1. Overview of Strimzi

Strimzi is based on Apache Kafka, a popular platform for streaming data delivery and processing. Strimzi makes it easy to run Apache Kafka on Kubernetes.

Strimzi provides three operators:

Cluster Operator

Responsible for deploying and managing Apache Kafka clusters within a Kubernetes cluster.

Topic Operator

Responsible for managing Kafka topics within a Kafka cluster running within a Kubernetes cluster.

User Operator

Responsible for managing Kafka users within a Kafka cluster running within a Kubernetes cluster.

Note
 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

This guide describes how to install and use Strimzi.

1.1. Kafka Key Features

  • Designed for horizontal scalability

  • Message ordering guarantee at the partition level

  • Message rewind/replay

    • "Long term" storage allows the reconstruction of an application state by replaying the messages

    • Combines with compacted topics to use Kafka as a key-value store

Additional resources

1.2. 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 works on all types of clusters, from public and private clouds to local deployments intended for development. This guide assumes that a Kubernetes cluster is available and the kubectl command-line tool is installed and configured to connect to the running cluster.

When no existing Kubernetes cluster is available, minikube can be used to create a local cluster. More details can be found in Installing Kubernetes clusters. Strimzi supports a few specific OpenShift-specific features. If you need an OpenShift cluster to use these and none is available minishift can be used to create a local cluster. More details can be found in Installing OpenShift clusters.

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. Installing Strimzi and deploying components

To install Strimzi, download the release artefacts from GitHub.

The folder contains several YAML files to help you deploy the components of Strimzi to Kubernetes, perform common operations, and configure your Kafka cluster. The YAML files are referenced throughout this documentation.

Additionally, a Helm Chart is provided for deploying the Cluster Operator using Helm. The container images are available through the Docker Hub.

The remainder of this chapter provides an overview of each component and instructions for deploying the components to Kubernetes using the YAML files provided.

Note
 Although container images for Strimzi are available in the Docker Hub, we recommend that you use the YAML files provided instead.

2.2. Custom resources

Custom resource definitions (CRDs) extend the Kubernetes API, providing definitions to create and modify custom resources to a Kubernetes cluster. Custom resources are created as instances of CRDs.

In Strimzi, CRDs introduce custom resources specific to Strimzi to a Kubernetes cluster, such as Kafka, Kafka Connect, Kafka Mirror Maker, and users and topics custom resources. CRDs provide configuration instructions, defining the schemas used to instantiate and manage the Strimzi-specific resources. CRDs also allow Strimzi resources to benefit from native Kubernetes features like CLI accessibility and configuration validation.

CRDs require a one-time installation in a cluster. Depending on the cluster setup, installation typically requires cluster admin privileges.

Note
 Access to manage custom resources is limited to Strimzi administrators.

CRDs and custom resources are defined as YAML files.

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.
Additional resources

2.2.1. Strimzi custom resource example

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

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

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)
      # ...
  validation: (5)
    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. 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

  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.

    The name is used by the Topic Operator and User Operator to identify the Kafka cluster when creating a topic or user.

  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.

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.

2.2.2. Strimzi custom resource status

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

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

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.

KafkaMirrorMaker

KafkaMirrorMakerStatus schema reference

The Kafka MirrorMaker tool, if deployed.

KafkaUser

KafkaUserStatus schema reference

Kafka users in your 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 most recently observed by the 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
    type: tls
  - addresses:
    - host: 172.29.49.180
      port: 9094
    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 status for external listeners is still under development and does not provide a specific IP address for external listeners of type nodeport.
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 Checking the status of a custom resource.

2.3. Cluster Operator

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

2.3.1. Overview of the Cluster Operator component

Strimzi uses the Cluster Operator to deploy and manage clusters for:

  • Kafka (including Zookeeper)

  • Kafka Connect

  • Kafka Mirror Maker

  • 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 Entity Operator 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 topic management through KafkaUser custom resources

For more information on the configuration options supported by the Kafka resource, see Kafka cluster configuration.

Note
 On OpenShift, a Kafka Connect deployment can incorporate a Source2Image feature to provides a convenient way to include connectors.
Example architecture for the Cluster Operator

Cluster Operator

2.3.2. Watch options for a Cluster Operator deployment

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

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

Note
 Strimzi provides example YAML files to make the deployment process easier.

