j2.properties key. MirrorMaker has a single logger called mirrormaker.root.logger. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.

    13. Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).

    14. Prometheus metrics, which are enabled with configuration for the Prometheus JMX exporter in this example. You can enable metrics without further configuration using metrics: {}.

    15. JVM configuration options to optimize performance for the Virtual Machine (VM) running Kafka MirrorMaker.

    16. ADVANCED OPTION: Container image configuration, which is recommended only in special situations.

    17. Template customization. Here a pod is scheduled based with anti-affinity, so the pod is not scheduled on nodes with the same hostname.

    Warning
    		 With the abortOnSendFailure property set to false, the producer attempts to send the next message in a topic. The original message might be lost, as there is no attempt to resend a failed message.

  2. Create or update the resource:

    kubectl apply -f <your-file>

3.4.2. Kafka MirrorMaker configuration properties

Use the spec configuration properties of the KafkaMirrorMaker resource to set up your MirrorMaker deployment.

Supported properties are described here for your reference.

Replicas

Use the replicas property to configure replicas.

You can run multiple MirrorMaker replicas to provide better availability and scalability. When running Kafka MirrorMaker on Kubernetes it is not absolutely necessary to run multiple replicas of the Kafka MirrorMaker for high availability. When the node where the Kafka MirrorMaker has deployed crashes, Kubernetes will automatically reschedule the Kafka MirrorMaker pod to a different node. However, running Kafka MirrorMaker with multiple replicas can provide faster failover times as the other nodes will be up and running.

Bootstrap servers

Use the consumer.bootstrapServers and producer.bootstrapServers properties to configure lists of bootstrap servers for the consumer and producer.

Kafka MirrorMaker always works together with two Kafka clusters (source and target). The source and the target Kafka clusters are specified in the form of two lists of comma-separated list of <hostname>:‍<port> pairs. Each comma-separated list contains one or more Kafka brokers or a Service pointing to Kafka brokers specified as a <hostname>:<port> pairs.

The bootstrap server lists can refer to Kafka clusters that do not need to be deployed in the same Kubernetes cluster. They can even refer to a Kafka cluster not deployed by Strimzi, or deployed by Strimzi but on a different Kubernetes cluster accessible outside.

If on the same Kubernetes cluster, each list must ideally contain the Kafka cluster bootstrap service which is named <cluster-name>-kafka-bootstrap and a port of 9092 for plain traffic or 9093 for encrypted traffic. If deployed by Strimzi but on different Kubernetes clusters, the list content depends on the approach used for exposing the clusters (routes, nodeports or loadbalancers).

When using Kafka MirrorMaker with a Kafka cluster not managed by Strimzi, you can specify the bootstrap servers list according to the configuration of the given cluster.

Whitelist

Use the whitelist property to configure a list of topics that Kafka MirrorMaker mirrors from the source to the target Kafka cluster.

The property allows any regular expression from the simplest case with a single topic name to complex patterns. For example, you can mirror topics A and B using "A|B" or all topics using "*". You can also pass multiple regular expressions separated by commas to the Kafka MirrorMaker.

Consumer group identifier

Use the consumer.groupId property to configure a consumer group identifier for the consumer.

Kafka MirrorMaker uses a Kafka consumer to consume messages, behaving like any other Kafka consumer client. Messages consumed from the source Kafka cluster are mirrored to a target Kafka cluster. A group identifier is required, as the consumer needs to be part of a consumer group for the assignment of partitions.

Consumer streams

Use the consumer.numStreams property to configure the number of streams for the consumer.

You can increase the throughput in mirroring topics by increasing the number of consumer threads. Consumer threads belong to the consumer group specified for Kafka MirrorMaker. Topic partitions are assigned across the consumer threads, which consume messages in parallel.

Offset auto-commit interval

Use the consumer.offsetCommitInterval property to configure an offset auto-commit interval for the consumer.

You can specify the regular time interval at which an offset is committed after Kafka MirrorMaker has consumed data from the source Kafka cluster. The time interval is set in milliseconds, with a default value of 60,000.

Abort on message send failure

Use the producer.abortOnSendFailure property to configure how to handle message send failure from the producer.

By default, if an error occurs when sending a message from Kafka MirrorMaker to a Kafka cluster:

  • The Kafka MirrorMaker container is terminated in Kubernetes.

  • The container is then recreated.

If the abortOnSendFailure option is set to false, message sending errors are ignored.

Kafka producer and consumer

Use the consumer.config and producer.config properties to configure Kafka options for the consumer and producer.

The config property contains the Kafka MirrorMaker consumer and producer configuration options as keys, with values set in one of the following JSON types:

  • String

  • Number

  • Boolean

Exceptions

You can specify and configure standard Kafka consumer and producer options:

However, there are exceptions for options automatically configured and managed directly by Strimzi related to:

  • Kafka cluster bootstrap address

  • Security (encryption, authentication, and authorization)

  • Consumer group identifier

Specifically, all configuration options with keys equal to or starting with one of the following strings are forbidden:

  • ssl.

  • sasl.

  • security.

  • bootstrap.servers

  • group.id

When a forbidden option is present in the config property, it is ignored and a warning message is printed to the Custer Operator log file. All other options are passed to Kafka MirrorMaker.

Important
 The Cluster Operator does not validate keys or values in the provided config object. When an invalid configuration is provided, the Kafka MirrorMaker might not start or might become unstable. In such cases, the configuration in the KafkaMirrorMaker.spec.consumer.config or KafkaMirrorMaker.spec.producer.config object should be fixed and the Cluster Operator will roll out the new configuration for Kafka MirrorMaker.
CPU and memory resources

Use the reources.requests and resources.limits properties to configure resource requests and limits.

For every deployed container, Strimzi allows you to request specific resources and define the maximum consumption of those resources.

Strimzi supports requests and limits for the following types of resources:

  • cpu

  • memory

Strimzi uses the Kubernetes syntax for specifying these resources.

For more information about managing computing resources on Kubernetes, see Managing Compute Resources for Containers.

Resource requests

Requests specify the resources to reserve for a given container. Reserving the resources ensures that they are always available.

Important
 If the resource request is for more than the available free resources in the Kubernetes cluster, the pod is not scheduled.

A request may be configured for one or more supported resources.

Resource limits

Limits specify the maximum resources that can be consumed by a given container. The limit is not reserved and might not always be available. A container can use the resources up to the limit only when they are available. Resource limits should be always higher than the resource requests.

A resource may be configured for one or more supported limits.

Supported CPU formats

CPU requests and limits are supported in the following formats:

  • Number of CPU cores as integer (5 CPU core) or decimal (2.5 CPU core).

  • Number or millicpus / millicores (100m) where 1000 millicores is the same 1 CPU core.

Note
 The computing power of 1 CPU core may differ depending on the platform where Kubernetes is deployed.

For more information on CPU specification, see the Meaning of CPU.

Supported memory formats

Memory requests and limits are specified in megabytes, gigabytes, mebibytes, and gibibytes.

  • To specify memory in megabytes, use the M suffix. For example 1000M.

  • To specify memory in gigabytes, use the G suffix. For example 1G.

  • To specify memory in mebibytes, use the Mi suffix. For example 1000Mi.

  • To specify memory in gibibytes, use the Gi suffix. For example 1Gi.

For more details about memory specification and additional supported units, see Meaning of memory.

Kafka MirrorMaker loggers

Kafka MirrorMaker has its own configurable logger:

  • mirrormaker.root.logger

MirrorMaker uses the Apache log4j logger implementation.

Use the logging property to configure loggers and logger levels.

You can set the log levels by specifying the logger and level directly (inline) or use a custom (external) ConfigMap. If a ConfigMap is used, you set logging.name property to the name of the ConfigMap containing the external logging configuration. Inside the ConfigMap, the logging configuration is described using log4j.properties.

Here we see examples of inline and external logging:

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
spec:
  # ...
  logging:
    type: inline
    loggers:
      mirrormaker.root.logger: "INFO"
  # ...
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
spec:
  # ...
  logging:
    type: external
    name: customConfigMap
  # ...
Additional resources
Healthchecks

Use the livenessProbe and readinessProbe properties to configure healthcheck probes supported in Strimzi.

Healthchecks are periodical tests which verify the health of an application. When a Healthcheck probe fails, Kubernetes assumes that the application is not healthy and attempts to fix it.

For more details about the probes, see Configure Liveness and Readiness Probes.

Both livenessProbe and readinessProbe support the following options:

  • initialDelaySeconds

  • timeoutSeconds

  • periodSeconds

  • successThreshold

  • failureThreshold

An example of liveness and readiness probe configuration
# ...
readinessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
livenessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
# ...

For more information about the livenessProbe and readinessProbe options, see Probe schema reference.

Prometheus metrics

Use the metrics property to enable and configure Prometheus metrics.

The metrics property can also contain additional configuration for the Prometheus JMX exporter. Strimzi supports Prometheus metrics using Prometheus JMX exporter to convert the JMX metrics supported by Apache Kafka and ZooKeeper to Prometheus metrics.

To enable Prometheus metrics export without any further configuration, you can set it to an empty object ({}).

When metrics are enabled, they are exposed on port 9404.

When the metrics property is not defined in the resource, the Prometheus metrics are disabled.

For more information about setting up and deploying Prometheus and Grafana, see Introducing Metrics to Kafka.

JVM Options

Use the jvmOptions property to configure supported options for the JVM on which the component is running.

Supported JVM options help to optimize performance for different platforms and architectures.

For more information on the supported options, see JVM configuration.

Container images

Use the image property to configure the container image used by the component.

Overriding container images is recommended only in special situations where you need to use a different container registry or a customized image.

For example, if your network does not allow access to the container repository used by Strimzi, you can copy the Strimzi images or build them from the source. However, if the configured image is not compatible with Strimzi images, it might not work properly.

A copy of the container image might also be customized and used for debugging.

For more information see Container image configurations.

3.4.3. List of resources created as part of Kafka MirrorMaker

The following resources are created by the Cluster Operator in the Kubernetes cluster:

<mirror-maker-name>-mirror-maker

Deployment which is responsible for creating the Kafka MirrorMaker pods.

<mirror-maker-name>-config

ConfigMap which contains ancillary configuration for the the Kafka MirrorMaker, and is mounted as a volume by the Kafka broker pods.

<mirror-maker-name>-mirror-maker

Pod Disruption Budget configured for the Kafka MirrorMaker worker nodes.

3.4.4. Using Strimzi with MirrorMaker 2.0.

This section describes using Strimzi with MirrorMaker 2.0.

MirrorMaker 2.0 is used to replicate data between two or more active Kafka clusters, within or across data centers.

Data replication across clusters supports scenarios that require:

  • Recovery of data in the event of a system failure

  • Aggregation of data for analysis

  • Restriction of data access to a specific cluster

  • Provision of data at a specific location to improve latency

Note
 MirrorMaker 2.0 has features not supported by the previous version of MirrorMaker.
MirrorMaker 2.0 data replication

MirrorMaker 2.0 consumes messages from a source Kafka cluster and writes them to a target Kafka cluster.

MirrorMaker 2.0 uses:

  • Source cluster configuration to consume data from the source cluster

  • Target cluster configuration to output data to the target cluster

MirrorMaker 2.0 is based on the Kafka Connect framework, connectors managing the transfer of data between clusters. A MirrorMaker 2.0 MirrorSourceConnector replicates topics from a source cluster to a target cluster.

The process of mirroring data from one cluster to another cluster is asynchronous. The recommended pattern is for messages to be produced locally alongside the source Kafka cluster, then consumed remotely close to the target Kafka cluster.

MirrorMaker 2.0 can be used with more than one source cluster.

MirrorMaker 2.0 replication
Figure 1. Replication across two clusters
Cluster configuration

You can use MirrorMaker 2.0 in active/passive or active/active cluster configurations.

  • In an active/passive configuration, the data from an active cluster is replicated in a passive cluster, which remains on standby, for example, for data recovery in the event of system failure.

  • In an active/active configuration, both clusters are active and provide the same data simultaneously, which is useful if you want to make the same data available locally in different geographical locations.

The expectation is that producers and consumers connect to active clusters only.

Bidirectional replication

The MirrorMaker 2.0 architecture supports bidirectional replication in an active/active cluster configuration. A MirrorMaker 2.0 cluster is required at each target destination.

Each cluster replicates the data of the other cluster using the concept of source and remote topics. As the same topics are stored in each cluster, remote topics are automatically renamed by MirrorMaker 2.0 to represent the source cluster.

MirrorMaker 2.0 bidirectional architecture
Figure 2. Topic renaming

By flagging the originating cluster, topics are not replicated back to that cluster.

The concept of replication through remote topics is useful when configuring an architecture that requires data aggregation. Consumers can subscribe to source and remote topics within the same cluster, without the need for a separate aggregation cluster.

Topic configuration synchronization

Topic configuration is automatically synchronized between source and target clusters. By synchronizing configuration properties, the need for rebalancing is reduced.

Data integrity

MirrorMaker 2.0 monitors source topics and propagates any configuration changes to remote topics, checking for and creating missing partitions. Only MirrorMaker 2.0 can write to remote topics.

Offset tracking

MirrorMaker 2.0 tracks offsets for consumer groups using internal topics.

  • The offset sync topic maps the source and target offsets for replicated topic partitions from record metadata

  • The checkpoint topic maps the last committed offset in the source and target cluster for replicated topic partitions in each consumer group

Offsets for the checkpoint topic are tracked at predetermined intervals through configuration. Both topics enable replication to be fully restored from the correct offset position on failover.

MirrorMaker 2.0 uses its MirrorCheckpointConnector to emit checkpoints for offset tracking.

Connectivity checks

A heartbeat internal topic checks connectivity between clusters.

The heartbeat topic is replicated from the source cluster.

Target clusters use the topic to check:

  • The connector managing connectivity between clusters is running

  • The source cluster is available

MirrorMaker 2.0 uses its MirrorHeartbeatConnector to emit heartbeats that perform these checks.

ACL rules synchronization

ACL access to remote topics is possible if you are not using the User Operator.

If SimpleAclAuthorizer is being used, without the User Operator, ACL rules that manage access to brokers also apply to remote topics. Users that can read a source topic can read its remote equivalent.

Note
 OAuth 2.0 authorization does not support access to remote topics in this way.
Synchronizing data between Kafka clusters using MirrorMaker 2.0

Use MirrorMaker 2.0 to synchronize data between Kafka clusters through configuration.

The previous version of MirrorMaker continues to be supported. If you wish to use the resources configured for the previous version, they must be updated to the format supported by MirrorMaker 2.0.

The configuration must specify:

  • Each Kafka cluster

  • Connection information for each cluster, including TLS authentication

  • The replication flow and direction

    • Cluster to cluster

    • Topic to topic

Use the properties of the KafkaMirrorMaker2 resource to configure your Kafka MirrorMaker 2.0 deployment.

MirrorMaker 2.0 provides default configuration values for properties such as replication factors. A minimal configuration, with defaults left unchanged, would be something like this example:

apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker2
spec:
  version: 2.5.0
  connectCluster: "my-cluster-target"
  clusters:
  - alias: "my-cluster-source"
    bootstrapServers: my-cluster-source-kafka-bootstrap:9092
  - alias: "my-cluster-target"
    bootstrapServers: my-cluster-target-kafka-bootstrap:9092
  mirrors:
  - sourceCluster: "my-cluster-source"
    targetCluster: "my-cluster-target"
    sourceConnector: {}

You can configure access control for source and target clusters using TLS or SASL authentication. This procedure shows a configuration that uses TLS encryption and authentication for the source and target cluster.

Prerequisites
Procedure

  1. Edit the spec properties for the KafkaMirrorMaker2 resource.

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

    apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaMirrorMaker2
metadata:
  name: my-mirror-maker2
spec:
  version: 2.5.0 (1)
  replicas: 3 (2)
  connectCluster: "my-cluster-target" (3)
  clusters: (4)
  - alias: "my-cluster-source" (5)
    authentication: (6)
      certificateAndKey:
        certificate: source.crt
        key: source.key
        secretName: my-user-source
      type: tls
    bootstrapServers: my-cluster-source-kafka-bootstrap:9092 (7)
    tls: (8)
      trustedCertificates:
      - certificate: ca.crt
        secretName: my-cluster-source-cluster-ca-cert
  - alias: "my-cluster-target" (9)
    authentication: (10)
      certificateAndKey:
        certificate: target.crt
        key: target.key
        secretName: my-user-target
      type: tls
    bootstrapServers: my-cluster-target-kafka-bootstrap:9092 (11)
    config: (12)
      config.storage.replication.factor: 1
      offset.storage.replication.factor: 1
      status.storage.replication.factor: 1
    tls: (13)
      trustedCertificates:
      - certificate: ca.crt
        secretName: my-cluster-target-cluster-ca-cert
  mirrors: (14)
  - sourceCluster: "my-cluster-source" (15)
    targetCluster: "my-cluster-target" (16)
    sourceConnector: (17)
      config:
        replication.factor: 1 (18)
        offset-syncs.topic.replication.factor: 1 (19)
        sync.topic.acls.enabled: "false" (20)
    heartbeatConnector: (21)
      config:
        heartbeats.topic.replication.factor: 1 (22)
    checkpointConnector: (23)
      config:
        checkpoints.topic.replication.factor: 1 (24)
    topicsPattern: ".*" (25)
    groupsPattern: "group1|group2|group3" (26)

    1. The Kafka Connect version.

    2. The number of replica nodes.

    3. The cluster alias for Kafka Connect.

    4. Specification for the Kafka clusters being synchronized.

    5. The cluster alias for the source Kafka cluster.

    6. Authentication for the source cluster, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-512 or PLAIN mechanism.

    7. Bootstrap server for connection to the source Kafka cluster.

    8. TLS encryption with key names under which TLS certificates are stored in X.509 format for the source Kafka cluster. For more details see KafkaMirrorMaker2Tls schema reference.

    9. The cluster alias for the target Kafka cluster.

    10. Authentication for the target Kafka cluster is configured in the same way as for the source Kafka cluster.

    11. Bootstrap server for connection to the target Kafka cluster.

    12. Kafka Connect configuration. Standard Apache Kafka configuration may be provided, restricted to those properties not managed directly by Strimzi.

    13. TLS encryption for the target Kafka cluster is configured in the same way as for the source Kafka cluster.

    14. MirrorMaker 2.0 connectors.

    15. The alias of the source cluster used by the MirrorMaker 2.0 connectors.

    16. The alias of the target cluster used by the MirrorMaker 2.0 connectors.

    17. The configuration for the MirrorSourceConnector that creates remote topics. The config overrides the default configuration options.

    18. The replication factor for mirrored topics created at the target cluster.

    19. The replication factor for the MirrorSourceConnector offset-syncs internal topic that maps the offsets of the source and target clusters.

    20. When enabled, ACLs are applied to synchronized topics. The default is true.

    21. The configuration for the MirrorHeartbeatConnector that performs connectivity checks. The config overrides the default configuration options.

    22. The replication factor for the heartbeat topic created at the target cluster.

    23. The configuration for the MirrorCheckpointConnector that tracks offsets. The config overrides the default configuration options.

    24. The replication factor for the checkpoints topic created at the target cluster.

    25. Topic replication from the source cluster defined as regular expression patterns. Here we request all topics.

    26. Consumer group replication from the source cluster defined as regular expression patterns. Here we request three consumer groups by name. You can use comma-separated lists.

  2. Create or update the resource:

    kubectl apply -f <your-file>

3.5. Kafka Bridge configuration

The full schema of the KafkaBridge resource is described in the KafkaBridge schema reference. All labels that are applied to the desired KafkaBridge resource will also be applied to the Kubernetes resources making up the Kafka Bridge cluster. This provides a convenient mechanism for resources to be labeled as required.

3.5.1. Replicas

Kafka Bridge can run multiple nodes. The number of nodes is defined in the KafkaBridge resource. Running a Kafka Bridge with multiple nodes can provide better availability and scalability. However, when running Kafka Bridge on Kubernetes it is not absolutely necessary to run multiple nodes of Kafka Bridge for high availability.

Important
 If a node where Kafka Bridge is deployed to crashes, Kubernetes will automatically reschedule the Kafka Bridge pod to a different node. In order to prevent issues arising when client consumer requests are processed by different Kafka Bridge instances, addressed-based routing must be employed to ensure that requests are routed to the right Kafka Bridge instance. Additionally, each independent Kafka Bridge instance must have a replica. A Kafka Bridge instance has its own state which is not shared with another instances.
Configuring the number of nodes

The number of Kafka Bridge nodes is configured using the replicas property in KafkaBridge.spec.

Prerequisites

  • An Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the replicas property in the KafkaBridge resource. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  replicas: 3
  # ...

  2. Create or update the resource.

    kubectl apply -f your-file

3.5.2. Bootstrap servers

A Kafka Bridge always works in combination with a Kafka cluster. A Kafka cluster is specified as a list of bootstrap servers. On Kubernetes, the list must ideally contain the Kafka cluster bootstrap service named cluster-name-kafka-bootstrap, and a port of 9092 for plain traffic or 9093 for encrypted traffic.

The list of bootstrap servers is configured in the bootstrapServers property in KafkaBridge.kafka.spec. The servers must be defined as a comma-separated list specifying one or more Kafka brokers, or a service pointing to Kafka brokers specified as a hostname:_port_ pairs.

When using Kafka Bridge with a Kafka cluster not managed by Strimzi, you can specify the bootstrap servers list according to the configuration of the cluster.

Configuring bootstrap servers
Prerequisites

  • An Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the bootstrapServers property in the KafkaBridge resource. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  bootstrapServers: my-cluster-kafka-bootstrap:9092
  # ...

  2. Create or update the resource.

    kubectl apply -f your-file

3.5.3. Connecting to Kafka brokers using TLS

By default, Kafka Bridge tries to connect to Kafka brokers using a plain text connection. If you prefer to use TLS, additional configuration is required.

TLS support for Kafka connection to the Kafka Bridge

TLS support for Kafka connection is configured in the tls property in KafkaBridge.spec. The tls property contains a list of secrets with key names under which the certificates are stored. The certificates must be stored in X509 format.

An example showing TLS configuration with multiple certificates
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  tls:
    trustedCertificates:
    - secretName: my-secret
      certificate: ca.crt
    - secretName: my-other-secret
      certificate: certificate.crt
  # ...

When multiple certificates are stored in the same secret, it can be listed multiple times.

An example showing TLS configuration with multiple certificates from the same secret
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  tls:
    trustedCertificates:
    - secretName: my-secret
      certificate: ca.crt
    - secretName: my-secret
      certificate: ca2.crt
  # ...
Configuring TLS in Kafka Bridge
Prerequisites

  • An Kubernetes cluster

  • A running Cluster Operator

  • If they exist, the name of the Secret for the certificate used for TLS Server Authentication, and the key under which the certificate is stored in the Secret

Procedure

  1. (Optional) If they do not already exist, prepare the TLS certificate used in authentication in a file and create a Secret.

    Note
    		 The secrets created by the Cluster Operator for Kafka cluster may be used directly. 
    kubectl create secret generic my-secret --from-file=my-file.crt

  2. Edit the tls property in the KafkaBridge resource. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  tls:
	  trustedCertificates:
	  - secretName: my-cluster-cluster-cert
	    certificate: ca.crt
  # ...

  3. Create or update the resource.

    kubectl apply -f your-file

3.5.4. Connecting to Kafka brokers with Authentication

By default, Kafka Bridge will try to connect to Kafka brokers without authentication. Authentication is enabled through the KafkaBridge resources.

Authentication support in Kafka Bridge

Authentication is configured through the authentication property in KafkaBridge.spec. The authentication property specifies the type of the authentication mechanisms which should be used and additional configuration details depending on the mechanism. The currently supported authentication types are:

TLS Client Authentication

To use TLS client authentication, set the type property to the value tls. TLS client authentication uses a TLS certificate to authenticate. The certificate is specified in the certificateAndKey property and is always loaded from an Kubernetes secret. In the secret, the certificate must be stored in X509 format under two different keys: public and private.

Note
 TLS client authentication can be used only with TLS connections. For more details about TLS configuration in Kafka Bridge see Connecting to Kafka brokers using TLS.
An example TLS client authentication configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  authentication:
    type: tls
    certificateAndKey:
      secretName: my-secret
      certificate: public.crt
      key: private.key
  # ...
SCRAM-SHA-512 authentication

To configure Kafka Bridge to use SASL-based SCRAM-SHA-512 authentication, set the type property to scram-sha-512. This authentication mechanism requires a username and password.

  • Specify the username in the username property.

  • In the passwordSecret property, specify a link to a Secret containing the password. The secretName property contains the name of the Secret and the password property contains the name of the key under which the password is stored inside the Secret.

Important
 Do not specify the actual password in the password field.
An example SASL based SCRAM-SHA-512 client authentication configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  authentication:
    type: scram-sha-512
    username: my-bridge-user
    passwordSecret:
      secretName: my-bridge-user
      password: my-bridge-password-key
  # ...
SASL-based PLAIN authentication

To configure Kafka Bridge to use SASL-based PLAIN authentication, set the type property to plain. This authentication mechanism requires a username and password.

Warning
 The SASL PLAIN mechanism will transfer the username and password across the network in cleartext. Only use SASL PLAIN authentication if TLS encryption is enabled.

  • Specify the username in the username property.

  • In the passwordSecret property, specify a link to a Secret containing the password. The secretName property contains the name the Secret and the password property contains the name of the key under which the password is stored inside the Secret.

Important
 Do not specify the actual password in the password field.
An example showing SASL based PLAIN client authentication configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  authentication:
    type: plain
    username: my-bridge-user
    passwordSecret:
      secretName: my-bridge-user
      password: my-bridge-password-key
  # ...
Configuring TLS client authentication in Kafka Bridge
Prerequisites

  • An Kubernetes cluster

  • A running Cluster Operator

  • If they exist, the name of the Secret with the public and private keys used for TLS Client Authentication, and the keys under which they are stored in the Secret

Procedure

  1. (Optional) If they do not already exist, prepare the keys used for authentication in a file and create the Secret.

    Note
    		 Secrets created by the User Operator may be used. 
    kubectl create secret generic my-secret --from-file=my-public.crt --from-file=my-private.key

  2. Edit the authentication property in the KafkaBridge resource. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  authentication:
  type: tls
  certificateAndKey:
    secretName: my-secret
    certificate: my-public.crt
    key: my-private.key
  # ...

  3. Create or update the resource.

    kubectl apply -f your-file
Configuring SCRAM-SHA-512 authentication in Kafka Bridge
Prerequisites

  • An Kubernetes cluster

  • A running Cluster Operator

  • Username of the user which should be used for authentication

  • If they exist, the name of the Secret with the password used for authentication and the key under which the password is stored in the Secret

Procedure

  1. (Optional) If they do not already exist, prepare a file with the password used in authentication and create the Secret.

    Note
    		 Secrets created by the User Operator may be used. 
    echo -n '<password>' > <my-password.txt>
kubectl create secret generic <my-secret> --from-file=<my-password.txt>

  2. Edit the authentication property in the KafkaBridge resource. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  authentication:
    type: scram-sha-512
    username: _<my-username>_
    passwordSecret:
      secretName: _<my-secret>_
      password: _<my-password.txt>_
  # ...

  3. Create or update the resource.

    kubectl apply -f your-file

3.5.5. Kafka Bridge configuration

Strimzi allows you to customize the configuration of Apache Kafka Bridge nodes by editing certain options listed in Apache Kafka configuration documentation for consumers and Apache Kafka configuration documentation for producers.

Configuration options that can be configured relate to:

  • Kafka cluster bootstrap address

  • Security (Encryption, Authentication, and Authorization)

  • Consumer configuration

  • Producer configuration

  • HTTP configuration

Kafka Bridge Consumer configuration

Kafka Bridge consumer is configured using the properties in KafkaBridge.spec.consumer. This property contains the Kafka Bridge consumer configuration options as keys. The values can be one of the following JSON types:

  • String

  • Number

  • Boolean

Users can specify and configure the options listed in the Apache Kafka configuration documentation for consumers with the exception of those options which are managed directly by Strimzi. Specifically, all configuration options with keys equal to or starting with one of the following strings are forbidden:

  • ssl.

  • sasl.

  • security.

  • bootstrap.servers

  • group.id

When one of the forbidden options is present in the config property, it will be ignored and a warning message will be printed to the Custer Operator log file. All other options will be passed to Kafka

Important
 The Cluster Operator does not validate keys or values in the config object provided. When an invalid configuration is provided, the Kafka Bridge cluster might not start or might become unstable. In this circumstance, fix the configuration in the KafkaBridge.spec.consumer.config object, then the Cluster Operator can roll out the new configuration to all Kafka Bridge nodes.
Example Kafka Bridge consumer configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  consumer:
    config:
      auto.offset.reset: earliest
      enable.auto.commit: true
  # ...
Kafka Bridge Producer configuration

Kafka Bridge producer is configured using the properties in KafkaBridge.spec.producer. This property contains the Kafka Bridge producer configuration options as keys. The values can be one of the following JSON types:

  • String

  • Number

  • Boolean

Users can specify and configure the options listed in the Apache Kafka configuration documentation for producers with the exception of those options which are managed directly by Strimzi. Specifically, all configuration options with keys equal to or starting with one of the following strings are forbidden:

  • ssl.

  • sasl.

  • security.

  • bootstrap.servers

Important
 The Cluster Operator does not validate keys or values in the config object provided. When an invalid configuration is provided, the Kafka Bridge cluster might not start or might become unstable. In this circumstance, fix the configuration in the KafkaBridge.spec.producer.config object, then the Cluster Operator can roll out the new configuration to all Kafka Bridge nodes.
Example Kafka Bridge producer configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  producer:
    config:
      acks: 1
      delivery.timeout.ms: 300000
  # ...
Kafka Bridge HTTP configuration

Kafka Bridge HTTP configuration is set using the properties in KafkaBridge.spec.http. This property contains the Kafka Bridge HTTP configuration options.

  • port

Example Kafka Bridge HTTP configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  http:
    port: 8080
  # ...
Configuring Kafka Bridge
Prerequisites

  • An Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the kafka, http, consumer or producer property in the KafkaBridge resource. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  bootstrapServers: my-cluster-kafka:9092
  http:
    port: 8080
  consumer:
    config:
      auto.offset.reset: earliest
  producer:
    config:
      delivery.timeout.ms: 300000
  # ...

  2. Create or update the resource.

    kubectl apply -f your-file

3.5.6. CPU and memory resources

For every deployed container, Strimzi allows you to request specific resources and define the maximum consumption of those resources.

Strimzi supports two types of resources:

  • CPU

  • Memory

Strimzi uses the Kubernetes syntax for specifying CPU and memory resources.

Resource limits and requests

Resource limits and requests are configured using the resources property in the following resources:

  • Kafka.spec.kafka

  • Kafka.spec.kafka.tlsSidecar

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.entityOperator.tlsSidecar

  • Kafka.spec.KafkaExporter

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaBridge.spec

Additional resources
Resource requests

Requests specify the resources to reserve for a given container. Reserving the resources ensures that they are always available.

Important
 If the resource request is for more than the available free resources in the Kubernetes cluster, the pod is not scheduled.

Resources requests are specified in the requests property. Resources requests currently supported by Strimzi:

  • cpu

  • memory

A request may be configured for one or more supported resources.

Example resource request configuration with all resources
# ...
resources:
  requests:
    cpu: 12
    memory: 64Gi
# ...
Resource limits

Limits specify the maximum resources that can be consumed by a given container. The limit is not reserved and might not always be available. A container can use the resources up to the limit only when they are available. Resource limits should be always higher than the resource requests.

Resource limits are specified in the limits property. Resource limits currently supported by Strimzi:

  • cpu

  • memory

A resource may be configured for one or more supported limits.

Example resource limits configuration
# ...
resources:
  limits:
    cpu: 12
    memory: 64Gi
# ...
Supported CPU formats

CPU requests and limits are supported in the following formats:

  • Number of CPU cores as integer (5 CPU core) or decimal (2.5 CPU core).

  • Number or millicpus / millicores (100m) where 1000 millicores is the same 1 CPU core.

Example CPU units
# ...
resources:
  requests:
    cpu: 500m
  limits:
    cpu: 2.5
# ...
Note
 The computing power of 1 CPU core may differ depending on the platform where Kubernetes is deployed.
Additional resources
Supported memory formats

Memory requests and limits are specified in megabytes, gigabytes, mebibytes, and gibibytes.

  • To specify memory in megabytes, use the M suffix. For example 1000M.

  • To specify memory in gigabytes, use the G suffix. For example 1G.

  • To specify memory in mebibytes, use the Mi suffix. For example 1000Mi.

  • To specify memory in gibibytes, use the Gi suffix. For example 1Gi.

An example of using different memory units
# ...
resources:
  requests:
    memory: 512Mi
  limits:
    memory: 2Gi
# ...
Additional resources

  • For more details about memory specification and additional supported units, see Meaning of memory.

Configuring resource requests and limits
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the resources property in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    resources:
      requests:
        cpu: "8"
        memory: 64Gi
      limits:
        cpu: "12"
        memory: 128Gi
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Additional resources

3.5.7. Kafka Bridge loggers

Kafka Bridge has its own configurable loggers:

  • log4j.logger.io.strimzi.kafka.bridge

  • log4j.logger.http.openapi.operation.<operation-id>

You can replace <operation-id> in the log4j.logger.http.openapi.operation.<operation-id> logger to set log levels for specific operations:

  • createConsumer

  • deleteConsumer

  • subscribe

  • unsubscribe

  • poll

  • assign

  • commit

  • send

  • sendToPartition

  • seekToBeginning

  • seekToEnd

  • seek

  • healthy

  • ready

  • openapi

Each operation is defined according OpenAPI specification, and has a corresponding API endpoint through which the bridge receives requests from HTTP clients. You can change the log level on each endpoint to create fine-grained logging information about the incoming and outgoing HTTP requests.

Kafka Bridge uses the Apache log4j logger implementation. Loggers are defined in the log4j.properties file, which has the following default configuration for healthy and ready endpoints:

log4j.logger.http.openapi.operation.healthy=WARN, out
log4j.additivity.http.openapi.operation.healthy=false
log4j.logger.http.openapi.operation.ready=WARN, out
log4j.additivity.http.openapi.operation.ready=false

The log level of all other operations is set to INFO by default.

Use the logging property to configure loggers and logger levels.

You can set the log levels by specifying the logger and level directly (inline) or use a custom (external) ConfigMap. If a ConfigMap is used, you set logging.name property to the name of the ConfigMap containing the external logging configuration. Inside the ConfigMap, the logging configuration is described using log4j.properties.

Here we see examples of inline and external logging.

Inline logging
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaBridge
spec:
  # ...
  logging:
    type: inline
    loggers:
      log4j.logger.io.strimzi.kafka.bridge: "INFO"
  # ...
External logging
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaBridge
spec:
  # ...
  logging:
    type: external
    name: customConfigMap
  # ...
Additional resources

3.5.8. JVM Options

The following components of Strimzi run inside a Virtual Machine (VM):

  • Apache Kafka

  • Apache ZooKeeper

  • Apache Kafka Connect

  • Apache Kafka MirrorMaker

  • Strimzi Kafka Bridge

JVM configuration options optimize the performance for different platforms and architectures. Strimzi allows you to configure some of these options.

JVM configuration

JVM options can be configured using the jvmOptions property in following resources:

  • Kafka.spec.kafka

  • Kafka.spec.zookeeper

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaMirrorMaker.spec

  • KafkaBridge.spec

Only a selected subset of available JVM options can be configured. The following options are supported:

-Xms and -Xmx

-Xms configures the minimum initial allocation heap size when the JVM starts. -Xmx configures the maximum heap size.

Note
 The units accepted by JVM settings such as -Xmx and -Xms are those accepted by the JDK java binary in the corresponding image. Accordingly, 1g or 1G means 1,073,741,824 bytes, and Gi is not a valid unit suffix. This is in contrast to the units used for memory requests and limits, which follow the Kubernetes convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes

The default values used for -Xms and -Xmx depends on whether there is a memory request limit configured for the container:

  • If there is a memory limit then the JVM’s minimum and maximum memory will be set to a value corresponding to the limit.

  • If there is no memory limit then the JVM’s minimum memory will be set to 128M and the JVM’s maximum memory will not be defined. This allows for the JVM’s memory to grow as-needed, which is ideal for single node environments in test and development.

Important

Setting -Xmx explicitly requires some care:

  • The JVM’s overall memory usage will be approximately 4 × the maximum heap, as configured by -Xmx.

  • If -Xmx is set without also setting an appropriate Kubernetes memory limit, it is possible that the container will be killed should the Kubernetes node experience memory pressure (from other Pods running on it).

  • If -Xmx is set without also setting an appropriate Kubernetes memory request, it is possible that the container will be scheduled to a node with insufficient memory. In this case, the container will not start but crash (immediately if -Xms is set to -Xmx, or some later time if not).

When setting -Xmx explicitly, it is recommended to:

  • set the memory request and the memory limit to the same value,

  • use a memory request that is at least 4.5 × the -Xmx,

  • consider setting -Xms to the same value as -Xmx.

Important
 Containers doing lots of disk I/O (such as Kafka broker containers) will need to leave some memory available for use as operating system page cache. On such containers, the requested memory should be significantly higher than the memory used by the JVM.
Example fragment configuring -Xmx and -Xms
# ...
jvmOptions:
  "-Xmx": "2g"
  "-Xms": "2g"
# ...

In the above example, the JVM will use 2 GiB (=2,147,483,648 bytes) for its heap. Its total memory usage will be approximately 8GiB.

Setting the same value for initial (-Xms) and maximum (-Xmx) heap sizes avoids the JVM having to allocate memory after startup, at the cost of possibly allocating more heap than is really needed. For Kafka and ZooKeeper pods such allocation could cause unwanted latency. For Kafka Connect avoiding over allocation may be the most important concern, especially in distributed mode where the effects of over-allocation will be multiplied by the number of consumers.

-server

-server enables the server JVM. This option can be set to true or false.

Example fragment configuring -server
# ...
jvmOptions:
  "-server": true
# ...
Note
 When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.
-XX

-XX object can be used for configuring advanced runtime options of a JVM. The -server and -XX options are used to configure the KAFKA_JVM_PERFORMANCE_OPTS option of Apache Kafka.

Example showing the use of the -XX object
jvmOptions:
  "-XX":
    "UseG1GC": true
    "MaxGCPauseMillis": 20
    "InitiatingHeapOccupancyPercent": 35
    "ExplicitGCInvokesConcurrent": true
    "UseParNewGC": false

The example configuration above will result in the following JVM options:

-XX:+UseG1GC -XX:MaxGCPauseMillis=20 -XX:InitiatingHeapOccupancyPercent=35 -XX:+ExplicitGCInvokesConcurrent -XX:-UseParNewGC
Note
 When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.
Garbage collector logging

The jvmOptions section also allows you to enable and disable garbage collector (GC) logging. GC logging is disabled by default. To enable it, set the gcLoggingEnabled property as follows:

Example of enabling GC logging
# ...
jvmOptions:
  gcLoggingEnabled: true
# ...
Configuring JVM options
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the jvmOptions property in the Kafka, KafkaConnect, KafkaConnectS2I, KafkaMirrorMaker, or KafkaBridge resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    jvmOptions:
      "-Xmx": "8g"
      "-Xms": "8g"
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.5.9. Healthchecks

Healthchecks are periodical tests which verify the health of an application. When a Healthcheck probe fails, Kubernetes assumes that the application is not healthy and attempts to fix it.

Kubernetes supports two types of Healthcheck probes:

  • Liveness probes

  • Readiness probes

For more details about the probes, see Configure Liveness and Readiness Probes. Both types of probes are used in Strimzi components.

Users can configure selected options for liveness and readiness probes.

Healthcheck configurations

Liveness and readiness probes can be configured using the livenessProbe and readinessProbe properties in following resources:

  • Kafka.spec.kafka

  • Kafka.spec.kafka.tlsSidecar

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator.tlsSidecar

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.KafkaExporter

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaMirrorMaker.spec

  • KafkaBridge.spec

Both livenessProbe and readinessProbe support the following options:

  • initialDelaySeconds

  • timeoutSeconds

  • periodSeconds

  • successThreshold

  • failureThreshold

For more information about the livenessProbe and readinessProbe options, see Probe schema reference.