The Cluster Operator watches the following resources:

  • Kafka for the Kafka cluster.

  • KafkaConnect for the Kafka Connect cluster.

  • KafkaConnectS2I for the Kafka Connect cluster with Source2Image support.

  • KafkaMirrorMaker for the Kafka Mirror Maker 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.

2.3.3. Deploying the Cluster Operator to watch a single namespace

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.

  • Modify the installation files according to the namespace the Cluster Operator is going to be installed in.

    On Linux, use:

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

    On MacOS, use:

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

  • Deploy the Cluster Operator:

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

2.3.4. Deploying the Cluster Operator to watch multiple namespaces

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.

  • Edit the installation files according to the namespace the Cluster Operator is going to be installed in.

    On Linux, use:

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

    On MacOS, use:

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

  1. Edit the file install/cluster-operator/050-Deployment-strimzi-cluster-operator.yaml and in the environment variable STRIMZI_NAMESPACE list all the namespaces where Cluster Operator should watch for resources. For example:

    apiVersion: extensions/v1beta1
kind: Deployment
spec:
  template:
    spec:
      serviceAccountName: strimzi-cluster-operator
      containers:
      - name: strimzi-cluster-operator
        image: strimzi/operator:0.13.0
        imagePullPolicy: IfNotPresent
        env:
        - name: STRIMZI_NAMESPACE
          value: watched-namespace-1,watched-namespace-2,watched-namespace-3

  2. For all namespaces which should be watched by the Cluster Operator (watched-namespace-1, watched-namespace-2, watched-namespace-3 in the above example), install the RoleBindings. Replace the watched-namespace with the namespace used in the previous step.

    This can be done using kubectl apply:

    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

  3. Deploy the Cluster Operator

    This can be done using kubectl apply:

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

2.3.5. Deploying the Cluster Operator to watch all namespaces

You can configure 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.

  • Your Kubernetes cluster is running.

Procedure

  1. Configure the Cluster Operator to watch all namespaces:

    1. Edit the 050-Deployment-strimzi-cluster-operator.yaml file.

    2. Set the value of the STRIMZI_NAMESPACE environment variable to *.

      apiVersion: extensions/v1beta1
kind: Deployment
spec:
  template:
    spec:
      # ...
      serviceAccountName: strimzi-cluster-operator
      containers:
      - name: strimzi-cluster-operator
        image: strimzi/operator:0.13.0
        imagePullPolicy: IfNotPresent
        env:
        - name: STRIMZI_NAMESPACE
          value: "*"
        # ...

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

    Use the kubectl create clusterrolebinding command:

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

    Replace my-namespace with the namespace in which you want to install the Cluster Operator.

  3. Deploy the Cluster Operator to your Kubernetes cluster.

    Use the kubectl apply command:

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

2.3.6. Deploying the Cluster Operator using Helm Chart

Prerequisites

  • Helm client has to be installed on the local machine.

  • Helm has to be installed in the Kubernetes cluster.

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 whether the Cluster Operator has been deployed successfully using the Helm command line tool:

    helm ls
Additional resources

2.3.7. 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.

2.4. Kafka cluster

You can use Strimzi to deploy an ephemeral or persistent Kafka cluster to Kubernetes. When installing Kafka, Strimzi also installs a Zookeeper cluster and adds the necessary configuration to connect Kafka with Zookeeper.

Ephemeral cluster

In general, an ephemeral (that is, 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.

Strimzi includes two templates for deploying a Kafka cluster:

  • kafka-ephemeral.yaml deploys an ephemeral cluster, named my-cluster by default.

  • kafka-persistent.yaml deploys a persistent cluster, 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 resource in the relevant YAML file.

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

2.4.1. Deploying the Kafka cluster

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

Prerequisites

  • The Cluster Operator is deployed.

Procedure

  1. If you plan to use the cluster for development or testing purposes, you can create and deploy an ephemeral cluster using kubectl apply.

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

  2. If you plan to use the cluster in production, create and deploy a persistent cluster using kubectl apply.

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

2.5. Kafka Connect

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

You can use Kafka Connect to:

  • Build connector plug-ins (as JAR files) for your Kafka cluster

  • Run connectors

Kafka Connect includes the following built-in 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).