An example of liveness and readiness probe configuration
# ...
readinessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
livenessProbe:
  initialDelaySeconds: 15
  timeoutSeconds: 5
# ...
Configuring healthchecks
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the livenessProbe or readinessProbe property in the Kafka, KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    readinessProbe:
      initialDelaySeconds: 15
      timeoutSeconds: 5
    livenessProbe:
      initialDelaySeconds: 15
      timeoutSeconds: 5
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.5.10. Container images

Strimzi allows you to configure container images which will be used for its components. Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by Strimzi. In such a case, you should either copy the Strimzi images or build them from the source. If the configured image is not compatible with Strimzi images, it might not work properly.

Container image configurations

You can specify which container image to use for each component using the image property in the following resources:

  • Kafka.spec.kafka

  • Kafka.spec.kafka.tlsSidecar

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.entityOperator.tlsSidecar

  • Kafka.spec.jmxTrans

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaBridge.spec

Configuring the image property for Kafka, Kafka Connect, and Kafka MirrorMaker

Kafka, Kafka Connect (including Kafka Connect with S2I support), and Kafka MirrorMaker support multiple versions of Kafka. Each component requires its own image. The default images for the different Kafka versions are configured in the following environment variables:

  • STRIMZI_KAFKA_IMAGES

  • STRIMZI_KAFKA_CONNECT_IMAGES

  • STRIMZI_KAFKA_CONNECT_S2I_IMAGES

  • STRIMZI_KAFKA_MIRROR_MAKER_IMAGES

These environment variables contain mappings between the Kafka versions and their corresponding images. The mappings are used together with the image and version properties:

  • If neither image nor version are given in the custom resource then the version will default to the Cluster Operator’s default Kafka version, and the image will be the one corresponding to this version in the environment variable.

  • If image is given but version is not, then the given image is used and the version is assumed to be the Cluster Operator’s default Kafka version.

  • If version is given but image is not, then the image that corresponds to the given version in the environment variable is used.

  • If both version and image are given, then the given image is used. The image is assumed to contain a Kafka image with the given version.

The image and version for the different components can be configured in the following properties:

  • For Kafka in spec.kafka.image and spec.kafka.version.

  • For Kafka Connect, Kafka Connect S2I, and Kafka MirrorMaker in spec.image and spec.version.

Warning
 It is recommended to provide only the version and leave the image property unspecified. This reduces the chance of making a mistake when configuring the custom resource. If you need to change the images used for different versions of Kafka, it is preferable to configure the Cluster Operator’s environment variables.
Configuring the image property in other resources

For the image property in the other custom resources, the given value will be used during deployment. If the image property is missing, the image specified in the Cluster Operator configuration will be used. If the image name is not defined in the Cluster Operator configuration, then the default value will be used.

  • For Kafka broker TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_KAFKA_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.18.0-kafka-2.5.0 container image.

  • For ZooKeeper nodes:

  • For ZooKeeper node TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_ZOOKEEPER_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.18.0-kafka-2.5.0 container image.

  • For Topic Operator:

    1. Container image specified in the STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/operator:0.18.0 container image.

  • For User Operator:

    1. Container image specified in the STRIMZI_DEFAULT_USER_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/operator:0.18.0 container image.

  • For Entity Operator TLS sidecar:

    1. Container image specified in the STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.18.0-kafka-2.5.0 container image.

  • For Kafka Exporter:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_EXPORTER_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.18.0-kafka-2.5.0 container image.

  • For Kafka Bridge:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_BRIDGE_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka-bridge:0.16.0 container image.

  • For Kafka broker initializer:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_INIT_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/operator:0.18.0 container image.

  • For Kafka broker initializer:

    1. Container image specified in the STRIMZI_DEFAULT_JMXTRANS_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/operator:0.18.0 container image.

Warning
 Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by Strimzi. In such case, you should either copy the Strimzi images or build them from source. In case the configured image is not compatible with Strimzi images, it might not work properly.
Example of container image configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    image: my-org/my-image:latest
    # ...
  zookeeper:
    # ...
Configuring container images
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the image property in the Kafka, KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    image: my-org/my-image:latest
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.5.11. Configuring pod scheduling

Important
 When two applications are scheduled to the same Kubernetes node, both applications might use the same resources like disk I/O and impact performance. That can lead to performance degradation. Scheduling Kafka pods in a way that avoids sharing nodes with other critical workloads, using the right nodes or dedicated a set of nodes only for Kafka are the best ways how to avoid such problems.
Scheduling pods based on other applications
Avoid critical applications to share the node

Pod anti-affinity can be used to ensure that critical applications are never scheduled on the same disk. When running Kafka cluster, it is recommended to use pod anti-affinity to ensure that the Kafka brokers do not share the nodes with other workloads like databases.

Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod

  • Kafka.spec.zookeeper.template.pod

  • Kafka.spec.entityOperator.template.pod

  • KafkaConnect.spec.template.pod

  • KafkaConnectS2I.spec.template.pod

  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

The format of the affinity property follows the Kubernetes specification. For more details, see the Kubernetes node and pod affinity documentation.

Configuring pod anti-affinity in Kafka components
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the affinity property in the resource specifying the cluster deployment. Use labels to specify the pods which should not be scheduled on the same nodes. The topologyKey should be set to kubernetes.io/hostname to specify that the selected pods should not be scheduled on nodes with the same hostname. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    template:
      pod:
        affinity:
          podAntiAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              - labelSelector:
                  matchExpressions:
                    - key: application
                      operator: In
                      values:
                        - postgresql
                        - mongodb
                topologyKey: "kubernetes.io/hostname"
    # ...
  zookeeper:
    # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Scheduling pods to specific nodes
Node scheduling

The Kubernetes cluster usually consists of many different types of worker nodes. Some are optimized for CPU heavy workloads, some for memory, while other might be optimized for storage (fast local SSDs) or network. Using different nodes helps to optimize both costs and performance. To achieve the best possible performance, it is important to allow scheduling of Strimzi components to use the right nodes.

Kubernetes uses node affinity to schedule workloads onto specific nodes. Node affinity allows you to create a scheduling constraint for the node on which the pod will be scheduled. The constraint is specified as a label selector. You can specify the label using either the built-in node label like beta.kubernetes.io/instance-type or custom labels to select the right node.

Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod

  • Kafka.spec.zookeeper.template.pod

  • Kafka.spec.entityOperator.template.pod

  • KafkaConnect.spec.template.pod

  • KafkaConnectS2I.spec.template.pod

  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

The format of the affinity property follows the Kubernetes specification. For more details, see the Kubernetes node and pod affinity documentation.

Configuring node affinity in Kafka components
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Label the nodes where Strimzi components should be scheduled.

    This can be done using kubectl label:

    kubectl label node your-node node-type=fast-network

    Alternatively, some of the existing labels might be reused.

  2. Edit the affinity property in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    template:
      pod:
        affinity:
          nodeAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
                - matchExpressions:
                  - key: node-type
                    operator: In
                    values:
                    - fast-network
    # ...
  zookeeper:
    # ...

  3. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Using dedicated nodes
Dedicated nodes

Cluster administrators can mark selected Kubernetes nodes as tainted. Nodes with taints are excluded from regular scheduling and normal pods will not be scheduled to run on them. Only services which can tolerate the taint set on the node can be scheduled on it. The only other services running on such nodes will be system services such as log collectors or software defined networks.

Taints can be used to create dedicated nodes. Running Kafka and its components on dedicated nodes can have many advantages. There will be no other applications running on the same nodes which could cause disturbance or consume the resources needed for Kafka. That can lead to improved performance and stability.

To schedule Kafka pods on the dedicated nodes, configure node affinity and tolerations.

Affinity

Affinity can be configured using the affinity property in following resources:

  • Kafka.spec.kafka.template.pod

  • Kafka.spec.zookeeper.template.pod

  • Kafka.spec.entityOperator.template.pod

  • KafkaConnect.spec.template.pod

  • KafkaConnectS2I.spec.template.pod

  • KafkaBridge.spec.template.pod

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

The format of the affinity property follows the Kubernetes specification. For more details, see the Kubernetes node and pod affinity documentation.

Tolerations

Tolerations can be configured using the tolerations property in following resources:

  • Kafka.spec.kafka.template.pod

  • Kafka.spec.zookeeper.template.pod

  • Kafka.spec.entityOperator.template.pod

  • KafkaConnect.spec.template.pod

  • KafkaConnectS2I.spec.template.pod

  • KafkaBridge.spec.template.pod

The format of the tolerations property follows the Kubernetes specification. For more details, see the Kubernetes taints and tolerations.

Setting up dedicated nodes and scheduling pods on them
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Select the nodes which should be used as dedicated.

  2. Make sure there are no workloads scheduled on these nodes.

  3. Set the taints on the selected nodes:

    This can be done using kubectl taint:

    kubectl taint node your-node dedicated=Kafka:NoSchedule

  4. Additionally, add a label to the selected nodes as well.

    This can be done using kubectl label:

    kubectl label node your-node dedicated=Kafka

  5. Edit the affinity and tolerations properties in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    template:
      pod:
        tolerations:
          - key: "dedicated"
            operator: "Equal"
            value: "Kafka"
            effect: "NoSchedule"
        affinity:
          nodeAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
              - matchExpressions:
                - key: dedicated
                  operator: In
                  values:
                  - Kafka
    # ...
  zookeeper:
    # ...

  6. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

3.5.12. List of resources created as part of Kafka Bridge cluster

The following resources are created by the Cluster Operator in the Kubernetes cluster:

bridge-cluster-name-bridge

Deployment which is in charge to create the Kafka Bridge worker node pods.

bridge-cluster-name-bridge-service

Service which exposes the REST interface of the Kafka Bridge cluster.

bridge-cluster-name-bridge-config

ConfigMap which contains the Kafka Bridge ancillary configuration and is mounted as a volume by the Kafka broker pods.

bridge-cluster-name-bridge

Pod Disruption Budget configured for the Kafka Bridge worker nodes.

3.6. Using OAuth 2.0 token-based authentication

Strimzi supports the use of OAuth 2.0 authentication using the SASL OAUTHBEARER mechanism.

OAuth 2.0 enables standardized token-based authentication and authorization between applications, using a central authorization server to issue tokens that grant limited access to resources.

You can configure OAuth 2.0 authentication, then OAuth 2.0 authorization. OAuth 2.0 authentication can also be used in conjunction with ACL-based Kafka authorization.

Using OAuth 2.0 token-based authentication, application clients can access resources on application servers (called resource servers) without exposing account credentials.

The application client passes an access token as a means of authenticating, which application servers can also use to determine the level of access to grant. The authorization server handles the granting of access and inquiries about access.

In the context of Strimzi:

  • Kafka brokers act as OAuth 2.0 resource servers

  • Kafka clients act as OAuth 2.0 application clients

Kafka clients authenticate to Kafka brokers. The brokers and clients communicate with the OAuth 2.0 authorization server, as necessary, to obtain or validate access tokens.

For a deployment of Strimzi, OAuth 2.0 integration provides:

  • Server-side OAuth 2.0 support for Kafka brokers

  • Client-side OAuth 2.0 support for Kafka MirrorMaker, Kafka Connect and the Kafka Bridge

Additional resources

3.6.1. OAuth 2.0 authentication mechanism

The Kafka SASL OAUTHBEARER mechanism is used to establish authenticated sessions with a Kafka broker.

A Kafka client initiates a session with the Kafka broker using the SASL OAUTHBEARER mechanism for credentials exchange, where credentials take the form of an access token.

Kafka brokers and clients need to be configured to use OAuth 2.0.

3.6.2. OAuth 2.0 Kafka broker configuration

Kafka broker configuration for OAuth 2.0 involves:

  • Creating the OAuth 2.0 client in the authorization server

  • Configuring OAuth 2.0 authentication in the Kafka custom resource

Note
 In relation to the authorization server, Kafka brokers and Kafka clients are both regarded as OAuth 2.0 clients.
OAuth 2.0 client configuration on an authorization server

To configure a Kafka broker to validate the token received during session initiation, the recommended approach is to create an OAuth 2.0 client definition in an authorization server, configured as confidential, with the following client credentials enabled:

  • Client ID of kafka (for example)

  • Client ID and Secret as the authentication mechanism

Note
 You only need to use a client ID and secret when using a non-public introspection endpoint of the authorization server. The credentials are not typically required when using public authorization server endpoints, as with fast local JWT token validation.
OAuth 2.0 authentication configuration in the Kafka cluster

To use OAuth 2.0 authentication in the Kafka cluster, you specify for example a TLS listener configuration for your Kafka cluster custom resource with the authentication method oauth:

Assigining the authentication method type for OAuth 2.0
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    listeners:
      tls:
        authentication:
          type: oauth
          #...

You can configure plain, tls and external listeners, as described in Kafka broker listeners, but it is recommended not to use plain listeners or external listeners with disabled TLS encryption with OAuth 2.0 as this creates a vulnerability to network eavesdropping and unauthorized access through token theft.

You configure an external listener with type: oauth for a secure transport layer to communicate with the client.

Using OAuth 2.0 with an external listener
# ...
listeners:
  tls:
    authentication:
      type: oauth
  external:
    type: loadbalancer
    tls: true
    authentication:
      type: oauth
    #...

The tls property is true by default, so it can be left out.

When you’ve defined the type of authentication as OAuth 2.0, you add configuration based on the type of validation, either as fast local JWT validation or token validation using an introspection endpoint.

The procedure to configure OAuth 2.0 for listeners, with descriptions and examples, is described in Configuring OAuth 2.0 support for Kafka brokers.

Fast local JWT token validation configuration

Fast local JWT token validation checks a JWT token signature locally.

The local check ensures that a token:

  • Conforms to type by containing a (typ) claim value of Bearer for an access token

  • Is valid (not expired)

  • Has an issuer that matches a validIssuerURI

You specify a validIssuerUrI attribute when you configure the listener, so that any tokens not issued by the authorization server are rejected.

The authorization server does not need to be contacted during fast local JWT token validation. You activate fast local JWT token validation by specifying a jwksEndpointUri attribute, the endpoint exposed by the OAuth 2.0 authorization server. The endpoint contains the public keys used to validate signed JWT tokens, which are sent as credentials by Kafka clients.

Note
 All communication with the authorization server should be performed using TLS encryption.

You can configure a certificate truststore as a Kubernetes Secret in your Strimzi project namespace, and use a tlsTrustedCertificates attribute to point to the Kubernetes Secret containing the truststore file.

You might want to configure a userNameClaim to properly extract a username from the JWT token. If you want to use Kafka ACL authorization, you need to identify the user by their username during authentication. (The sub claim in JWT tokens is typically a unique ID, not a username.)

Example configuration for fast local JWT token validation
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    listeners:
      tls:
        authentication:
          type: oauth
          validIssuerUri: <https://<auth-server-address>/auth/realms/tls>
          jwksEndpointUri: <https://<auth-server-address>/auth/realms/tls/protocol/openid-connect/certs>
          userNameClaim: preferred_username
          tlsTrustedCertificates:
          - secretName: oauth-server-cert
            certificate: ca.crt
OAuth 2.0 introspection endpoint configuration

Token validation using an OAuth 2.0 introspection endpoint treats a received access token as opaque. The Kafka broker sends an access token to the introspection endpoint, which responds with the token information necessary for validation. Importantly, it returns up-to-date information if the specific access token is valid, and also information about when the token expires.

To configure OAuth 2.0 introspection-based validation, you specify an introspectionEndpointUri attribute rather than the jwksEndpointUri attribute specified for fast local JWT token validation. Depending on the authorization server, you typically have to specify a clientId and clientSecret, because the introspection endpoint is usually protected.

Example configuration for an introspection endpoint
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    listeners:
      tls:
        authentication:
          type: oauth
          clientId: kafka-broker
          clientSecret:
            secretName: my-cluster-oauth
            key: clientSecret
          validIssuerUri: <https://<auth-server-address>/auth/realms/tls>
          introspectionEndpointUri: <https://<auth-server-address>/auth/realms/tls/protocol/openid-connect/token/introspect>
          userNameClaim: preferred_username
          tlsTrustedCertificates:
          - secretName: oauth-server-cert
            certificate: ca.crt

3.6.3. OAuth 2.0 Kafka client configuration

A Kafka client is configured with either:

  • The credentials required to obtain a valid access token from an authorization server (client ID and Secret)

  • A valid long-lived access token or refresh token, obtained using tools provided by an authorization server

The only information ever sent to the Kafka broker is an access token. The credentials used to authenticate with the authorization server to obtain the access token are never sent to the broker.

When a client obtains an access token, no further communication with the authorization server is needed.

The simplest mechanism is authentication with a client ID and Secret. Using a long-lived access token, or a long-lived refresh token, adds more complexity because there is an additional dependency on authorization server tools.

Note
 If you are using long-lived access tokens, you may need to configure the client in the authorization server to increase the maximum lifetime of the token.

If the Kafka client is not configured with an access token directly, the client exchanges credentials for an access token during Kafka session initiation by contacting the authorization server. The Kafka client exchanges either:

  • Client ID and Secret

  • Client ID, refresh token, and (optionally) a Secret

3.6.4. OAuth 2.0 client authentication flow

In this section, we explain and visualize the communication flow between Kafka client, Kafka broker, and authorization server during Kafka session initiation. The flow depends on the client and server configuration.

When a Kafka client sends an access token as credentials to a Kafka broker, the token needs to be validated.

Depending on the authorization server used, and the configuration options available, you may prefer to use:

  • Fast local token validation based on JWT signature checking and local token introspection, without contacting the authorization server

  • An OAuth 2.0 introspection endpoint provided by the authorization server

Using fast local token validation requires the authorization server to provide a JWKS endpoint with public certificates that are used to validate signatures on the tokens.

Another option is to use an OAuth 2.0 introspection endpoint on the authorization server. Each time a new Kafka broker connection is established, the broker passes the access token received from the client to the authorization server, and checks the response to confirm whether or not the token is valid.

Kafka client credentials can also be configured for:

  • Direct local access using a previously generated long-lived access token

  • Contact with the authorization server for a new access token to be issued

Note
 An authorization server might only allow the use of opaque access tokens, which means that local token validation is not possible.
Example client authentication flows

Here you can see the communication flows, for different configurations of Kafka clients and brokers, during Kafka session authentication.

Client using client ID and secret, with broker delegating validation to authorization server

Client using client ID and secret with broker delegating validation to authorization server

  1. Kafka client requests access token from authorization server, using client ID and secret, and optionally a refresh token.

  2. Authorization server generates a new access token.

  3. Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the access token.

  4. Kafka broker validates the access token by calling a token introspection endpoint on authorization server, using its own client ID and secret.

  5. Kafka client session is established if the token is valid.

Client using client ID and secret, with broker performing fast local token validation

Client using client ID and secret with broker performing fast local token validation

  1. Kafka client authenticates with authorization server from the token endpoint, using a client ID and secret, and optionally a refresh token.

  2. Authorization server generates a new access token.

  3. Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the access token.

  4. Kafka broker validates the access token locally using a JWT token signature check, and local token introspection.

Client using long-lived access token, with broker delegating validation to authorization server

Client using long-lived access token with broker delegating validation to authorization server

  1. Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the long-lived access token.

  2. Kafka broker validates the access token by calling a token introspection endpoint on authorization server, using its own client ID and secret.

  3. Kafka client session is established if the token is valid.

Client using long-lived access token, with broker performing fast local validation

Client using long-lived access token with broker performing fast local validation

  1. Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the long-lived access token.

  2. Kafka broker validates the access token locally using JWT token signature check, and local token introspection.

Warning
 Fast local JWT token signature validation is suitable only for short-lived tokens as there is no check with the authorization server if a token has been revoked. Token expiration is written into the token, but revocation can happen at any time, so cannot be accounted for without contacting the authorization server. Any issued token would be considered valid until it expires.

3.6.5. Configuring OAuth 2.0 authentication

OAuth 2.0 is used for interaction between Kafka clients and Strimzi components.

In order to use OAuth 2.0 for Strimzi, you must:

  1. Configure an OAuth 2.0 authorization server for the Strimzi cluster and Kafka clients

  2. Deploy or update the Kafka cluster with Kafka broker listeners configured to use OAuth 2.0

  3. Update your Java-based Kafka clients to use OAuth 2.0

  4. Update Kafka component clients to use OAuth 2.0

Configuring an OAuth 2.0 authorization server

This procedure describes in general what you need to do to configure an authorization server for integration with Strimzi.

These instructions are not product specific.

The steps are dependent on the chosen authorization server. Consult the product documentation for the authorization server for information on how to set up OAuth 2.0 access.

Note
 If you already have an authorization server deployed, you can skip the deployment step and use your current deployment.
Procedure

  1. Deploy the authorization server to your cluster.

  2. Access the CLI or admin console for the authorization server to configure OAuth 2.0 for Strimzi.

    Now prepare the authorization server to work with Strimzi.

  3. Configure a kafka-broker client.

  4. Configure clients for each Kafka client component of your application.

What to do next

After deploying and configuring the authorization server, configure the Kafka brokers to use OAuth 2.0.

Configuring OAuth 2.0 support for Kafka brokers

This procedure describes how to configure Kafka brokers so that the broker listeners are enabled to use OAuth 2.0 authentication using an authorization server.

We advise use of OAuth 2.0 over an encrypted interface through configuration of TLS listeners. Plain listeners are not recommended.

If the authorization server is using certificates signed by the trusted CA and matching the OAuth 2.0 server hostname, TLS connection works using the default settings. Otherwise, you have two connection options for your listener configuration when delegating token validation to the authorization server:

Before you start

For more information on the configuration of OAuth 2.0 authentication for Kafka broker listeners, see:

Prerequisites

  • Strimzi and Kafka are running

  • An OAuth 2.0 authorization server is deployed

Procedure

  1. Update the Kafka broker configuration (Kafka.spec.kafka) of your Kafka resource in an editor.

    kubectl edit kafka my-cluster

  2. Configure the Kafka broker listeners configuration.

    The configuration for each type of listener does not have to be the same, as they are independent.

    The examples here show the configuration options as configured for external listeners.

    Example 1: Configuring fast local JWT token validation
    external:
  type: loadbalancer
  authentication:
    type: oauth (1)
    validIssuerUri: <https://<auth-server-address>/auth/realms/external> (2)
    jwksEndpointUri: <https://<auth-server-address>/auth/realms/external/protocol/openid-connect/certs> (3)
    userNameClaim: preferred_username (4)
    tlsTrustedCertificates: (5)
    - secretName: oauth-server-cert
      certificate: ca.crt
    disableTlsHostnameVerification: true (6)
    jwksExpirySeconds: 360 (7)
    jwksRefreshSeconds: 300 (8)
    enableECDSA: "true" (9)

    1. Listener type set to oauth.

    2. URI of the token issuer used for authentication.

    3. URI of the JWKS certificate endpoint used for local JWT validation.

    4. The token claim (or key) that contains the actual user name in the token. The user name is the principal used to identify the user. The userNameClaim value will depend on the authentication flow and the authorization server used.

    5. (Optional) Trusted certificates for TLS connection to the authorization server.

    6. (Optional) Disable TLS hostname verification. Default is false.

    7. The duration the JWKs certificates are considered valid before they expire. Default is 360 seconds. If you specify a longer time, consider the risk of allowing access to revoked certificates.

    8. The period between refreshes of JWKs certificates. The interval must be at least 60 seconds shorter than the expiry interval. Default is 300 seconds.

    9. (Optional) If ECDSA is used for signing JWT tokens on authorization server, then this needs to be enabled. It installs additional crypto providers using BouncyCastle crypto library. Default is false.

    Example 2: Configuring token validation using an introspection endpoint
    external:
  type: loadbalancer
  authentication:
    type: oauth
    validIssuerUri: <https://<auth-server-address>/auth/realms/external>
    introspectionEndpointUri: <https://<auth-server-address>/auth/realms/external/protocol/openid-connect/token/introspect> (1)
    clientId: kafka-broker (2)
    clientSecret: (3)
      secretName: my-cluster-oauth
      key: clientSecret
    userNameClaim: preferred_username (4)

    1. URI of the token introspection endpoint.

    2. Client ID to identify the client.

    3. Client Secret and client ID is used for authentication.

    4. The token claim (or key) that contains the actual user name in the token. The user name is the principal used to identify the user. The userNameClaim value will depend on the authorization server used.

    Depending on how you apply OAuth 2.0 authentication, and the type of authorization server, there are additional (optional) configuration settings you can use:

      # ...
  authentication:
    type: oauth
    # ...
    checkIssuer: false (1)
    fallbackUserNameClaim: client_id (2)
    fallbackUserNamePrefix: client-account- (3)
    validTokenType: bearer (4)
    userInfoEndpointUri: <https://<auth-server-address>/auth/realms/external/protocol/openid-connect/userinfo> (5)

    1. If your authorization server does not provide an iss claim, it is not possible to perform an issuer check. In this situation, set checkIssuer to false and do not specify a validIssuerUri. Default is true.

    2. An authorization server may not provide a single attribute to identify both regular users and clients. A client authenticating in its own name might provide a client_id. But a user authenticating using a username and password, to obtain a refresh token or an access token, might provide a username attribute in addition to a client_id. Use this fallback option to specify the username claim (attribute) to use if a primary user id attribute is not available.

    3. In situations where fallbackUserNameClaim is applicable, it may also be necessary to prevent name collisions between the values of the username claim, and those of the fallback username claim. Consider a situation where a client called producer exists, but also a regular user called producer exists. In order to differentiate between the two, you can use this property to add a prefix to the user id of the client.

    4. (Only applicable when using introspectionEndpointUri) Depending on the authorization server you are using, the Introspection Endpoint may or may not return the token_type attribute., or it may contain different values. You can specify a valid token type value that the response from the Introspection Endpoint has to contain.

    5. (Only applicable when using introspectionEndpointUri) The authorization server may be configured or implemented in such a way to not provide any identifiable information in an Introspection Endpoint response. In order to obtain the user id, you can configure the URI of the User Info Endpoint as a fallback. The userNameClaim, fallbackUserNameClaim, and fallbackUserNamePrefix settings are applied to the response of User Info Endpoint.

  3. Save and exit the editor, then wait for rolling updates to complete.

  4. Check the update in the logs or by watching the pod state transitions:

    kubectl logs -f ${POD_NAME} -c ${CONTAINER_NAME}
kubectl get po -w

    The rolling update configures the brokers to use OAuth 2.0 authentication.

What to do next
Configuring Kafka Java clients to use OAuth 2.0

This procedure describes how to configure Kafka producer and consumer APIs to use OAuth 2.0 for interaction with Kafka brokers.

Add a client callback plugin to your pom.xml file, and configure the system properties.

Prerequisites

  • Strimzi and Kafka are running

  • An OAuth 2.0 authorization server is deployed and configured for OAuth access to Kafka brokers

  • Kafka brokers are configured for OAuth 2.0

Procedure

  1. Add the client library with OAuth 2.0 support to the pom.xml file for the Kafka client:

    <dependency>
 <groupId>io.strimzi</groupId>
 <artifactId>kafka-oauth-client</artifactId>
 <version>0.2.0</version>
</dependency>

  2. Configure the system properties for the callback:

    For example:

    System.setProperty(ClientConfig.OAUTH_TOKEN_ENDPOINT_URI, “https://<auth-server-address>/auth/realms/master/protocol/openid-connect/token”); (1)
System.setProperty(ClientConfig.OAUTH_CLIENT_ID, "<client-name>"); (2)
System.setProperty(ClientConfig.OAUTH_CLIENT_SECRET, "<client-secret>"); (3)

    1. URI of the authorization server token endpoint.

    2. Client ID, which is the name used when creating the client in the authorization server.

    3. Client secret created when creating the client in the authorization server.

  3. Enable the SASL OAUTHBEARER mechanism on a TLS encrypted connection in the Kafka client configuration:

    For example:

    props.put("sasl.jaas.config", "org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required;");
props.put("security.protocol", "SASL_SSL"); (1)
props.put("sasl.mechanism", "OAUTHBEARER");
props.put("sasl.login.callback.handler.class", "io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler");

    1. Here we use SASL_SSL for use over TLS connections. Use SASL_PLAINTEXT over unencrypted connections.

  4. Verify that the Kafka client can access the Kafka brokers.

What to do next
Configuring OAuth 2.0 for Kafka components

This procedure describes how to configure Kafka components to use OAuth 2.0 authentication using an authorization server.

You can configure authentication for:

  • Kafka Connect

  • Kafka MirrorMaker

  • Kafka Bridge

In this scenario, the Kafka component and the authorization server are running in the same cluster.

Before you start

For more information on the configuration of OAuth 2.0 authentication for Kafka components, see:

Prerequisites

  • Strimzi and Kafka are running

  • An OAuth 2.0 authorization server is deployed and configured for OAuth access to Kafka brokers

  • Kafka brokers are configured for OAuth 2.0

Procedure

  1. Create a client secret and mount it to the component as an environment variable.

    For example, here we are creating a client Secret for the Kafka Bridge:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Secret
metadata:
 name: my-bridge-oauth
type: Opaque
data:
 clientSecret: MGQ1OTRmMzYtZTllZS00MDY2LWI5OGEtMTM5MzM2NjdlZjQw (1)

    1. The clientSecret key must be in base64 format.

  2. Create or edit the resource for the Kafka component so that OAuth 2.0 authentication is configured for the authentication property.

    For OAuth 2.0 authentication, you can use:

    • Client ID and secret

    • Client ID and refresh token

    • Access token

    • TLS

    KafkaClientAuthenticationOAuth schema reference provides examples of each.

    For example, here OAuth 2.0 is assigned to the Kafka Bridge client using a client ID and secret, and TLS:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  # ...
  authentication:
    type: oauth (1)
    tokenEndpointUri: https://<auth-server-address>/auth/realms/master/protocol/openid-connect/token (2)
    clientId: kafka-bridge
    clientSecret:
      secretName: my-bridge-oauth
      key: clientSecret
    tlsTrustedCertificates: (3)
    - secretName: oauth-server-cert
      certificate: tls.crt

    1. Authentication type set to oauth.

    2. URI of the token endpoint for authentication.

    3. Trusted certificates for TLS connection to the authorization server.

    Depending on how you apply OAuth 2.0 authentication, and the type of authorization server, there are additional configuration options you can use:

    # ...
spec:
  # ...
  authentication:
    # ...
    disableTlsHostnameVerification: true (1)
    checkAccessTokenType: false (2)
    accessTokenIsJwt: false (3)
    scope: any (4)

    1. (Optional) Disable TLS hostname verification. Default is false.

    2. If the authorization server does not return a typ (type) claim inside the JWT token, you can apply checkAccessTokenType: false to skip the token type check. Default is true.

    3. If you are using opaque tokens, you can apply accessTokenIsJwt: false so that access tokens are not treated as JWT tokens.

    4. If the authorization server requires the client to specify the scope when requesting the token from the token endpoint, you can provide it - in this case it is any.

  3. Apply the changes to the deployment of your Kafka resource.

    kubectl apply -f your-file

  4. Check the update in the logs or by watching the pod state transitions:

    kubectl logs -f ${POD_NAME} -c ${CONTAINER_NAME}
kubectl get pod -w

    The rolling updates configure the component for interaction with Kafka brokers using OAuth 2.0 authentication.

3.6.6. Authorization server examples

When choosing an authorization server, consider the features that best support configuration of your chosen authentication flow.

For the purposes of testing OAuth 2.0 with Strimzi, Keycloak and ORY Hydra were implemented as the OAuth 2.0 authorization server.

For more information, see:

3.7. Using OAuth 2.0 token-based authorization

If you are using OAuth 2.0 with Keycloak for token-based authentication, you can also use Keycloak to configure authorization rules to constrain client access to Kafka brokers. Authentication establishes the identity of a user. Authorization decides the level of access for that user.

Strimzi supports the use of OAuth 2.0 token-based authorization through Keycloak Authorization Services, which allows you to manage security policies and permissions centrally.

Security policies and permissions defined in Keycloak are used to grant access to resources on Kafka brokers. Users and clients are matched against policies that permit access to perform specific actions on Kafka brokers.

Kafka allows all users full access to brokers by default, and also provides the SimpleACLAuthorizer plugin to configure authorization based on Access Control Lists (ACLs). ZooKeeper stores ACL rules that grant or deny access to resources based on username. However, OAuth 2.0 token-based authorization with Keycloak offers far greater flexibility on how you wish to implement access control to Kafka brokers. In addition, you can configure your Kafka brokers to use OAuth 2.0 authorization and ACLs.

Additional resources

3.7.1. OAuth 2.0 authorization mechanism

OAuth 2.0 authorization in Strimzi uses Keycloak server Authorization Services REST endpoints to extend token-based authentication with Keycloak by applying defined security policies on a particular user, and providing a list of permissions granted on different resources for that user. Policies use roles and groups to match permissions to users. OAuth 2.0 authorization enforces permissions locally based on the received list of grants for the user from Keycloak Authorization Services.

Kafka broker custom authorizer

A Keycloak authorizer (KeycloakRBACAuthorizer) is provided with Strimzi. To be able to use the Keycloak REST endpoints for Authorization Services provided by Keycloak, you configure a custom authorizer on the Kafka broker.

The authorizer fetches a list of granted permissions from the authorization server as needed, and enforces authorization locally on the Kafka Broker, making rapid authorization decisions for each client request.

3.7.2. Configuring OAuth 2.0 authorization support

This procedure describes how to configure Kafka brokers to use OAuth 2.0 authorization using Keycloak Authorization Services.

Before you begin

Consider the access you require or want to limit for certain users. You can use a combination of Keycloak groups, roles, clients, and users to configure access in Keycloak.

Typically, groups are used to match users based on organizational departments or geographical locations. And roles are used to match users based on their function.

With Keycloak, you can store users and groups in LDAP, whereas clients and roles cannot be stored this way. Storage and access to user data may be a factor in how you choose to configure authorization policies.

Note
 Super users always have unconstrained access to a Kafka broker regardless of the authorization implemented on the Kafka broker.
Prerequisites

  • Strimzi must be configured to use OAuth 2.0 with Keycloak for token-based authentication. You use the same Keycloak server endpoint when you set up authorization.

  • You need to understand how to manage policies and permissions for Keycloak Authorization Services, as described in the Keycloak documentation.

Procedure

  1. Access the Keycloak Admin Console or use the Keycloak Admin CLI to enable Authorization Services for the Kafka broker client you created when setting up OAuth 2.0 authentication.

  2. Use Authorization Services to define resources, authorization scopes, policies, and permissions for the client.

  3. Bind the permissions to users and clients by assigning them roles and groups.

  4. Configure the Kafka brokers to use Keycloak authorization by updating the Kafka broker configuration (Kafka.spec.kafka) of your Kafka resource in an editor.

    kubectl edit kafka my-cluster

  5. Configure the Kafka broker kafka configuration to use keycloak authorization, and to be able to access the authorization server and Authorization Services.

    For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka
  # ...
  authorization:
    type: keycloak (1)
    tokenEndpointUri: <https://<auth-server-address>/auth/realms/external/protocol/openid-connect/token> (2)
    clientId: kafka (3)
    delegateToKafkaAcls: false (4)
    disableTlsHostnameVerification: false (5)
    superUsers: (6)
      - CN=fred
      - sam
      - CN=edward
    tlsTrustedCertificates: (7)
    - secretName: oauth-server-cert
      certificate: ca.crt
  #...

    1. Type keycloak enables Keycloak authorization.

    2. URI of the Keycloak token endpoint. For production, always use HTTPs.

    3. The client ID of the OAuth 2.0 client definition in Keycloak that has Authorization Services enabled. Typically, kafka is used as the ID.

    4. (Optional) Delegate authorization to Kafka SimpleACLAuthorizer if access is denied by Keycloak Authorization Services policies. The default is false.

    5. (Optional) Disable TLS hostname verification. Default is false.

    6. (Optional) Designated super users.

    7. (Optional) Trusted certificates for TLS connection to the authorization server.

  6. Save and exit the editor, then wait for rolling updates to complete.

  7. Check the update in the logs or by watching the pod state transitions:

    kubectl logs -f ${POD_NAME} -c kafka
kubectl get po -w

    The rolling update configures the brokers to use OAuth 2.0 authorization.

  8. Verify the configured permissions by accessing Kafka brokers as clients or users with specific roles, making sure they have the necessary access, or do not have the access they are not supposed to have.

3.8. Customizing deployments

Strimzi creates several Kubernetes resources, such as Deployments, StatefulSets, Pods, and Services, which are managed by Kubernetes operators. Only the operator that is responsible for managing a particular Kubernetes resource can change that resource. If you try to manually change an operator-managed Kubernetes resource, the operator will revert your changes back.

However, changing an operator-managed Kubernetes resource can be useful if you want to perform certain tasks, such as:

  • Adding custom labels or annotations that control how Pods are treated by Istio or other services;

  • Managing how Loadbalancer-type Services are created by the cluster.

You can make these types of changes using the template property in the Strimzi custom resources.

3.8.1. Template properties

You can use the template property to configure aspects of the resource creation process. You can include it in the following resources and properties:

  • Kafka.spec.kafka

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator

  • Kafka.spec.kafkaExporter

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaMirrorMakerSpec

  • KafkaBridge.spec

In the following example, the template property is used to modify the labels in a Kafka broker’s StatefulSet:

apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
  labels:
    app: my-cluster
spec:
  kafka:
    # ...
    template:
      statefulset:
        metadata:
          labels:
            mylabel: myvalue
    # ...
Supported template properties for a Kafka cluster
statefulset

Configures the StatefulSet used by the Kafka broker.

pod

Configures the Kafka broker Pods created by the StatefulSet.

bootstrapService

Configures the bootstrap service used by clients running within Kubernetes to connect to the Kafka broker.

brokersService

Configures the headless service.

externalBootstrapService

Configures the bootstrap service used by clients connecting to Kafka brokers from outside of Kubernetes.

perPodService

Configures the per-Pod services used by clients connecting to the Kafka broker from outside Kubernetes to access individual brokers.

externalBootstrapRoute

Configures the bootstrap route used by clients connecting to the Kafka brokers from outside of OpenShift using OpenShift Routes.

perPodRoute

Configures the per-Pod routes used by clients connecting to the Kafka broker from outside OpenShift to access individual brokers using OpenShift Routes.

podDisruptionBudget

Configures the Pod Disruption Budget for Kafka broker StatefulSet.

kafkaContainer

Configures the container used to run the Kafka broker, including custom environment variables.

tlsSidecarContainer

Configures the TLS sidecar container, including custom environment variables.

initContainer

Configures the container used to initialize the brokers.

persistentVolumeClaim

Configures the metadata of the Kafka PersistentVolumeClaims.

Additional resources

KafkaClusterTemplate schema reference.

Supported template properties for a ZooKeeper cluster
statefulset

Configures the ZooKeeper StatefulSet.

pod

Configures the ZooKeeper Pods created by the StatefulSet.

clientsService

Configures the service used by clients to access ZooKeeper.

nodesService

Configures the headless service.

podDisruptionBudget

Configures the Pod Disruption Budget for ZooKeeper StatefulSet.

zookeeperContainer

Configures the container used to run the ZooKeeper Node, including custom environment variables.

tlsSidecarContainer

Configures the TLS sidecar container, including custom environment variables.

persistentVolumeClaim

Configures the metadata of the ZooKeeper PersistentVolumeClaims.

Additional resources

ZookeeperClusterTemplate schema reference.

Supported template properties for Entity Operator
deployment

Configures the Deployment used by the Entity Operator.

pod

Configures the Entity Operator Pod created by the Deployment.

topicOperatorContainer

Configures the container used to run the Topic Operator, including custom environment variables.

userOperatorContainer

Configures the container used to run the User Operator, including custom environment variables.

tlsSidecarContainer

Configures the TLS sidecar container, including custom environment variables.

Additional resources

EntityOperatorTemplate schema reference.

Supported template properties for Kafka Exporter
deployment

Configures the Deployment used by Kafka Exporter.

pod

Configures the Kafka Exporter Pod created by the Deployment.

services

Configures the Kafka Exporter services.

container

Configures the container used to run Kafka Exporter, including custom environment variables.

Additional resources

KafkaExporterTemplate schema reference.

Supported template properties for Kafka Connect and Kafka Connect with Source2Image support
deployment

Configures the Kafka Connect Deployment.

pod

Configures the Kafka Connect Pods created by the Deployment.

apiService

Configures the service used by the Kafka Connect REST API.

podDisruptionBudget

Configures the Pod Disruption Budget for Kafka Connect Deployment.

connectContainer

Configures the container used to run Kafka Connect, including custom environment variables.

Additional resources

KafkaConnectTemplate schema reference.

Supported template properties for Kafka MirrorMaker
deployment

Configures the Kafka MirrorMaker Deployment.

pod

Configures the Kafka MirrorMaker Pods created by the Deployment.

podDisruptionBudget

Configures the Pod Disruption Budget for Kafka MirrorMaker Deployment.

mirrorMakerContainer

Configures the container used to run Kafka MirrorMaker, including custom environment variables.