In Strimzi, you can use the Cluster Operator to deploy a Kafka Connect to your Kubernetes cluster. OpenShift also supports Kafka Connect Source-2-Image (S2I) clusters.

A Kafka Connect cluster is implemented as a Deployment with a configurable number of workers. The Kafka Connect REST API is available on port 8083, as the <connect-cluster-name>-connect-api service.

For more information on deploying a Kafka Connect S2I cluster on OpenShift, see Creating a container image using OpenShift builds and Source-to-Image.

2.5.1. Deploying Kafka Connect to your cluster

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

Prerequisites
Procedure

  • Use the kubectl apply command to create a KafkaConnect resource based on the kafka-connect.yaml file:

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

2.5.2. Extending Kafka Connect with connector plug-ins

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

  • Creating a container image from the Kafka Connect base image (manually or using your CI (continuous integration), for example).

  • Creating a container image using OpenShift builds and Source-to-Image (S2I) - available only on OpenShift.

Creating a Docker image from the Kafka Connect base image

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.

The following procedure explains how to create your custom image and add it to the /opt/kafka/plugins directory. 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:0.13.0-kafka-2.3.0 as the base image:

    FROM strimzi/kafka:0.13.0-kafka-2.3.0
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. Edit the KafkaConnect.spec.image property of the KafkaConnect custom resource to point to the new container image. If set, this property overrides the STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE variable referred to in the next step.

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
  name: my-connect-cluster
spec:
  #...
  image: my-new-container-image

  5. In the install/cluster-operator/050-Deployment-strimzi-cluster-operator.yaml file, edit the STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE variable to point to the new container image.

Additional resources
Creating a container image using OpenShift builds and Source-to-Image

You can use OpenShift builds and the Source-to-Image (S2I) framework to create new container images. 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:0.13.0-kafka-2.3.0 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. Once the build has finished, the new image is used automatically by the Kafka Connect deployment.

2.6. Kafka Mirror Maker

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

For information about example resources and the format for deploying Kafka Mirror Maker, see Kafka Mirror Maker configuration.

2.6.1. Deploying Kafka Mirror Maker

Prerequisites

  • Before deploying Kafka Mirror Maker, the Cluster Operator must be deployed.

Procedure

  • Create a Kafka Mirror Maker cluster from the command-line:

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

  • For more information about deploying the Cluster Operator, see Cluster Operator

2.7. Kafka Bridge

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

For information about example resources and the format for deploying Kafka Bridge, see Kafka Bridge configuration.

2.7.1. Deploying Kafka Bridge to your Kubernetes cluster

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

Prerequisites
Procedure

  • Use the kubectl apply command to create a KafkaBridge resource based on the kafka-bridge.yaml file:

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

2.8. Deploying example clients

Prerequisites

  • An existing Kafka cluster for the client to connect to.

Procedure

  1. Deploy the producer.

    Use kubectl run:

    kubectl run kafka-producer -ti --image=strimzi/kafka:0.13.0-kafka-2.3.0 --rm=true --restart=Never -- bin/kafka-console-producer.sh --broker-list cluster-name-kafka-bootstrap:9092 --topic my-topic

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

  3. Press Enter to send the message.

  4. Deploy the consumer.

    Use kubectl run:

    kubectl run kafka-consumer -ti --image=strimzi/kafka:0.13.0-kafka-2.3.0 --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.

2.9. Topic Operator

The Topic Operator is responsible for managing Kafka topics within a Kafka cluster running within a Kubernetes cluster.

2.9.1. Overview of the Topic Operator component

The Topic Operator provides a way of managing topics in a Kafka cluster via 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 operator will create the topic it describes

  • Deleted, the operator will delete the topic it describes

  • Changed, the operator will update the topic it describes

And also, in the other direction, if a topic is:

  • Created within the Kafka cluster, the operator will create a KafkaTopic describing it

  • Deleted from the Kafka cluster, the operator will delete the KafkaTopic describing it

  • Changed in the Kafka cluster, the operator will update the KafkaTopic describing it

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.

For more details about creating, modifying and deleting topics, see Using the Topic Operator.