Additional resources

KafkaMirrorMakerTemplate schema reference.

3.8.2. Labels and Annotations

For every resource, you can configure additional Labels and Annotations. Labels and Annotations are used to identify and organize resources, and are configured in the metadata property.

For example:

# ...
template:
    statefulset:
        metadata:
            labels:
                label1: value1
                label2: value2
            annotations:
                annotation1: value1
                annotation2: value2
# ...

The labels and annotations fields can contain any labels or annotations that do not contain the reserved string strimzi.io. Labels and annotations containing strimzi.io are used internally by Strimzi and cannot be configured.

For Kafka Connect, annotations on the KafkaConnect resource are used to enable the creation and management of connectors using KafkaConnector resources. For more information, see Enabling KafkaConnector resources.

Note
 The metadata property is not applicable to container templates, such as the kafkaContainer.

3.8.3. Customizing Pods

In addition to Labels and Annotations, you can customize some other fields on Pods. These fields are described in the following table and affect how the Pod is created.

Field Description

terminationGracePeriodSeconds

Defines the period of time, in seconds, by which the Pod must have terminated gracefully. After the grace period, the Pod and its containers are forcefully terminated (killed). The default value is 30 seconds.

NOTE: You might need to increase the grace period for very large Kafka clusters, so that the Kafka brokers have enough time to transfer their work to another broker before they are terminated.

imagePullSecrets

Defines a list of references to Kubernetes Secrets that can be used for pulling container images from private repositories. For more information about how to create a Secret with the credentials, see Pull an Image from a Private Registry.

NOTE: When the STRIMZI_IMAGE_PULL_SECRETS environment variable in Cluster Operator and the imagePullSecrets option are specified, only the imagePullSecrets variable is used. The STRIMZI_IMAGE_PULL_SECRETS variable is ignored.

securityContext

Configures pod-level security attributes for containers running as part of a given Pod. For more information about configuring SecurityContext, see Configure a Security Context for a Pod or Container.

priorityClassName

Configures the name of the Priority Class which will be used for given a Pod. For more information about Priority Classes, see Pod Priority and Preemption.

schedulerName

The name of the scheduler used to dispatch this Pod. If not specified, the default scheduler will be used.

These fields are effective on each type of cluster (Kafka and ZooKeeper; Kafka Connect and Kafka Connect with S2I support; and Kafka MirrorMaker).

The following example shows these customized fields on a template property:

# ...
template:
  pod:
    metadata:
      labels:
        label1: value1
    imagePullSecrets:
      - name: my-docker-credentials
    securityContext:
      runAsUser: 1000001
      fsGroup: 0
    terminationGracePeriodSeconds: 120
# ...
Additional resources

3.8.4. Customizing containers with environment variables

You can set custom environment variables for a container by using the relevant template container property. The following table lists the Strimzi containers and the relevant template configuration property (defined under spec) for each custom resource.

Table 1. Table Container environment variable properties
Strimzi Element Container Configuration property

Kafka

Kafka Broker

kafka.template.kafkaContainer.env

Kafka

Kafka Broker TLS Sidecar

kafka.template.tlsSidecarContainer.env

Kafka

Kafka Initialization

kafka.template.initContainer.env

Kafka

ZooKeeper Node

zookeeper.template.zookeeperContainer.env

Kafka

ZooKeeper TLS Sidecar

zookeeper.template.tlsSidecarContainer.env

Kafka

Topic Operator

entityOperator.template.topicOperatorContainer.env

Kafka

User Operator

entityOperator.template.userOperatorContainer.env

Kafka

Entity Operator TLS Sidecar

entityOperator.template.tlsSidecarContainer.env

KafkaConnect

Connect and ConnectS2I

template.connectContainer.env

KafkaMirrorMaker

MirrorMaker

template.mirrorMakerContainer.env

KafkaBridge

Bridge

template.bridgeContainer.env

The environment variables are defined under the env property as a list of objects with name and value fields. The following example shows two custom environment variables set for the Kafka broker containers:

# ...
kind: Kafka
spec:
    kafka:
        template:
            kafkaContainer:
                env:
                    - name: TEST_ENV_1
                      value: test.env.one
                    - name: TEST_ENV_2
                      value: test.env.two
# ...

Environment variables prefixed with KAFKA_ are internal to Strimzi and should be avoided. If you set a custom environment variable that is already in use by Strimzi, it is ignored and a warning is recorded in the log.

Additional resources

3.8.5. Customizing external Services

When exposing Kafka outside of Kubernetes using loadbalancers or node ports, you can use additional customization properties in addition to labels and annotations. The properties for external services are described in the following table and affect how a Service is created.

Field Description

externalTrafficPolicy

Specifies whether the service routes external traffic to node-local or cluster-wide endpoints. Cluster may cause a second hop to another node and obscures the client source IP. Local avoids a second hop for LoadBalancer and Nodeport type services and preserves the client source IP (when supported by the infrastructure). If unspecified, Kubernetes will use Cluster as the default.

loadBalancerSourceRanges

A list of CIDR ranges (for example 10.0.0.0/8 or 130.211.204.1/32) from which clients can connect to load balancer type listeners. If supported by the platform, traffic through the loadbalancer is restricted to the specified CIDR ranges. This field is applicable only for loadbalancer type services, and is ignored if the cloud provider does not support the feature.

For more information, see https://kubernetes.io/docs/tasks/access-application-cluster/configure-cloud-provider-firewall/.

These properties are available for externalBootstrapService and perPodService. The following example shows these customized properties for a template:

# ...
template:
  externalBootstrapService:
    externalTrafficPolicy: Local
    loadBalancerSourceRanges:
      - 10.0.0.0/8
      - 88.208.76.87/32
  perPodService:
    externalTrafficPolicy: Local
    loadBalancerSourceRanges:
      - 10.0.0.0/8
      - 88.208.76.87/32
# ...
Additional resources

3.8.6. Customizing the image pull policy

Strimzi allows you to customize the image pull policy for containers in all pods deployed by the Cluster Operator. The image pull policy is configured using the environment variable STRIMZI_IMAGE_PULL_POLICY in the Cluster Operator deployment. The STRIMZI_IMAGE_PULL_POLICY environment variable can be set to three different values:

Always

Container images are pulled from the registry every time the pod is started or restarted.

IfNotPresent

Container images are pulled from the registry only when they were not pulled before.

Never

Container images are never pulled from the registry.

The image pull policy can be currently customized only for all Kafka, Kafka Connect, and Kafka MirrorMaker clusters at once. Changing the policy will result in a rolling update of all your Kafka, Kafka Connect, and Kafka MirrorMaker clusters.

Additional resources

  • For more information about Cluster Operator configuration, see Cluster Operator.

  • For more information about Image Pull Policies, see Disruptions.

3.8.7. Customizing Pod Disruption Budgets

Strimzi creates a pod disruption budget for every new StatefulSet or Deployment. By default, these pod disruption budgets only allow a single pod to be unavailable at a given time by setting the maxUnavailable value in the PodDisruptionBudget.spec resource to 1. You can change the amount of unavailable pods allowed by changing the default value of maxUnavailable in the pod disruption budget template. This template applies to each type of cluster (Kafka and ZooKeeper; Kafka Connect and Kafka Connect with S2I support; and Kafka MirrorMaker).

The following example shows customized podDisruptionBudget fields on a template property:

# ...
template:
    podDisruptionBudget:
        metadata:
            labels:
                key1: label1
                key2: label2
            annotations:
                key1: label1
                key2: label2
        maxUnavailable: 1
# ...
Additional resources

3.8.8. Customizing deployments

This procedure describes how to customize Labels of a Kafka cluster.

Prerequisites

  • A Kubernetes cluster.

  • A running Cluster Operator.

Procedure

  1. Edit the template property in the Kafka, KafkaConnect, KafkaConnectS2I, or KafkaMirrorMaker resource. For example, to modify the labels for the Kafka broker StatefulSet, use:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
  labels:
    app: my-cluster
spec:
  kafka:
    # ...
    template:
      statefulset:
        metadata:
          labels:
            mylabel: myvalue
    # ...

  2. Create or update the resource.

    Use kubectl apply:

    kubectl apply -f your-file

    Alternatively, use kubectl edit:

    kubectl edit Resource ClusterName

3.9. External logging

When setting the logging levels for a resource, you can specify them inline directly in the spec.logging property of the resource YAML:

spec:
  # ...
  logging:
    type: inline
    loggers:
      kafka.root.logger.level: "INFO"

Or you can specify external logging:

spec:
  # ...
  logging:
    type: external
    name: customConfigMap

With external logging, logging properties are defined in a ConfigMap. The name of the ConfigMap is referenced in the spec.logging.name property.

The advantages of using a ConfigMap are that the logging properties are maintained in one place and are accessible to more than one resource.

3.9.1. Creating a ConfigMap for logging

To use a ConfigMap to define logging properties, you create the ConfigMap and then reference it as part of the logging definition in the spec of a resource.

The ConfigMap must contain the appropriate logging configuration.

  • log4j.properties for Kafka components, ZooKeeper, and the Kafka Bridge

  • log4j2.properties for the Topic Operator and User Operator

The configuration must be placed under these properties.

Here we demonstrate how a ConfigMap defines a root logger for a Kafka resource.

Procedure

  1. Create the ConfigMap.

    You can create the ConfigMap as a YAML file or from a properties file using kubectl at the command line.

    ConfigMap example with a root logger definition for Kafka:

    kind: ConfigMap
apiVersion: kafka.strimzi.io/v1beta1
metadata:
  name: logging-configmap
data:
  log4j.properties:
    kafka.root.logger.level="INFO"

    From the command line, using a properties file:

    kubectl create configmap logging-configmap --from-file=log4j.properties

    The properties file defines the logging configuration:

    # Define the root logger
kafka.root.logger.level="INFO"
# ...

  2. Define external logging in the spec of the resource, setting the logging.name to the name of the ConfigMap.

    spec:
  # ...
  logging:
    type: external
    name: logging-configmap

  3. Create or update the resource.

    kubectl apply -f kafka.yaml

4. Operators

4.1. Cluster Operator

Use the Cluster Operator to deploy a Kafka cluster and other Kafka components.

The Cluster Operator is deployed using YAML installation files. For information on deploying the Cluster Operator, see the Deploying the Cluster Operator.

For information on the deployment options available for Kafka, see Kafka Cluster configuration.

Note
 On OpenShift, a Kafka Connect deployment can incorporate a Source2Image feature to provide a convenient way to add additional connectors.

4.1.1. Cluster Operator

Strimzi 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 Kubernetes 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

Cluster Operator

4.1.2. Reconciliation

Although the operator reacts to all notifications about the desired cluster resources received from the Kubernetes cluster, if the operator is not running, or if a notification is not received for any reason, the desired resources will get out of sync with the state of the running Kubernetes cluster.

In order to handle failovers properly, a periodic reconciliation process is executed by the Cluster Operator so that it can compare the state of the desired resources with the current cluster deployments in order to have a consistent state across all of them. You can set the time interval for the periodic reconciliations using the STRIMZI_FULL_RECONCILIATION_INTERVAL_MS variable.

4.1.3. Cluster Operator Configuration

The Cluster Operator can be configured through the following supported environment variables:

STRIMZI_NAMESPACE

A comma-separated list of namespaces that the operator should operate in. When not set, set to empty string, or to * the Cluster Operator will operate in all namespaces. The Cluster Operator deployment might use the Kubernetes Downward API to set this automatically to the namespace the Cluster Operator is deployed in. See the example below:

env:
  - name: STRIMZI_NAMESPACE
    valueFrom:
      fieldRef:
        fieldPath: metadata.namespace
STRIMZI_FULL_RECONCILIATION_INTERVAL_MS

Optional, default is 120000 ms. The interval between periodic reconciliations, in milliseconds.

STRIMZI_LOG_LEVEL

Optional, default INFO. The level for printing logging messages. The value can be set to: ERROR, WARNING, INFO, DEBUG, and TRACE.

STRIMZI_OPERATION_TIMEOUT_MS

Optional, default 300000 ms. The timeout for internal operations, in milliseconds. This value should be increased when using Strimzi on clusters where regular Kubernetes operations take longer than usual (because of slow downloading of Docker images, for example).

STRIMZI_KAFKA_IMAGES

Required. This provides a mapping from Kafka version to the corresponding Docker image containing a Kafka broker of that version. The required syntax is whitespace or comma separated <version>=<image> pairs. For example 2.4.1=strimzi/kafka:0.18.0-kafka-2.4.1, 2.5.0=strimzi/kafka:0.18.0-kafka-2.5.0. This is used when a Kafka.spec.kafka.version property is specified but not the Kafka.spec.kafka.image, as described in Container images.

STRIMZI_DEFAULT_KAFKA_INIT_IMAGE

Optional, default strimzi/operator:0.18.0. The image name to use as default for the init container started before the broker for initial configuration work (that is, rack support), if no image is specified as the kafka-init-image in the Container images.

STRIMZI_DEFAULT_TLS_SIDECAR_KAFKA_IMAGE

Optional, default strimzi/kafka:0.18.0-kafka-2.5.0. The image name to use as the default when deploying the sidecar container which provides TLS support for Kafka, if no image is specified as the Kafka.spec.kafka.tlsSidecar.image in the Container images.

STRIMZI_KAFKA_CONNECT_IMAGES

Required. This provides a mapping from the Kafka version to the corresponding Docker image containing a Kafka connect of that version. The required syntax is whitespace or comma separated <version>=<image> pairs. For example 2.4.1=strimzi/kafka:0.18.0-kafka-2.4.1, 2.5.0=strimzi/kafka:0.18.0-kafka-2.5.0. This is used when a KafkaConnect.spec.version property is specified but not the KafkaConnect.spec.image, as described in Container images.

STRIMZI_KAFKA_CONNECT_S2I_IMAGES

Required. This provides a mapping from the Kafka version to the corresponding Docker image containing a Kafka connect of that version. The required syntax is whitespace or comma separated <version>=<image> pairs. For example 2.4.1=strimzi/kafka:0.18.0-kafka-2.4.1, 2.5.0=strimzi/kafka:0.18.0-kafka-2.5.0. This is used when a KafkaConnectS2I.spec.version property is specified but not the KafkaConnectS2I.spec.image, as described in Container images.

STRIMZI_KAFKA_MIRROR_MAKER_IMAGES

Required. This provides a mapping from the Kafka version to the corresponding Docker image containing a Kafka mirror maker of that version. The required syntax is whitespace or comma separated <version>=<image> pairs. For example 2.4.1=strimzi/kafka:0.18.0-kafka-2.4.1, 2.5.0=strimzi/kafka:0.18.0-kafka-2.5.0. This is used when a KafkaMirrorMaker.spec.version property is specified but not the KafkaMirrorMaker.spec.image, as described in Container images.

STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE

Optional, default strimzi/operator:0.18.0. The image name to use as the default when deploying the topic operator, if no image is specified as the Kafka.spec.entityOperator.topicOperator.image in the Container images of the Kafka resource.

STRIMZI_DEFAULT_USER_OPERATOR_IMAGE

Optional, default strimzi/operator:0.18.0. The image name to use as the default when deploying the user operator, if no image is specified as the Kafka.spec.entityOperator.userOperator.image in the Container images of the Kafka resource.

STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGE

Optional, default strimzi/kafka:0.18.0-kafka-2.5.0. The image name to use as the default when deploying the sidecar container which provides TLS support for the Entity Operator, if no image is specified as the Kafka.spec.entityOperator.tlsSidecar.image in the Container images.

STRIMZI_IMAGE_PULL_POLICY

Optional. The ImagePullPolicy which will be applied to containers in all pods managed by Strimzi Cluster Operator. The valid values are Always, IfNotPresent, and Never. If not specified, the Kubernetes defaults will be used. Changing the policy will result in a rolling update of all your Kafka, Kafka Connect, and Kafka MirrorMaker clusters.

STRIMZI_IMAGE_PULL_SECRETS

Optional. A comma-separated list of Secret names. The secrets referenced here contain the credentials to the container registries where the container images are pulled from. The secrets are used in the imagePullSecrets field for all Pods created by the Cluster Operator. Changing this list results in a rolling update of all your Kafka, Kafka Connect, and Kafka MirrorMaker clusters.

STRIMZI_KUBERNETES_VERSION

Optional. Overrides the Kubernetes version information detected from the API server. See the example below:

env:
  - name: STRIMZI_KUBERNETES_VERSION
    value: |
           major=1
           minor=16
           gitVersion=v1.16.2
           gitCommit=c97fe5036ef3df2967d086711e6c0c405941e14b
           gitTreeState=clean
           buildDate=2019-10-15T19:09:08Z
           goVersion=go1.12.10
           compiler=gc
           platform=linux/amd64
KUBERNETES_SERVICE_DNS_DOMAIN

Optional. Overrides the default Kubernetes DNS domain name suffix.

By default, services assigned in the Kubernetes cluster have a DNS domain name that uses the default suffix cluster.local.

For example, for broker kafka-0:

<cluster-name>-kafka-0.<cluster-name>-kafka-brokers.<namespace>.svc.cluster.local

The DNS domain name is added to the Kafka broker certificates used for hostname verification.

If you are using a different DNS domain name suffix in your cluster, change the KUBERNETES_SERVICE_DNS_DOMAIN environment variable from the default to the one you are using in order to establish a connection with the Kafka brokers.

4.1.4. Role-Based Access Control (RBAC)

Provisioning Role-Based Access Control (RBAC) for the Cluster Operator

For the Cluster Operator to function it needs permission within the Kubernetes cluster to interact with resources such as Kafka, KafkaConnect, and so on, as well as the managed resources, such as ConfigMaps, Pods, Deployments, StatefulSets, Services, and so on. Such permission is described in terms of Kubernetes role-based access control (RBAC) resources:

  • ServiceAccount,

  • Role and ClusterRole,

  • RoleBinding and ClusterRoleBinding.

In addition to running under its own ServiceAccount with a ClusterRoleBinding, the Cluster Operator manages some RBAC resources for the components that need access to Kubernetes resources.

Kubernetes also includes privilege escalation protections that prevent components operating under one ServiceAccount from granting other ServiceAccounts privileges that the granting ServiceAccount does not have. Because the Cluster Operator must be able to create the ClusterRoleBindings, and RoleBindings needed by resources it manages, the Cluster Operator must also have those same privileges.

Delegated privileges

When the Cluster Operator deploys resources for a desired Kafka resource it also creates ServiceAccounts, RoleBindings, and ClusterRoleBindings, as follows:

  • The Kafka broker pods use a ServiceAccount called cluster-name-kafka

    • When the rack feature is used, the strimzi-cluster-name-kafka-init ClusterRoleBinding is used to grant this ServiceAccount access to the nodes within the cluster via a ClusterRole called strimzi-kafka-broker

    • When the rack feature is not used no binding is created

  • The ZooKeeper pods use a ServiceAccount called cluster-name-zookeeper

  • The Entity Operator pod uses a ServiceAccount called cluster-name-entity-operator

    • The Topic Operator produces Kubernetes events with status information, so the ServiceAccount is bound to a ClusterRole called strimzi-entity-operator which grants this access via the strimzi-entity-operator RoleBinding

  • The pods for KafkaConnect and KafkaConnectS2I resources use a ServiceAccount called cluster-name-cluster-connect

  • The pods for KafkaMirrorMaker use a ServiceAccount called cluster-name-mirror-maker

  • The pods for KafkaBridge use a ServiceAccount called cluster-name-bridge

ServiceAccount

The Cluster Operator is best run using a ServiceAccount:

Example ServiceAccount for the Cluster Operator
apiVersion: v1
kind: ServiceAccount
metadata:
  name: strimzi-cluster-operator
  labels:
    app: strimzi

The Deployment of the operator then needs to specify this in its spec.template.spec.serviceAccountName:

Partial example of Deployment for the Cluster Operator
apiVersion: apps/v1
kind: Deployment
metadata:
  name: strimzi-cluster-operator
  labels:
    app: strimzi
spec:
  replicas: 1
  selector:
    matchLabels:
      name: strimzi-cluster-operator
      strimzi.io/kind: cluster-operator
  template:
      # ...

Note line 12, where the the strimzi-cluster-operator ServiceAccount is specified as the serviceAccountName.

ClusterRoles

The Cluster Operator needs to operate using ClusterRoles that gives access to the necessary resources. Depending on the Kubernetes cluster setup, a cluster administrator might be needed to create the ClusterRoles.

Note
 Cluster administrator rights are only needed for the creation of the ClusterRoles. The Cluster Operator will not run under the cluster admin account.

The ClusterRoles follow the principle of least privilege and contain only those privileges needed by the Cluster Operator to operate Kafka, Kafka Connect, and ZooKeeper clusters. The first set of assigned privileges allow the Cluster Operator to manage Kubernetes resources such as StatefulSets, Deployments, Pods, and ConfigMaps.

Cluster Operator uses ClusterRoles to grant permission at the namespace-scoped resources level and cluster-scoped resources level:

ClusterRole with namespaced resources for the Cluster Operator
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: strimzi-cluster-operator-namespaced
  labels:
    app: strimzi
rules:
- apiGroups:
  - ""
  resources:
  # The cluster operator needs to access and manage service accounts to grant Strimzi components cluster permissions
  - serviceaccounts
  verbs:
  - get
  - create
  - delete
  - patch
  - update
- apiGroups:
  - "rbac.authorization.k8s.io"
  resources:
  # The cluster operator needs to access and manage rolebindings to grant Strimzi components cluster permissions
  - rolebindings
  verbs:
  - get
  - create
  - delete
  - patch
  - update
- apiGroups:
  - ""
  resources:
  # The cluster operator needs to access and manage config maps for Strimzi components configuration
  - configmaps
  # The cluster operator needs to access and manage services to expose Strimzi components to network traffic
  - services
  # The cluster operator needs to access and manage secrets to handle credentials
  - secrets
  # The cluster operator needs to access and manage persistent volume claims to bind them to Strimzi components for persistent data
  - persistentvolumeclaims
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - "kafka.strimzi.io"
  resources:
  # The cluster operator runs the KafkaAssemblyOperator, which needs to access and manage Kafka resources
  - kafkas
  - kafkas/status
  # The cluster operator runs the KafkaConnectAssemblyOperator, which needs to access and manage KafkaConnect resources
  - kafkaconnects
  - kafkaconnects/status
  # The cluster operator runs the KafkaConnectS2IAssemblyOperator, which needs to access and manage KafkaConnectS2I resources
  - kafkaconnects2is
  - kafkaconnects2is/status
  # The cluster operator runs the KafkaConnectorAssemblyOperator, which needs to access and manage KafkaConnector resources
  - kafkaconnectors
  - kafkaconnectors/status
  # The cluster operator runs the KafkaMirrorMakerAssemblyOperator, which needs to access and manage KafkaMirrorMaker resources
  - kafkamirrormakers
  - kafkamirrormakers/status
  # The cluster operator runs the KafkaBridgeAssemblyOperator, which needs to access and manage BridgeMaker resources
  - kafkabridges
  - kafkabridges/status
  # The cluster operator runs the KafkaMirrorMaker2AssemblyOperator, which needs to access and manage KafkaMirrorMaker2 resources
  - kafkamirrormaker2s
  - kafkamirrormaker2s/status
  # The cluster operator runs the KafkaRebalanceAssemblyOperator, which needs to access and manage KafkaRebalance resources
  - kafkarebalances
  - kafkarebalances/status
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - ""
  resources:
  # The cluster operator needs to access and delete pods, this is to allow it to monitor pod health and coordinate rolling updates
  - pods
  verbs:
  - get
  - list
  - watch
  - delete
- apiGroups:
  - ""
  resources:
  - endpoints
  verbs:
  - get
  - list
  - watch
- apiGroups:
  # The cluster operator needs the extensions api as the operator supports Kubernetes version 1.11+
  # apps/v1 was introduced in Kubernetes 1.14
  - "extensions"
  resources:
  # The cluster operator needs to access and manage deployments to run deployment based Strimzi components
  - deployments
  - deployments/scale
  # The cluster operator needs to access replica sets to manage Strimzi components and to determine error states
  - replicasets
  # The cluster operator needs to access and manage replication controllers to manage replicasets
  - replicationcontrollers
  # The cluster operator needs to access and manage network policies to lock down communication between Strimzi components
  - networkpolicies
  # The cluster operator needs to access and manage ingresses which allow external access to the services in a cluster
  - ingresses
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - "apps"
  resources:
  # The cluster operator needs to access and manage deployments to run deployment based Strimzi components
  - deployments
  - deployments/scale
  - deployments/status
  # The cluster operator needs to access and manage stateful sets to run stateful sets based Strimzi components
  - statefulsets
  # The cluster operator needs to access replica-sets to manage Strimzi components and to determine error states
  - replicasets
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - ""
  resources:
  # The cluster operator needs to be able to create events and delegate permissions to do so
  - events
  verbs:
  - create
- apiGroups:
  # OpenShift S2I requirements
  - apps.openshift.io
  resources:
  - deploymentconfigs
  - deploymentconfigs/scale
  - deploymentconfigs/status
  - deploymentconfigs/finalizers
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  # OpenShift S2I requirements
  - build.openshift.io
  resources:
  - buildconfigs
  - builds
  verbs:
  - create
  - delete
  - get
  - list
  - patch
  - watch
  - update
- apiGroups:
  # OpenShift S2I requirements
  - image.openshift.io
  resources:
  - imagestreams
  - imagestreams/status
  verbs:
  - create
  - delete
  - get
  - list
  - watch
  - patch
  - update
- apiGroups:
  - networking.k8s.io
  resources:
  # The cluster operator needs to access and manage network policies to lock down communication between Strimzi components
  - networkpolicies
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - route.openshift.io
  resources:
  # The cluster operator needs to access and manage routes to expose Strimzi components for external access
  - routes
  - routes/custom-host
  verbs:
  - get
  - list
  - create
  - delete
  - patch
  - update
- apiGroups:
  - policy
  resources:
  # The cluster operator needs to access and manage pod disruption budgets this limits the number of concurrent disruptions
  # that a Strimzi component experiences, allowing for higher availability
  - poddisruptionbudgets
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update

The second includes the permissions needed for cluster-scoped resources.

ClusterRole with cluster-scoped resources for the Cluster Operator
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: strimzi-cluster-operator-global
  labels:
    app: strimzi
rules:
- apiGroups:
  - "rbac.authorization.k8s.io"
  resources:
  # The cluster operator needs to create and manage cluster role bindings in the case of an install where a user
  # has specified they want their cluster role bindings generated
  - clusterrolebindings
  verbs:
  - get
  - create
  - delete
  - patch
  - update
  - watch
- apiGroups:
  - storage.k8s.io
  resources:
  # The cluster operator requires "get" permissions to view storage class details
  # This is because only a persistent volume of a supported storage class type can be resized
  - storageclasses
  verbs:
  - get
- apiGroups:
  - ""
  resources:
  # The cluster operator requires "list" permissions to view all nodes in a cluster
  # The listing is used to determine the node addresses when NodePort access is configured
  # These addresses are then exposed in the custom resource states
  - nodes
  verbs:
  - list

The strimzi-kafka-broker ClusterRole represents the access needed by the init container in Kafka pods that is used for the rack feature. As described in the Delegated privileges section, this role is also needed by the Cluster Operator in order to be able to delegate this access.

ClusterRole for the Cluster Operator allowing it to delegate access to Kubernetes nodes to the Kafka broker pods
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: strimzi-kafka-broker
  labels:
    app: strimzi
rules:
- apiGroups:
  - ""
  resources:
  # The Kafka Brokers require "get" permissions to view the node they are on
  # This information is used to generate a Rack ID that is used for High Availability configurations
  - nodes
  verbs:
  - get

The strimzi-topic-operator ClusterRole represents the access needed by the Topic Operator. As described in the Delegated privileges section, this role is also needed by the Cluster Operator in order to be able to delegate this access.

ClusterRole for the Cluster Operator allowing it to delegate access to events to the Topic Operator
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: strimzi-entity-operator
  labels:
    app: strimzi
rules:
- apiGroups:
  - "kafka.strimzi.io"
  resources:
  # The entity operator runs the KafkaTopic assembly operator, which needs to access and manage KafkaTopic resources
  - kafkatopics
  - kafkatopics/status
  # The entity operator runs the KafkaUser assembly operator, which needs to access and manage KafkaUser resources
  - kafkausers
  - kafkausers/status
  verbs:
  - get
  - list
  - watch
  - create
  - patch
  - update
  - delete
- apiGroups:
  - ""
  resources:
  - events
  verbs:
  # The entity operator needs to be able to create events
  - create
- apiGroups:
  - ""
  resources:
  # The entity operator user-operator needs to access and manage secrets to store generated credentials
  - secrets
  verbs:
  - get
  - list
  - create
  - patch
  - update
  - delete
ClusterRoleBindings

The operator needs ClusterRoleBindings and RoleBindings which associates its ClusterRole with its ServiceAccount: ClusterRoleBindings are needed for ClusterRoles containing cluster-scoped resources.

Example ClusterRoleBinding for the Cluster Operator
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: strimzi-cluster-operator
  labels:
    app: strimzi
subjects:
- kind: ServiceAccount
  name: strimzi-cluster-operator
  namespace: myproject
roleRef:
  kind: ClusterRole
  name: strimzi-cluster-operator-global
  apiGroup: rbac.authorization.k8s.io

ClusterRoleBindings are also needed for the ClusterRoles needed for delegation:

Examples RoleBinding for the Cluster Operator
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: strimzi-cluster-operator-kafka-broker-delegation
  labels:
    app: strimzi
# The Kafka broker cluster role must be bound to the cluster operator service account so that it can delegate the cluster role to the Kafka brokers.
# This must be done to avoid escalating privileges which would be blocked by Kubernetes.
subjects:
- kind: ServiceAccount
  name: strimzi-cluster-operator
  namespace: myproject
roleRef:
  kind: ClusterRole
  name: strimzi-kafka-broker
  apiGroup: rbac.authorization.k8s.io

ClusterRoles containing only namespaced resources are bound using RoleBindings only.

apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: strimzi-cluster-operator
  labels:
    app: strimzi
subjects:
- kind: ServiceAccount
  name: strimzi-cluster-operator
  namespace: myproject
roleRef:
  kind: ClusterRole
  name: strimzi-cluster-operator-namespaced
  apiGroup: rbac.authorization.k8s.io
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: strimzi-cluster-operator-entity-operator-delegation
  labels:
    app: strimzi
# The Entity Operator cluster role must be bound to the cluster operator service account so that it can delegate the cluster role to the Entity Operator.
# This must be done to avoid escalating privileges which would be blocked by Kubernetes.
subjects:
- kind: ServiceAccount
  name: strimzi-cluster-operator
  namespace: myproject
roleRef:
  kind: ClusterRole
  name: strimzi-entity-operator
  apiGroup: rbac.authorization.k8s.io

4.2. Topic Operator

The Topic Operator manages Kafka topics through custom resources.

The Topic Operator is deployed:

4.2.1. Topic Operator

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

Example architecture for the Topic Operator

Topic Operator

The role of the Topic Operator is to keep a set of KafkaTopic Kubernetes 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.

4.2.2. Identifying a Kafka cluster for topic handling

A KafkaTopic resource includes a label that defines the appropriate name of the Kafka cluster (derived from the name of the Kafka resource) to which it belongs.

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaTopic
metadata:
  name: my-topic
  labels:
    strimzi.io/cluster: my-cluster

The label is used by the Topic Operator to identify the KafkaTopic resource and create a new topic, and also in subsequent handling of the topic.

If the label does not match the Kafka cluster, the Topic Operator cannot identify the KafkaTopic and the topic is not created.

4.2.3. Understanding the Topic Operator

A fundamental problem that the operator has to solve is that there is no single source of truth: Both the KafkaTopic resource and the topic within Kafka can be modified independently of the operator. Complicating this, the Topic Operator might not always be able to observe changes at each end in real time (for example, the operator might be down).

To resolve this, the operator maintains its own private copy of the information about each topic. When a change happens either in the Kafka cluster, or in Kubernetes, it looks at both the state of the other system and at its private copy in order to determine what needs to change to keep everything in sync. The same thing happens whenever the operator starts, and periodically while it is running.

For example, suppose the Topic Operator is not running, and a KafkaTopic my-topic gets created. When the operator starts it will lack a private copy of "my-topic", so it can infer that the KafkaTopic has been created since it was last running. The operator will create the topic corresponding to "my-topic" and also store a private copy of the metadata for "my-topic".

The private copy allows the operator to cope with scenarios where the topic configuration gets changed both in Kafka and in Kubernetes, so long as the changes are not incompatible (for example, both changing the same topic config key, but to different values). In the case of incompatible changes, the Kafka configuration wins, and the KafkaTopic will be updated to reflect that.

The private copy is held in the same ZooKeeper ensemble used by Kafka itself. This mitigates availability concerns, because if ZooKeeper is not running then Kafka itself cannot run, so the operator will be no less available than it would even if it was stateless.

4.2.4. Configuring the Topic Operator with resource requests and limits

You can allocate resources, such as CPU and memory, to the Topic Operator and set a limit on the amount of resources it can consume.

Prerequisites

  • The Cluster Operator is running.

Procedure

  1. Update the Kafka cluster configuration in an editor, as required:

    Use kubectl edit:

    kubectl edit kafka my-cluster

  2. In the spec.entityOperator.topicOperator.resources property in the Kafka resource, set the resource requests and limits for the Topic Operator.

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  # kafka and zookeeper sections...
  entityOperator:
    topicOperator:
      resources:
        request:
          cpu: "1"
          memory: 500Mi
        limit:
          cpu: "1"
          memory: 500Mi

  3. Apply the new configuration to create or update the resource.

    Use kubectl apply:

    kubectl apply -f kafka.yaml
Additional resources

4.3. User Operator

The User Operator manages Kafka users through custom resources.

The User Operator is deployed:

4.3.1. 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 Kubernetes 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.

4.3.2. Identifying a Kafka cluster for user handling

A KafkaUser resource includes a label that defines the appropriate name of the Kafka cluster (derived from the name of the Kafka resource) to which it belongs.

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster

The label is used by the User Operator to identify the KafkaUser resource and create a new user, and also in subsequent handling of the user.

If the label does not match the Kafka cluster, the User Operator cannot identify the kafkaUser and the user is not created.

4.3.3. Configuring the User Operator with resource requests and limits

You can allocate resources, such as CPU and memory, to the User Operator and set a limit on the amount of resources it can consume.

Prerequisites

  • The Cluster Operator is running.

Procedure

  1. Update the Kafka cluster configuration in an editor, as required:

    kubectl edit kafka my-cluster

  2. In the spec.entityOperator.userOperator.resources property in the Kafka resource, set the resource requests and limits for the User Operator.

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  # kafka and zookeeper sections...
  entityOperator:
    userOperator:
      resources:
        request:
          cpu: "1"
          memory: 500Mi
        limit:
          cpu: "1"
          memory: 500Mi

    Save the file and exit the editor. The Cluster Operator will apply the changes automatically.

Additional resources

4.4. Monitoring Operators

4.4.1. Prometheus metrics

Strimzi operators expose Prometheus metrics. The metrics are automatically enabled and contain information about:

  • Number of reconciliations

  • Number of Custom Resources the operator is processing

  • Duration of reconciliations

  • JVM metrics from the operators

Additionally, we provide an example Grafana dashboard.

For more information about Prometheus, see the Introducing Metrics to Kafka.

5. Using the Topic Operator

5.1. Topic Operator usage recommendations

When working with topics, be consistent and always operate on either KafkaTopic resources or topics directly. Avoid routinely switching between both methods for a given topic.

Use topic names that reflect the nature of the topic, and remember that names cannot be changed later.

If creating a topic in Kafka, use a name that is a valid Kubernetes resource name, otherwise the Topic Operator will need to create the corresponding KafkaTopic with a name that conforms to the Kubernetes rules.

Note
 Recommendations for identifiers and names in Kubernetes are outlined in Identifiers and Names in Kubernetes community article.
Kafka topic naming conventions

Kafka and Kubernetes impose their own validation rules for the naming of topics in Kafka and KafkaTopic.metadata.name respectively. There are valid names for each which are invalid in the other.

Using the spec.topicName property, it is possible to create a valid topic in Kafka with a name that would be invalid for the KafkaTopic in Kubernetes.

The spec.topicName property inherits Kafka naming validation rules:

  • The name must not be longer than 249 characters.

  • Valid characters for Kafka topics are ASCII alphanumerics, ., _, and -.

  • The name cannot be . or .., though . can be used in a name, such as exampleTopic. or .exampleTopic.

spec.topicName must not be changed.

For example:

kind: KafkaTopic
metadata:
  name: topic-name-1
spec:
  topicName: topicName-1 # Upper case is invalid in Kubernetes
  # ...

cannot be changed to

kind: KafkaTopic
metadata:
  name: topic-name-1
spec:
  topicName: name-2
  # ...
Note
 Some Kafka client applications, such as Kafka Streams, can create topics in Kafka programmatically. If those topics have names that are invalid Kubernetes resource names, the Topic Operator gives them valid names based on the Kafka names. Invalid characters are replaced and a hash is appended to the name.

5.2. Creating a topic

This procedure describes how to create a Kafka topic using a KafkaTopic Kubernetes resource.

Prerequisites
Procedure

  1. Prepare a file containing the KafkaTopic to be created

    An example KafkaTopic
    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaTopic
metadata:
  name: orders
  labels:
    strimzi.io/cluster: my-cluster
spec:
  partitions: 10
  replicas: 2
    Note
    		 It is recommended that the topic name given is a valid Kubernetes resource name, as it is then not necessary to set the KafkaTopic.spec.topicName property. The KafkaTopic.spec.topicName cannot be changed after creation.
    Note
    		 The KafkaTopic.spec.partitions cannot be decreased.

  2. Create the KafkaTopic resource in Kubernetes.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Additional resources

5.3. Changing a topic

This procedure describes how to change the configuration of an existing Kafka topic by using a KafkaTopic resource.

Before you start

It is important that you consider the following before making your changes:

  • Kafka does not support making the following changes through the KafkaTopic resource:

    • Changing topic names using spec.topicName

    • Decreasing partition size using spec.partitions

  • You cannot change the number of replicas initially specified using spec.replicas.

  • Increasing spec.partitions for topics with keys will change how records are partitioned, which can be particularly problematic when the topic uses semantic partitioning.

Prerequisites
Procedure

  1. Prepare a file containing the desired KafkaTopic

    For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaTopic
metadata:
  name: orders
  labels:
    strimzi.io/cluster: my-cluster
spec:
  partitions: 16
  replicas: 2
    Tip
    		 You can get the current version of the resource using kubectl get kafkatopic orders -o yaml.

  2. Apply the changes to the deployment of your KafkaTopic resource.

    kubectl apply -f your-file
Additional resources

5.4. Deleting a topic

This procedure describes how to delete a Kafka topic using a KafkaTopic Kubernetes resource.

Prerequisites

  • A running Kafka cluster.

  • A running Topic Operator (typically deployed with the Entity Operator).

  • An existing KafkaTopic to be deleted.

  • delete.topic.enable=true (default)

Note
 The delete.topic.enable property must be set to true in Kafka.spec.kafka.config. Otherwise, the steps outlined here will delete the KafkaTopic resource, but the Kafka topic and its data will remain. After reconciliation by the Topic Operator, the custom resource is then recreated.
Procedure

  • Delete the KafkaTopic resource in Kubernetes.

    This can be done using kubectl delete:

    kubectl delete kafkatopic your-topic-name
Additional resources

6. Using the User Operator

When you create, modify or delete a user using the KafkaUser resource, the User Operator ensures those changes are reflected in the Kafka cluster.

6.1. Kafka user resource

The KafkaUser resource is used to configure the authentication mechanism, authorization mechanism, and access rights for a user.

The full schema for KafkaUser is described in KafkaUser schema reference.

6.1.1. User authentication

Authentication is configured using the authentication property in KafkaUser.spec. The authentication mechanism enabled for the user is specified using the type field.

Supported authentication mechanisms:

  • TLS client authentication

  • SCRAM-SHA-512 authentication

When no authentication mechanism is specified, the User Operator does not create the user or its credentials.

Additional resources
TLS Client Authentication

To use TLS client authentication, you set the type field to tls.

An example KafkaUser with TLS client authentication enabled
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster
spec:
  authentication:
    type: tls
  # ...

When the user is created by the User Operator, it creates a new Secret with the same name as the KafkaUser resource. The Secret contains a private and public key for TLS client authentication. The public key is contained in a user certificate, which is signed by the client Certificate Authority (CA).

All keys are in X.509 format.