2.9.2. Deploying the Topic Operator using the Cluster Operator

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

Prerequisites

  • A running Cluster Operator

  • A Kafka resource to be created or updated

Procedure

  1. Ensure that the Kafka.spec.entityOperator object exists in the Kafka resource. This configures the Entity Operator.

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  #...
  entityOperator:
    topicOperator: {}
    userOperator: {}

  2. Configure the Topic Operator using the fields described in EntityTopicOperatorSpec schema reference.

  3. Create or update the Kafka resource in Kubernetes.

    Use kubectl apply:

    kubectl apply -f your-file
Additional resources

  • For more information about deploying the Cluster Operator, see Cluster Operator.

  • For more information about deploying the Entity Operator, see Entity Operator.

  • For more information about the Kafka.spec.entityOperator object used to configure the Topic Operator when deployed by the Cluster Operator, see EntityOperatorSpec schema reference.

2.10. User Operator

The User Operator is responsible for managing Kafka users within a Kafka cluster running within a Kubernetes cluster.

2.10.1. Overview of the User Operator component

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 will create the user it describes

  • if a KafkaUser is deleted, the User Operator will delete the user it describes

  • if a KafkaUser is changed, the User Operator will update the user it describes

Unlike the Topic Operator, the User Operator does not sync any changes from the Kafka cluster with the Kubernetes resources. Unlike the Kafka topics which might be created by applications directly in Kafka, it is not expected that the users will be managed directly in the Kafka cluster in parallel with the User Operator, so this should not be needed.

The User Operator allows you to declare a KafkaUser as part of your application’s deployment. When the user is created, the user credentials will be 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 rights in the KafkaUser declaration.

2.10.2. Deploying the User Operator using the Cluster Operator

Prerequisites

  • A running Cluster Operator

  • A Kafka resource to be created or updated.

Procedure

  1. Edit the Kafka resource ensuring it has a Kafka.spec.entityOperator.userOperator object that configures the User Operator how you want.

  2. Create or update the Kafka resource in Kubernetes.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Additional resources

  • For more information about deploying the Cluster Operator, see Cluster Operator.

  • For more information about the Kafka.spec.entityOperator object used to configure the User Operator when deployed by the Cluster Operator, see EntityOperatorSpec schema reference.

2.11. Strimzi Administrators

Strimzi includes several custom resources. By default, permission to create, edit, and delete these resources is limited to Kubernetes cluster administrators. If you want to allow non-cluster administators to manage Strimzi resources, you must assign them the Strimzi Administrator role.

2.11.1. Designating Strimzi Administrators

Prerequisites

  • Strimzi CustomResourceDefinitions are installed.

Procedure

  1. Create the strimzi-admin cluster role in Kubernetes.

    Use kubectl apply:

    kubectl apply -f install/strimzi-admin

  2. Assign the strimzi-admin ClusterRole to one or more existing users in the Kubernetes cluster.

    Use kubectl create:

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

2.12. Container images

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 installation YAML files

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

Kafka

  • docker.io/strimzi/kafka:0.13.0-kafka-2.1.0

  • docker.io/strimzi/kafka:0.13.0-kafka-2.1.1

  • docker.io/strimzi/kafka:0.13.0-kafka-2.2.0

  • docker.io/strimzi/kafka:0.13.0-kafka-2.2.1

  • docker.io/strimzi/kafka:0.13.0-kafka-2.3.0

Strimzi image for running Kafka, including:

  • Kafka Broker

  • Kafka Connect / S2I

  • Kafka Mirror Maker

  • Zookeeper

  • TLS Sidecars

Operator

  • docker.io/strimzi/operator:0.13.0

Strimzi image for running the operators:

  • Cluster Operator

  • Topic Operator

  • User Operator

  • Kafka Initializer

Kafka Bridge

  • docker.io/strimzi/kafka-bridge:0.13.0

Strimzi image for running the Strimzi kafka Bridge

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 Mirror Maker

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: 0.13.0 (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
    authorization: (9)
      type: simple
    config: (10)
      auto.create.topics.enable: "false"
      offsets.topic.replication.factor: 3
      transaction.state.log.replication.factor: 3
      transaction.state.log.min.isr: 2
    storage: (11)
      type: persistent-claim (12)
      size: 10000Gi (13)
    rack: (14)
      topologyKey: failure-domain.beta.kubernetes.io/zone
    metrics: (15)
      lowercaseOutputName: true
      rules: (16)
      # 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: (17)
    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: (18)
    topicOperator:
      resources:
        requests:
          memory: 512Mi
          cpu: "1"
        limits:
          memory: 512Mi
          cpu: "1"
    userOperator:
      resources:
        requests:
          memory: 512Mi
          cpu: "1"
        limits:
          memory: 512Mi
          cpu: "1"

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

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

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

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

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

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

  15. Kafka metrics configuration for use with Prometheus.

  16. 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.

  17. Zookeeper-specific configuration, which contains properties similar to the Kafka configuration.

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

3.1.2. Data storage considerations

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

Strimzi requires block storage and is designed to work optimally with cloud-based block storage solutions, including Amazon Elastic Block Store (EBS). The use of file storage (for example, NFS) is not recommended.

Choose local storage (local persistent volumes) when possible. If local storage is not available, you can use a Storage Area Network (SAN) accessed by a protocol such as Fibre Channel or iSCSI.

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.
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.

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.

Important
 The Cluster Operator does not validate keys or values in the provided config object. If invalid configuration is provided, the Kafka cluster might not start or might become unstable. In such cases, you must fix the configuration in the Kafka.spec.kafka.config object and the Cluster Operator will roll out the new configuration to all Kafka brokers.
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

Strimzi allows users to configure the listeners which will be enabled in Kafka brokers. Three types of listener are supported:

  • Plain listener on port 9092 (without encryption)

  • TLS listener on port 9093 (with encryption)

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

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 link:https://github.com/kubernetes-incubator/external-dns[External DNS^] annotations
# ...
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"
# ...
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. Additional NodePort type 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. When selecting the node address, the different address types are used with the following priority:

  1. ExternalDNS

  2. ExternalIP

  3. Hostname

  4. InternalDNS

  5. InternalIP

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 link:https://github.com/kubernetes-incubator/external-dns[External DNS^] annotations
# ...
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
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

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

    An example configuration with an external listener configured to use node ports:

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

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f your-file

  3. Find the port number of the bootstrap service.

    This can be done using kubectl get:

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

    The port should be used in the Kafka bootstrap address.

  4. Find the address of the Kubernetes node.

    This can be done using kubectl get:

    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.

    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
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.

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 link:https://github.com/kubernetes-incubator/external-dns[External DNS^] annotations
# ...
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

  • SASL SCRAM-SHA-512

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 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; only Simple authorization is currently supported.

You can optionally designate a list of super users in the superUsers field.

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
# ...
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 -ti 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.

Configuration

The Entity Operator can be configured using the entityOperator property in Kafka.spec

The entityOperator property supports several sub-properties:

  • tlsSidecar

  • topicOperator

  • userOperator

  • template

The tlsSidecar property can be used to configure the TLS sidecar container which is used to communicate with Zookeeper. For more details about configuring the TLS sidecar, see TLS sidecar.

The template property can be used to configure details of the Entity Operator pod, such as labels, annotations, affinity, tolerations and so on.

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.

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

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

Topic Operator

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

The Topic Operator has its own configurable logger:

  • rootLogger.level

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

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

watchedNamespace

The Kubernetes namespace in which the topic 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.

The User Operator has its own configurable logger:

  • rootLogger.level

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

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.entityOperator.tlsSidecar

  • 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. Logging

This section provides information on loggers and how to configure log levels.

You can set the log levels by specifying the loggers and their levels directly (inline) or use a custom (external) config map.

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

    • zookeeper.root.logger

Specifying inline logging
Procedure

  1. Edit the YAML file to specify the loggers and logging level for the required components.

    For example, the logging level here is set to INFO:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    logging:
      type: inline
      loggers:
        logger.name: "INFO"
    # ...
  zookeeper:
    # ...
    logging:
      type: inline
      loggers:
        logger.name: "INFO"
    # ...
  entityOperator:
    # ...
    topicOperator:
      # ...
      logging:
        type: inline
        loggers:
          logger.name: "INFO"
    # ...
    # ...
    userOperator:
      # ...
      logging:
        type: inline
        loggers:
          logger.name: "INFO"
    # ...