Secrets provide private keys and certificates in PEM and PKCS #12 formats. For more information on securing Kafka communication with Secrets, see Security.

An example Secret with user credentials
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 that contains the public key of the user
  user.key: # Private key of the user
  user.p12: # PKCS #12 archive file for storing certificates and keys
  user.password: # Password for protecting the PKCS #12 archive file
SCRAM-SHA-512 Authentication

To use SCRAM-SHA-512 authentication mechanism, you set the type field to scram-sha-512.

An example KafkaUser with SCRAM-SHA-512 authentication enabled
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster
spec:
  authentication:
    type: scram-sha-512
  # ...

When the user is created by the User Operator, it creates a new secret with the same name as the KafkaUser resource. The secret contains the generated password in the password key, which is encoded with base64. In order to use the password, it must be decoded.

An example Secret with user credentials
apiVersion: v1
kind: Secret
metadata:
  name: my-user
  labels:
    strimzi.io/kind: KafkaUser
    strimzi.io/cluster: my-cluster
type: Opaque
data:
  password: Z2VuZXJhdGVkcGFzc3dvcmQ= # Generated password

Decoding the generated password:

echo "Z2VuZXJhdGVkcGFzc3dvcmQ=" | base64 --decode

6.1.2. User authorization

User authorization is configured using the authorization property in KafkaUser.spec. The authorization type enabled for a user is specified using the type field.

If no authorization is specified, the User Operator does not provision any access rights for the user.

To use simple authorization, you set the type property to simple in KafkaUser.spec. Simple authorization uses the default Kafka authorization plugin, SimpleAclAuthorizer.

Alternatively, if you are using OAuth 2.0 token based authentication, you can also configure OAuth 2.0 authorization.

ACL rules

SimpleAclAuthorizer uses ACL rules to manage access to Kafka brokers.

ACL rules grant access rights to the user, which you specify in the acls property.

An AclRule is specified as a set of properties:

resource

The resource property specifies the resource that the rule applies to.

Simple authorization supports four resource types, which are specified in the type property:

  • Topics (topic)

  • Consumer Groups (group)

  • Clusters (cluster)

  • Transactional IDs (transactionalId)

For Topic, Group, and Transactional ID resources you can specify the name of the resource the rule applies to in the name property.

Cluster type resources have no name.

A name is specified as a literal or a prefix using the patternType property.

  • Literal names are taken exactly as they are specified in the name field.

  • Prefix names use the value from the name as a prefix, and will apply the rule to all resources with names starting with the value.

type

The type property specifies the type of ACL rule, allow or deny.

The type field is optional. If type is unspecified, the ACL rule is treated as an allow rule.

operation

The operation specifies the operation to allow or deny.

The following operations are supported:

  • Read

  • Write

  • Delete

  • Alter

  • Describe

  • All

  • IdempotentWrite

  • ClusterAction

  • Create

  • AlterConfigs

  • DescribeConfigs

Only certain operations work with each resource.

For more details about SimpleAclAuthorizer, ACLs and supported combinations of resources and operations, see Authorization and ACLs.

host

The host property specifies a remote host from which the rule is allowed or denied.

Use an asterisk (*) to allow or deny the operation from all hosts. The host field is optional. If host is unspecified, the * value is used by default.

For more information about the AclRule object, see AclRule schema reference.

An example KafkaUser with authorization
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster
spec:
  # ...
  authorization:
    type: simple
    acls:
      - 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: prefix
        operation: Read
Super user access to Kafka brokers

If a user is added to a list of super users in a Kafka broker configuration, the user is allowed unlimited access to the cluster regardless of any authorization constraints defined in ACLs.

For more information on configuring super users, see authentication and authorization of Kafka brokers.

6.1.3. User quotas

You can configure the spec for the KafkaUser resource to enforce quotas so that a user does not exceed access to Kafka brokers based on a byte threshold or a time limit of CPU utilization.

An example KafkaUser with user quotas
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster
spec:
  # ...
  quotas:
    producerByteRate: 1048576 (1)
    consumerByteRate: 2097152 (2)
    requestPercentage: 55 (3)

  1. Byte-per-second quota on the amount of data the user can push to a Kafka broker

  2. Byte-per-second quota on the amount of data the user can fetch from a Kafka broker

  3. CPU utilization limit as a percentage of time for a client group

For more information on these properties, see the KafkaUserQuotas schema reference

6.2. Configuring a Kafka user

Use the properties of the KafkaUser resource to configure a Kafka user.

You can use kubectl apply to create or modify users, and kubectl delete to delete existing users.

For example:

  • kubectl apply -f <user-config-file>

  • kubectl delete KafkaUser <user-name>

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

This procedure shows how a user is created with TLS authentication. You can also create a user with SCRAM-SHA authentication.

The authentication required depends on the type of authentication configured for the Kafka broker listener.

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.
Prerequisites

If you are using SCRAM-SHA authentication, you need a running Kafka cluster configured with a Kafka broker listener using SCRAM-SHA authentication.

Procedure

  1. Prepare a YAML file containing the KafkaUser to be created.

    An example KafkaUser
    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster
spec:
  authentication: (1)
    type: tls
  authorization:
    type: simple (2)
    acls:
      - 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. User authentication mechanism, defined as mutual tls or scram-sha-512.

    2. Simple authorization, which requires an accompanying list of ACL rules.

  2. Create the KafkaUser resource in Kubernetes.

    kubectl apply -f <user-config-file>

  3. Use the credentials from the my-user secret in your client application.

7. Kafka Bridge

This chapter provides an overview of the Strimzi Kafka Bridge and helps you get started using its REST API to interact with Strimzi. To try out the Kafka Bridge in your local environment, see the Kafka Bridge quickstart later in this chapter.

7.1. Kafka Bridge overview

You can use the Kafka Bridge as an interface to make specific types of request to the Kafka cluster.

7.1.1. Kafka Bridge interface

Strimzi Kafka Bridge provides a RESTful interface that allows HTTP-based clients to interact with a Kafka cluster.  Kafka Bridge offers the advantages of a web API connection to Strimzi, without the need for client applications to interpret the Kafka protocol.

The API has two main resources — consumers and topics — that are exposed and made accessible through endpoints to interact with consumers and producers in your Kafka cluster. The resources relate only to the Kafka Bridge, not the consumers and producers connected directly to Kafka.

HTTP requests

The Kafka Bridge supports HTTP requests to a Kafka cluster, with methods to:

  • Send messages to a topic.

  • Retrieve messages from topics.

  • Create and delete consumers.

  • Subscribe consumers to topics, so that they start receiving messages from those topics.

  • Retrieve a list of topics that a consumer is subscribed to.

  • Unsubscribe consumers from topics.

  • Assign partitions to consumers.

  • Commit a list of consumer offsets.

  • Seek on a partition, so that a consumer starts receiving messages from the first or last offset position, or a given offset position.

The methods provide JSON responses and HTTP response code error handling. Messages can be sent in JSON or binary formats.

Clients can produce and consume messages without the requirement to use the native Kafka protocol.

Additional resources

  • To view the API documentation, including example requests and responses, see the Kafka Bridge API reference on the Strimzi website.

7.1.2. Supported clients for the Kafka Bridge

You can use the Kafka Bridge to integrate both internal and external HTTP client applications with your Kafka cluster.

Internal clients

Internal clients are container-based HTTP clients running in the same Kubernetes cluster as the Kafka Bridge itself. Internal clients can access the Kafka Bridge on the host and port defined in the KafkaBridge custom resource.

External clients

External clients are HTTP clients running outside the Kubernetes cluster in which the Kafka Bridge is deployed and running. External clients can access the Kafka Bridge through an OpenShift Route, a loadbalancer service, or using an Ingress.

HTTP internal and external client integration

Kafka Bridge

7.1.3. Securing the Kafka Bridge

Strimzi does not currently provide any encryption, authentication, or authorization for the Kafka Bridge. This means that requests sent from external clients to the Kafka Bridge are:

  • Not encrypted, and must use HTTP rather than HTTPS

  • Sent without authentication

However, you can secure the Kafka Bridge using other methods, such as:

  • Kubernetes Network Policies that define which pods can access the Kafka Bridge.

  • Reverse proxies with authentication or authorization, for example, OAuth2 proxies.

  • API Gateways.

  • Ingress or OpenShift Routes with TLS termination.

The Kafka Bridge supports TLS encryption and TLS and SASL authentication when connecting to the Kafka Brokers. Within your Kubernetes cluster, you can configure:

  • TLS or SASL-based authentication between the Kafka Bridge and your Kafka cluster

  • A TLS-encrypted connection between the Kafka Bridge and your Kafka cluster.

For more information, see Authentication support in Kafka Bridge.

You can use ACLs in Kafka brokers to restrict the topics that can be consumed and produced using the Kafka Bridge.

7.1.4. Accessing the Kafka Bridge outside of Kubernetes

After deployment, the Strimzi Kafka Bridge can only be accessed by applications running in the same Kubernetes cluster. These applications use the kafka-bridge-name-bridge-service Service to access the API.

If you want to make the Kafka Bridge accessible to applications running outside of the Kubernetes cluster, you can expose it manually by using one of the following features:

  • Services of types LoadBalancer or NodePort

  • Ingress resources

  • OpenShift Routes

If you decide to create Services, use the following labels in the selector to configure the pods to which the service will route the traffic:

  # ...
  selector:
    strimzi.io/cluster: kafka-bridge-name (1)
    strimzi.io/kind: KafkaBridge
  #...

  1. Name of the Kafka Bridge custom resource in your Kubernetes cluster.

7.1.5. Requests to the Kafka Bridge

Specify data formats and HTTP headers to ensure valid requests are submitted to the Kafka Bridge.

Content Type headers

API request and response bodies are always encoded as JSON.

  • When performing consumer operations, POST requests must provide the following Content-Type header if there is a non-empty body:

    Content-Type: application/vnd.kafka.v2+json

  • When performing producer operations, POST requests must provide Content-Type headers specifying the desired embedded data format, either json or binary, as shown in the following table.

    Embedded data format Content-Type header

    JSON

    Content-Type: application/vnd.kafka.json.v2+json

    Binary

    Content-Type: application/vnd.kafka.binary.v2+json

You set the embedded data format when creating a consumer using the consumers/groupid endpoint—​for more information, see the next section.

The Content-Type must not be set if the POST request has an empty body. An empty body can be used to create a consumer with the default values.

Embedded data format

The embedded data format is the format of the Kafka messages that are transmitted, over HTTP, from a producer to a consumer using the Kafka Bridge. Two embedded data formats are supported: JSON and binary.

When creating a consumer using the /consumers/groupid endpoint, the POST request body must specify an embedded data format of either JSON or binary. This is specified in the format field, for example:

{
  "name": "my-consumer",
  "format": "binary", (1)
...
}

  1. A binary embedded data format.

The embedded data format specified when creating a consumer must match the data format of the Kafka messages it will consume.

If you choose to specify a binary embedded data format, subsequent producer requests must provide the binary data in the request body as Base64-encoded strings. For example, when sending messages using the /topics/topicname endpoint, records.value must be encoded in Base64:

{
  "records": [
    {
      "key": "my-key",
      "value": "ZWR3YXJkdGhldGhyZWVsZWdnZWRjYXQ="
    },
  ]
}

Producer requests must also provide a Content-Type header that corresponds to the embedded data format, for example, Content-Type: application/vnd.kafka.binary.v2+json.

Accept headers

After creating a consumer, all subsequent GET requests must provide an Accept header in the following format:

Accept: application/vnd.kafka.embedded-data-format.v2+json

The embedded-data-format is either json or binary.

For example, when retrieving records for a subscribed consumer using an embedded data format of JSON, include this Accept header:

Accept: application/vnd.kafka.json.v2+json

7.1.6. Kafka Bridge API resources

For the full list of REST API endpoints and descriptions, including example requests and responses, see the Kafka Bridge API reference on the Strimzi website.

7.1.7. Kafka Bridge deployment

You deploy the Kafka Bridge into your Kubernetes cluster by using the Cluster Operator.

After the Kafka Bridge is deployed, the Cluster Operator creates Kafka Bridge objects in your Kubernetes cluster. Objects include the deployment, service, and pod, each named after the name given in the custom resource for the Kafka Bridge.

Additional resources

7.2. Kafka Bridge quickstart

Use this quickstart to try out the Strimzi Kafka Bridge in your local development environment. You will learn how to:

  • Deploy the Kafka Bridge to your Kubernetes cluster

  • Expose the Kafka Bridge service to your local machine by using port-forwarding

  • Produce messages to topics and partitions in your Kafka cluster

  • Create a Kafka Bridge consumer

  • Perform basic consumer operations, such as subscribing the consumer to topics and retrieving the messages that you produced

In this quickstart, HTTP requests are formatted as curl commands that you can copy and paste to your terminal. Access to a Kubernetes cluster is required; to run and manage a local Kubernetes cluster, use a tool such as Minikube, CodeReady Containers, or MiniShift.

Ensure you have the prerequisites and then follow the tasks in the order provided in this chapter.

About data formats

In this quickstart, you will produce and consume messages in JSON format, not binary. For more information on the data formats and HTTP headers used in the example requests, see Requests to the Kafka Bridge.

Prerequisites for the quickstart

  • Cluster administrator access to a local or remote Kubernetes cluster.

  • Strimzi is installed.

  • A running Kafka cluster, deployed by the Cluster Operator, in a Kubernetes namespace.

  • The Entity Operator is deployed and running as part of the Kafka cluster.

7.2.1. Deploying the Kafka Bridge to your Kubernetes cluster

Strimzi includes a YAML example that specifies the configuration of the Strimzi Kafka Bridge. Make some minimal changes to this file and then deploy an instance of the Kafka Bridge to your Kubernetes cluster.

Procedure

  1. Edit the examples/kafka-bridge/kafka-bridge.yaml file.

    apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
  name: quickstart (1)
spec:
  replicas: 1
  bootstrapServers: <cluster-name>-kafka-bootstrap:9092 (2)
  http:
    port: 8080

    1. When the Kafka Bridge is deployed, -bridge is appended to the name of the deployment and other related resources. In this example, the Kafka Bridge deployment is named quickstart-bridge and the accompanying Kafka Bridge service is named quickstart-bridge-service.

    2. In the bootstrapServers property, enter the name of the Kafka cluster as the <cluster-name>.

  2. Deploy the Kafka Bridge to your Kubernetes cluster:

    kubectl apply -f examples/kafka-bridge/kafka-bridge.yaml

    A quickstart-bridge deployment, service, and other related resources are created in your Kubernetes cluster.

  3. Verify that the Kafka Bridge was successfully deployed:

    kubectl get deployments
    NAME                             READY   UP-TO-DATE   AVAILABLE   AGE
quickstart-bridge                  1/1     1            1          34m
my-cluster-connect                 1/1     1            1          24h
my-cluster-entity-operator         1/1     1            1          24h
#...
What to do next

After deploying the Kafka Bridge to your Kubernetes cluster, expose the Kafka Bridge service to your local machine.

Additional resources

7.2.2. Exposing the Kafka Bridge service to your local machine

Next, use port forwarding to expose the Strimzi Kafka Bridge service to your local machine on http://localhost:8080.

Note
 Port forwarding is only suitable for development and testing purposes.
Procedure

  1. List the names of the pods in your Kubernetes cluster:

    kubectl get pods -o name

pod/kafka-consumer
# ...
pod/quickstart-bridge-589d78784d-9jcnr
pod/strimzi-cluster-operator-76bcf9bc76-8dnfm

  2. Connect to the quickstart-bridge pod on port 8080:

    kubectl port-forward pod/quickstart-bridge-589d78784d-9jcnr 8080:8080 &
    Note
    		 If port 8080 on your local machine is already in use, use an alternative HTTP port, such as 8008.

API requests are now forwarded from port 8080 on your local machine to port 8080 in the Kafka Bridge pod.

7.2.3. Producing messages to topics and partitions

Next, produce messages to topics in JSON format by using the topics endpoint. You can specify destination partitions for messages in the request body, as shown here. The partitions endpoint provides an alternative method for specifying a single destination partition for all messages as a path parameter.

Procedure

  1. In a text editor, create a YAML definition for a Kafka topic with three partitions.

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaTopic
metadata:
  name: bridge-quickstart-topic
  labels:
    strimzi.io/cluster: <kafka-cluster-name> (1)
spec:
  partitions: 3 (2)
  replicas: 1
  config:
    retention.ms: 7200000
    segment.bytes: 1073741824

    1. The name of the Kafka cluster in which the Kafka Bridge is deployed.

    2. The number of partitions for the topic.

  2. Save the file to the examples/topic directory as bridge-quickstart-topic.yaml.

  3. Create the topic in your Kubernetes cluster:

    kubectl apply -f examples/topic/bridge-quickstart-topic.yaml

  4. Using the Kafka Bridge, produce three messages to the topic you created:

    curl -X POST \
  http://localhost:8080/topics/bridge-quickstart-topic \
  -H 'content-type: application/vnd.kafka.json.v2+json' \
  -d '{
    "records": [
        {
            "key": "my-key",
            "value": "sales-lead-0001"
        },
        {
            "value": "sales-lead-0002",
            "partition": 2
        },
        {
            "value": "sales-lead-0003"
        }
    ]
}'

    • sales-lead-0001 is sent to a partition based on the hash of the key.

    • sales-lead-0002 is sent directly to partition 2.

    • sales-lead-0003 is sent to a partition in the bridge-quickstart-topic topic using a round-robin method.

  5. If the request is successful, the Kafka Bridge returns an offsets array, along with a 200 code and a content-type header of application/vnd.kafka.v2+json. For each message, the offsets array describes:

    • The partition that the message was sent to

    • The current message offset of the partition

      Example response
      #...
{
  "offsets":[
    {
      "partition":0,
      "offset":0
    },
    {
      "partition":2,
      "offset":0
    },
    {
      "partition":0,
      "offset":1
    }
  ]
}
What to do next

After producing messages to topics and partitions, create a Kafka Bridge consumer.

Additional resources

7.2.4. Creating a Kafka Bridge consumer

Before you can perform any consumer operations in the Kafka cluster, you must first create a consumer by using the consumers endpoint. The consumer is referred to as a Kafka Bridge consumer.

Procedure

  1. Create a Kafka Bridge consumer in a new consumer group named bridge-quickstart-consumer-group:

    curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group \
  -H 'content-type: application/vnd.kafka.v2+json' \
  -d '{
    "name": "bridge-quickstart-consumer",
    "auto.offset.reset": "earliest",
    "format": "json",
    "enable.auto.commit": false,
    "fetch.min.bytes": 512,
    "consumer.request.timeout.ms": 30000
  }'

    • The consumer is named bridge-quickstart-consumer and the embedded data format is set as json.

    • Some basic configuration settings are defined.

    • The consumer will not commit offsets to the log automatically because the enable.auto.commit setting is false. You will commit the offsets manually later in this quickstart.

      If the request is successful, the Kafka Bridge returns the consumer ID (instance_id) and base URL (base_uri) in the response body, along with a 200 code.

      Example response
      #...
{
  "instance_id": "bridge-quickstart-consumer",
  "base_uri":"http://<bridge-name>-bridge-service:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer"
}

  2. Copy the base URL (base_uri) to use in the other consumer operations in this quickstart.

What to do next

Now that you have created a Kafka Bridge consumer, you can subscribe it to topics.

Additional resources

7.2.5. Subscribing a Kafka Bridge consumer to topics

After you have created a Kafka Bridge consumer, subscribe it to one or more topics by using the subscription endpoint. Once subscribed, the consumer starts receiving all messages that are produced to the topic.

Procedure

  • Subscribe the consumer to the bridge-quickstart-topic topic that you created earlier, in Producing messages to topics and partitions:

    curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/subscription \
  -H 'content-type: application/vnd.kafka.v2+json' \
  -d '{
    "topics": [
        "bridge-quickstart-topic"
    ]
}'

    The topics array can contain a single topic (as shown here) or multiple topics. If you want to subscribe the consumer to multiple topics that match a regular expression, you can use the topic_pattern string instead of the topics array.

    If the request is successful, the Kafka Bridge returns a 204 (No Content) code only.

What to do next

After subscribing a Kafka Bridge consumer to topics, you can retrieve messages from the consumer.

Additional resources

7.2.6. Retrieving the latest messages from a Kafka Bridge consumer

Next, retrieve the latest messages from the Kafka Bridge consumer by requesting data from the records endpoint. In production, HTTP clients can call this endpoint repeatedly (in a loop).

Procedure

  1. Produce additional messages to the Kafka Bridge consumer, as described in Producing messages to topics and partitions.

  2. Submit a GET request to the records endpoint:

    curl -X GET http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/records \
  -H 'accept: application/vnd.kafka.json.v2+json'

    After creating and subscribing to a Kafka Bridge consumer, a first GET request will return an empty response because the poll operation starts a rebalancing process to assign partitions.

  3. Repeat step two to retrieve messages from the Kafka Bridge consumer.

    The Kafka Bridge returns an array of messages — describing the topic name, key, value, partition, and offset — in the response body, along with a 200 code. Messages are retrieved from the latest offset by default.

    HTTP/1.1 200 OK
content-type: application/vnd.kafka.json.v2+json
#...
[
  {
    "topic":"bridge-quickstart-topic",
    "key":"my-key",
    "value":"sales-lead-0001",
    "partition":0,
    "offset":0
  },
  {
    "topic":"bridge-quickstart-topic",
    "key":null,
    "value":"sales-lead-0003",
    "partition":0,
    "offset":1
  },
#...
    Note
    		 If an empty response is returned, produce more records to the consumer as described in Producing messages to topics and partitions, and then try retrieving messages again.
What to do next

After retrieving messages from a Kafka Bridge consumer, try committing offsets to the log.

Additional resources

7.2.7. Commiting offsets to the log

Next, use the offsets endpoint to manually commit offsets to the log for all messages received by the Kafka Bridge consumer. This is required because the Kafka Bridge consumer that you created earlier, in Creating a Kafka Bridge consumer, was configured with the enable.auto.commit setting as false.

Procedure

  • Commit offsets to the log for the bridge-quickstart-consumer:

    curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/offsets

    Because no request body is submitted, offsets are committed for all the records that have been received by the consumer. Alternatively, the request body can contain an array (OffsetCommitSeekList) that specifies the topics and partitions that you want to commit offsets for.

    If the request is successful, the Kafka Bridge returns a 204 code only.

What to do next

After committing offsets to the log, try out the endpoints for seeking to offsets.

Additional resources

7.2.8. Seeking to offsets for a partition

Next, use the positions endpoints to configure the Kafka Bridge consumer to retrieve messages for a partition from a specific offset, and then from the latest offset. This is referred to in Apache Kafka as a seek operation.

Procedure

  1. Seek to a specific offset for partition 0 of the quickstart-bridge-topic topic:

    curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/positions \
  -H 'content-type: application/vnd.kafka.v2+json' \
  -d '{
    "offsets": [
        {
            "topic": "bridge-quickstart-topic",
            "partition": 0,
            "offset": 2
        }
    ]
}'

    If the request is successful, the Kafka Bridge returns a 204 code only.

  2. Submit a GET request to the records endpoint:

    curl -X GET http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/records \
  -H 'accept: application/vnd.kafka.json.v2+json'

    The Kafka Bridge returns messages from the offset that you seeked to.

  3. Restore the default message retrieval behavior by seeking to the last offset for the same partition. This time, use the positions/end endpoint.

    curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/positions/end \
  -H 'content-type: application/vnd.kafka.v2+json' \
  -d '{
    "partitions": [
        {
            "topic": "bridge-quickstart-topic",
            "partition": 0
        }
    ]
}'

    If the request is successful, the Kafka Bridge returns another 204 code.

Note
 You can also use the positions/beginning endpoint to seek to the first offset for one or more partitions.
What to do next

In this quickstart, you have used the Strimzi Kafka Bridge to perform several common operations on a Kafka cluster. You can now delete the Kafka Bridge consumer that you created earlier.

Additional resources

7.2.9. Deleting a Kafka Bridge consumer

Finally, delete the Kafa Bridge consumer that you used throughout this quickstart.

Procedure

  • Delete the Kafka Bridge consumer by sending a DELETE request to the instances endpoint.

    curl -X DELETE http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer

    If the request is successful, the Kafka Bridge returns a 204 code only.

Additional resources

8. Cruise Control

You can deploy Cruise Control and access a subset of its features through Strimzi custom resources.

Example YAML files for deploying Cruise Control and setting optimization goals are provided in examples/cruise-control/.

8.1. Why use Cruise Control?

Cruise Control reduces the time and effort involved in running an efficient and balanced Kafka cluster.

A typical cluster can become unevenly loaded over time. Partitions that handle large amounts of message traffic might be unevenly distributed across the available brokers. To rebalance the cluster, administrators must monitor the load on brokers and manually reassign busy partitions to brokers with spare capacity.

Cruise Control automates the cluster rebalancing process. It constructs a workload model of resource utilization for the cluster—​based on CPU, disk, and network load—​and generates optimization proposals (that you can approve or reject) for more balanced partition assignments. A set of configurable optimization goals is used to calculate these proposals.

When you approve an optimization proposal, Cruise Control applies it to your Kafka cluster. When the cluster rebalancing operation is complete, the broker pods are used more effectively and the Kafka cluster is more evenly balanced.

Additional resources

8.2. Optimization goals overview

To rebalance a Kafka cluster, Cruise Control uses optimization goals to generate optimization proposals, which you can approve or reject.

Optimization goals are constraints on workload redistribution and resource utilization across a Kafka cluster. With a few exceptions, Strimzi supports all the optimization goals developed in the Cruise Control project. These are as follows, in descending priority order:

  1. Rack-awareness

  2. Replica capacity

  3. Capacity: Disk capacity, Network inbound capacity, Network outbound capacity

  4. Replica distribution

  5. Potential network output

  6. Resource distribution: Disk utilization distribution, Network inbound utilization distribution, Network outbound utilization distribution

  7. Leader bytes-in rate distribution

  8. Topic replica distribution

  9. Leader replica distribution

  10. Preferred leader election

For more information on each optimization goal, see Goals in the Cruise Control Wiki.

Note
 CPU goals, intra-broker disk goals, "Write your own" goals, and Kafka assigner goals are not yet supported.

As described in the following sections, you can customize the supported optimization goals by reordering them in terms of priority, and disabling goals to exclude from optimization proposal calculations.

Goals configuration in Strimzi

You configure optimization goals in the Kafka and KafkaRebalance custom resources. Cruise Control has configurations for hard optimization goals that must be satisfied, as well as master, default, and user-provided optimization goals.

The following sections describe each goal configuration in more detail.

Hard goals and soft goals

Hard goals are goals that must be satisfied in optimization proposals. Goals that are not hard goals are known as soft goals, and might not be satisfied by optimization proposals. You can think of soft goals as best effort goals: they do not need to be satisfied in optimization proposals, but are included in optimization calculations. An optimization proposal that violates one or more soft goals, but satisfies all hard goals, is valid.

Cruise control will calculate optimization proposals that satisfy all the hard goals and as many soft goals as possible (in their priority order). An optimization proposal that does not satisfy all the hard goals is rejected by Cruise Control and not sent to the user for approval.

Note
 For example, you might have a soft goal to distribute a topic’s replicas evenly across the cluster (the topic replica distribution goal). Cruise Control will ignore this goal if doing so enables all the configured hard goals to be met.

In Cruise Control, six of the master optimization goals are preset as hard goals:

RackAwareGoal; ReplicaCapacityGoal; DiskCapacityGoal; NetworkInboundCapacityGoal; NetworkOutboundCapacityGoal; CpuCapacityGoal

Hard goals are controlled in the Cruise Control deployment configuration, by editing the hard.goals property in Kafka.spec.cruiseControl.config.

  • To inherit the six preset hard goals from Cruise Control, do not specify the hard.goals property in Kafka.spec.cruiseControl.config

  • To change the preset hard goals, specify the desired goals in the hard.goals configuration option.

Increasing the number of hard goals will reduce the likelihood of Cruise Control generating valid optimization proposals.

Note
 If skipHardGoalCheck: true is specified in the KafkaRebalance custom resource, Cruise Control does not check that the list of user-provided optimization goals (goals) contains all the configured hard goals (hard.goals). Therefore, if some, but not all, of the user-provided optimization goals are in the hard.goals list, Cruise Control will still treat them as hard goals even if skipHardGoalCheck: true is specified.
Master optimization goals

The master optimization goals are available to all users. Goals that are not listed in the master optimization goals are not available to use for Cruise Control operations.

Unless you change the Cruise Control deployment configuration, Strimzi will inherit the following master optimization goals from Cruise Control, in descending priority order:

RackAwareGoal; ReplicaCapacityGoal; DiskCapacityGoal; NetworkInboundCapacityGoal; NetworkOutboundCapacityGoal; CpuCapacityGoal; ReplicaDistributionGoal; PotentialNwOutGoal; DiskUsageDistributionGoal; NetworkInboundUsageDistributionGoal; NetworkOutboundUsageDistributionGoal; CpuUsageDistributionGoal; TopicReplicaDistributionGoal; LeaderReplicaDistributionGoal; LeaderBytesInDistributionGoal; PreferredLeaderElectionGoal

Six of these goals are preset as hard goals.

To reduce complexity, we recommend that you use the inherited master optimization goals, unless you need to completely exclude one or more goals from use in KafkaRebalance resources. The priority order of the master optimization goals can be modified, if desired, in the default optimization goals.

Master optimization goals are controlled, if needed, in the Cruise Control deployment configuration: Kafka.spec.cruiseControl.config.goals

  • To accept the inherited master optimization goals, do not specify the goals property in Kafka.spec.cruiseControl.config.

  • If you need to modify the inherited master optimization goals, specify a list of goals in the goals configuration option.

Default optimization goals

Cruise Control uses the default optimization goals to generate the cached optimization proposal. For more information about the cached optimization proposal, see Optimization proposals overview.

You can override the default optimization goals by setting user-provided optimization goals in a KafkaRebalance custom resource.

Unless you change the Cruise Control deployment configuration, the default optimization goals are the same as the master optimization goals.

  • To use the master optimization goals as the default goals, do not specify the default.goals property in Kafka.spec.cruiseControl.config.

  • To modify the default optimization goals, edit the default.goals property in Kafka.spec.cruiseControl.config. You must use a subset of the master optimization goals.

Example Kafka configuration for default optimization goals
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    topicOperator: {}
    userOperator: {}
  cruiseControl: {}
    capacity:
      networkIn: 10000KB/s
      networkOut: 10000KB/s
    config:
      default.goals: >
         com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal,
         com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal,
         com.linkedin.kafka.cruisecontrol.analyzer.goals.DiskCapacityGoal
      # ...
Note
 For an example of the complete deployment configuration, see Deploying Cruise Control.

If no default optimization goals are specified, the cached proposal is generated using the master optimization goals.

User-provided optimization goals

User-provided optimization goals narrow down the default goals. You can set them, if required, in the KafkaRebalance custom resource for a particular optimization proposal: KafkaRebalance.spec.goals

They are useful for generating an optimization proposal that addresses a particular scenario. For example, you might want to optimize leader replica distribution across the Kafka cluster without considering goals for disk capacity or disk utilization. So, you create a KafkaRebalance custom resource containing a user-provided goal for leader replica distribution only.

User-provided optimization goals must:

  • Include all configured hard goals, or an error occurs

  • Be a subset of the master optimization goals

To ignore the configured hard goals in an optimization proposal, add the skipHardGoalCheck: true option to the KafkaRebalance custom resource.

Additional resources

8.3. Optimization proposals overview

An optimization proposal is a summary of proposed changes that would produce a more balanced Kafka cluster, with partition workloads distributed more evenly among the brokers. Each optimization proposal is based on the set of optimization goals that were used to generate it.

Use the summary (shown in the status of the KafkaRebalance resource) to decide whether to:

  • Approve the optimization proposal. This instructs Cruise Control to apply the proposal to the Kafka cluster and start a cluster rebalance operation.

  • Reject the optimization proposal. You can change the optimization goals and then generate another proposal.

Note
 All optimization proposals are dry runs: you cannot approve a cluster rebalance without first generating an optimization proposal. There is no limit to the number of optimization proposals that can be generated.

Cached optimization proposal

Cruise Control maintains a cached optimization proposal based on the configured default optimization goals. Generated from the workload model, the cached optimization proposal is updated every 15 minutes to reflect the current state of the Kafka cluster. If you generate an optimization proposal using the default optimization goals, Cruise Control returns the most recent cached proposal.

To change the cached optimization proposal refresh interval, edit the proposal.expiration.ms setting in the Cruise Control deployment configuration. Consider a shorter interval for fast changing clusters, although this increases the load on the Cruise Control server.

Contents of optimization proposals

The following table explains the properties contained in an optimization proposal:

JSON property Description

numIntraBrokerReplicaMovements

The total number of partition replicas that will be transferred between the disks of the cluster’s brokers.

Performance impact during rebalance operation: Relatively high, but lower than numReplicaMovements.

excludedBrokersForLeadership

Not yet supported. An empty list is returned.

numReplicaMovements

The number of partition replicas that will be moved between separate brokers.

Performance impact during rebalance operation: Relatively high.

onDemandBalancednessScoreBefore, onDemandBalancednessScoreAfter

A measurement of the overall balancedness of a Kafka Cluster, before and after the optimization proposal was generated.

The calculation is 100 minus the sum of the BalancednessScore of each violated soft goal. Cruise Control assigns a BalancednessScore to every optimization goal based on several factors, including priority (the goal’s position in the list of default.goals or user-provided goals).

The Before score is based on the current configuration of the Kafka cluster. The After score is based on the generated optimization proposal.

intraBrokerDataToMoveMB

The sum of the size of each partition replica that will be moved between disks on the same broker (see also numIntraBrokerReplicaMovements).

Performance impact during rebalance operation: Variable. The larger the number, the longer the cluster rebalance will take to complete. Moving a large amount of data between disks on the same broker has less impact than between separate brokers (see dataToMoveMB).

recentWindows

The number of metrics windows upon which the optimization proposal is based.

dataToMoveMB

The sum of the size of each partition replica that will be moved to a separate broker (see also numReplicaMovements).

Performance impact during rebalance operation: Variable. The larger the number, the longer the cluster rebalance will take to complete.

monitoredPartitionsPercentage

The percentage of partitions in the Kafka cluster covered by the optimization proposal. Affected by the number of excludedTopics.

excludedTopics

Not yet supported. An empty list is returned.

numLeaderMovements

The number of partitions whose leaders will be switched to different replicas. This involves a change to ZooKeeper configuration.

Performance impact during rebalance operation: Relatively low.

excludedBrokersForReplicaMove

Not yet supported. An empty list is returned.
Additional resources

8.4. Deploying Cruise Control

Cruise Control is configured using the cruiseControl property in the Kafka resource. Once configured, you can deploy a Cruise Control instance to your Strimzi cluster by creating or updating the Kafka resource.

For an overview of the Kafka resource, see the sample Kafka YAML configuration.

You can configure cruiseControl properties as part of a deployment or redeployment of a Kafka cluster. Deploy one instance of Cruise Control per Kafka cluster.

This procedure uses the following example file provided with Strimzi:

  • examples/cruise-control/cruise-control-topic.yaml

This creates a strimzi.cruisecontrol.metrics topic in your Kafka cluster, which collects information from the Cruise Control Metrics Reporter for use in generating rebalance proposals. You do not need to interact with this topic after Cruise Control is deployed.

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Create the Cruise Control metrics topic in your Kafka cluster:

    kubectl apply -f examples/cruise-control/cruise-control-topic.yaml

  2. Edit the cruiseControl property of 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:
  # ...
  cruiseControl:
    capacity: (1)
      networkIn: 10000KB/s
      networkOut: 10000KB/s
      # ...
    config: (2)
      default.goals: >
         com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal,
         com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal
         # ...
      goals: {}
      cpu.balance.threshold: 1.1
      metadata.max.age.ms: 300000
      send.buffer.bytes: 131072
      # ...
    resources: (3)
      requests:
        cpu: 200m
        memory: 64Mi
      limits:
        cpu: 500m
        memory: 128Mi
    logging: (4)
        type: inline
        loggers:
          cruisecontrol.root.logger: "INFO"
    template: (5)
      pod:
        metadata:
          labels:
            label1: value1
        securityContext:
          runAsUser: 1000001
          fsGroup: 0
        terminationGracePeriodSeconds: 120
    readinessProbe: (6)
      initialDelaySeconds: 15
      timeoutSeconds: 5
    livenessProbe: (7)
      initialDelaySeconds: 15
      timeoutSeconds: 5
# ...

    1. Specifies capacity limits for broker resources. For more information, see Capacity configuration.

    2. Defines the Cruise Control configuration, including default and enabled optimization goals. You can provide any standard configuration option apart from those managed directly by Strimzi.

    3. CPU and memory resources reserved for Cruise Control. For more information, see CPU and memory resources.

    4. Defined loggers and log levels added directly (inline) or indirectly (external) through a ConfigMap. A custom ConfigMap must be placed under the log4j.properties key. Cruise Control has a single logger called cruisecontrol.root.logger. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. For more information, see Logging configuration.

    5. Customization of deployment templates and pods.

    6. Healthcheck readiness probes.

    7. Healthcheck liveness probes.

  3. Create or update the resource:

    kubectl apply -f kafka.yaml

  4. Verify that Cruise Control was successfully deployed:

    kubectl get deployments -l app.kubernetes.io/name=strimzi
What to do next

After configuring and deploying Cruise Control, you can generate optimization proposals.

Additional resources

CruiseControlTemplate schema reference.

8.5. Cruise Control configuration

The config property in Kafka.spec.cruiseControl contains configuration options as keys with values as one of the following JSON types:

  • String

  • Number

  • Boolean

Note
 Strings that look like JSON or YAML will need to be explicitly quoted.

You can specify and configure all the options listed in the "Configurations" section of the Cruise Control documentation, apart from those managed directly by Strimzi. Specifically, you cannot modify configuration options with keys equal to or starting with one of the following strings:

  • bootstrap.servers

  • zookeeper.

  • ssl.

  • security.

  • failed.brokers.zk.path

  • webserver.http.port

  • webserver.http.address

  • webserver.api.urlprefix

  • metric.reporter.sampler.bootstrap.servers

  • metric.reporter.topic

  • metric.reporter.topic.pattern

  • partition.metric.sample.store.topic

  • broker.metric.sample.store.topic

  • capacity.config.file

  • skip.sample.store.topic.rack.awareness.check

  • cruise.control.metrics.topic

  • sasl.

If restricted options are specified, they are ignored and a warning message is printed to the Cluster Operator log file. All supported options are passed to Cruise Control.

An example Cruise Control configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  # ...
  cruiseControl:
    # ...
    config:
      default.goals: >
         com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal,
         com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal
      cpu.balance.threshold: 1.1
      metadata.max.age.ms: 300000
      send.buffer.bytes: 131072
    # ...

8.5.1. Capacity configuration

Cruise Control uses capacity limits to determine if optimization goals for broker resources are being broken. An optimization will fail if a goal is broken, preventing the optimization from being used to generate an optimization proposal for balanced partition assignments. For example, an optimization that would cause a broker to exceed its CPU capacity would not be used if the CpuCapacityGoal is set as a hard goal.

You specify capacity limits for broker resources in the brokerCapacity property in Kafka.spec.cruiseControl . Capacity limits can be set for the following broker resources in the described units:

  • disk - Disk storage in bytes

  • cpuUtilization - CPU utilization as a percent (0-100)

  • inboundNetwork - Inbound network throughput in bytes per second

  • outboundNetwork - Outbound network throughput in bytes per second

Because Strimzi Kafka brokers are homogeneous, Cruise Control applies the same capacity limits to every broker it is monitoring.

An example Cruise Control brokerCapacity configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  # ...
  cruiseControl:
    # ...
    brokerCapacity:
      disk: 100G
      cpuUtilization: 100
      inboundNetwork: 10000KB/s
      outboundNetwork: 10000KB/s
    # ...
Additional resources

For more information, refer to the BrokerCapacity schema reference.

8.5.2. Topic creation and configuration

Cruise Control requires the following three topics to be created in order to function properly:

  • strimzi.cruisecontrol.partitionmetricsamples

  • strimzi.cruisecontrol.modeltrainingsamples

  • strimzi.cruisecontrol.metrics

Cruise Control will create the strimzi.cruisecontrol.partitionmetricsamples and strimzi.cruisecontrol.modeltrainingsamples topics after the Cruise Control metric reporters create the strimzi.cruisecontrol.metrics topic. However, if automatic topic creation is disabled in Kafka with a configuration of auto.create.topics.enable: false in spec.kafka.config when starting a new Kafka cluster, the Cruise Control metric reporters will be unable to create the strimzi.cruisecontrol.metrics topic. In this situation, the strimzi.cruisecontrol.metrics topic will need to be created manually.

To manually create the strimzi.cruisecontrol.metrics Cruise Control topic in your Kubernetes cluster:

kubectl apply -f examples/cruise-control/cruise-control-topic.yaml

Since log compaction may remove records needed by Cruise Control, all topics created by Cruise Control must be configured with cleanup.policy=delete to disable log compaction. Cruise Control will automatically disable log compaction for the strimzi.cruisecontrol.partitionmetricsamples and strimzi.cruisecontrol.modeltrainingsamples topics. The Cruise Control metric reporters will attempt to disable log compaction for the strimzi.cruisecontrol.metrics topic but will fail when being started with a new Kafka cluster. This will only become a problem when log compaction is enabled in Kafka with the setting log.cleanup.policy=compact in the spec.kafka.config. In this situation, log compaction will be enabled for strimzi.cruisecontrol.metrics topic and will need to be overridden with a cleanup.policy=delete in the strimzi.cruisecontrol.metrics KafkaTopic.

Here we see an example of log compaction being disabled in a Cruise Control KafkaTopic.

apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaTopic
metadata:
  name: strimzi.cruisecontrol.metrics
spec:
  partitions: 1
  replicas: 1
  config:
    cleanup.policy: delete

8.5.3. Logging configuration

Cruise Control has its own configurable logger:

  • cruisecontrol.root.logger

Cruise Control uses the Apache log4j logger implementation.

Use the logging property to configure loggers and logger levels.

You can set the log levels by specifying the logger and level directly (inline) or use a custom (external) ConfigMap. If a ConfigMap is used, you set logging.name property to the name of the ConfigMap containing the external logging configuration. Inside the ConfigMap, the logging configuration is described using log4j.properties.

Here we see examples of inline and external logging.

Inline logging
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
# ...
spec:
  cruiseControl:
    # ...
    logging:
      type: inline
      loggers:
        cruisecontrol.root.logger: "INFO"
    # ...
External logging
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
# ...
spec:
  cruiseControl:
    # ...
    logging:
      type: external
      name: customConfigMap
    # ...

8.6. Generating optimization proposals

When you create or update a KafkaRebalance resource, Cruise Control generates an optimization proposal for the Kafka cluster based on the configured optimization goals.

You can then analyze the summary information in the optimization proposal and decide whether to approve it.

Prerequisites
Procedure

  1. Create a KafkaRebalance resource:

    1. To use the default optimization goals defined in the Kafka resource, leave the spec property empty:

      apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaRebalance
metadata:
  name: my-rebalance
  labels:
    strimzi.io/cluster: my-cluster
spec: {}

    2. To set user-provided optimization goals instead of using the default goals, edit the goals property and enter one or more goals.

      In the following example, rack awareness and replica capacity are user-provided optimization goals:

      apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaRebalance
metadata:
  name: my-rebalance
  labels:
    strimzi.io/cluster: my-cluster
spec:
  goals:
    - RackAwareGoal
    - ReplicaCapacityGoal

  2. Create or update the resource:

    kubectl apply -f your-file

    The Cluster Operator requests the optimization proposal from Cruise Control. This might take a few minutes depending on the size of the Kafka cluster.

  3. Check the status of the KafkaRebalance resource:

    kubectl describe kafkarebalance rebalance-cr-name

    Cruise Control returns one of two statuses:

    • PendingProposal: The rebalance operator is polling the Cruise Control API to check if the optimization proposal is ready.

    • ProposalReady: The optimization proposal is ready for review and, if desired, approval. The status contains the optimization proposal, in the status.summary property of the KafkaRebalance resource.

  4. Review the optimization proposal.

    kubectl describe kafkarebalance rebalance-cr-name

    Here is an example proposal:

    Status:
  Conditions:
    Last Transition Time:  2020-05-19T13:50:12.533Z
    Status:                ProposalReady
    Type:                  State
  Observed Generation:     1
  Optimization Result:
    Data To Move MB:  0
    Excluded Brokers For Leadership:
    Excluded Brokers For Replica Move:
    Excluded Topics:
    Intra Broker Data To Move MB:         0
    Monitored Partitions Percentage:      100
    Num Intra Broker Replica Movements:   0
    Num Leader Movements:                 0
    Num Replica Movements:                26
    On Demand Balancedness Score After:   81.8666802863978
    On Demand Balancedness Score Before:  78.01176356230222
    Recent Windows:                       1
  Session Id:                             05539377-ca7b-45ef-b359-e13564f1458c

    For information about the different properties, see Contents of optimization proposals.

What to do next

Approving an optimization proposal

Additional resources

8.7. Approving an optimization proposal

You can approve an optimization proposal generated by Cruise Control, if its status is ProposalReady. Cruise Control will then apply the optimization proposal to the Kafka cluster, reassigning partitions to brokers and changing partition leadership.

Caution

This is not a dry run. Before you approve an optimization proposal, you must:
Prerequisites
Procedure

Perform these steps for the optimization proposal that you want to approve:

  1. Unless the optimization proposal is newly generated, check that it is based on current information about the state of the Kafka cluster. To do so, refresh the optimization proposal to make sure it uses the latest cluster metrics:

    1. Annotate the KafkaRebalance resource in Kubernetes:

      kubectl annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=refresh

    2. Check the status of the KafkaRebalance resource:

      kubectl describe kafkarebalance rebalance-cr-name

    3. Wait until the status changes to ProposalReady.

  2. Approve the optimization proposal that you want Cruise Control to apply.

    Annotate the KafkaRebalance resource in Kubernetes:

    kubectl annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=approve

  3. The Cluster Operator detects the annotated resource and instructs Cruise Control to rebalance the Kafka cluster.

  4. Check the status of the KafkaRebalance resource:

    kubectl describe kafkarebalance rebalance-cr-name

  5. Cruise Control returns one of three statuses:

Additional resources

8.8. Stopping a cluster rebalance

Once started, a cluster rebalance operation might take some time to complete and affect the overall performance of the Kafka cluster.

If you want to stop a cluster rebalance operation that is in progress, apply a stop annotation to the KafkaRebalance custom resource. This causes Cruise Control to finish the current batch of partition reassignments and then stop the rebalance. Any partition reassignments before this point will have been successfully applied, so the Kafka cluster will be in a different state compared to before the rebalance operation started. Therefore, if further rebalancing is required, you should generate a new optimization proposal.

Note
 The performance of the Kafka cluster in the intermediate (stopped) state might be worse than in the initial state.
Prerequisites

  • You have approved the optimization proposal by annotating the KafkaRebalance custom resource with approve.

  • The status of the KafkaRebalance custom resource is Rebalancing.

Procedure

  1. Annotate the KafkaRebalance resource in Kubernetes:

    kubectl annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=stop

  2. Check the status of the KafkaRebalance resource:

    kubectl get kafka kafkarebalance -o jsonpath='{.status}'

  3. Wait until the status changes to Stopped.

Additional resources

8.9. Fixing problems with a KafkaRebalance resource

If an issue occurs in the process of creating a KafkaRebalance resource, or when interacting with Cruise Control, the error is reported in the resource status, along with details of how to fix it. The resource also moves to the NotReady state.

To continue with the cluster rebalance operation, you must fix the problem in the KafkaRebalance resource itself. Problems can include the following:

  • A misconfigured parameter.

  • The Cruise Control server is not reachable.

When you believe that you have fixed the issue, you need to add the refresh annotation to the resource. During a “refresh”, a new optimization proposal is requested from the Cruise Control server.

Prerequisites
Procedure

  1. Get information about the error from the KafkaRebalance status and resolve the issue if possible.

  2. Annotate the KafkaRebalance resource in Kubernetes:

    kubectl annotate kafkarebalance cluster-rebalance-name strimzi.io/rebalance=refresh

  3. Check the status of the KafkaRebalance resource:

    kubectl describe kafkarebalance rebalance-cr-name

  4. Wait until the status changes to PendingProposal, or directly to ProposalReady.

Additional resources

9. Distributed tracing

This chapter outlines the support for distributed tracing in Strimzi, using Jaeger.

How you configure distributed tracing varies by Strimzi client and component.

  • You instrument Kafka Producer, Consumer, and Streams API applications for distributed tracing using an OpenTracing client library. This involves adding instrumentation code to these clients, which monitors the execution of individual transactions in order to generate trace data.

  • Distributed tracing support is built in to the Kafka Connect, MirrorMaker, and Kafka Bridge components of Strimzi. To configure these components for distributed tracing, you configure and update the relevant custom resources.

Before configuring distributed tracing in Strimzi clients and components, you must first initialize and configure a Jaeger tracer in the Kafka cluster, as described in Initializing a Jaeger tracer for Kafka clients.

Note
 Distributed tracing is not supported for Kafka brokers.

9.1. Overview of distributed tracing in Strimzi

Distributed tracing allows developers and system administrators to track the progress of transactions between applications (and services in a microservice architecture) in a distributed system. This information is useful for monitoring application performance and investigating issues with target systems and end-user applications.

In Strimzi and data streaming platforms in general, distributed tracing facilitates the end-to-end tracking of messages: from source systems to the Kafka cluster and then to target systems and applications.

As an aspect of system observability, distributed tracing complements the metrics that are available to view in Grafana dashboards and the available loggers for each component.

OpenTracing overview

Distributed tracing in Strimzi is implemented using the open source OpenTracing and Jaeger projects.

The OpenTracing specification defines APIs that developers can use to instrument applications for distributed tracing. It is independent from the tracing system.

When instrumented, applications generate traces for individual transactions. Traces are composed of spans, which define specific units of work.

To simplify the instrumentation of the Kafka Bridge and Kafka Producer, Consumer, and Streams API applications, Strimzi includes the OpenTracing Apache Kafka Client Instrumentation library.

Note
 The OpenTracing project is merging with the OpenCensus project. The new, combined project is named OpenTelemetry. OpenTelemetry will provide compatibility for applications that are instrumented using the OpenTracing APIs.
Jaeger overview

Jaeger, a tracing system, is an implementation of the OpenTracing APIs used for monitoring and troubleshooting microservices-based distributed systems. It consists of four main components and provides client libraries for instrumenting applications. You can use the Jaeger user interface to visualize, query, filter, and analyze trace data.

An example of a query in the Jaeger user interface

Simple Jaeger query

9.1.1. Distributed tracing support in Strimzi

In Strimzi, distributed tracing is supported in:

  • Kafka Connect (including Kafka Connect with Source2Image support)

  • MirrorMaker

  • The Strimzi Kafka Bridge

You enable and configure distributed tracing for these components by setting template configuration properties in the relevant custom resource (for example, KafkaConnect and KafkaBridge).

To enable distributed tracing in Kafka Producer, Consumer, and Streams API applications, you can instrument application code using the OpenTracing Apache Kafka Client Instrumentation library. When instrumented, these clients generate traces for messages (for example, when producing messages or writing offsets to the log).

Traces are sampled according to a sampling strategy and then visualized in the Jaeger user interface. This trace data is useful for monitoring the performance of your Kafka cluster and debugging issues with target systems and applications.

Outline of procedures

To set up distributed tracing for Strimzi, follow these procedures:

This chapter covers setting up distributed tracing for Strimzi clients and components only. Setting up distributed tracing for applications and systems beyond Strimzi is outside the scope of this chapter. To learn more about this subject, see the OpenTracing documentation and search for "inject and extract".

Before you start

Before you set up distributed tracing for Strimzi, it is helpful to understand:

Prerequisites

9.2. Setting up tracing for Kafka clients

This section describes how to initialize a Jaeger tracer to allow you to instrument your client applications for distributed tracing.

9.2.1. Initializing a Jaeger tracer for Kafka clients

Configure and initialize a Jaeger tracer using a set of tracing environment variables.

Procedure

Perform the following steps for each client application.

  1. Add Maven dependencies for Jaeger to the pom.xml file for the client application:

    <dependency>
    <groupId>io.jaegertracing</groupId>
    <artifactId>jaeger-client</artifactId>
    <version>1.1.0</version>
</dependency>

  2. Define the configuration of the Jaeger tracer using the tracing environment variables.

  3. Create the Jaeger tracer from the environment variables that you defined in step two:

    Tracer tracer = Configuration.fromEnv().getTracer();
    Note
    		 For alternative ways to initialize a Jaeger tracer, see the Java OpenTracing library documentation.

  4. Register the Jaeger tracer as a global tracer:

    GlobalTracer.register(tracer);

A Jaeger tracer is now initialized for the client application to use.

9.2.2. Tracing environment variables

Use these environment variables when configuring a Jaeger tracer for Kafka clients.

Note
 The tracing environment variables are part of the Jaeger project and are subject to change. For the latest environment variables, see the Jaeger documentation.
Property Required Description

JAEGER_SERVICE_NAME

Yes

The name of the Jaeger tracer service.

JAEGER_AGENT_HOST

No

The hostname for communicating with the jaeger-agent through the User Datagram Protocol (UDP).

JAEGER_AGENT_PORT

No

The port used for communicating with the jaeger-agent through UDP.

JAEGER_ENDPOINT

No

The traces endpoint. Only define this variable if the client application will bypass the jaeger-agent and connect directly to the jaeger-collector.

JAEGER_AUTH_TOKEN

No

The authentication token to send to the endpoint as a bearer token.

JAEGER_USER

No

The username to send to the endpoint if using basic authentication.

JAEGER_PASSWORD

No

The password to send to the endpoint if using basic authentication.

JAEGER_PROPAGATION

No

A comma-separated list of formats to use for propagating the trace context. Defaults to the standard Jaeger format. Valid values are jaeger and b3.

JAEGER_REPORTER_LOG_SPANS

No

Indicates whether the reporter should also log the spans.

JAEGER_REPORTER_MAX_QUEUE_SIZE

No

The reporter’s maximum queue size.

JAEGER_REPORTER_FLUSH_INTERVAL

No

The reporter’s flush interval, in ms. Defines how frequently the Jaeger reporter flushes span batches.

JAEGER_SAMPLER_TYPE

No

The sampling strategy to use for client traces: Constant, Probabilistic, Rate Limiting, or Remote (the default type).

To sample all traces, use the Constant sampling strategy with a parameter of 1.

For more information, see the Jaeger documentation.

JAEGER_SAMPLER_PARAM

No

The sampler parameter (number).

JAEGER_SAMPLER_MANAGER_HOST_PORT

No

The hostname and port to use if a Remote sampling strategy is selected.

JAEGER_TAGS

No

A comma-separated list of tracer-level tags that are added to all reported spans.

The value can also refer to an environment variable using the format ${envVarName:default}. :default is optional and identifies a value to use if the environment variable cannot be found.
Additional resources

9.3. Instrumenting Kafka clients with tracers

This section describes how to instrument Kafka Producer, Consumer, and Streams API applications for distributed tracing.

9.3.1. Instrumenting Kafka Producers and Consumers for tracing

Use a Decorator pattern or Interceptors to instrument your Java Producer and Consumer application code for distributed tracing.

Procedure

Perform these steps in the application code of each Kafka Producer and Consumer application.

  1. Add the Maven dependency for OpenTracing to the Producer or Consumer’s pom.xml file.

    <dependency>
    <groupId>io.opentracing.contrib</groupId>
    <artifactId>opentracing-kafka-client</artifactId>
    <version>0.1.12</version>
</dependency>

  2. Instrument your client application code using either a Decorator pattern or Interceptors.

    • If you prefer to use a Decorator pattern, use following example:

      // Create an instance of the KafkaProducer:
KafkaProducer<Integer, String> producer = new KafkaProducer<>(senderProps);

// Create an instance of the TracingKafkaProducer:
TracingKafkaProducer<Integer, String> tracingProducer = new TracingKafkaProducer<>(producer,
        tracer);

// Send:
tracingProducer.send(...);

// Create an instance of the KafkaConsumer:
KafkaConsumer<Integer, String> consumer = new KafkaConsumer<>(consumerProps);

// Create an instance of the TracingKafkaConsumer:
TracingKafkaConsumer<Integer, String> tracingConsumer = new TracingKafkaConsumer<>(consumer,
        tracer);

// Subscribe:
tracingConsumer.subscribe(Collections.singletonList("messages"));

// Get messages:
ConsumerRecords<Integer, String> records = tracingConsumer.poll(1000);

// Retrieve SpanContext from polled record (consumer side):
ConsumerRecord<Integer, String> record = ...
SpanContext spanContext = TracingKafkaUtils.extractSpanContext(record.headers(), tracer);

    • If you prefer to use Interceptors, use the following example:

      // Register the tracer with GlobalTracer:
GlobalTracer.register(tracer);

// Add the TracingProducerInterceptor to the sender properties:
senderProps.put(ProducerConfig.INTERCEPTOR_CLASSES_CONFIG,
          TracingProducerInterceptor.class.getName());

// Create an instance of the KafkaProducer:
KafkaProducer<Integer, String> producer = new KafkaProducer<>(senderProps);

// Send:
producer.send(...);

// Add the TracingConsumerInterceptor to the consumer properties:
consumerProps.put(ConsumerConfig.INTERCEPTOR_CLASSES_CONFIG,
          TracingConsumerInterceptor.class.getName());

// Create an instance of the KafkaConsumer:
KafkaConsumer<Integer, String> consumer = new KafkaConsumer<>(consumerProps);

// Subscribe:
consumer.subscribe(Collections.singletonList("messages"));

// Get messages:
ConsumerRecords<Integer, String> records = consumer.poll(1000);

// Retrieve the SpanContext from a polled message (consumer side):
ConsumerRecord<Integer, String> record = ...
SpanContext spanContext = TracingKafkaUtils.extractSpanContext(record.headers(), tracer);
Custom span names in a Decorator pattern

A span is a logical unit of work in Jaeger, with an operation name, start time, and duration.

If you use a Decorator pattern to instrument your Kafka Producer and Consumer applications, you can define custom span names by passing a BiFunction object as an additional argument when creating the TracingKafkaProducer and TracingKafkaConsumer objects. The OpenTracing Apache Kafka Client Instrumentation library includes several built-in span names, which are described below.

Example: Using custom span names to instrument client application code in a Decorator pattern
// Create a BiFunction for the KafkaProducer that operates on (String operationName, ProducerRecord consumerRecord) and returns a String to be used as the name:

BiFunction<String, ProducerRecord, String> producerSpanNameProvider =
    (operationName, producerRecord) -> "CUSTOM_PRODUCER_NAME";

// Create an instance of the KafkaProducer:
KafkaProducer<Integer, String> producer = new KafkaProducer<>(senderProps);

// Create an instance of the TracingKafkaProducer
TracingKafkaProducer<Integer, String> tracingProducer = new TracingKafkaProducer<>(producer,
        tracer,
        producerSpanNameProvider);

// Spans created by the tracingProducer will now have "CUSTOM_PRODUCER_NAME" as the span name.

// Create a BiFunction for the KafkaConsumer that operates on (String operationName, ConsumerRecord consumerRecord) and returns a String to be used as the name:

BiFunction<String, ConsumerRecord, String> consumerSpanNameProvider =
    (operationName, consumerRecord) -> operationName.toUpperCase();

// Create an instance of the KafkaConsumer:
KafkaConsumer<Integer, String> consumer = new KafkaConsumer<>(consumerProps);

// Create an instance of the TracingKafkaConsumer, passing in the consumerSpanNameProvider BiFunction:

TracingKafkaConsumer<Integer, String> tracingConsumer = new TracingKafkaConsumer<>(consumer,
        tracer,
        consumerSpanNameProvider);

// Spans created by the tracingConsumer will have the operation name as the span name, in upper-case.
// "receive" -> "RECEIVE"
Built-in span names

When defining custom span names, you can use the following BiFunctions in the ClientSpanNameProvider class. If no spanNameProvider is specified, CONSUMER_OPERATION_NAME and PRODUCER_OPERATION_NAME are used.

BiFunction Description

CONSUMER_OPERATION_NAME, PRODUCER_OPERATION_NAME

Returns the operationName as the span name: "receive" for Consumers and "send" for Producers.

CONSUMER_PREFIXED_OPERATION_NAME(String prefix), PRODUCER_PREFIXED_OPERATION_NAME(String prefix)

Returns a String concatenation of prefix and operationName.

CONSUMER_TOPIC, PRODUCER_TOPIC

Returns the name of the topic that the message was sent to or retrieved from in the format (record.topic()).

PREFIXED_CONSUMER_TOPIC(String prefix), PREFIXED_PRODUCER_TOPIC(String prefix)

Returns a String concatenation of prefix and the topic name in the format (record.topic()).

CONSUMER_OPERATION_NAME_TOPIC, PRODUCER_OPERATION_NAME_TOPIC

Returns the operation name and the topic name: "operationName - record.topic()".

CONSUMER_PREFIXED_OPERATION_NAME_TOPIC(String prefix), PRODUCER_PREFIXED_OPERATION_NAME_TOPIC(String prefix)

Returns a String concatenation of prefix and "operationName - record.topic()".

9.3.2. Instrumenting Kafka Streams applications for tracing

This section describes how to instrument Kafka Streams API applications for distributed tracing.

Procedure

Perform the following steps for each Kafka Streams API application.

  1. Add the opentracing-kafka-streams dependency to the pom.xml file for your Kafka Streams API application:

    <dependency>
    <groupId>io.opentracing.contrib</groupId>
    <artifactId>opentracing-kafka-streams</artifactId>
    <version>0.1.12</version>
</dependency>

  2. Create an instance of the TracingKafkaClientSupplier supplier interface:

    KafkaClientSupplier supplier = new TracingKafkaClientSupplier(tracer);

  3. Provide the supplier interface to KafkaStreams:

    KafkaStreams streams = new KafkaStreams(builder.build(), new StreamsConfig(config), supplier);
streams.start();

9.4. Setting up tracing for MirrorMaker, Kafka Connect, and the Kafka Bridge

Distributed tracing is supported for MirrorMaker, Kafka Connect (including Kafka Connect with Source2Image support), and the Strimzi Kafka Bridge.

Tracing in MirrorMaker

For MirrorMaker, messages are traced from the source cluster to the target cluster; the trace data records messages entering and leaving the MirrorMaker component.

Tracing in Kafka Connect

Only messages produced and consumed by Kafka Connect itself are traced. To trace messages sent between Kafka Connect and external systems, you must configure tracing in the connectors for those systems. For more information, see Kafka Connect cluster configuration.

Tracing in the Kafka Bridge

Messages produced and consumed by the Kafka Bridge are traced. Incoming HTTP requests from client applications to send and receive messages through the Kafka Bridge are also traced. In order to have end-to-end tracing, you must configure tracing in your HTTP clients.

9.4.1. Enabling tracing in MirrorMaker, Kafka Connect, and Kafka Bridge resources

Update the configuration of KafkaMirrorMaker, KafkaConnect, KafkaConnectS2I, and KafkaBridge custom resources to specify and configure a Jaeger tracer service for each resource. Updating a tracing-enabled resource in your Kubernetes cluster triggers two events:

  • Interceptor classes are updated in the integrated consumers and producers in MirrorMaker, Kafka Connect, or the Strimzi Kafka Bridge.

  • For MirrorMaker and Kafka Connect, the tracing agent initializes a Jaeger tracer based on the tracing configuration defined in the resource.

  • For the Kafka Bridge, a Jaeger tracer based on the tracing configuration defined in the resource is initialized by the Kafka Bridge itself.

Procedure

Perform these steps for each KafkaMirrorMaker, KafkaConnect, KafkaConnectS2I, and KafkaBridge resource.

  1. In the spec.template property, configure the Jaeger tracer service. For example:

    Jaeger tracer configuration for Kafka Connect
    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect-cluster
spec:
  #...
  template:
    connectContainer: (1)
      env:
        - name: JAEGER_SERVICE_NAME
          value: my-jaeger-service
        - name: JAEGER_AGENT_HOST
          value: jaeger-agent-name
        - name: JAEGER_AGENT_PORT
          value: "6831"
  tracing: (2)
    type: jaeger
  #...
    Jaeger tracer configuration for MirrorMaker
    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  #...
  template:
    mirrorMakerContainer:
      env:
        - name: JAEGER_SERVICE_NAME
          value: my-jaeger-service
        - name: JAEGER_AGENT_HOST
          value: jaeger-agent-name
        - name: JAEGER_AGENT_PORT
          value: "6831"
  tracing:
    type: jaeger
#...
    Jaeger tracer configuration for the Kafka Bridge
    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaBridge
metadata:
  name: my-bridge
spec:
  #...
  template:
    bridgeContainer:
      env:
        - name: JAEGER_SERVICE_NAME
          value: my-jaeger-service
        - name: JAEGER_AGENT_HOST
          value: jaeger-agent-name
        - name: JAEGER_AGENT_PORT
          value: "6831"
  tracing:
    type: jaeger
#...

    1. Use the tracing environment variables as template configuration properties.

    2. Set the spec.tracing.type property to jaeger.

  2. Create or update the resource:

    kubectl apply -f your-file
Additional resources

10. Security

Strimzi supports encrypted communication between the Kafka and Strimzi components using the TLS protocol. Communication between Kafka brokers (interbroker communication), between ZooKeeper nodes (internodal communication), and between these and the Strimzi operators is always encrypted. Communication between Kafka clients and Kafka brokers is encrypted according to how the cluster is configured. For the Kafka and Strimzi components, TLS certificates are also used for authentication.

The Cluster Operator automatically sets up and renews TLS certificates to enable encryption and authentication within your cluster. It also sets up other TLS certificates if you want to enable encryption or TLS authentication between Kafka brokers and clients. Certificates provided by users are not renewed.

You can provide your own server certificates, called Kafka listener certificates, for TLS listeners or external listeners which have TLS encryption enabled. For more information, see Kafka listener certificates.

Secure Communication
Figure 3. Example architecture diagram of the communication secured by TLS.

10.1. Certificate Authorities

To support encryption, each Strimzi component needs its own private keys and public key certificates. All component certificates are signed by an internal Certificate Authority (CA) called the cluster CA.

Similarly, each Kafka client application connecting to Strimzi using TLS client authentication needs to provide private keys and certificates. A second internal CA, named the clients CA, is used to sign certificates for the Kafka clients.

10.1.1. CA certificates

Both the cluster CA and clients CA have a self-signed public key certificate.

Kafka brokers are configured to trust certificates signed by either the cluster CA or clients CA. Components that clients do not need to connect to, such as ZooKeeper, only trust certificates signed by the cluster CA. Unless TLS encryption for external listeners is disabled, client applications must trust certificates signed by the cluster CA. This is also true for client applications that perform mutual TLS authentication.

By default, Strimzi automatically generates and renews CA certificates issued by the cluster CA or clients CA. You can configure the management of these CA certificates in the Kafka.spec.clusterCa and Kafka.spec.clientsCa objects. Certificates provided by users are not renewed.

You can provide your own CA certificates for the cluster CA or clients CA. For more information, see Installing your own CA certificates. If you provide your own certificates, you must manually renew them when needed.

10.1.2. Validity periods of CA certificates

CA certificate validity periods are expressed as a number of days after certificate generation. You can configure the validity period of:

  • Cluster CA certificates in Kafka.spec.clusterCa.validityDays

  • Client CA certificates in Kafka.spec.clientsCa.validityDays

10.1.3. Installing your own CA certificates

This procedure describes how to install your own CA certificates and private keys instead of using CA certificates and private keys generated by the Cluster Operator.

Prerequisites

  • The Cluster Operator is running.

  • A Kafka cluster is not yet deployed.

  • Your own X.509 certificates and keys in PEM format for the cluster CA or clients CA.

    • If you want to use a cluster or clients CA which is not a Root CA, you have to include the whole chain in the certificate file. The chain should be in the following order:

      1. The cluster or clients CA

      2. One or more intermediate CAs

      3. The root CA

    • All CAs in the chain should be configured as a CA in the X509v3 Basic Constraints.

Procedure

  1. Put your CA certificate in the corresponding Secret (<cluster>-cluster-ca-cert for the cluster CA or <cluster>-clients-ca-cert for the clients CA):

    Run the following commands:

    # Delete any existing secret (ignore "Not Exists" errors)
kubectl delete secret <ca-cert-secret>
# Create and label the new secret
kubectl create secret generic <ca-cert-secret> --from-file=ca.crt=<ca-cert-file>

  2. Put your CA key in the corresponding Secret (<cluster>-cluster-ca for the cluster CA or <cluster>-clients-ca for the clients CA):

    # Delete the existing secret
kubectl delete secret <ca-key-secret>
# Create the new one
kubectl create secret generic <ca-key-secret> --from-file=ca.key=<ca-key-file>

  3. Label both Secrets with the labels strimzi.io/kind=Kafka and strimzi.io/cluster=<my-cluster>:

    kubectl label secret <ca-cert-secret> strimzi.io/kind=Kafka strimzi.io/cluster=<my-cluster>
kubectl label secret <ca-key-secret> strimzi.io/kind=Kafka strimzi.io/cluster=<my-cluster>

  4. Create the Kafka resource for your cluster, configuring either the Kafka.spec.clusterCa or the Kafka.spec.clientsCa object to not use generated CAs:

    Example fragment Kafka resource configuring the cluster CA to use certificates you supply for yourself
    kind: Kafka
version: kafka.strimzi.io/v1beta1
spec:
  # ...
  clusterCa:
    generateCertificateAuthority: false
Additional resources

10.2. Secrets

Strimzi uses Secrets to store private keys and certificates for Kafka cluster components and clients. Secrets are used for establishing TLS encrypted connections between Kafka brokers, and between brokers and clients. They are also used for mutual TLS authentication.

  • A Cluster Secret contains a cluster CA certificate to sign Kafka broker certificates, and is used by a connecting client to establish a TLS encrypted connection with the Kafka cluster to validate broker identity.

  • A Client Secret contains a client CA certificate for a user to sign its own client certificate to allow mutual authentication against the Kafka cluster. The broker validates the client identity through the client CA certificate itself.

  • A User Secret contains a private key and certificate, which are generated and signed by the client CA certificate when a new user is created. The key and certificate are used for authentication and authorization when accessing the cluster.

Secrets provide private keys and certificates in PEM and PKCS #12 formats. Using private keys and certificates in PEM format means that users have to get them from the Secrets, and generate a corresponding truststore (or keystore) to use in their Java applications. PKCS #12 storage provides a truststore (or keystore) that can be used directly.

All keys are 2048 bits in size.

10.2.1. PKCS #12 storage

PKCS #12 defines an archive file format (.p12) for storing cryptography objects into a single file with password protection. You can use PKCS #12 to manage certificates and keys in one place.

Each Secret contains fields specific to PKCS #12.

  • The .p12 field contains the certificates and keys.

  • The .password field is the password that protects the archive.

10.2.2. Cluster CA Secrets

Table 2. Cluster CA Secrets managed by the Cluster Operator in <cluster>
Secret name Field within Secret Description

<cluster>-cluster-ca

ca.key

The current private key for the cluster CA.

<cluster>-cluster-ca-cert

ca.p12

PKCS #12 archive file for storing certificates and keys.

ca.password

Password for protecting the PKCS #12 archive file.

ca.crt

The current certificate for the cluster CA.

<cluster>-kafka-brokers

<cluster>-kafka-<num>.p12

PKCS #12 archive file for storing certificates and keys.

<cluster>-kafka-<num>.password

Password for protecting the PKCS #12 archive file.

<cluster>-kafka-<num>.crt

Certificate for Kafka broker pod <num>. Signed by a current or former cluster CA private key in <cluster>-cluster-ca.

<cluster>-kafka-<num>.key

Private key for Kafka broker pod <num>.

<cluster>-zookeeper-nodes

<cluster>-zookeeper-<num>.p12

PKCS #12 archive file for storing certificates and keys.

<cluster>-zookeeper-<num>.password

Password for protecting the PKCS #12 archive file.

<cluster>-zookeeper-<num>.crt

Certificate for ZooKeeper node <num>. Signed by a current or former cluster CA private key in <cluster>-cluster-ca.

<cluster>-zookeeper-<num>.key

Private key for ZooKeeper pod <num>.

<cluster>-entity-operator-certs

entity-operator_.p12

PKCS #12 archive file for storing certificates and keys.

entity-operator_.password

Password for protecting the PKCS #12 archive file.

entity-operator_.crt

Certificate for TLS communication between the Entity Operator and Kafka or ZooKeeper. Signed by a current or former cluster CA private key in <cluster>-cluster-ca.

entity-operator.key

Private key for TLS communication between the Entity Operator and Kafka or ZooKeeper

The CA certificates in <cluster>-cluster-ca-cert must be trusted by Kafka client applications so that they validate the Kafka broker certificates when connecting to Kafka brokers over TLS.

Note
 Only <cluster>-cluster-ca-cert needs to be used by clients. All other Secrets in the table above only need to be accessed by the Strimzi components. You can enforce this using Kubernetes role-based access controls if necessary.

10.2.3. Client CA Secrets

Table 3. Clients CA Secrets managed by the Cluster Operator in <cluster>
Secret name Field within Secret Description

<cluster>-clients-ca

ca.key

The current private key for the clients CA.

<cluster>-clients-ca-cert

ca.p12

PKCS #12 archive file for storing certificates and keys.

ca.password

Password for protecting the PKCS #12 archive file.

ca.crt

The current certificate for the clients CA.

The certificates in <cluster>-clients-ca-cert are those which the Kafka brokers trust.

Note
 <cluster>-clients-ca is used to sign certificates of client applications. It needs to be accessible to the Strimzi components and for administrative access if you are intending to issue application certificates without using the User Operator. You can enforce this using Kubernetes role-based access controls if necessary.

10.2.4. User Secrets

Table 4. Secrets managed by the User Operator
Secret name Field within Secret Description

<user>

user.p12

PKCS #12 archive file for storing certificates and keys.

user.password

Password for protecting the PKCS #12 archive file.

user.crt

Certificate for the user, signed by the clients CA

user.key

Private key for the user

10.3. Certificate renewal

The cluster CA and clients CA certificates are only valid for a limited time period, known as the validity period. This is usually defined as a number of days since the certificate was generated. For auto-generated CA certificates, you can configure the validity period in Kafka.spec.clusterCa.validityDays and Kafka.spec.clientsCa.validityDays. The default validity period for both certificates is 365 days. Manually-installed CA certificates should have their own validity period defined.

When a CA certificate expires, components and clients which still trust that certificate will not accept TLS connections from peers whose certificate were signed by the CA private key. The components and clients need to trust the new CA certificate instead.

To allow the renewal of CA certificates without a loss of service, the Cluster Operator will initiate certificate renewal before the old CA certificates expire. You can configure the renewal period in Kafka.spec.clusterCa.renewalDays and Kafka.spec.clientsCa.renewalDays (both default to 30 days). The renewal period is measured backwards, from the expiry date of the current certificate.

Not Before                                     Not After
    |                                              |
    |<--------------- validityDays --------------->|
                              <--- renewalDays --->|

The behavior of the Cluster Operator during the renewal period depends on whether the relevant setting is enabled, in either Kafka.spec.clusterCa.generateCertificateAuthority or Kafka.spec.clientsCa.generateCertificateAuthority.

10.3.1. Renewal process with generated CAs

The Cluster Operator performs the following process to renew CA certificates:

  1. Generate a new CA certificate, but retain the existing key. The new certificate replaces the old one with the name ca.crt within the corresponding Secret.

  2. Generate new client certificates (for ZooKeeper nodes, Kafka brokers, and the Entity Operator). This is not strictly necessary because the signing key has not changed, but it keeps the validity period of the client certificate in sync with the CA certificate.

  3. Restart ZooKeeper nodes so that they will trust the new CA certificate and use the new client certificates.

  4. Restart Kafka brokers so that they will trust the new CA certificate and use the new client certificates.

  5. Restart the Topic and User Operators so that they will trust the new CA certificate and use the new client certificates.

10.3.2. Client applications

The Cluster Operator is not aware of the client applications using the Kafka cluster.

When connecting to the cluster, and to ensure they operate correctly, client applications must:

  • Trust the cluster CA certificate published in the <cluster>-cluster-ca-cert Secret.

  • Use the credentials published in their <user-name> Secret to connect to the cluster.

    The User Secret provides credentials in PEM and PKCS #12 format, or it can provide a password when using SCRAM-SHA authentication. The User Operator creates the user credentials when a user is created.

For workloads running inside the same Kubernetes cluster and namespace, Secrets can be mounted as a volume so the client Pods construct their keystores and truststores from the current state of the Secrets. For more details on this procedure, see Configuring internal clients to trust the cluster CA.

Client certificate renewal

You must ensure clients continue to work after certificate renewal. The renewal process depends on how the clients are configured.

If you are provisioning client certificates and keys manually, you must generate new client certificates and ensure the new certificates are used by clients within the renewal period. Failure to do this by the end of the renewal period could result in client applications being unable to connect to the cluster.

10.3.3. Renewing CA certificates manually

Unless the Kafka.spec.clusterCa.generateCertificateAuthority and Kafka.spec.clientsCa.generateCertificateAuthority objects are set to false, the cluster and clients CA certificates will auto-renew at the start of their respective certificate renewal periods. You can manually renew one or both of these certificates before the certificate renewal period starts, if required for security reasons. A renewed certificate uses the same private key as the old certificate.

Prerequisites

  • The Cluster Operator is running.

  • A Kafka cluster in which CA certificates and private keys are installed.

Procedure

  • Apply the strimzi.io/force-renew annotation to the Secret that contains the CA certificate that you want to renew.

    Certificate Secret Annotate command

    Cluster CA

    <cluster-name>-cluster-ca-cert

    kubectl annotate secret <cluster-name>-cluster-ca-cert strimzi.io/force-renew=true

    Clients CA

    <cluster-name>-clients-ca-cert

    kubectl annotate secret <cluster-name>-clients-ca-cert strimzi.io/force-renew=true

At the next reconciliation the Cluster Operator will generate a new CA certificate for the Secret that you annotated. If maintenance time windows are configured, the Cluster Operator will generate the new CA certificate at the first reconciliation within the next maintenance time window.

Client applications must reload the cluster and clients CA certificates that were renewed by the Cluster Operator.

Additional resources

10.3.4. Renewing your own CA certificates

This procedure describes how to renew CA certificates and private keys that you previously installed. You will need to follow this procedure during the renewal period in order to replace CA certificates which will soon expire.

Prerequisites

  • The Cluster Operator is running.

  • A Kafka cluster in which you previously installed your own CA certificates and private keys.

  • New cluster and clients X.509 certificates and keys in PEM format. These could be generated using openssl using a command such as:

    openssl req -x509 -new -days <validity> --nodes -out ca.crt -keyout ca.key
Procedure

  1. Establish what CA certificates already exist in the Secret:

    Use the following commands:

    kubectl describe secret <ca-cert-secret>

  2. Prepare a directory containing the existing CA certificates in the secret.

    mkdir new-ca-cert-secret
cd new-ca-cert-secret

    For each certificate <ca-certificate> from the previous step, run:

    # Fetch the existing secret
kubectl get secret <ca-cert-secret> -o 'jsonpath={.data.<ca-certificate>}' | base64 -d > <ca-certificate>

  3. Rename the old ca.crt file to ca_<date>_.crt, where <date> is the certificate expiry date in the format <year>-<month>-<day>_T<hour>_-<minute>-_<second>_Z, for example ca-2018-09-27T17-32-00Z.crt.

    mv ca.crt ca-$(date -u -d$(openssl x509 -enddate -noout -in ca.crt | sed 's/.*=//') +'%Y-%m-%dT%H-%M-%SZ').crt

  4. Copy the new CA certificate into the directory, naming it ca.crt

    cp <path-to-new-cert> ca.crt

  5. Replace the CA certificate Secret (<cluster>-cluster-ca or <cluster>-clients-ca). This can be done using the following commands:

    # Delete the existing secret
kubectl delete secret <ca-cert-secret>
# Re-create the secret with the new private key
kubectl create secret generic <ca-cert-secret> --from-file=.

    You can now delete the directory you created:

    cd ..
rm -r new-ca-cert-secret

  6. Replace the CA key Secret (<cluster>-cluster-ca or <cluster>-clients-ca). This can be done using the following commands:

    # Delete the existing secret
kubectl delete secret <ca-key-secret>
# Re-create the secret with the new private key
kubectl create secret generic <ca-key-secret> --from-file=ca.key=<ca-key-file>

10.4. Replacing private keys

You can replace the private keys used by the cluster CA and clients CA certificates. When a private key is replaced, the Cluster Operator generates a new CA certificate for the new private key.

Prerequisites

  • The Cluster Operator is running.

  • A Kafka cluster in which CA certificates and private keys are installed.

Procedure

  • Apply the strimzi.io/force-replace annotation to the Secret that contains the private key that you want to renew.

    Private key for Secret Annotate command

    Cluster CA

    <cluster-name>-cluster-ca

    kubectl annotate secret <cluster-name>-cluster-ca strimzi.io/force-replace=true

    Clients CA

    <cluster-name>-clients-ca

    kubectl annotate secret <cluster-name>-clients-ca strimzi.io/force-replace=true

At the next reconciliation the Cluster Operator will:

  • Generate a new private key for the Secret that you annotated

  • Generate a new CA certificate

If maintenance time windows are configured, the Cluster Operator will generate the new private key and CA certificate at the first reconciliation within the next maintenance time window.

Client applications must reload the cluster and clients CA certificates that were renewed by the Cluster Operator.

Additional resources

10.5. TLS connections

10.5.1. ZooKeeper communication

ZooKeeper does not support TLS itself. By deploying a TLS sidecar within every ZooKeeper pod, the Cluster Operator is able to provide data encryption and authentication between ZooKeeper nodes in a cluster. ZooKeeper only communicates with the TLS sidecar over the loopback interface. The TLS sidecar then proxies all ZooKeeper traffic, TLS decrypting data upon entry into a ZooKeeper pod, and TLS encrypting data upon departure from a ZooKeeper pod.

This TLS encrypting stunnel proxy is instantiated from the spec.zookeeper.stunnelImage specified in the Kafka resource.

10.5.2. Kafka interbroker communication

Communication between Kafka brokers is done through an internal listener on port 9091, which is encrypted by default and not accessible to Kafka clients.

Communication between Kafka brokers and ZooKeeper nodes uses a TLS sidecar, as described above.

10.5.3. Topic and User Operators

Like the Cluster Operator, the Topic and User Operators each use a TLS sidecar when communicating with ZooKeeper. The Topic Operator connects to Kafka brokers on port 9091.

10.5.4. Kafka Client connections

Encrypted communication between Kafka brokers and clients running within the same Kubernetes cluster can be provided by configuring the spec.kafka.listeners.tls listener, which listens on port 9093.

Encrypted communication between Kafka brokers and clients running outside the same Kubernetes cluster can be provided by configuring the spec.kafka.listeners.external listener (the port of the external listener depends on its type).

Note
 Unencrypted client communication with brokers can be configured by spec.kafka.listeners.plain, which listens on port 9092.

10.6. Configuring internal clients to trust the cluster CA

This procedure describes how to configure a Kafka client that resides inside the Kubernetes cluster — connecting to the tls listener on port 9093 — to trust the cluster CA certificate.

The easiest way to achieve this for an internal client is to use a volume mount to access the Secrets containing the necessary certificates and keys.

Follow the steps to configure trust certificates that are signed by the cluster CA for Java-based Kafka Producer, Consumer, and Streams APIs.

Choose the steps to follow according to the certificate format of the cluster CA: PKCS #12 (.p12) or PEM (.crt).

The steps describe how to mount the Cluster Secret that verifies the identity of the Kafka cluster to the client pod.

Prerequisites

  • The Cluster Operator must be running.

  • There needs to be a Kafka resource within the Kubernetes cluster.

  • You need a Kafka client application outside the Kubernetes cluster that will connect using TLS, and needs to trust the cluster CA certificate.

  • The client application must be running in the same namespace as the Kafka resource.

Using PKCS #12 format (.p12)

  1. Mount the cluster Secret as a volume when defining the client pod.

    For example:

    kind: Pod
apiVersion: {API_Version}
metadata:
  name: client-pod
spec:
  containers:
  - name: client-name
    image: client-name
    volumeMounts:
    - name: secret-volume
      mountPath: /data/p12
    env:
    - name: SECRET_PASSWORD
      valueFrom:
        secretKeyRef:
          name: my-secret
          key: my-password
  volumes:
  - name: secret-volume
    secret:
      secretName: my-cluster-cluster-cert

    Here we’re mounting:

    • The PKCS #12 file into an exact path, which can be configured

    • The password into an environment variable, where it can be used for Java configuration

  2. Configure the Kafka client with the following properties:

    • A security protocol option:

      • security.protocol: SSL when using TLS for encryption (with or without TLS authentication).

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

    • ssl.truststore.location with the truststore location where the certificates were imported.

    • ssl.truststore.password with the password for accessing the truststore.

    • ssl.truststore.type=PKCS12 to identify the truststore type.

Using PEM format (.crt)

  1. Mount the cluster Secret as a volume when defining the client pod.

    For example:

    kind: Pod
apiVersion: {API_Version}
metadata:
  name: client-pod
spec:
  containers:
  - name: client-name
    image: client-name
    volumeMounts:
    - name: secret-volume
      mountPath: /data/crt
  volumes:
  - name: secret-volume
    secret:
      secretName: my-cluster-cluster-cert

  2. Use the certificate with clients that use certificates in X.509 format.

10.7. Configuring external clients to trust the cluster CA

This procedure describes how to configure a Kafka client that resides outside the Kubernetes cluster – connecting to the external listener on port 9094 – to trust the cluster CA certificate. Follow this procedure when setting up the client and during the renewal period, when the old clients CA certificate is replaced.

Follow the steps to configure trust certificates that are signed by the cluster CA for Java-based Kafka Producer, Consumer, and Streams APIs.

Choose the steps to follow according to the certificate format of the cluster CA: PKCS #12 (.p12) or PEM (.crt).

The steps describe how to obtain the certificate from the Cluster Secret that verifies the identity of the Kafka cluster.

Important
 The <cluster-name>-cluster-ca-cert Secret will contain more than one CA certificate during the CA certificate renewal period. Clients must add all of them to their truststores.
Prerequisites

  • The Cluster Operator must be running.

  • There needs to be a Kafka resource within the Kubernetes cluster.

  • You need a Kafka client application outside the Kubernetes cluster that will connect using TLS, and needs to trust the cluster CA certificate.

Using PKCS #12 format (.p12)

  1. Extract the cluster CA certificate and password from the generated <cluster-name>-cluster-ca-cert Secret.

    kubectl get secret <cluster-name>-cluster-ca-cert -o jsonpath='{.data.ca\.p12}' | base64 -d > ca.p12
    kubectl get secret <cluster-name>-cluster-ca-cert -o jsonpath='{.data.ca\.password}' | base64 -d > ca.password

  2. Configure the Kafka client with the following properties:

    • A security protocol option:

      • security.protocol: SSL when using TLS for encryption (with or without TLS authentication).

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

    • ssl.truststore.location with the truststore location where the certificates were imported.

    • ssl.truststore.password with the password for accessing the truststore. This property can be omitted if it is not needed by the truststore.

    • ssl.truststore.type=PKCS12 to identify the truststore type.

Using PEM format (.crt)

  1. Extract the cluster CA certificate from the generated <cluster-name>-cluster-ca-cert Secret.

    kubectl get secret <cluster-name>-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt

  2. Use the certificate with clients that use certificates in X.509 format.

10.8. Kafka listener certificates

You can provide your own server certificates and private keys for the following types of listeners:

  • TLS listeners for inter-cluster communication

  • External listeners (route, loadbalancer, ingress, and nodeport types) which have TLS encryption enabled, for communication between Kafka clients and Kafka brokers

These user-provided certificates are called Kafka listener certificates.

Providing Kafka listener certificates for external listeners allows you to leverage existing security infrastructure, such as your organization’s private CA or a public CA. Kafka clients will connect to Kafka brokers using Kafka listener certificates rather than certificates signed by the cluster CA or clients CA.

You must manually renew Kafka listener certificates when needed.

10.8.1. Providing your own Kafka listener certificates

This procedure shows how to configure a listener to use your own private key and server certificate, called a Kafka listener certificate.

Your client applications should use the CA public key as a trusted certificate in order to verify the identity of the Kafka broker.

Prerequisites

  • A Kubernetes cluster.

  • The Cluster Operator is running.

  • For each listener, a compatible server certificate signed by an external CA.

Procedure

  1. Create a Secret containing your private key and server certificate:

    kubectl create secret generic my-secret --from-file=my-listener-key.key --from-file=my-listener-certificate.crt

  2. Edit the Kafka resource for your cluster. Configure the listener to use your Secret, certificate file, and private key file in the configuration.brokerCertChainAndKey property.

    Example configuration for a loadbalancer external listener with TLS encryption enabled
    # ...
listeners:
  plain: {}
  external:
    type: loadbalancer
    configuration:
      brokerCertChainAndKey:
        secretName: my-secret
        certificate: my-listener-certificate.crt
        key: my-listener-key.key
    tls: true
    authentication:
      type: tls
# ...
    Example configuration for a TLS listener
    # ...
listeners:
  plain: {}
  tls:
    configuration:
      brokerCertChainAndKey:
        secretName: my-secret
        certificate: my-listener-certificate.pem
        key: my-listener-key.key
    authentication:
      type: tls
# ...

  3. Apply the new configuration to create or update the resource:

    kubectl apply -f kafka.yaml

    The Cluster Operator starts a rolling update of the Kafka cluster, which updates the configuration of the listeners.

    Note
    		 A rolling update is also started if you update a Kafka listener certificate in a Secret that is already used by a TLS or external listener.
Additional resources

10.8.2. Alternative subjects in server certificates for Kafka listeners

In order to use TLS hostname verification with your own Kafka listener certificates, you must use the correct Subject Alternative Names (SANs) for each listener. The certificate SANs must specify hostnames for:

  • All of the Kafka brokers in your cluster

  • The Kafka cluster bootstrap service

You can use wildcard certificates if they are supported by your CA.

TLS listener SAN examples

Use the following examples to help you specify hostnames of the SANs in your certificates for TLS listeners.

Wildcards example
//Kafka brokers
*.<cluster-name>-kafka-brokers
*.<cluster-name>-kafka-brokers.<namespace>.svc

// Bootstrap service
<cluster-name>-kafka-bootstrap
<cluster-name>-kafka-bootstrap.<namespace>.svc
Non-wildcards example
// Kafka brokers
<cluster-name>-kafka-0.<cluster-name>-kafka-brokers
<cluster-name>-kafka-0.<cluster-name>-kafka-brokers.<namespace>.svc
<cluster-name>-kafka-1.<cluster-name>-kafka-brokers
<cluster-name>-kafka-1.<cluster-name>-kafka-brokers.<namespace>.svc
# ...

// Bootstrap service
<cluster-name>-kafka-bootstrap
<cluster-name>-kafka-bootstrap.<namespace>.svc
External listener SAN examples

For external listeners which have TLS encryption enabled, the hostnames you need to specify in certificates depends on the external listener type.

External listener type In the SANs, specify…​

Route

Addresses of all Kafka broker Routes and the address of the bootstrap Route.

You can use a matching wildcard name.

loadbalancer

Addresses of all Kafka broker loadbalancers and the bootstrap loadbalancer address.

You can use a matching wildcard name.

NodePort

Addresses of all Kubernetes worker nodes that the Kafka broker pods might be scheduled to.

You can use a matching wildcard name.
Additional resources

11. Managing Strimzi

This chapter covers tasks to maintain a deployment of Strimzi.

11.1. Discovering services using labels and annotations

Service discovery makes it easier for client applications running in the same Kubernetes cluster as Strimzi to interact with a Kafka cluster.

A service discovery label and annotation is generated for services used to access the Kafka cluster:

  • Internal Kafka bootstrap service

  • HTTP Bridge service

The label helps to make the service discoverable, and the annotation provides connection details that a client application can use to make the connection.

The service discovery label, strimzi.io/discovery, is set as true for the Service resources. The service discovery annotation has the same key, providing connection details in JSON format for each service.

Example internal Kafka bootstrap service

apiVersion: v1
kind: Service
metadata:
  annotations:
    strimzi.io/discovery: |-
      [ {
        "port" : 9092,
        "tls" : false,
        "protocol" : "kafka",
        "auth" : "scram-sha-512"
      }, {
        "port" : 9093,
        "tls" : true,
        "protocol" : "kafka",
        "auth" : "tls"
      } ]
  labels:
    strimzi.io/cluster: my-cluster
    strimzi.io/discovery: "true"
    strimzi.io/kind: Kafka
    strimzi.io/name: my-cluster-kafka-bootstrap
  name: my-cluster-kafka-bootstrap
spec:
  #...

Example HTTP Bridge service

apiVersion: v1
kind: Service
metadata:
  annotations:
    strimzi.io/discovery: |-
      [ {
        "port" : 8080,
        "tls" : false,
        "auth" : "none",
        "protocol" : "http"
      } ]
  labels:
    strimzi.io/cluster: my-bridge
    strimzi.io/discovery: "true"
    strimzi.io/kind: KafkaBridge
    strimzi.io/name: my-bridge-bridge-service

11.1.1. Returning connection details on services

You can find the services by specifying the discovery label when fetching services from the command line or a corresponding API call.

kubectl get service -l strimzi.io/discovery=true

The connection details are returned when retrieving the service discovery label.

11.2. Checking the status of a custom resource

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

11.2.1. Strimzi custom resource status information

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

Strimzi resource Schema reference Publishes status information on…​

Kafka

KafkaStatus schema reference

The Kafka cluster.

KafkaConnect

KafkaConnectStatus schema reference

The Kafka Connect cluster, if deployed.

KafkaConnectS2I

KafkaConnectS2IStatus schema reference

The Kafka Connect cluster with Source-to-Image support, if deployed.

KafkaConnector

KafkaConnectorStatus schema reference

KafkaConnector resources, if deployed.

KafkaMirrorMaker

KafkaMirrorMakerStatus schema reference

The Kafka MirrorMaker tool, if deployed.

KafkaTopic

KafkaTopicStatus schema reference

Kafka topics in your Kafka cluster.

KafkaUser

KafkaUserStatus schema reference

Kafka users in your Kafka cluster.

KafkaBridge

KafkaBridgeStatus schema reference

The Strimzi Kafka Bridge, if deployed.

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

  • Current state, in the status.conditions property

  • Last observed generation, in the status.observedGeneration property

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

  • KafkaConnectStatus provides the REST API endpoint for Kafka Connect connectors.

  • KafkaUserStatus provides the user name of the Kafka user and the Secret in which their credentials are stored.

  • KafkaBridgeStatus provides the HTTP address at which external client applications can access the Bridge service.

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

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

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

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

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

  1. Status conditions describe criteria related to the status that cannot be deduced from the existing resource information, or are specific to the instance of a resource.

  2. The Ready condition indicates whether the Cluster Operator currently considers the Kafka cluster able to handle traffic.

  3. The observedGeneration indicates the generation of the Kafka custom resource that was last reconciled by the Cluster Operator.

  4. The listeners describe the current Kafka bootstrap addresses by type.

    Important
    		 The address in the custom resource status for external listeners with type nodeport is currently not supported.
Note
 The Kafka bootstrap addresses listed in the status do not signify that those endpoints or the Kafka cluster is in a ready state.
Accessing status information

You can access status information for a resource from the command line. For more information, see Finding the status of a custom resource.

11.2.2. Finding the status of a custom resource

This procedure describes how to find the status of a custom resource.

Prerequisites

  • A Kubernetes cluster.

  • The Cluster Operator is running.

Procedure

  • Specify the custom resource and use the -o jsonpath option to apply a standard JSONPath expression to select the status property:

    kubectl get kafka <kafka_resource_name> -o jsonpath='{.status}'

    This expression returns all the status information for the specified custom resource. You can use dot notation, such as status.listeners or status.observedGeneration, to fine-tune the status information you wish to see.

Additional resources

11.3. Recovering a cluster from persistent volumes

You can recover a Kafka cluster from persistent volumes (PVs) if they are still present.

You might want to do this, for example, after:

  • A namespace was deleted unintentionally

  • A whole Kubernetes cluster is lost, but the PVs remain in the infrastructure

11.3.1. Recovery from namespace deletion

Recovery from namespace deletion is possible because of the relationship between persistent volumes and namespaces. A PersistentVolume (PV) is a storage resource that lives outside of a namespace. A PV is mounted into a Kafka pod using a PersistentVolumeClaim (PVC), which lives inside a namespace.

The reclaim policy for a PV tells a cluster how to act when a namespace is deleted. If the reclaim policy is set as:

  • Delete (default), PVs are deleted when PVCs are deleted within a namespace

  • Retain, PVs are not deleted when a namespace is deleted

To ensure that you can recover from a PV if a namespace is deleted unintentionally, the policy must be reset from Delete to Retain in the PV specification using the persistentVolumeReclaimPolicy property:

apiVersion: v1
kind: PersistentVolume
# ...
spec:
  # ...
  persistentVolumeReclaimPolicy: Retain

Alternatively, PVs can inherit the reclaim policy of an associated storage class. Storage classes are used for dynamic volume allocation.

By configuring the reclaimPolicy property for the storage class, PVs that use the storage class are created with the appropriate reclaim policy. The storage class is configured for the PV using the storageClassName property.

apiVersion: v1
kind: StorageClass
metadata:
  name: gp2-retain
parameters:
  # ...
# ...
reclaimPolicy: Retain
apiVersion: v1
kind: PersistentVolume
# ...
spec:
  # ...
  storageClassName: gp2-retain
Note
 If you are using Retain as the reclaim policy, but you want to delete an entire cluster, you need to delete the PVs manually. Otherwise they will not be deleted, and may cause unnecessary expenditure on resources.

11.3.2. Recovery from loss of a Kubernetes cluster

When a cluster is lost, you can use the data from disks/volumes to recover the cluster if they were preserved within the infrastructure. The recovery procedure is the same as with namespace deletion, assuming PVs can be recovered and they were created manually.

11.3.3. Recovering a deleted cluster from persistent volumes

This procedure describes how to recover a deleted cluster from persistent volumes (PVs).

In this situation, the Topic Operator identifies that topics exist in Kafka, but the KafkaTopic resources do not exist.

When you get to the step to recreate your cluster, you have two options:

  1. Use Option 1 when you can recover all KafkaTopic resources.

    The KafkaTopic resources must therefore be recovered before the cluster is started so that the corresponding topics are not deleted by the Topic Operator.

  2. Use Option 2 when you are unable to recover all KafkaTopic resources.

    This time you deploy your cluster without the Topic Operator, delete the Topic Operator data in ZooKeeper, and then redeploy it so that the Topic Operator can recreate the KafkaTopic resources from the corresponding topics.

Note
 If the Topic Operator is not deployed, you only need to recover the PersistentVolumeClaim (PVC) resources.
Before you begin

In this procedure, it is essential that PVs are mounted into the correct PVC to avoid data corruption. A volumeName is specified for the PVC and this must match the name of the PV.

For more information, see:

Note
 The procedure does not include recovery of KafkaUser resources, which must be recreated manually. If passwords and certificates need to be retained, secrets must be recreated before creating the KafkaUser resources.
Procedure

  1. Check information on the PVs in the cluster:

    kubectl get pv

    Information is presented for PVs with data.

    Example output showing columns important to this procedure:

    NAME                                         RECLAIMPOLICY CLAIM
pvc-5e9c5c7f-3317-11ea-a650-06e1eadd9a4c ... Retain ...    myproject/data-my-cluster-zookeeper-1
pvc-5e9cc72d-3317-11ea-97b0-0aef8816c7ea ... Retain ...    myproject/data-my-cluster-zookeeper-0
pvc-5ead43d1-3317-11ea-97b0-0aef8816c7ea ... Retain ...    myproject/data-my-cluster-zookeeper-2
pvc-7e1f67f9-3317-11ea-a650-06e1eadd9a4c ... Retain ...    myproject/data-0-my-cluster-kafka-0
pvc-7e21042e-3317-11ea-9786-02deaf9aa87e ... Retain ...    myproject/data-0-my-cluster-kafka-1
pvc-7e226978-3317-11ea-97b0-0aef8816c7ea ... Retain ...    myproject/data-0-my-cluster-kafka-2

    • NAME shows the name of each PV.

    • RECLAIM POLICY shows that PVs are retained.

    • CLAIM shows the link to the original PVCs.

  2. Recreate the original namespace:

    kubectl create namespace myproject

  3. Recreate the original PVC resource specifications, linking the PVCs to the appropriate PV:

    For example:

    apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: data-0-my-cluster-kafka-0
spec:
  accessModes:
  - ReadWriteOnce
  resources:
    requests:
      storage: 100Gi
  storageClassName: gp2-retain
  volumeMode: Filesystem
  volumeName: pvc-7e1f67f9-3317-11ea-a650-06e1eadd9a4c

  4. Edit the PV specifications to delete the claimRef properties that bound the original PVC.

    For example:

    apiVersion: v1
kind: PersistentVolume
metadata:
  annotations:
    kubernetes.io/createdby: aws-ebs-dynamic-provisioner
    pv.kubernetes.io/bound-by-controller: "yes"
    pv.kubernetes.io/provisioned-by: kubernetes.io/aws-ebs
  creationTimestamp: "<date>"
  finalizers:
  - kubernetes.io/pv-protection
  labels:
    failure-domain.beta.kubernetes.io/region: eu-west-1
    failure-domain.beta.kubernetes.io/zone: eu-west-1c
  name: pvc-7e226978-3317-11ea-97b0-0aef8816c7ea
  resourceVersion: "39431"
  selfLink: /api/v1/persistentvolumes/pvc-7e226978-3317-11ea-97b0-0aef8816c7ea
  uid: 7efe6b0d-3317-11ea-a650-06e1eadd9a4c
spec:
  accessModes:
  - ReadWriteOnce
  awsElasticBlockStore:
    fsType: xfs
    volumeID: aws://eu-west-1c/vol-09db3141656d1c258
  capacity:
    storage: 100Gi
  claimRef:
    apiVersion: v1
    kind: PersistentVolumeClaim
    name: data-0-my-cluster-kafka-2
    namespace: myproject
    resourceVersion: "39113"
    uid: 54be1c60-3319-11ea-97b0-0aef8816c7ea
  nodeAffinity:
    required:
      nodeSelectorTerms:
      - matchExpressions:
        - key: failure-domain.beta.kubernetes.io/zone
          operator: In
          values:
          - eu-west-1c
        - key: failure-domain.beta.kubernetes.io/region
          operator: In
          values:
          - eu-west-1
  persistentVolumeReclaimPolicy: Retain
  storageClassName: gp2-retain
  volumeMode: Filesystem

    In the example, the following properties are deleted:

    claimRef:
  apiVersion: v1
  kind: PersistentVolumeClaim
  name: data-0-my-cluster-kafka-2
  namespace: myproject
  resourceVersion: "39113"
  uid: 54be1c60-3319-11ea-97b0-0aef8816c7ea

  5. Deploy the Cluster Operator.

    kubectl apply -f install/cluster-operator -n my-project

  6. Recreate your cluster.

    Follow the steps depending on whether or not you have all the KafkaTopic resources needed to recreate your cluster.

    Option 1: If you have all the KafkaTopic resources that existed before you lost your cluster, including internal topics such as committed offsets from __consumer_offsets:

    1. Recreate all KafkaTopic resources.

      It is essential that you recreate the resources before deploying the cluster, or the Topic Operator will delete the topics.

    2. Deploy the Kafka cluster.

      For example:

      kubectl apply -f kafka.yaml

    Option 2: If you do not have all the KafkaTopic resources that existed before you lost your cluster:

    1. Deploy the Kafka cluster, as with the first option, but without the Topic Operator by removing the topicOperator property from the Kafka resource before deploying.

      If you include the Topic Operator in the deployment, the Topic Operator will delete all the topics.

    2. Run an exec command to one of the Kafka broker pods to open the ZooKeeper shell script.

      For example, where my-cluster-kafka-0 is the name of the broker pod:

      kubectl exec my-cluster-kafka-0 bin/zookeeper-shell.sh localhost:2181

    3. Delete the whole /strimzi path to remove the Topic Operator storage:

      deleteall /strimzi

    4. Enable the Topic Operator by redeploying the Kafka cluster with the topicOperator property to recreate the KafkaTopic resources.

      For example:

      apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  #...
  entityOperator:
    topicOperator: {} (1)
    #...

    1. Here we show the default configuration, which has no additional properties. You specify the required configuration using the properties described in EntityTopicOperatorSpec schema reference.

  7. Verify the recovery by listing the KafkaTopic resources:

    kubectl get KafkaTopic

11.4. Uninstalling Strimzi

This procedure describes how to uninstall Strimzi and remove resources related to the deployment.

Prerequisites

In order to perform this procedure, identify resources created specifically for a deployment and referenced from the Strimzi resource.

Such resources include:

  • Secrets (Custom CAs and certificates, Kafka Connect secrets, and other Kafka secrets)

  • Logging ConfigMaps (of type external)

These are resources referenced by Kafka, KafkaConnect, KafkaConnectS2I, KafkaMirrorMaker, or KafkaBridge configuration.

Procedure

  1. Delete the Cluster Operator Deployment, related CustomResourceDefinitions, and RBAC resources:

    kubectl delete -f install/cluster-operator
    Warning
    		 Deleting CustomResourceDefinitions results in the garbage collection of the corresponding custom resources (Kafka, KafkaConnect, KafkaConnectS2I, KafkaMirrorMaker, or KafkaBridge) and the resources dependent on them (Deployments, StatefulSets, and other dependent resources).

  2. Delete the resources you identified in the prerequisites.

Appendix A: Frequently asked questions

A.1.1. Why do I need cluster administrator privileges to install Strimzi?

To install Strimzi, you need to be able to create the following cluster-scoped resources:

  • Custom Resource Definitions (CRDs) to instruct Kubernetes about resources that are specific to Strimzi, such as Kafka and KafkaConnect

  • ClusterRoles and ClusterRoleBindings

Cluster-scoped resources, which are not scoped to a particular Kubernetes namespace, typically require cluster administrator privileges to install.

As a cluster administrator, you can inspect all the resources being installed (in the /install/ directory) to ensure that the ClusterRoles do not grant unnecessary privileges.

After installation, the Cluster Operator runs as a regular Deployment, so any standard (non-admin) Kubernetes user with privileges to access the Deployment can configure it. The cluster administrator can grant standard users the privileges necessary to manage Kafka custom resources.

See also:

A.1.2. Why does the Cluster Operator need to create ClusterRoleBindings?

Kubernetes has built-in privilege escalation prevention, which means that the Cluster Operator cannot grant privileges it does not have itself, specifically, it cannot grant such privileges in a namespace it cannot access. Therefore, the Cluster Operator must have the privileges necessary for all the components it orchestrates.

The Cluster Operator needs to be able to grant access so that:

  • The Topic Operator can manage KafkaTopics, by creating Roles and RoleBindings in the namespace that the operator runs in

  • The User Operator can manage KafkaUsers, by creating Roles and RoleBindings in the namespace that the operator runs in

  • The failure domain of a Node is discovered by Strimzi, by creating a ClusterRoleBinding

When using rack-aware partition assignment, the broker pod needs to be able to get information about the Node it is running on, for example, the Availability Zone in Amazon AWS. A Node is a cluster-scoped resource, so access to it can only be granted through a ClusterRoleBinding, not a namespace-scoped RoleBinding.

A.1.3. Can standard Kubernetes users create Kafka custom resources?

By default, standard Kubernetes users will not have the privileges necessary to manage the custom resources handled by the Cluster Operator. The cluster administrator can grant a user the necessary privileges using Kubernetes RBAC resources.

For more information, see Strimzi Administrators.

A.1.4. What do the failed to acquire lock warnings in the log mean?

For each cluster, the Cluster Operator executes only one operation at a time. The Cluster Operator uses locks to make sure that there are never two parallel operations running for the same cluster. Other operations must wait until the current operation completes before the lock is released.

INFO

Examples of cluster operations include cluster creation, rolling update, scale down , and scale up.

If the waiting time for the lock takes too long, the operation times out and the following warning message is printed to the log:

2018-03-04 17:09:24 WARNING AbstractClusterOperations:290 - Failed to acquire lock for kafka cluster lock::kafka::myproject::my-cluster

Depending on the exact configuration of STRIMZI_FULL_RECONCILIATION_INTERVAL_MS and STRIMZI_OPERATION_TIMEOUT_MS, this warning message might appear occasionally without indicating any underlying issues. Operations that time out are picked up in the next periodic reconciliation, so that the operation can acquire the lock and execute again.

Should this message appear periodically, even in situations when there should be no other operations running for a given cluster, it might indicate that the lock was not properly released due to an error. If this is the case, try restarting the Cluster Operator.

A.1.5. Why is hostname verification failing when connecting to NodePorts using TLS?

Currently, off-cluster access using NodePorts with TLS encryption enabled does not support TLS hostname verification. As a result, the clients that verify the hostname will fail to connect. For example, the Java client will fail with the following exception:

Caused by: java.security.cert.CertificateException: No subject alternative names matching IP address 168.72.15.231 found
 at sun.security.util.HostnameChecker.matchIP(HostnameChecker.java:168)
 at sun.security.util.HostnameChecker.match(HostnameChecker.java:94)
 at sun.security.ssl.X509TrustManagerImpl.checkIdentity(X509TrustManagerImpl.java:455)
 at sun.security.ssl.X509TrustManagerImpl.checkIdentity(X509TrustManagerImpl.java:436)
 at sun.security.ssl.X509TrustManagerImpl.checkTrusted(X509TrustManagerImpl.java:252)
 at sun.security.ssl.X509TrustManagerImpl.checkServerTrusted(X509TrustManagerImpl.java:136)
 at sun.security.ssl.ClientHandshaker.serverCertificate(ClientHandshaker.java:1501)
 ... 17 more

To connect, you must disable hostname verification. In the Java client, you can do this by setting the configuration option ssl.endpoint.identification.algorithm to an empty string.

When configuring the client using a properties file, you can do it this way:

ssl.endpoint.identification.algorithm=

When configuring the client directly in Java, set the configuration option to an empty string:

props.put("ssl.endpoint.identification.algorithm", "");

Appendix B: Custom Resource API Reference

B.1. Kafka schema reference

Property Description

spec

The specification of the Kafka and ZooKeeper clusters, and Topic Operator.

KafkaSpec

status

The status of the Kafka and ZooKeeper clusters, and Topic Operator.

KafkaStatus

B.2. KafkaSpec schema reference

Used in: Kafka

Property Description

kafka

Configuration of the Kafka cluster.

KafkaClusterSpec

zookeeper

Configuration of the ZooKeeper cluster.

ZookeeperClusterSpec

topicOperator

The property topicOperator has been deprecated. This feature should now be configured at path spec.entityOperator.topicOperator. Configuration of the Topic Operator.

TopicOperatorSpec

entityOperator

Configuration of the Entity Operator.

EntityOperatorSpec

clusterCa

Configuration of the cluster certificate authority.

CertificateAuthority

clientsCa

Configuration of the clients certificate authority.

CertificateAuthority

cruiseControl

Configuration for Cruise Control deployment. Deploys a Cruise Control instance when specified.

CruiseControlSpec

jmxTrans

Configuration for JmxTrans. When the property is present a JmxTrans deployment is created for gathering JMX metrics from each Kafka broker. For more information see JmxTrans GitHub.

JmxTransSpec

kafkaExporter

Configuration of the Kafka Exporter. Kafka Exporter can provide additional metrics, for example lag of consumer group at topic/partition.

KafkaExporterSpec

maintenanceTimeWindows

A list of time windows for maintenance tasks (that is, certificates renewal). Each time window is defined by a cron expression.

string array

B.3. KafkaClusterSpec schema reference

Used in: KafkaSpec

Property Description

replicas

The number of pods in the cluster.

integer

image

The docker image for the pods. The default value depends on the configured Kafka.spec.kafka.version.

string

storage

Storage configuration (disk). Cannot be updated. The type depends on the value of the storage.type property within the given object, which must be one of [ephemeral, persistent-claim, jbod].

EphemeralStorage, PersistentClaimStorage, JbodStorage

listeners

Configures listeners of Kafka brokers.

KafkaListeners

authorization

Authorization configuration for Kafka brokers. The type depends on the value of the authorization.type property within the given object, which must be one of [simple, keycloak].

KafkaAuthorizationSimple, KafkaAuthorizationKeycloak

config

The kafka broker config. Properties with the following prefixes cannot be set: listeners, advertised., broker., listener., host.name, port, inter.broker.listener.name, sasl., ssl., security., password., principal.builder.class, log.dir, zookeeper.connect, zookeeper.set.acl, authorizer., super.user, cruise.control.metrics.topic, cruise.control.metrics.reporter.bootstrap.servers (with the exception of: zookeeper.connection.timeout.ms, ssl.cipher.suites, ssl.protocol, ssl.enabled.protocols,cruise.control.metrics.topic.num.partitions, cruise.control.metrics.topic.replication.factor, cruise.control.metrics.topic.retention.ms).

map

rack

Configuration of the broker.rack broker config.

Rack

brokerRackInitImage

The image of the init container used for initializing the broker.rack.

string

affinity

The property affinity has been deprecated. This feature should now be configured at path spec.kafka.template.pod.affinity. The pod’s affinity rules. See external documentation of core/v1 affinity.

Affinity

tolerations

The property tolerations has been deprecated. This feature should now be configured at path spec.kafka.template.pod.tolerations. The pod’s tolerations. See external documentation of core/v1 toleration.

Toleration array

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

jvmOptions

JVM Options for pods.

JvmOptions

jmxOptions

JMX Options for Kafka brokers.

KafkaJmxOptions

resources

CPU and memory resources to reserve.

ResourceRequirements

metrics

The Prometheus JMX Exporter configuration. See https://github.com/prometheus/jmx_exporter for details of the structure of this configuration.

map

logging

Logging configuration for Kafka. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

tlsSidecar

TLS sidecar configuration.

TlsSidecar

template

Template for Kafka cluster resources. The template allows users to specify how are the StatefulSet, Pods and Services generated.

KafkaClusterTemplate

version

The kafka broker version. Defaults to 2.5.0. Consult the user documentation to understand the process required to upgrade or downgrade the version.

string

B.4. EphemeralStorage schema reference

Used in: JbodStorage, KafkaClusterSpec, ZookeeperClusterSpec

The type property is a discriminator that distinguishes the use of the type EphemeralStorage from PersistentClaimStorage. It must have the value ephemeral for the type EphemeralStorage.

Property Description

id

Storage identification number. It is mandatory only for storage volumes defined in a storage of type 'jbod'.

integer

sizeLimit

When type=ephemeral, defines the total amount of local storage required for this EmptyDir volume (for example 1Gi).

string

type

Must be ephemeral.

string

B.5. PersistentClaimStorage schema reference

Used in: JbodStorage, KafkaClusterSpec, ZookeeperClusterSpec

The type property is a discriminator that distinguishes the use of the type PersistentClaimStorage from EphemeralStorage. It must have the value persistent-claim for the type PersistentClaimStorage.

Property Description

type

Must be persistent-claim.

string

size

When type=persistent-claim, defines the size of the persistent volume claim (i.e 1Gi). Mandatory when type=persistent-claim.

string

selector

Specifies a specific persistent volume to use. It contains key:value pairs representing labels for selecting such a volume.

map

deleteClaim

Specifies if the persistent volume claim has to be deleted when the cluster is un-deployed.

boolean

class

The storage class to use for dynamic volume allocation.

string

id

Storage identification number. It is mandatory only for storage volumes defined in a storage of type 'jbod'.

integer

overrides

Overrides for individual brokers. The overrides field allows to specify a different configuration for different brokers.

PersistentClaimStorageOverride array

B.6. PersistentClaimStorageOverride schema reference

Used in: PersistentClaimStorage

Property Description

class

The storage class to use for dynamic volume allocation for this broker.

string

broker

Id of the kafka broker (broker identifier).

integer

B.7. JbodStorage schema reference

Used in: KafkaClusterSpec

The type property is a discriminator that distinguishes the use of the type JbodStorage from EphemeralStorage, PersistentClaimStorage. It must have the value jbod for the type JbodStorage.

Property Description

type

Must be jbod.

string

volumes

List of volumes as Storage objects representing the JBOD disks array.

EphemeralStorage, PersistentClaimStorage array

B.8. KafkaListeners schema reference

Used in: KafkaClusterSpec

Property Description

plain

Configures plain listener on port 9092.

KafkaListenerPlain

tls

Configures TLS listener on port 9093.

KafkaListenerTls

external

Configures external listener on port 9094. The type depends on the value of the external.type property within the given object, which must be one of [route, loadbalancer, nodeport, ingress].

KafkaListenerExternalRoute, KafkaListenerExternalLoadBalancer, KafkaListenerExternalNodePort, KafkaListenerExternalIngress

B.9. KafkaListenerPlain schema reference

Used in: KafkaListeners

Property Description

authentication

Authentication configuration for this listener. Since this listener does not use TLS transport you cannot configure an authentication with type: tls. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, oauth].

KafkaListenerAuthenticationTls, KafkaListenerAuthenticationScramSha512, KafkaListenerAuthenticationOAuth

networkPolicyPeers

List of peers which should be able to connect to this listener. Peers in this list are combined using a logical OR operation. If this field is empty or missing, all connections will be allowed for this listener. If this field is present and contains at least one item, the listener only allows the traffic which matches at least one item in this list. See external documentation of networking.k8s.io/v1 networkpolicypeer.

NetworkPolicyPeer array

B.10. KafkaListenerAuthenticationTls schema reference

Used in: KafkaListenerExternalIngress, KafkaListenerExternalLoadBalancer, KafkaListenerExternalNodePort, KafkaListenerExternalRoute, KafkaListenerPlain, KafkaListenerTls

The type property is a discriminator that distinguishes the use of the type KafkaListenerAuthenticationTls from KafkaListenerAuthenticationScramSha512, KafkaListenerAuthenticationOAuth. It must have the value tls for the type KafkaListenerAuthenticationTls.

Property Description

type

Must be tls.

string

B.11. KafkaListenerAuthenticationScramSha512 schema reference

Used in: KafkaListenerExternalIngress, KafkaListenerExternalLoadBalancer, KafkaListenerExternalNodePort, KafkaListenerExternalRoute, KafkaListenerPlain, KafkaListenerTls

The type property is a discriminator that distinguishes the use of the type KafkaListenerAuthenticationScramSha512 from KafkaListenerAuthenticationTls, KafkaListenerAuthenticationOAuth. It must have the value scram-sha-512 for the type KafkaListenerAuthenticationScramSha512.

Property Description

type

Must be scram-sha-512.

string

B.12. KafkaListenerAuthenticationOAuth schema reference

Used in: KafkaListenerExternalIngress, KafkaListenerExternalLoadBalancer, KafkaListenerExternalNodePort, KafkaListenerExternalRoute, KafkaListenerPlain, KafkaListenerTls

The type property is a discriminator that distinguishes the use of the type KafkaListenerAuthenticationOAuth from KafkaListenerAuthenticationTls, KafkaListenerAuthenticationScramSha512. It must have the value oauth for the type KafkaListenerAuthenticationOAuth.

Property Description

accessTokenIsJwt

Configure whether the access token is treated as JWT. This must be set to false if the authorization server returns opaque tokens. Defaults to true.

boolean

checkAccessTokenType

Configure whether the access token type check is performed or not. This should be set to false if the authorization server does not include 'typ' claim in JWT token. Defaults to true.

boolean

checkIssuer

Enable or disable issuer checking. By default issuer is checked using the value configured by validIssuerUri. Default value is true.

boolean

clientId

OAuth Client ID which the Kafka broker can use to authenticate against the authorization server and use the introspect endpoint URI.

string

clientSecret

Link to Kubernetes Secret containing the OAuth client secret which the Kafka broker can use to authenticate against the authorization server and use the introspect endpoint URI.

GenericSecretSource

disableTlsHostnameVerification

Enable or disable TLS hostname verification. Default value is false.

boolean

enableECDSA

Enable or disable ECDSA support by installing BouncyCastle crypto provider. Default value is false.

boolean

fallbackUserNameClaim

The fallback username claim to be used for the user id if the claim specified by userNameClaim is not present. This is useful when client_credentials authentication only results in the client id being provided in another claim. It only takes effect if userNameClaim is set.

string

fallbackUserNamePrefix

The prefix to use with the value of fallbackUserNameClaim to construct the user id. This only takes effect if fallbackUserNameClaim is true, and the value is present for the claim. Mapping usernames and client ids into the same user id space is useful in preventing name collisions.

string

introspectionEndpointUri

URI of the token introspection endpoint which can be used to validate opaque non-JWT tokens.

string

jwksEndpointUri

URI of the JWKS certificate endpoint, which can be used for local JWT validation.

string

jwksExpirySeconds

Configures how often are the JWKS certificates considered valid. The expiry interval has to be at least 60 seconds longer then the refresh interval specified in jwksRefreshSeconds. Defaults to 360 seconds.

integer

jwksRefreshSeconds

Configures how often are the JWKS certificates refreshed. The refresh interval has to be at least 60 seconds shorter then the expiry interval specified in jwksExpirySeconds. Defaults to 300 seconds.

integer

tlsTrustedCertificates

Trusted certificates for TLS connection to the OAuth server.

CertSecretSource array

type

Must be oauth.

string

userInfoEndpointUri

URI of the User Info Endpoint to use as a fallback to obtaining the user id when the Introspection Endpoint does not return information that can be used for the user id.

string

userNameClaim

Name of the claim from the JWT authentication token, Introspection Endpoint response or User Info Endpoint response which will be used to extract the user id. Defaults to sub.

string

validIssuerUri

URI of the token issuer used for authentication.

string

validTokenType

Valid value for the token_type attribute returned by the Introspection Endpoint. No default value, and not checked by default.

string

B.13. GenericSecretSource schema reference

Used in: KafkaClientAuthenticationOAuth, KafkaListenerAuthenticationOAuth

Property Description

key

The key under which the secret value is stored in the Kubernetes Secret.

string

secretName

The name of the Kubernetes Secret containing the secret value.

string

B.14. CertSecretSource schema reference

Used in: KafkaAuthorizationKeycloak, KafkaBridgeTls, KafkaClientAuthenticationOAuth, KafkaConnectTls, KafkaListenerAuthenticationOAuth, KafkaMirrorMaker2Tls, KafkaMirrorMakerTls

Property Description

certificate

The name of the file certificate in the Secret.

string

secretName

The name of the Secret containing the certificate.

string

B.15. KafkaListenerTls schema reference

Used in: KafkaListeners

Property Description

authentication

Authentication configuration for this listener. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, oauth].

KafkaListenerAuthenticationTls, KafkaListenerAuthenticationScramSha512, KafkaListenerAuthenticationOAuth

configuration

Configuration of TLS listener.

TlsListenerConfiguration

networkPolicyPeers

List of peers which should be able to connect to this listener. Peers in this list are combined using a logical OR operation. If this field is empty or missing, all connections will be allowed for this listener. If this field is present and contains at least one item, the listener only allows the traffic which matches at least one item in this list. See external documentation of networking.k8s.io/v1 networkpolicypeer.

NetworkPolicyPeer array

B.16. TlsListenerConfiguration schema reference

Used in: KafkaListenerTls

Property Description

brokerCertChainAndKey

Reference to the Secret which holds the certificate and private key pair. The certificate can optionally contain the whole chain.

CertAndKeySecretSource

B.17. CertAndKeySecretSource schema reference

Used in: IngressListenerConfiguration, KafkaClientAuthenticationTls, KafkaListenerExternalConfiguration, NodePortListenerConfiguration, TlsListenerConfiguration

Property Description

certificate

The name of the file certificate in the Secret.

string

key

The name of the private key in the Secret.

string

secretName

The name of the Secret containing the certificate.

string

B.18. KafkaListenerExternalRoute schema reference

Used in: KafkaListeners

The type property is a discriminator that distinguishes the use of the type KafkaListenerExternalRoute from KafkaListenerExternalLoadBalancer, KafkaListenerExternalNodePort, KafkaListenerExternalIngress. It must have the value route for the type KafkaListenerExternalRoute.

Property Description

type

Must be route.

string

authentication

Authentication configuration for Kafka brokers. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, oauth].

KafkaListenerAuthenticationTls, KafkaListenerAuthenticationScramSha512, KafkaListenerAuthenticationOAuth

overrides

Overrides for external bootstrap and broker services and externally advertised addresses.

RouteListenerOverride

configuration

External listener configuration.

KafkaListenerExternalConfiguration

networkPolicyPeers

List of peers which should be able to connect to this listener. Peers in this list are combined using a logical OR operation. If this field is empty or missing, all connections will be allowed for this listener. If this field is present and contains at least one item, the listener only allows the traffic which matches at least one item in this list. See external documentation of networking.k8s.io/v1 networkpolicypeer.

NetworkPolicyPeer array

B.19. RouteListenerOverride schema reference

Used in: KafkaListenerExternalRoute

Property Description

bootstrap

External bootstrap service configuration.

RouteListenerBootstrapOverride

brokers

External broker services configuration.

RouteListenerBrokerOverride array

B.20. RouteListenerBootstrapOverride schema reference

Used in: RouteListenerOverride

Property Description

address

Additional address name for the bootstrap service. The address will be added to the list of subject alternative names of the TLS certificates.

string

host

Host for the bootstrap route. This field will be used in the spec.host field of the OpenShift Route.

string

B.21. RouteListenerBrokerOverride schema reference

Used in: RouteListenerOverride

Property Description

broker

Id of the kafka broker (broker identifier).

integer

advertisedHost

The host name which will be used in the brokers' advertised.brokers.

string

advertisedPort

The port number which will be used in the brokers' advertised.brokers.

integer

host

Host for the broker route. This field will be used in the spec.host field of the OpenShift Route.

string

B.22. KafkaListenerExternalConfiguration schema reference

Used in: KafkaListenerExternalLoadBalancer, KafkaListenerExternalRoute

Property Description

brokerCertChainAndKey

Reference to the Secret which holds the certificate and private key pair. The certificate can optionally contain the whole chain.

CertAndKeySecretSource

B.23. KafkaListenerExternalLoadBalancer schema reference

Used in: KafkaListeners

The type property is a discriminator that distinguishes the use of the type KafkaListenerExternalLoadBalancer from KafkaListenerExternalRoute, KafkaListenerExternalNodePort, KafkaListenerExternalIngress. It must have the value loadbalancer for the type KafkaListenerExternalLoadBalancer.

Property Description

type

Must be loadbalancer.

string

authentication

Authentication configuration for Kafka brokers. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, oauth].

KafkaListenerAuthenticationTls, KafkaListenerAuthenticationScramSha512, KafkaListenerAuthenticationOAuth

overrides

Overrides for external bootstrap and broker services and externally advertised addresses.

LoadBalancerListenerOverride

configuration

External listener configuration.

KafkaListenerExternalConfiguration

networkPolicyPeers

List of peers which should be able to connect to this listener. Peers in this list are combined using a logical OR operation. If this field is empty or missing, all connections will be allowed for this listener. If this field is present and contains at least one item, the listener only allows the traffic which matches at least one item in this list. See external documentation of networking.k8s.io/v1 networkpolicypeer.

NetworkPolicyPeer array

tls

Enables TLS encryption on the listener. By default set to true for enabled TLS encryption.

boolean

B.24. LoadBalancerListenerOverride schema reference

Used in: KafkaListenerExternalLoadBalancer

Property Description

bootstrap

External bootstrap service configuration.

LoadBalancerListenerBootstrapOverride

brokers

External broker services configuration.

LoadBalancerListenerBrokerOverride array

B.25. LoadBalancerListenerBootstrapOverride schema reference

Used in: LoadBalancerListenerOverride

Property Description

address

Additional address name for the bootstrap service. The address will be added to the list of subject alternative names of the TLS certificates.

string

dnsAnnotations

Annotations that will be added to the Service resource. You can use this field to configure DNS providers such as External DNS.

map

loadBalancerIP

The loadbalancer is requested with the IP address specified in this field. This feature depends on whether the underlying cloud provider supports specifying the loadBalancerIP when a load balancer is created. This field is ignored if the cloud provider does not support the feature.

string

B.26. LoadBalancerListenerBrokerOverride schema reference

Used in: LoadBalancerListenerOverride

Property Description

broker

Id of the kafka broker (broker identifier).

integer

advertisedHost

The host name which will be used in the brokers' advertised.brokers.

string

advertisedPort

The port number which will be used in the brokers' advertised.brokers.

integer

dnsAnnotations

Annotations that will be added to the Service resources for individual brokers. You can use this field to configure DNS providers such as External DNS.

map

loadBalancerIP

The loadbalancer is requested with the IP address specified in this field. This feature depends on whether the underlying cloud provider supports specifying the loadBalancerIP when a load balancer is created. This field is ignored if the cloud provider does not support the feature.

string

B.27. KafkaListenerExternalNodePort schema reference

Used in: KafkaListeners

The type property is a discriminator that distinguishes the use of the type KafkaListenerExternalNodePort from KafkaListenerExternalRoute, KafkaListenerExternalLoadBalancer, KafkaListenerExternalIngress. It must have the value nodeport for the type KafkaListenerExternalNodePort.

Property Description

type

Must be nodeport.

string

authentication

Authentication configuration for Kafka brokers. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, oauth].

KafkaListenerAuthenticationTls, KafkaListenerAuthenticationScramSha512, KafkaListenerAuthenticationOAuth

overrides

Overrides for external bootstrap and broker services and externally advertised addresses.

NodePortListenerOverride

configuration

External listener configuration.

NodePortListenerConfiguration

networkPolicyPeers

List of peers which should be able to connect to this listener. Peers in this list are combined using a logical OR operation. If this field is empty or missing, all connections will be allowed for this listener. If this field is present and contains at least one item, the listener only allows the traffic which matches at least one item in this list. See external documentation of networking.k8s.io/v1 networkpolicypeer.

NetworkPolicyPeer array

tls

Enables TLS encryption on the listener. By default set to true for enabled TLS encryption.

boolean

B.28. NodePortListenerOverride schema reference

Used in: KafkaListenerExternalNodePort

Property Description

bootstrap

External bootstrap service configuration.

NodePortListenerBootstrapOverride

brokers

External broker services configuration.

NodePortListenerBrokerOverride array

B.29. NodePortListenerBootstrapOverride schema reference

Used in: NodePortListenerOverride

Property Description

address

Additional address name for the bootstrap service. The address will be added to the list of subject alternative names of the TLS certificates.

string

dnsAnnotations

Annotations that will be added to the Service resource. You can use this field to configure DNS providers such as External DNS.

map

nodePort

Node port for the bootstrap service.

integer

B.30. NodePortListenerBrokerOverride schema reference

Used in: NodePortListenerOverride

Property Description

broker

Id of the kafka broker (broker identifier).

integer

advertisedHost

The host name which will be used in the brokers' advertised.brokers.

string

advertisedPort

The port number which will be used in the brokers' advertised.brokers.

integer

nodePort

Node port for the broker service.

integer

dnsAnnotations

Annotations that will be added to the Service resources for individual brokers. You can use this field to configure DNS providers such as External DNS.

map

B.31. NodePortListenerConfiguration schema reference

Used in: KafkaListenerExternalNodePort

Property Description

brokerCertChainAndKey

Reference to the Secret which holds the certificate and private key pair. The certificate can optionally contain the whole chain.

CertAndKeySecretSource

preferredAddressType

Defines which address type should be used as the node address. Available types are: ExternalDNS, ExternalIP, InternalDNS, InternalIP and Hostname. By default, the addresses will be used in the following order (the first one found will be used): * ExternalDNS * ExternalIP * InternalDNS * InternalIP * Hostname

This field can be used to select the address type which will be used as the preferred type and checked first. In case no address will be found for this address type, the other types will be used in the default order..

string (one of [ExternalDNS, ExternalIP, Hostname, InternalIP, InternalDNS])

B.32. KafkaListenerExternalIngress schema reference

Used in: KafkaListeners

The type property is a discriminator that distinguishes the use of the type KafkaListenerExternalIngress from KafkaListenerExternalRoute, KafkaListenerExternalLoadBalancer, KafkaListenerExternalNodePort. It must have the value ingress for the type KafkaListenerExternalIngress.

Property Description

type

Must be ingress.

string

authentication

Authentication configuration for Kafka brokers. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, oauth].

KafkaListenerAuthenticationTls, KafkaListenerAuthenticationScramSha512, KafkaListenerAuthenticationOAuth

class

Configures the Ingress class that defines which Ingress controller will be used. If not set, the Ingress class is set to nginx.

string

configuration

External listener configuration.

IngressListenerConfiguration

networkPolicyPeers

List of peers which should be able to connect to this listener. Peers in this list are combined using a logical OR operation. If this field is empty or missing, all connections will be allowed for this listener. If this field is present and contains at least one item, the listener only allows the traffic which matches at least one item in this list. See external documentation of networking.k8s.io/v1 networkpolicypeer.

NetworkPolicyPeer array

B.33. IngressListenerConfiguration schema reference

Used in: KafkaListenerExternalIngress

Property Description

bootstrap

External bootstrap ingress configuration.

IngressListenerBootstrapConfiguration

brokers

External broker ingress configuration.

IngressListenerBrokerConfiguration array

brokerCertChainAndKey

Reference to the Secret which holds the certificate and private key pair. The certificate can optionally contain the whole chain.

CertAndKeySecretSource

B.34. IngressListenerBootstrapConfiguration schema reference

Used in: IngressListenerConfiguration

Property Description

address

Additional address name for the bootstrap service. The address will be added to the list of subject alternative names of the TLS certificates.

string

dnsAnnotations

Annotations that will be added to the Ingress resource. You can use this field to configure DNS providers such as External DNS.

map

host

Host for the bootstrap route. This field will be used in the Ingress resource.

string

B.35. IngressListenerBrokerConfiguration schema reference

Used in: IngressListenerConfiguration

Property Description

broker

Id of the kafka broker (broker identifier).

integer

advertisedHost

The host name which will be used in the brokers' advertised.brokers.

string

advertisedPort

The port number which will be used in the brokers' advertised.brokers.

integer

host

Host for the broker ingress. This field will be used in the Ingress resource.

string

dnsAnnotations

Annotations that will be added to the Ingress resources for individual brokers. You can use this field to configure DNS providers such as External DNS.

map

B.36. KafkaAuthorizationSimple schema reference

Used in: KafkaClusterSpec

The type property is a discriminator that distinguishes the use of the type KafkaAuthorizationSimple from KafkaAuthorizationKeycloak. It must have the value simple for the type KafkaAuthorizationSimple.

Property Description

type

Must be simple.

string

superUsers

List of super users. Should contain list of user principals which should get unlimited access rights.

string array

B.37. KafkaAuthorizationKeycloak schema reference

Used in: KafkaClusterSpec

The type property is a discriminator that distinguishes the use of the type KafkaAuthorizationKeycloak from KafkaAuthorizationSimple. It must have the value keycloak for the type KafkaAuthorizationKeycloak.

Property Description

type

Must be keycloak.

string

clientId

OAuth Client ID which the Kafka client can use to authenticate against the OAuth server and use the token endpoint URI.

string

tokenEndpointUri

Authorization server token endpoint URI.

string

tlsTrustedCertificates

Trusted certificates for TLS connection to the OAuth server.

CertSecretSource array

disableTlsHostnameVerification

Enable or disable TLS hostname verification. Default value is false.

boolean

delegateToKafkaAcls

Whether authorization decision should be delegated to the 'Simple' authorizer if DENIED by Keycloak Authorization Services policies.Default value is false.

boolean

superUsers

List of super users. Should contain list of user principals which should get unlimited access rights.

string array

B.38. Rack schema reference

Used in: KafkaClusterSpec

Property Description

topologyKey

A key that matches labels assigned to the Kubernetes cluster nodes. The value of the label is used to set the broker’s broker.rack config.

string

B.39. Probe schema reference

Used in: CruiseControlSpec, EntityTopicOperatorSpec, EntityUserOperatorSpec, KafkaBridgeSpec, KafkaClusterSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaExporterSpec, KafkaMirrorMaker2Spec, KafkaMirrorMakerSpec, TlsSidecar, TopicOperatorSpec, ZookeeperClusterSpec

Property Description

failureThreshold

Minimum consecutive failures for the probe to be considered failed after having succeeded. Defaults to 3. Minimum value is 1.

integer

initialDelaySeconds

The initial delay before first the health is first checked.

integer

periodSeconds

How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1.

integer

successThreshold

Minimum consecutive successes for the probe to be considered successful after having failed. Defaults to 1. Must be 1 for liveness. Minimum value is 1.

integer

timeoutSeconds

The timeout for each attempted health check.

integer

B.40. JvmOptions schema reference

Used in: CruiseControlSpec, EntityTopicOperatorSpec, EntityUserOperatorSpec, KafkaBridgeSpec, KafkaClusterSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMaker2Spec, KafkaMirrorMakerSpec, TopicOperatorSpec, ZookeeperClusterSpec

Property Description

-XX

A map of -XX options to the JVM.

map

-Xms

-Xms option to to the JVM.

string

-Xmx

-Xmx option to to the JVM.

string

gcLoggingEnabled

Specifies whether the Garbage Collection logging is enabled. The default is false.

boolean

javaSystemProperties

A map of additional system properties which will be passed using the -D option to the JVM.

SystemProperty array

B.41. SystemProperty schema reference

Used in: JvmOptions

Property Description

name

The system property name.

string

value

The system property value.

string

B.42. KafkaJmxOptions schema reference

Used in: KafkaClusterSpec

Property Description

authentication

Authentication configuration for connecting to the Kafka JMX port. The type depends on the value of the authentication.type property within the given object, which must be one of [password].

KafkaJmxAuthenticationPassword

B.43. KafkaJmxAuthenticationPassword schema reference

Used in: KafkaJmxOptions

The type property is a discriminator that distinguishes the use of the type KafkaJmxAuthenticationPassword from other subtypes which may be added in the future. It must have the value password for the type KafkaJmxAuthenticationPassword.

Property Description

type

Must be password.

string

B.44. ResourceRequirements schema reference

Used in: CruiseControlSpec, EntityTopicOperatorSpec, EntityUserOperatorSpec, JmxTransSpec, KafkaBridgeSpec, KafkaClusterSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaExporterSpec, KafkaMirrorMaker2Spec, KafkaMirrorMakerSpec, TlsSidecar, TopicOperatorSpec, ZookeeperClusterSpec

Property Description

limits

map

requests

map

B.45. InlineLogging schema reference

Used in: CruiseControlSpec, EntityTopicOperatorSpec, EntityUserOperatorSpec, KafkaBridgeSpec, KafkaClusterSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMaker2Spec, KafkaMirrorMakerSpec, TopicOperatorSpec, ZookeeperClusterSpec

The type property is a discriminator that distinguishes the use of the type InlineLogging from ExternalLogging. It must have the value inline for the type InlineLogging.

Property Description

type

Must be inline.

string

loggers

A Map from logger name to logger level.

map

B.46. ExternalLogging schema reference

Used in: CruiseControlSpec, EntityTopicOperatorSpec, EntityUserOperatorSpec, KafkaBridgeSpec, KafkaClusterSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMaker2Spec, KafkaMirrorMakerSpec, TopicOperatorSpec, ZookeeperClusterSpec

The type property is a discriminator that distinguishes the use of the type ExternalLogging from InlineLogging. It must have the value external for the type ExternalLogging.

Property Description

type

Must be external.

string

name

The name of the ConfigMap from which to get the logging configuration.

string

B.47. TlsSidecar schema reference

Used in: CruiseControlSpec, EntityOperatorSpec, KafkaClusterSpec, TopicOperatorSpec, ZookeeperClusterSpec

Property Description

image

The docker image for the container.

string

livenessProbe

Pod liveness checking.

Probe

logLevel

The log level for the TLS sidecar. Default value is notice.

string (one of [emerg, debug, crit, err, alert, warning, notice, info])

readinessProbe

Pod readiness checking.

Probe

resources

CPU and memory resources to reserve.

ResourceRequirements

B.48. KafkaClusterTemplate schema reference

Used in: KafkaClusterSpec

Property Description

statefulset

Template for Kafka StatefulSet.

StatefulSetTemplate

pod

Template for Kafka Pods.

PodTemplate

bootstrapService

Template for Kafka bootstrap Service.

ResourceTemplate

brokersService

Template for Kafka broker Service.

ResourceTemplate

externalBootstrapService

Template for Kafka external bootstrap Service.

ExternalServiceTemplate

perPodService

Template for Kafka per-pod Services used for access from outside of Kubernetes.

ExternalServiceTemplate

externalBootstrapRoute

Template for Kafka external bootstrap Route.

ResourceTemplate

perPodRoute

Template for Kafka per-pod Routes used for access from outside of OpenShift.

ResourceTemplate

externalBootstrapIngress

Template for Kafka external bootstrap Ingress.

ResourceTemplate

perPodIngress

Template for Kafka per-pod Ingress used for access from outside of Kubernetes.

ResourceTemplate

persistentVolumeClaim

Template for all Kafka PersistentVolumeClaims.

ResourceTemplate

podDisruptionBudget

Template for Kafka PodDisruptionBudget.

PodDisruptionBudgetTemplate

kafkaContainer

Template for the Kafka broker container.

ContainerTemplate

tlsSidecarContainer

Template for the Kafka broker TLS sidecar container.

ContainerTemplate

initContainer

Template for the Kafka init container.

ContainerTemplate

B.49. StatefulSetTemplate schema reference

Used in: KafkaClusterTemplate, ZookeeperClusterTemplate

Property Description

metadata

Metadata which should be applied to the resource.

MetadataTemplate

podManagementPolicy

PodManagementPolicy which will be used for this StatefulSet. Valid values are Parallel and OrderedReady. Defaults to Parallel.

string (one of [OrderedReady, Parallel])

B.50. MetadataTemplate schema reference

Used in: ExternalServiceTemplate, PodDisruptionBudgetTemplate, PodTemplate, ResourceTemplate, StatefulSetTemplate

Property Description

labels

Labels which should be added to the resource template. Can be applied to different resources such as StatefulSets, Deployments, Pods, and Services.

map

annotations

Annotations which should be added to the resource template. Can be applied to different resources such as StatefulSets, Deployments, Pods, and Services.

map

B.51. PodTemplate schema reference

Used in: CruiseControlTemplate, EntityOperatorTemplate, JmxTransTemplate, KafkaBridgeTemplate, KafkaClusterTemplate, KafkaConnectTemplate, KafkaExporterTemplate, KafkaMirrorMakerTemplate, ZookeeperClusterTemplate

Property Description

metadata

Metadata applied to the resource.

MetadataTemplate

imagePullSecrets

List of references to secrets in the same namespace to use for pulling any of the images used by this Pod. See external documentation of core/v1 localobjectreference.

LocalObjectReference array

securityContext

Configures pod-level security attributes and common container settings. See external documentation of core/v1 podsecuritycontext.

PodSecurityContext

terminationGracePeriodSeconds

The grace period is the duration in seconds after the processes running in the pod are sent a termination signal and the time when the processes are forcibly halted with a kill signal. Set this value longer than the expected cleanup time for your process.Value must be non-negative integer. The value zero indicates delete immediately. Defaults to 30 seconds.

integer

affinity

The pod’s affinity rules. See external documentation of core/v1 affinity.

Affinity

priorityClassName

The name of the Priority Class to which these pods will be assigned.

string

schedulerName

The name of the scheduler used to dispatch this Pod. If not specified, the default scheduler will be used.

string

tolerations

The pod’s tolerations. See external documentation of core/v1 toleration.

Toleration array

B.52. ResourceTemplate schema reference

Used in: CruiseControlTemplate, EntityOperatorTemplate, JmxTransTemplate, KafkaBridgeTemplate, KafkaClusterTemplate, KafkaConnectTemplate, KafkaExporterTemplate, KafkaMirrorMakerTemplate, ZookeeperClusterTemplate

Property Description

metadata

Metadata which should be applied to the resource.

MetadataTemplate

B.53. ExternalServiceTemplate schema reference

Used in: KafkaClusterTemplate

Property Description

metadata

Metadata which should be applied to the resource.

MetadataTemplate

externalTrafficPolicy

Specifies whether the service routes external traffic to node-local or cluster-wide endpoints. Cluster may cause a second hop to another node and obscures the client source IP. Local avoids a second hop for LoadBalancer and Nodeport type services and preserves the client source IP (when supported by the infrastructure). If unspecified, Kubernetes will use Cluster as the default.

string (one of [Local, Cluster])

loadBalancerSourceRanges

A list of CIDR ranges (for example 10.0.0.0/8 or 130.211.204.1/32) from which clients can connect to load balancer type listeners. If supported by the platform, traffic through the loadbalancer is restricted to the specified CIDR ranges. This field is applicable only for loadbalancer type services and is ignored if the cloud provider does not support the feature. For more information, see https://kubernetes.io/docs/tasks/access-application-cluster/configure-cloud-provider-firewall/.

string array

B.54. PodDisruptionBudgetTemplate schema reference

Used in: CruiseControlTemplate, KafkaBridgeTemplate, KafkaClusterTemplate, KafkaConnectTemplate, KafkaMirrorMakerTemplate, ZookeeperClusterTemplate

Property Description

metadata

Metadata to apply to the PodDistruptionBugetTemplate resource.

MetadataTemplate

maxUnavailable

Maximum number of unavailable pods to allow automatic Pod eviction. A Pod eviction is allowed when the maxUnavailable number of pods or fewer are unavailable after the eviction. Setting this value to 0 prevents all voluntary evictions, so the pods must be evicted manually. Defaults to 1.

integer

B.55. ContainerTemplate schema reference

Used in: CruiseControlTemplate, EntityOperatorTemplate, JmxTransTemplate, KafkaBridgeTemplate, KafkaClusterTemplate, KafkaConnectTemplate, KafkaExporterTemplate, KafkaMirrorMakerTemplate, ZookeeperClusterTemplate

Property Description

env

Environment variables which should be applied to the container.

ContainerEnvVar array

securityContext

Security context for the container. See external documentation of core/v1 securitycontext.

SecurityContext

B.56. ContainerEnvVar schema reference

Used in: ContainerTemplate

Property Description

name

The environment variable key.

string

value

The environment variable value.

string

B.57. ZookeeperClusterSpec schema reference

Used in: KafkaSpec

Property Description

replicas

The number of pods in the cluster.

integer

image

The docker image for the pods.

string

storage

Storage configuration (disk). Cannot be updated. The type depends on the value of the storage.type property within the given object, which must be one of [ephemeral, persistent-claim].

EphemeralStorage, PersistentClaimStorage

config

The ZooKeeper broker config. Properties with the following prefixes cannot be set: server., dataDir, dataLogDir, clientPort, authProvider, quorum.auth, requireClientAuthScheme, snapshot.trust.empty, standaloneEnabled, reconfigEnabled, 4lw.commands.whitelist, secureClientPort, ssl., serverCnxnFactory, sslQuorum (with the exception of: ssl.protocol, ssl.quorum.protocol, ssl.enabledProtocols, ssl.quorum.enabledProtocols, ssl.ciphersuites, ssl.quorum.ciphersuites).

map

affinity

The property affinity has been deprecated. This feature should now be configured at path spec.zookeeper.template.pod.affinity. The pod’s affinity rules. See external documentation of core/v1 affinity.

Affinity

tolerations

The property tolerations has been deprecated. This feature should now be configured at path spec.zookeeper.template.pod.tolerations. The pod’s tolerations. See external documentation of core/v1 toleration.

Toleration array

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

jvmOptions

JVM Options for pods.

JvmOptions

resources

CPU and memory resources to reserve.

ResourceRequirements

metrics

The Prometheus JMX Exporter configuration. See https://github.com/prometheus/jmx_exporter for details of the structure of this configuration.

map

logging

Logging configuration for ZooKeeper. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

template

Template for ZooKeeper cluster resources. The template allows users to specify how are the StatefulSet, Pods and Services generated.

ZookeeperClusterTemplate

tlsSidecar

The property tlsSidecar has been deprecated. TLS sidecar configuration. The TLS sidecar is not used anymore and this option will be ignored.

TlsSidecar

B.58. ZookeeperClusterTemplate schema reference

Used in: ZookeeperClusterSpec

Property Description

statefulset

Template for ZooKeeper StatefulSet.

StatefulSetTemplate

pod

Template for ZooKeeper Pods.

PodTemplate

clientService

Template for ZooKeeper client Service.

ResourceTemplate

nodesService

Template for ZooKeeper nodes Service.

ResourceTemplate

persistentVolumeClaim

Template for all ZooKeeper PersistentVolumeClaims.

ResourceTemplate

podDisruptionBudget

Template for ZooKeeper PodDisruptionBudget.

PodDisruptionBudgetTemplate

zookeeperContainer

Template for the ZooKeeper container.

ContainerTemplate

tlsSidecarContainer

The property tlsSidecarContainer has been deprecated. Template for the Zookeeper server TLS sidecar container. The TLS sidecar is not used anymore and this option will be ignored.

ContainerTemplate

B.59. TopicOperatorSpec schema reference

Used in: KafkaSpec

Property Description

watchedNamespace

The namespace the Topic Operator should watch.

string

image

The image to use for the Topic Operator.

string

reconciliationIntervalSeconds

Interval between periodic reconciliations.

integer

zookeeperSessionTimeoutSeconds

Timeout for the ZooKeeper session.

integer

affinity

Pod affinity rules. See external documentation of core/v1 affinity.

Affinity

resources

CPU and memory resources to reserve.

ResourceRequirements

topicMetadataMaxAttempts

The number of attempts at getting topic metadata.

integer

tlsSidecar

TLS sidecar configuration.

TlsSidecar

logging

Logging configuration. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

jvmOptions

JVM Options for pods.

JvmOptions

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

B.60. EntityOperatorSpec schema reference

Used in: KafkaSpec

Property Description

topicOperator

Configuration of the Topic Operator.

EntityTopicOperatorSpec

userOperator

Configuration of the User Operator.

EntityUserOperatorSpec

affinity

The property affinity has been deprecated. This feature should now be configured at path spec.template.pod.affinity. The pod’s affinity rules. See external documentation of core/v1 affinity.

Affinity

tolerations

The property tolerations has been deprecated. This feature should now be configured at path spec.template.pod.tolerations. The pod’s tolerations. See external documentation of core/v1 toleration.

Toleration array

tlsSidecar

TLS sidecar configuration.

TlsSidecar

template

Template for Entity Operator resources. The template allows users to specify how is the Deployment and Pods generated.

EntityOperatorTemplate

B.61. EntityTopicOperatorSpec schema reference

Used in: EntityOperatorSpec

Property Description

watchedNamespace

The namespace the Topic Operator should watch.

string

image

The image to use for the Topic Operator.

string

reconciliationIntervalSeconds

Interval between periodic reconciliations.

integer

zookeeperSessionTimeoutSeconds

Timeout for the ZooKeeper session.

integer

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

resources

CPU and memory resources to reserve.

ResourceRequirements

topicMetadataMaxAttempts

The number of attempts at getting topic metadata.

integer

logging

Logging configuration. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

jvmOptions

JVM Options for pods.

JvmOptions

B.62. EntityUserOperatorSpec schema reference

Used in: EntityOperatorSpec

Property Description

watchedNamespace

The namespace the User Operator should watch.

string

image

The image to use for the User Operator.

string

reconciliationIntervalSeconds

Interval between periodic reconciliations.

integer

zookeeperSessionTimeoutSeconds

Timeout for the ZooKeeper session.

integer

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

resources

CPU and memory resources to reserve.

ResourceRequirements

logging

Logging configuration. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

jvmOptions

JVM Options for pods.

JvmOptions

B.63. EntityOperatorTemplate schema reference

Used in: EntityOperatorSpec

Property Description

deployment

Template for Entity Operator Deployment.

ResourceTemplate

pod

Template for Entity Operator Pods.

PodTemplate

tlsSidecarContainer

Template for the Entity Operator TLS sidecar container.

ContainerTemplate

topicOperatorContainer

Template for the Entity Topic Operator container.

ContainerTemplate

userOperatorContainer

Template for the Entity User Operator container.

ContainerTemplate

B.64. CertificateAuthority schema reference

Used in: KafkaSpec

Configuration of how TLS certificates are used within the cluster. This applies to certificates used for both internal communication within the cluster and to certificates used for client access via Kafka.spec.kafka.listeners.tls.

Property Description

generateCertificateAuthority

If true then Certificate Authority certificates will be generated automatically. Otherwise the user will need to provide a Secret with the CA certificate. Default is true.

boolean

validityDays

The number of days generated certificates should be valid for. The default is 365.

integer

renewalDays

The number of days in the certificate renewal period. This is the number of days before the a certificate expires during which renewal actions may be performed. When generateCertificateAuthority is true, this will cause the generation of a new certificate. When generateCertificateAuthority is true, this will cause extra logging at WARN level about the pending certificate expiry. Default is 30.

integer

certificateExpirationPolicy

How should CA certificate expiration be handled when generateCertificateAuthority=true. The default is for a new CA certificate to be generated reusing the existing private key.

string (one of [replace-key, renew-certificate])

B.65. CruiseControlSpec schema reference

Used in: KafkaSpec

Property Description

image

The docker image for the pods.

string

config

The Cruise Control configuration. For a full list of configuration options refer to https://github.com/linkedin/cruise-control/wiki/Configurations. Note that properties with the following prefixes cannot be set: bootstrap.servers, client.id, zookeeper., network., security., failed.brokers.zk.path,webserver.http., webserver.api.urlprefix, webserver.session.path, webserver.accesslog., two.step., request.reason.required,metric.reporter.sampler.bootstrap.servers, metric.reporter.topic, partition.metric.sample.store.topic, broker.metric.sample.store.topic,capacity.config.file, self.healing., anomaly.detection., ssl.

map

livenessProbe

Pod liveness checking for the Cruise Control container.

Probe

readinessProbe

Pod readiness checking for the Cruise Control container.

Probe

jvmOptions

JVM Options for the Cruise Control container.

JvmOptions

resources

CPU and memory resources to reserve for the Cruise Control container.

ResourceRequirements

logging

Logging configuration (log4j1) for Cruise Control. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

tlsSidecar

TLS sidecar configuration.

TlsSidecar

template

Template to specify how Cruise Control resources, Deployments and Pods, are generated.

CruiseControlTemplate

brokerCapacity

The Cruise Control brokerCapacity configuration.

BrokerCapacity

B.66. CruiseControlTemplate schema reference

Used in: CruiseControlSpec

Property Description

deployment

Template for Cruise Control Deployment.

ResourceTemplate

pod

Template for Cruise Control Pods.

PodTemplate

apiService

Template for Cruise Control API Service.

ResourceTemplate

podDisruptionBudget

Template for Cruise Control PodDisruptionBudget.

PodDisruptionBudgetTemplate

cruiseControlContainer

Template for the Cruise Control container.

ContainerTemplate

tlsSidecarContainer

Template for the Cruise Control TLS sidecar container.

ContainerTemplate

B.67. BrokerCapacity schema reference

Used in: CruiseControlSpec

Property Description

disk

Broker capacity for disk in bytes, for example, 100Gi.

string

cpuUtilization

Broker capacity for CPU resource utilization as a percentage (0 - 100).

integer

inboundNetwork

Broker capacity for inbound network throughput in bytes per second, for example, 10000KB/s.

string

outboundNetwork

Broker capacity for outbound network throughput in bytes per second, for example 10000KB/s.

string

B.68. JmxTransSpec schema reference

Used in: KafkaSpec

Property Description

image

The image to use for the JmxTrans.

string

outputDefinitions

Defines the output hosts that will be referenced later on. For more information on these properties see, JmxTransOutputDefinitionTemplate schema reference.

JmxTransOutputDefinitionTemplate array

logLevel

Sets the logging level of the JmxTrans deployment.For more information see, JmxTrans Logging Level.

string

kafkaQueries

Queries to send to the Kafka brokers to define what data should be read from each broker. For more information on these properties see, JmxTransQueryTemplate schema reference.

JmxTransQueryTemplate array

resources

CPU and memory resources to reserve.

ResourceRequirements

template

Template for JmxTrans resources.

JmxTransTemplate

B.69. JmxTransOutputDefinitionTemplate schema reference

Used in: JmxTransSpec

Property Description

outputType

Template for setting the format of the data that will be pushed.For more information see JmxTrans OutputWriters.

string

host

The DNS/hostname of the remote host that the data is pushed to.

string

port

The port of the remote host that the data is pushed to.

integer

flushDelayInSeconds

How many seconds the JmxTrans waits before pushing a new set of data out.

integer

typeNames

Template for filtering data to be included in response to a wildcard query. For more information see JmxTrans queries.

string array

name

Template for setting the name of the output definition. This is used to identify where to send the results of queries should be sent.

string

B.70. JmxTransQueryTemplate schema reference

Used in: JmxTransSpec

Property Description

targetMBean

If using wildcards instead of a specific MBean then the data is gathered from multiple MBeans. Otherwise if specifying an MBean then data is gathered from that specified MBean.

string

attributes

Determine which attributes of the targeted MBean should be included.

string array

outputs

List of the names of output definitions specified in the spec.kafka.jmxTrans.outputDefinitions that have defined where JMX metrics are pushed to, and in which data format.

string array

B.71. JmxTransTemplate schema reference

Used in: JmxTransSpec

Property Description

deployment

Template for JmxTrans Deployment.

ResourceTemplate

pod

Template for JmxTrans Pods.

PodTemplate

container

Template for JmxTrans container.

ContainerTemplate

B.72. KafkaExporterSpec schema reference

Used in: KafkaSpec

Property Description

image

The docker image for the pods.

string

groupRegex

Regular expression to specify which consumer groups to collect. Default value is .*.

string

topicRegex

Regular expression to specify which topics to collect. Default value is .*.

string

resources

CPU and memory resources to reserve.

ResourceRequirements

logging

Only log messages with the given severity or above. Valid levels: [debug, info, warn, error, fatal]. Default log level is info.

string

enableSaramaLogging

Enable Sarama logging, a Go client library used by the Kafka Exporter.

boolean

template

Customization of deployment templates and pods.

KafkaExporterTemplate

livenessProbe

Pod liveness check.

Probe

readinessProbe

Pod readiness check.

Probe

B.73. KafkaExporterTemplate schema reference

Used in: KafkaExporterSpec

Property Description

deployment

Template for Kafka Exporter Deployment.

ResourceTemplate

pod

Template for Kafka Exporter Pods.

PodTemplate

service

Template for Kafka Exporter Service.

ResourceTemplate

container

Template for the Kafka Exporter container.

ContainerTemplate

B.74. KafkaStatus schema reference

Used in: Kafka

Property Description

conditions

List of status conditions.

Condition array

observedGeneration

The generation of the CRD that was last reconciled by the operator.

integer

listeners

Addresses of the internal and external listeners.

ListenerStatus array

B.75. Condition schema reference

Used in: KafkaBridgeStatus, KafkaConnectorStatus, KafkaConnectS2IStatus, KafkaConnectStatus, KafkaMirrorMaker2Status, KafkaMirrorMakerStatus, KafkaRebalanceStatus, KafkaStatus, KafkaTopicStatus, KafkaUserStatus

Property Description

type

The unique identifier of a condition, used to distinguish between other conditions in the resource.

string

status

The status of the condition, either True, False or Unknown.

string

lastTransitionTime

Last time the condition of a type changed from one status to another. The required format is 'yyyy-MM-ddTHH:mm:ssZ', in the UTC time zone.

string

reason

The reason for the condition’s last transition (a single word in CamelCase).

string

message

Human-readable message indicating details about the condition’s last transition.

string

B.76. ListenerStatus schema reference

Used in: KafkaStatus

Property Description

type

The type of the listener. Can be one of the following three types: plain, tls, and external.

string

addresses

A list of the addresses for this listener.

ListenerAddress array

bootstrapServers

A comma-separated list of host:port pairs for connecting to the Kafka cluster using this listener.

string

certificates

A list of TLS certificates which can be used to verify the identity of the server when connecting to the given listener. Set only for tls and external listeners.

string array

B.77. ListenerAddress schema reference

Used in: ListenerStatus

Property Description

host

The DNS name or IP address of the Kafka bootstrap service.

string

port

The port of the Kafka bootstrap service.

integer

B.78. KafkaConnect schema reference

Property Description

spec

The specification of the Kafka Connect cluster.

KafkaConnectSpec

status

The status of the Kafka Connect cluster.

KafkaConnectStatus

B.79. KafkaConnectSpec schema reference

Used in: KafkaConnect

Property Description

replicas

The number of pods in the Kafka Connect group.

integer

version

The Kafka Connect version. Defaults to 2.5.0. Consult the user documentation to understand the process required to upgrade or downgrade the version.

string

image

The docker image for the pods.

string

bootstrapServers

Bootstrap servers to connect to. This should be given as a comma separated list of <hostname>:‍<port> pairs.

string

tls

TLS configuration.

KafkaConnectTls

authentication

Authentication configuration for Kafka Connect. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, plain, oauth].

KafkaClientAuthenticationTls, KafkaClientAuthenticationScramSha512, KafkaClientAuthenticationPlain, KafkaClientAuthenticationOAuth

config

The Kafka Connect configuration. Properties with the following prefixes cannot be set: ssl., sasl., security., listeners, plugin.path, rest., bootstrap.servers, consumer.interceptor.classes, producer.interceptor.classes (with the exception of: ssl.endpoint.identification.algorithm, ssl.cipher.suites, ssl.protocol, ssl.enabled.protocols).

map

resources

The maximum limits for CPU and memory resources and the requested initial resources.

ResourceRequirements

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

jvmOptions

JVM Options for pods.

JvmOptions

affinity

The property affinity has been deprecated. This feature should now be configured at path spec.template.pod.affinity. The pod’s affinity rules. See external documentation of core/v1 affinity.

Affinity

tolerations

The property tolerations has been deprecated. This feature should now be configured at path spec.template.pod.tolerations. The pod’s tolerations. See external documentation of core/v1 toleration.

Toleration array

logging

Logging configuration for Kafka Connect. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

metrics

The Prometheus JMX Exporter configuration. See https://github.com/prometheus/jmx_exporter for details of the structure of this configuration.

map

tracing

The configuration of tracing in Kafka Connect. The type depends on the value of the tracing.type property within the given object, which must be one of [jaeger].

JaegerTracing

template

Template for Kafka Connect and Kafka Connect S2I resources. The template allows users to specify how the Deployment, Pods and Service are generated.

KafkaConnectTemplate

externalConfiguration

Pass data from Secrets or ConfigMaps to the Kafka Connect pods and use them to configure connectors.

ExternalConfiguration

B.80. KafkaConnectTls schema reference

Used in: KafkaConnectS2ISpec, KafkaConnectSpec

Property Description

trustedCertificates

Trusted certificates for TLS connection.

CertSecretSource array

B.81. KafkaClientAuthenticationTls schema reference

Used in: KafkaBridgeSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMaker2ClusterSpec, KafkaMirrorMakerConsumerSpec, KafkaMirrorMakerProducerSpec

To use TLS client authentication, set the type property to the value tls. TLS client authentication uses a TLS certificate to authenticate. The certificate is specified in the certificateAndKey property and is always loaded from a Kubernetes secret. In the secret, the certificate must be stored in X509 format under two different keys: public and private.

Note
 TLS client authentication can only be used with TLS connections.
An example TLS client authentication configuration
authentication:
  type: tls
  certificateAndKey:
    secretName: my-secret
    certificate: public.crt
    key: private.key

The type property is a discriminator that distinguishes the use of the type KafkaClientAuthenticationTls from KafkaClientAuthenticationScramSha512, KafkaClientAuthenticationPlain, KafkaClientAuthenticationOAuth. It must have the value tls for the type KafkaClientAuthenticationTls.

Property Description

certificateAndKey

Reference to the Secret which holds the certificate and private key pair.

CertAndKeySecretSource

type

Must be tls.

string

B.82. KafkaClientAuthenticationScramSha512 schema reference

Used in: KafkaBridgeSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMaker2ClusterSpec, KafkaMirrorMakerConsumerSpec, KafkaMirrorMakerProducerSpec

To configure SASL-based SCRAM-SHA-512 authentication, set the type property to scram-sha-512. The SCRAM-SHA-512 authentication mechanism requires a username and password.

  • Specify the username in the username property.

  • In the passwordSecret property, specify a link to a Secret containing the password. The secretName property contains the name of the Secret and the password property contains the name of the key under which the password is stored inside the Secret.

Important
 Do not specify the actual password in the password field.
An example SASL based SCRAM-SHA-512 client authentication configuration
authentication:
  type: scram-sha-512
  username: my-connect
  passwordSecret:
    secretName: my-connect
    password: password

The type property is a discriminator that distinguishes the use of the type KafkaClientAuthenticationScramSha512 from KafkaClientAuthenticationTls, KafkaClientAuthenticationPlain, KafkaClientAuthenticationOAuth. It must have the value scram-sha-512 for the type KafkaClientAuthenticationScramSha512.

Property Description

passwordSecret

Reference to the Secret which holds the password.

PasswordSecretSource

type

Must be scram-sha-512.

string

username

Username used for the authentication.

string

B.83. PasswordSecretSource schema reference

Used in: KafkaClientAuthenticationPlain, KafkaClientAuthenticationScramSha512

Property Description

password

The name of the key in the Secret under which the password is stored.

string

secretName

The name of the Secret containing the password.

string

B.84. KafkaClientAuthenticationPlain schema reference

Used in: KafkaBridgeSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMaker2ClusterSpec, KafkaMirrorMakerConsumerSpec, KafkaMirrorMakerProducerSpec

To configure SASL-based PLAIN authentication, set the type property to plain. SASL PLAIN authentication mechanism requires a username and password.

Warning
 The SASL PLAIN mechanism will transfer the username and password across the network in cleartext. Only use SASL PLAIN authentication if TLS encryption is enabled.

  • Specify the username in the username property.

  • In the passwordSecret property, specify a link to a Secret containing the password. The secretName property contains the name of such a Secret and the password property contains the name of the key under which the password is stored inside the Secret.

Important
 Do not specify the actual password in the password field.
An example SASL based PLAIN client authentication configuration
authentication:
  type: plain
  username: my-connect
  passwordSecret:
    secretName: my-connect
    password: password

The type property is a discriminator that distinguishes the use of the type KafkaClientAuthenticationPlain from KafkaClientAuthenticationTls, KafkaClientAuthenticationScramSha512, KafkaClientAuthenticationOAuth. It must have the value plain for the type KafkaClientAuthenticationPlain.

Property Description

passwordSecret

Reference to the Secret which holds the password.

PasswordSecretSource

type

Must be plain.

string

username

Username used for the authentication.

string

B.85. KafkaClientAuthenticationOAuth schema reference

Used in: KafkaBridgeSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMaker2ClusterSpec, KafkaMirrorMakerConsumerSpec, KafkaMirrorMakerProducerSpec

To use OAuth client authentication, set the type property to the value oauth. OAuth authentication can be configured using:

  • Client ID and secret

  • Client ID and refresh token

  • Access token

  • TLS

Client ID and secret

You can configure the address of your authorization server in the tokenEndpointUri property together with the client ID and client secret used in authentication. The OAuth client will connect to the OAuth server, authenticate using the client ID and secret and get an access token which it will use to authenticate with the Kafka broker. In the clientSecret property, specify a link to a Secret containing the client secret.

An example of OAuth client authentication using client ID and client secret
authentication:
  type: oauth
  tokenEndpointUri: https://sso.myproject.svc:8443/auth/realms/internal/protocol/openid-connect/token
  clientId: my-client-id
  clientSecret:
    secretName: my-client-oauth-secret
    key: client-secret
Client ID and refresh token

You can configure the address of your OAuth server in the tokenEndpointUri property together with the OAuth client ID and refresh token. The OAuth client will connect to the OAuth server, authenticate using the client ID and refresh token and get an access token which it will use to authenticate with the Kafka broker. In the refreshToken property, specify a link to a Secret containing the refresh token.

An example of OAuth client authentication using client ID and refresh token
authentication:
  type: oauth
  tokenEndpointUri: https://sso.myproject.svc:8443/auth/realms/internal/protocol/openid-connect/token
  clientId: my-client-id
  refreshToken:
    secretName: my-refresh-token-secret
    key: refresh-token
Access token

You can configure the access token used for authentication with the Kafka broker directly. In this case, you do not specify the tokenEndpointUri. In the accessToken property, specify a link to a Secret containing the access token.

An example of OAuth client authentication using only an access token
authentication:
  type: oauth
  accessToken:
    secretName: my-access-token-secret
    key: access-token
TLS

Accessing the OAuth server using the HTTPS protocol does not require any additional configuration as long as the TLS certificates used by it are signed by a trusted certification authority and its hostname is listed in the certificate.

If your OAuth server is using certificates which are self-signed or are signed by a certification authority which is not trusted, you can configure a list of trusted certificates in the custom resoruce. The tlsTrustedCertificates property contains a list of secrets with key names under which the certificates are stored. The certificates must be stored in X509 format.

An example of TLS certificates provided
authentication:
  type: oauth
  tokenEndpointUri: https://sso.myproject.svc:8443/auth/realms/internal/protocol/openid-connect/token
  clientId: my-client-id
  refreshToken:
    secretName: my-refresh-token-secret
    key: refresh-token
  tlsTrustedCertificates:
    - secretName: oauth-server-ca
      certificate: tls.crt

The OAuth client will by default verify that the hostname of your OAuth server matches either the certificate subject or one of the alternative DNS names. If it is not required, you can disable the hostname verification.

An example of disabled TLS hostname verification
authentication:
  type: oauth
  tokenEndpointUri: https://sso.myproject.svc:8443/auth/realms/internal/protocol/openid-connect/token
  clientId: my-client-id
  refreshToken:
    secretName: my-refresh-token-secret
    key: refresh-token
  disableTlsHostnameVerification: true

The type property is a discriminator that distinguishes the use of the type KafkaClientAuthenticationOAuth from KafkaClientAuthenticationTls, KafkaClientAuthenticationScramSha512, KafkaClientAuthenticationPlain. It must have the value oauth for the type KafkaClientAuthenticationOAuth.

Property Description

accessToken

Link to Kubernetes Secret containing the access token which was obtained from the authorization server.

GenericSecretSource

accessTokenIsJwt

Configure whether access token should be treated as JWT. This should be set to false if the authorization server returns opaque tokens. Defaults to true.

boolean

clientId

OAuth Client ID which the Kafka client can use to authenticate against the OAuth server and use the token endpoint URI.

string

clientSecret

Link to Kubernetes Secret containing the OAuth client secret which the Kafka client can use to authenticate against the OAuth server and use the token endpoint URI.

GenericSecretSource

disableTlsHostnameVerification

Enable or disable TLS hostname verification. Default value is false.

boolean

maxTokenExpirySeconds

Set or limit time-to-live of the access tokens to the specified number of seconds. This should be set if the authorization server returns opaque tokens.

integer

refreshToken

Link to Kubernetes Secret containing the refresh token which can be used to obtain access token from the authorization server.

GenericSecretSource

scope

OAuth scope to use when authenticating against the authorization server. Some authorization servers require this to be set. The possible values depend on how authorization server is configured. By default scope is not specified when doing the token endpoint request.

string

tlsTrustedCertificates

Trusted certificates for TLS connection to the OAuth server.

CertSecretSource array

tokenEndpointUri

Authorization server token endpoint URI.

string

type

Must be oauth.

string

B.86. JaegerTracing schema reference

Used in: KafkaBridgeSpec, KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMaker2Spec, KafkaMirrorMakerSpec

The type property is a discriminator that distinguishes the use of the type JaegerTracing from other subtypes which may be added in the future. It must have the value jaeger for the type JaegerTracing.

Property Description

type

Must be jaeger.

string

B.87. KafkaConnectTemplate schema reference

Used in: KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMaker2Spec

Property Description

deployment

Template for Kafka Connect Deployment.

ResourceTemplate

pod

Template for Kafka Connect Pods.

PodTemplate

apiService

Template for Kafka Connect API Service.

ResourceTemplate

connectContainer

Template for the Kafka Connect container.

ContainerTemplate

podDisruptionBudget

Template for Kafka Connect PodDisruptionBudget.

PodDisruptionBudgetTemplate

B.88. ExternalConfiguration schema reference

Used in: KafkaConnectS2ISpec, KafkaConnectSpec, KafkaMirrorMaker2Spec

Property Description

env

Allows to pass data from Secret or ConfigMap to the Kafka Connect pods as environment variables.

ExternalConfigurationEnv array

volumes

Allows to pass data from Secret or ConfigMap to the Kafka Connect pods as volumes.

ExternalConfigurationVolumeSource array

B.89. ExternalConfigurationEnv schema reference

Used in: ExternalConfiguration

Property Description

name

Name of the environment variable which will be passed to the Kafka Connect pods. The name of the environment variable cannot start with KAFKA_ or STRIMZI_.

string

valueFrom

Value of the environment variable which will be passed to the Kafka Connect pods. It can be passed either as a reference to Secret or ConfigMap field. The field has to specify exactly one Secret or ConfigMap.

ExternalConfigurationEnvVarSource

B.90. ExternalConfigurationEnvVarSource schema reference

Used in: ExternalConfigurationEnv

Property Description

configMapKeyRef

Refernce to a key in a ConfigMap. See external documentation of core/v1 configmapkeyselector.

ConfigMapKeySelector

secretKeyRef

Reference to a key in a Secret. See external documentation of core/v1 secretkeyselector.

SecretKeySelector

B.91. ExternalConfigurationVolumeSource schema reference

Used in: ExternalConfiguration

Property Description

configMap

Reference to a key in a ConfigMap. Exactly one Secret or ConfigMap has to be specified. See external documentation of core/v1 configmapvolumesource.

ConfigMapVolumeSource

name

Name of the volume which will be added to the Kafka Connect pods.

string

secret

Reference to a key in a Secret. Exactly one Secret or ConfigMap has to be specified. See external documentation of core/v1 secretvolumesource.

SecretVolumeSource

B.92. KafkaConnectStatus schema reference

Used in: KafkaConnect

Property Description

conditions

List of status conditions.

Condition array

observedGeneration

The generation of the CRD that was last reconciled by the operator.

integer

url

The URL of the REST API endpoint for managing and monitoring Kafka Connect connectors.

string

connectorPlugins

The list of connector plugins available in this Kafka Connect deployment.

ConnectorPlugin array

B.93. ConnectorPlugin schema reference

Used in: KafkaConnectS2IStatus, KafkaConnectStatus, KafkaMirrorMaker2Status

Property Description

type

The type of the connector plugin. The available types are sink and source.

string

version

The version of the connector plugin.

string

class

The class of the connector plugin.

string

B.94. KafkaConnectS2I schema reference

Property Description

spec

The specification of the Kafka Connect Source-to-Image (S2I) cluster.

KafkaConnectS2ISpec

status

The status of the Kafka Connect Source-to-Image (S2I) cluster.

KafkaConnectS2IStatus

B.95. KafkaConnectS2ISpec schema reference

Used in: KafkaConnectS2I

Property Description

replicas

The number of pods in the Kafka Connect group.

integer

image

The docker image for the pods.

string

buildResources

CPU and memory resources to reserve.

ResourceRequirements

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

jvmOptions

JVM Options for pods.

JvmOptions

affinity

The property affinity has been deprecated. This feature should now be configured at path spec.template.pod.affinity. The pod’s affinity rules. See external documentation of core/v1 affinity.

Affinity

logging

Logging configuration for Kafka Connect. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

metrics

The Prometheus JMX Exporter configuration. See https://github.com/prometheus/jmx_exporter for details of the structure of this configuration.

map

template

Template for Kafka Connect and Kafka Connect S2I resources. The template allows users to specify how the Deployment, Pods and Service are generated.

KafkaConnectTemplate

authentication

Authentication configuration for Kafka Connect. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, plain, oauth].

KafkaClientAuthenticationTls, KafkaClientAuthenticationScramSha512, KafkaClientAuthenticationPlain, KafkaClientAuthenticationOAuth

bootstrapServers

Bootstrap servers to connect to. This should be given as a comma separated list of <hostname>:‍<port> pairs.

string

config

The Kafka Connect configuration. Properties with the following prefixes cannot be set: ssl., sasl., security., listeners, plugin.path, rest., bootstrap.servers, consumer.interceptor.classes, producer.interceptor.classes (with the exception of: ssl.endpoint.identification.algorithm, ssl.cipher.suites, ssl.protocol, ssl.enabled.protocols).

map

externalConfiguration

Pass data from Secrets or ConfigMaps to the Kafka Connect pods and use them to configure connectors.

ExternalConfiguration

insecureSourceRepository

When true this configures the source repository with the 'Local' reference policy and an import policy that accepts insecure source tags.

boolean

resources

The maximum limits for CPU and memory resources and the requested initial resources.

ResourceRequirements

tls

TLS configuration.

KafkaConnectTls

tolerations

The property tolerations has been deprecated. This feature should now be configured at path spec.template.pod.tolerations. The pod’s tolerations. See external documentation of core/v1 toleration.

Toleration array

tracing

The configuration of tracing in Kafka Connect. The type depends on the value of the tracing.type property within the given object, which must be one of [jaeger].

JaegerTracing

version

The Kafka Connect version. Defaults to 2.5.0. Consult the user documentation to understand the process required to upgrade or downgrade the version.

string

B.96. KafkaConnectS2IStatus schema reference

Used in: KafkaConnectS2I

Property Description

conditions

List of status conditions.

Condition array

observedGeneration

The generation of the CRD that was last reconciled by the operator.

integer

url

The URL of the REST API endpoint for managing and monitoring Kafka Connect connectors.

string

connectorPlugins

The list of connector plugins available in this Kafka Connect deployment.

ConnectorPlugin array

buildConfigName

The name of the build configuration.

string

B.97. KafkaTopic schema reference

Property Description

spec

The specification of the topic.

KafkaTopicSpec

status

The status of the topic.

KafkaTopicStatus

B.98. KafkaTopicSpec schema reference

Used in: KafkaTopic

Property Description

partitions

The number of partitions the topic should have. This cannot be decreased after topic creation. It can be increased after topic creation, but it is important to understand the consequences that has, especially for topics with semantic partitioning.

integer

replicas

The number of replicas the topic should have.

integer

config

The topic configuration.

map

topicName

The name of the topic. When absent this will default to the metadata.name of the topic. It is recommended to not set this unless the topic name is not a valid Kubernetes resource name.

string

B.99. KafkaTopicStatus schema reference

Used in: KafkaTopic

Property Description

conditions

List of status conditions.

Condition array

observedGeneration

The generation of the CRD that was last reconciled by the operator.

integer

B.100. KafkaUser schema reference

Property Description

spec

The specification of the user.

KafkaUserSpec

status

The status of the Kafka User.

KafkaUserStatus

B.101. KafkaUserSpec schema reference

Used in: KafkaUser

Property Description

authentication

Authentication mechanism enabled for this Kafka user. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512].

KafkaUserTlsClientAuthentication, KafkaUserScramSha512ClientAuthentication

authorization

Authorization rules for this Kafka user. The type depends on the value of the authorization.type property within the given object, which must be one of [simple].

KafkaUserAuthorizationSimple

quotas

Quotas on requests to control the broker resources used by clients. Network bandwidth and request rate quotas can be enforced.Kafka documentation for Kafka User quotas can be found at http://kafka.apache.org/documentation/#design_quotas.

KafkaUserQuotas

B.102. KafkaUserTlsClientAuthentication schema reference

Used in: KafkaUserSpec

The type property is a discriminator that distinguishes the use of the type KafkaUserTlsClientAuthentication from KafkaUserScramSha512ClientAuthentication. It must have the value tls for the type KafkaUserTlsClientAuthentication.

Property Description

type

Must be tls.

string

B.103. KafkaUserScramSha512ClientAuthentication schema reference

Used in: KafkaUserSpec

The type property is a discriminator that distinguishes the use of the type KafkaUserScramSha512ClientAuthentication from KafkaUserTlsClientAuthentication. It must have the value scram-sha-512 for the type KafkaUserScramSha512ClientAuthentication.

Property Description

type

Must be scram-sha-512.

string

B.104. KafkaUserAuthorizationSimple schema reference

Used in: KafkaUserSpec

The type property is a discriminator that distinguishes the use of the type KafkaUserAuthorizationSimple from other subtypes which may be added in the future. It must have the value simple for the type KafkaUserAuthorizationSimple.

Property Description

type

Must be simple.

string

acls

List of ACL rules which should be applied to this user.

AclRule array

B.105. AclRule schema reference

Used in: KafkaUserAuthorizationSimple

Property Description

host

The host from which the action described in the ACL rule is allowed or denied.

string

operation

Operation which will be allowed or denied. Supported operations are: Read, Write, Create, Delete, Alter, Describe, ClusterAction, AlterConfigs, DescribeConfigs, IdempotentWrite and All.

string (one of [Read, Write, Delete, Alter, Describe, All, IdempotentWrite, ClusterAction, Create, AlterConfigs, DescribeConfigs])

resource

Indicates the resource for which given ACL rule applies. The type depends on the value of the resource.type property within the given object, which must be one of [topic, group, cluster, transactionalId].

AclRuleTopicResource, AclRuleGroupResource, AclRuleClusterResource, AclRuleTransactionalIdResource

type

The type of the rule. Currently the only supported type is allow. ACL rules with type allow are used to allow user to execute the specified operations. Default value is allow.

string (one of [allow, deny])

B.106. AclRuleTopicResource schema reference

Used in: AclRule

The type property is a discriminator that distinguishes the use of the type AclRuleTopicResource from AclRuleGroupResource, AclRuleClusterResource, AclRuleTransactionalIdResource. It must have the value topic for the type AclRuleTopicResource.

Property Description

type

Must be topic.

string

name

Name of resource for which given ACL rule applies. Can be combined with patternType field to use prefix pattern.

string

patternType

Describes the pattern used in the resource field. The supported types are literal and prefix. With literal pattern type, the resource field will be used as a definition of a full topic name. With prefix pattern type, the resource name will be used only as a prefix. Default value is literal.

string (one of [prefix, literal])

B.107. AclRuleGroupResource schema reference

Used in: AclRule

The type property is a discriminator that distinguishes the use of the type AclRuleGroupResource from AclRuleTopicResource, AclRuleClusterResource, AclRuleTransactionalIdResource. It must have the value group for the type AclRuleGroupResource.

Property Description

type

Must be group.

string

name

Name of resource for which given ACL rule applies. Can be combined with patternType field to use prefix pattern.

string

patternType

Describes the pattern used in the resource field. The supported types are literal and prefix. With literal pattern type, the resource field will be used as a definition of a full topic name. With prefix pattern type, the resource name will be used only as a prefix. Default value is literal.

string (one of [prefix, literal])

B.108. AclRuleClusterResource schema reference

Used in: AclRule

The type property is a discriminator that distinguishes the use of the type AclRuleClusterResource from AclRuleTopicResource, AclRuleGroupResource, AclRuleTransactionalIdResource. It must have the value cluster for the type AclRuleClusterResource.

Property Description

type

Must be cluster.

string

B.109. AclRuleTransactionalIdResource schema reference

Used in: AclRule

The type property is a discriminator that distinguishes the use of the type AclRuleTransactionalIdResource from AclRuleTopicResource, AclRuleGroupResource, AclRuleClusterResource. It must have the value transactionalId for the type AclRuleTransactionalIdResource.

Property Description

type

Must be transactionalId.

string

name

Name of resource for which given ACL rule applies. Can be combined with patternType field to use prefix pattern.

string

patternType

Describes the pattern used in the resource field. The supported types are literal and prefix. With literal pattern type, the resource field will be used as a definition of a full name. With prefix pattern type, the resource name will be used only as a prefix. Default value is literal.

string (one of [prefix, literal])

B.110. KafkaUserQuotas schema reference

Used in: KafkaUserSpec

Kafka allows a user to enforce certain quotas to control usage of resources by clients. Quotas split into two categories:

  • Network usage quotas, which are defined as the byte rate threshold for each group of clients sharing a quota

  • CPU utilization quotas, which are defined as the percentage of time a client can utilize on request handler I/O threads and network threads of each broker within a quota window

Using quotas for Kafka clients might be useful in a number of situations. Consider a wrongly configured Kafka producer which is sending requests at too high a rate. Such misconfiguration can cause a denial of service to other clients, so the problematic client ought to be blocked. By using a network limiting quota, it is possible to prevent this situation from significantly impacting other clients.

Strimzi supports user-level quotas, but not client-level quotas.

An example Kafka user quotas
spec:
  quotas:
    producerByteRate: 1048576
    consumerByteRate: 2097152
    requestPercentage: 55

For more info about Kafka user quotas visit Apache Kafka documentation.

Property Description

consumerByteRate

A quota on the maximum bytes per-second that each client group can fetch from a broker before the clients in the group are throttled. Defined on a per-broker basis.

integer

producerByteRate

A quota on the maximum bytes per-second that each client group can publish to a broker before the clients in the group are throttled. Defined on a per-broker basis.

integer

requestPercentage

A quota on the maximum CPU utilization of each client group as a percentage of network and I/O threads.

integer

B.111. KafkaUserStatus schema reference

Used in: KafkaUser

Property Description

conditions

List of status conditions.

Condition array

observedGeneration

The generation of the CRD that was last reconciled by the operator.

integer

username

Username.

string

secret

The name of Secret where the credentials are stored.

string

B.112. KafkaMirrorMaker schema reference

Property Description

spec

The specification of Kafka MirrorMaker.

KafkaMirrorMakerSpec

status

The status of Kafka MirrorMaker.

KafkaMirrorMakerStatus

B.113. KafkaMirrorMakerSpec schema reference

Used in: KafkaMirrorMaker

Property Description

replicas

The number of pods in the Deployment.

integer

image

The docker image for the pods.

string

whitelist

List of topics which are included for mirroring. This option allows any regular expression using Java-style regular expressions. Mirroring two topics named A and B is achieved by using the whitelist 'A|B'. Or, as a special case, you can mirror all topics using the whitelist '*'. You can also specify multiple regular expressions separated by commas.

string

consumer

Configuration of source cluster.

KafkaMirrorMakerConsumerSpec

producer

Configuration of target cluster.

KafkaMirrorMakerProducerSpec

resources

CPU and memory resources to reserve.

ResourceRequirements

affinity

The property affinity has been deprecated. This feature should now be configured at path spec.template.pod.affinity. The pod’s affinity rules. See external documentation of core/v1 affinity.

Affinity

tolerations

The property tolerations has been deprecated. This feature should now be configured at path spec.template.pod.tolerations. The pod’s tolerations. See external documentation of core/v1 toleration.

Toleration array

jvmOptions

JVM Options for pods.

JvmOptions

logging

Logging configuration for MirrorMaker. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

metrics

The Prometheus JMX Exporter configuration. See JMX Exporter documentation for details of the structure of this configuration.

map

tracing

The configuration of tracing in Kafka MirrorMaker. The type depends on the value of the tracing.type property within the given object, which must be one of [jaeger].

JaegerTracing

template

Template to specify how Kafka MirrorMaker resources, Deployments and Pods, are generated.

KafkaMirrorMakerTemplate

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

version

The Kafka MirrorMaker version. Defaults to 2.5.0. Consult the documentation to understand the process required to upgrade or downgrade the version.

string

B.114. KafkaMirrorMakerConsumerSpec schema reference

Used in: KafkaMirrorMakerSpec

Property Description

numStreams

Specifies the number of consumer stream threads to create.

integer

offsetCommitInterval

Specifies the offset auto-commit interval in ms. Default value is 60000.

integer

groupId

A unique string that identifies the consumer group this consumer belongs to.

string

bootstrapServers

A list of host:port pairs for establishing the initial connection to the Kafka cluster.

string

authentication

Authentication configuration for connecting to the cluster. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, plain, oauth].

KafkaClientAuthenticationTls, KafkaClientAuthenticationScramSha512, KafkaClientAuthenticationPlain, KafkaClientAuthenticationOAuth

config

The MirrorMaker consumer config. Properties with the following prefixes cannot be set: ssl., bootstrap.servers, group.id, sasl., security., interceptor.classes (with the exception of: ssl.endpoint.identification.algorithm, ssl.cipher.suites, ssl.protocol, ssl.enabled.protocols).

map

tls

TLS configuration for connecting MirrorMaker to the cluster.

KafkaMirrorMakerTls

B.115. KafkaMirrorMakerTls schema reference

Used in: KafkaMirrorMakerConsumerSpec, KafkaMirrorMakerProducerSpec

Use the tls property to configure TLS encryption. Provide a list of secrets with key names under which the certificates are stored in X.509 format.

An example TLS encryption configuration
tls:
  trustedCertificates:
    - secretName: my-cluster-cluster-ca-cert
      certificate: ca.crt
Property Description

trustedCertificates

Trusted certificates for TLS connection.

CertSecretSource array

B.116. KafkaMirrorMakerProducerSpec schema reference

Used in: KafkaMirrorMakerSpec

Property Description

bootstrapServers

A list of host:port pairs for establishing the initial connection to the Kafka cluster.

string

abortOnSendFailure

Flag to set the MirrorMaker to exit on a failed send. Default value is true.

boolean

authentication

Authentication configuration for connecting to the cluster. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, plain, oauth].

KafkaClientAuthenticationTls, KafkaClientAuthenticationScramSha512, KafkaClientAuthenticationPlain, KafkaClientAuthenticationOAuth

config

The MirrorMaker producer config. Properties with the following prefixes cannot be set: ssl., bootstrap.servers, sasl., security., interceptor.classes (with the exception of: ssl.endpoint.identification.algorithm, ssl.cipher.suites, ssl.protocol, ssl.enabled.protocols).

map

tls

TLS configuration for connecting MirrorMaker to the cluster.

KafkaMirrorMakerTls

B.117. KafkaMirrorMakerTemplate schema reference

Used in: KafkaMirrorMakerSpec

Property Description

deployment

Template for Kafka MirrorMaker Deployment.

ResourceTemplate

pod

Template for Kafka MirrorMaker Pods.

PodTemplate

mirrorMakerContainer

Template for Kafka MirrorMaker container.

ContainerTemplate

podDisruptionBudget

Template for Kafka MirrorMaker PodDisruptionBudget.

PodDisruptionBudgetTemplate

B.118. KafkaMirrorMakerStatus schema reference

Used in: KafkaMirrorMaker

Property Description

conditions

List of status conditions.

Condition array

observedGeneration

The generation of the CRD that was last reconciled by the operator.

integer

B.119. KafkaBridge schema reference

Property Description

spec

The specification of the Kafka Bridge.

KafkaBridgeSpec

status

The status of the Kafka Bridge.

KafkaBridgeStatus

B.120. KafkaBridgeSpec schema reference

Used in: KafkaBridge

Property Description

replicas

The number of pods in the Deployment.

integer

image

The docker image for the pods.

string

bootstrapServers

A list of host:port pairs for establishing the initial connection to the Kafka cluster.

string

tls

TLS configuration for connecting Kafka Bridge to the cluster.

KafkaBridgeTls

authentication

Authentication configuration for connecting to the cluster. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, plain, oauth].

KafkaClientAuthenticationTls, KafkaClientAuthenticationScramSha512, KafkaClientAuthenticationPlain, KafkaClientAuthenticationOAuth

http

The HTTP related configuration.

KafkaBridgeHttpConfig

consumer

Kafka consumer related configuration.

KafkaBridgeConsumerSpec

producer

Kafka producer related configuration.

KafkaBridgeProducerSpec

resources

CPU and memory resources to reserve.

ResourceRequirements

jvmOptions

Currently not supported JVM Options for pods.

JvmOptions

logging

Logging configuration for Kafka Bridge. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

metrics

Currently not supported The Prometheus JMX Exporter configuration. See JMX Exporter documentation for details of the structure of this configuration.

map

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

template

Template for Kafka Bridge resources. The template allows users to specify how is the Deployment and Pods generated.

KafkaBridgeTemplate

tracing

The configuration of tracing in Kafka Bridge. The type depends on the value of the tracing.type property within the given object, which must be one of [jaeger].

JaegerTracing

B.121. KafkaBridgeTls schema reference

Used in: KafkaBridgeSpec

Property Description

trustedCertificates

Trusted certificates for TLS connection.

CertSecretSource array

B.122. KafkaBridgeHttpConfig schema reference

Used in: KafkaBridgeSpec

Property Description

port

The port which is the server listening on.

integer

cors

CORS configuration for the HTTP Bridge.

KafkaBridgeHttpCors

B.123. KafkaBridgeHttpCors schema reference

Used in: KafkaBridgeHttpConfig

Property Description

allowedOrigins

List of allowed origins. Java regular expressions can be used.

string array

allowedMethods

List of allowed HTTP methods.

string array

B.124. KafkaBridgeConsumerSpec schema reference

Used in: KafkaBridgeSpec

Property Description

config

The Kafka consumer configuration used for consumer instances created by the bridge. Properties with the following prefixes cannot be set: ssl., bootstrap.servers, group.id, sasl., security. (with the exception of: ssl.endpoint.identification.algorithm, ssl.cipher.suites, ssl.protocol, ssl.enabled.protocols).

map

B.125. KafkaBridgeProducerSpec schema reference

Used in: KafkaBridgeSpec

Property Description

config

The Kafka producer configuration used for producer instances created by the bridge. Properties with the following prefixes cannot be set: ssl., bootstrap.servers, sasl., security. (with the exception of: ssl.endpoint.identification.algorithm, ssl.cipher.suites, ssl.protocol, ssl.enabled.protocols).

map

B.126. KafkaBridgeTemplate schema reference

Used in: KafkaBridgeSpec

Property Description

deployment

Template for Kafka Bridge Deployment.

ResourceTemplate

pod

Template for Kafka Bridge Pods.

PodTemplate

apiService

Template for Kafka Bridge API Service.

ResourceTemplate

bridgeContainer

Template for the Kafka Bridge container.

ContainerTemplate

podDisruptionBudget

Template for Kafka Bridge PodDisruptionBudget.

PodDisruptionBudgetTemplate

B.127. KafkaBridgeStatus schema reference

Used in: KafkaBridge

Property Description

conditions

List of status conditions.

Condition array

observedGeneration

The generation of the CRD that was last reconciled by the operator.

integer

url

The URL at which external client applications can access the Kafka Bridge.

string

B.128. KafkaConnector schema reference

Property Description

spec

The specification of the Kafka Connector.

KafkaConnectorSpec

status

The status of the Kafka Connector.

KafkaConnectorStatus

B.129. KafkaConnectorSpec schema reference

Used in: KafkaConnector

Property Description

class

The Class for the Kafka Connector.

string

tasksMax

The maximum number of tasks for the Kafka Connector.

integer

config

The Kafka Connector configuration. The following properties cannot be set: connector.class, tasks.max.

map

pause

Whether the connector should be paused. Defaults to false.

boolean

B.130. KafkaConnectorStatus schema reference

Used in: KafkaConnector

Property Description

conditions

List of status conditions.

Condition array

observedGeneration

The generation of the CRD that was last reconciled by the operator.

integer

connectorStatus

The connector status, as reported by the Kafka Connect REST API.

map

B.131. KafkaMirrorMaker2 schema reference

Property Description

spec

The specification of the Kafka MirrorMaker 2.0 cluster.

KafkaMirrorMaker2Spec

status

The status of the Kafka MirrorMaker 2.0 cluster.

KafkaMirrorMaker2Status

B.132. KafkaMirrorMaker2Spec schema reference

Used in: KafkaMirrorMaker2

Property Description

replicas

The number of pods in the Kafka Connect group.

integer

version

The Kafka Connect version. Defaults to 2.5.0. Consult the user documentation to understand the process required to upgrade or downgrade the version.

string

image

The docker image for the pods.

string

connectCluster

The cluster alias used for Kafka Connect. The alias must match a cluster in the list at spec.clusters.

string

clusters

Kafka clusters for mirroring.

KafkaMirrorMaker2ClusterSpec array

mirrors

Configuration of the MirrorMaker 2.0 connectors.

KafkaMirrorMaker2MirrorSpec array

resources

The maximum limits for CPU and memory resources and the requested initial resources.

ResourceRequirements

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

jvmOptions

JVM Options for pods.

JvmOptions

affinity

The property affinity has been deprecated. This feature should now be configured at path spec.template.pod.affinity. The pod’s affinity rules. See external documentation of core/v1 affinity.

Affinity

tolerations

The property tolerations has been deprecated. This feature should now be configured at path spec.template.pod.tolerations. The pod’s tolerations. See external documentation of core/v1 toleration.

Toleration array

logging

Logging configuration for Kafka Connect. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

metrics

The Prometheus JMX Exporter configuration. See https://github.com/prometheus/jmx_exporter for details of the structure of this configuration.

map

tracing

The configuration of tracing in Kafka Connect. The type depends on the value of the tracing.type property within the given object, which must be one of [jaeger].

JaegerTracing

template

Template for Kafka Connect and Kafka Connect S2I resources. The template allows users to specify how the Deployment, Pods and Service are generated.

KafkaConnectTemplate

externalConfiguration

Pass data from Secrets or ConfigMaps to the Kafka Connect pods and use them to configure connectors.

ExternalConfiguration

B.133. KafkaMirrorMaker2ClusterSpec schema reference

Used in: KafkaMirrorMaker2Spec

Property Description

alias

Alias used to reference the Kafka cluster.

string

bootstrapServers

A comma-separated list of host:port pairs for establishing the connection to the Kafka cluster.

string

config

The MirrorMaker 2.0 cluster config. Properties with the following prefixes cannot be set: ssl., sasl., security., listeners, plugin.path, rest., bootstrap.servers, consumer.interceptor.classes, producer.interceptor.classes (with the exception of: ssl.endpoint.identification.algorithm, ssl.cipher.suites, ssl.protocol, ssl.enabled.protocols).

map

tls

TLS configuration for connecting MirrorMaker 2.0 connectors to a cluster.

KafkaMirrorMaker2Tls

authentication

Authentication configuration for connecting to the cluster. The type depends on the value of the authentication.type property within the given object, which must be one of [tls, scram-sha-512, plain, oauth].

KafkaClientAuthenticationTls, KafkaClientAuthenticationScramSha512, KafkaClientAuthenticationPlain, KafkaClientAuthenticationOAuth

B.134. KafkaMirrorMaker2Tls schema reference

Used in: KafkaMirrorMaker2ClusterSpec

Property Description

trustedCertificates

Trusted certificates for TLS connection.

CertSecretSource array

B.135. KafkaMirrorMaker2MirrorSpec schema reference

Used in: KafkaMirrorMaker2Spec

Property Description

sourceCluster

The alias of the source cluster used by the Kafka MirrorMaker 2.0 connectors. The alias must match a cluster in the list at spec.clusters.

string

targetCluster

The alias of the target cluster used by the Kafka MirrorMaker 2.0 connectors. The alias must match a cluster in the list at spec.clusters.

string

sourceConnector

The specification of the Kafka MirrorMaker 2.0 source connector.

KafkaMirrorMaker2ConnectorSpec

checkpointConnector

The specification of the Kafka MirrorMaker 2.0 checkpoint connector.

KafkaMirrorMaker2ConnectorSpec

heartbeatConnector

The specification of the Kafka MirrorMaker 2.0 heartbeat connector.

KafkaMirrorMaker2ConnectorSpec

topicsPattern

A regular expression matching the topics to be mirrored, for example, "topic1|topic2|topic3". Comma-separated lists are also supported.

string

topicsBlacklistPattern

A regular expression matching the topics to exclude from mirroring. Comma-separated lists are also supported.

string

groupsPattern

A regular expression matching the consumer groups to be mirrored. Comma-separated lists are also supported.

string

groupsBlacklistPattern

A regular expression matching the consumer groups to exclude from mirroring. Comma-separated lists are also supported.

string

B.136. KafkaMirrorMaker2ConnectorSpec schema reference

Used in: KafkaMirrorMaker2MirrorSpec

Property Description

tasksMax

The maximum number of tasks for the Kafka Connector.

integer

config

The Kafka Connector configuration. The following properties cannot be set: connector.class, tasks.max.

map

pause

Whether the connector should be paused. Defaults to false.

boolean

B.137. KafkaMirrorMaker2Status schema reference

Used in: KafkaMirrorMaker2

Property Description

conditions

List of status conditions.

Condition array

observedGeneration

The generation of the CRD that was last reconciled by the operator.

integer

url

The URL of the REST API endpoint for managing and monitoring Kafka Connect connectors.

string

connectorPlugins

The list of connector plugins available in this Kafka Connect deployment.

ConnectorPlugin array

connectors

List of MirrorMaker 2.0 connector statuses, as reported by the Kafka Connect REST API.

map array

B.138. KafkaRebalance schema reference

Property Description

spec

The specification of the Kafka rebalance.

KafkaRebalanceSpec

status

The status of the Kafka rebalance.

KafkaRebalanceStatus

B.139. KafkaRebalanceSpec schema reference

Used in: KafkaRebalance

Property Description

goals

A list of goals, ordered by decreasing priority, to use for generating and executing the rebalance proposal. The supported goals are available at https://github.com/linkedin/cruise-control#goals. If an empty goals list is provided, the goals declared in the default.goals Cruise Control configuration parameter are used.

string array

skipHardGoalCheck

Whether to allow the hard goals specified in the Kafka CR to be skipped in rebalance proposal generation. This can be useful when some of those hard goals are preventing a balance solution being found. Default is false.

boolean

B.140. KafkaRebalanceStatus schema reference

Used in: KafkaRebalance

Property Description

conditions

List of status conditions.

Condition array

observedGeneration

The generation of the CRD that was last reconciled by the operator.

integer

sessionId

The session identifier for requests to Cruise Control pertaining to this KafkaRebalance resource. This is used by the Kafka Rebalance operator to track the status of ongoing rebalancing operations.

string

optimizationResult

A JSON object describing the optimization result.

map