    You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.

    For more information about the log levels, see the log4j manual.

  2. Create or update the Kafka resource in Kubernetes.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Specifying an external ConfigMap for logging
Procedure

  1. Edit the YAML file to specify the name of the ConfigMap to use for the required components. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
spec:
  kafka:
    # ...
    logging:
      type: external
      name: customConfigMap
    # ...

    Remember to place your custom ConfigMap under the log4j.properties or log4j2.properties key.

  2. Create or update the Kafka resource in Kubernetes.

    This can be done using kubectl apply:

    kubectl apply -f your-file

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

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.zookeeper.tlsSidecar

  • Kafka.spec.entityOperator.tlsSidecar

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaBridge.spec

Both livenessProbe and readinessProbe support two additional options:

  • initialDelaySeconds

  • timeoutSeconds

The initialDelaySeconds property defines the initial delay before the probe is tried for the first time. Default is 15 seconds.

The timeoutSeconds property defines timeout of the probe. Default is 5 seconds.

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 configuring Prometheus and Grafana, see Metrics.

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. JVM Options

Apache Kafka and Apache Zookeeper run inside a Java Virtual Machine (JVM). 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

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 -Xms.

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 enabled by default. To disable it, set the gcLoggingEnabled property as follows:

Example of disabling GC logging
# ...
jvmOptions:
  gcLoggingEnabled: false
# ...
Configuring JVM options
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the jvmOptions property in the Kafka, KafkaConnect or KafkaConnectS2I 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

3.1.18. 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

Container image which should be used for given components can be specified using the image property in:

  • Kafka.spec.kafka

  • Kafka.spec.kafka.tlsSidecar

  • Kafka.spec.zookeeper

  • Kafka.spec.zookeeper.tlsSidecar

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.entityOperator.tlsSidecar

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaBridge.spec

Configuring the Kafka.spec.kafka.image property

The Kafka.spec.kafka.image property functions differently from the others, because Strimzi supports multiple versions of Kafka, each requiring the own image. The STRIMZI_KAFKA_IMAGES environment variable of the Cluster Operator configuration is used to provide a mapping between Kafka versions and the corresponding images. This is used in combination with the Kafka.spec.kafka.image and Kafka.spec.kafka.version properties as follows:

  • If neither Kafka.spec.kafka.image nor Kafka.spec.kafka.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 STRIMZI_KAFKA_IMAGES.

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

  • If Kafka.spec.kafka.version is given but Kafka.spec.kafka.image is not then image will be the one corresponding to this version in the STRIMZI_KAFKA_IMAGES.

  • Both Kafka.spec.kafka.version and Kafka.spec.kafka.image are given the given image will be used, and it will be assumed to contain a Kafka broker with the given version.

Warning
 It is best to provide just Kafka.spec.kafka.version and leave the Kafka.spec.kafka.image property unspecified. This reduces the chances of making a mistake in configuring the Kafka resource. If you need to change the images used for different versions of Kafka, it is better to configure the Cluster Operator’s STRIMZI_KAFKA_IMAGES environment variable.
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:0.13.0-kafka-2.3.0 container image.

  • For Zookeeper nodes:

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

    2. strimzi/kafka:0.13.0-kafka-2.3.0 container image.

  • 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:0.13.0-kafka-2.3.0 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:0.13.0 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:0.13.0 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:0.13.0-kafka-2.3.0 container image.

  • For Kafka Connect:

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

    2. strimzi/kafka:0.13.0-kafka-2.3.0 container image.

  • For Kafka Connect with Source2image support:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_CONNECT_S2I_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.13.0-kafka-2.3.0 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

3.1.19. 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

3.1.20. Configuring pod scheduling

Important
 When two application 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

3.1.21. 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

3.1.22. 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

3.1.23. 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).

3.1.24. 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

3.1.25. 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

3.1.26. 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

3.1.27. 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

3.1.28. 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

3.1.29. 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 Entitiy 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.

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 run multiple of 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 absolutely 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 currently supported authentication types are:

  • TLS client authentication

  • SASL-based authentication using the SCRAM-SHA-512 mechanism

  • SASL-based authentication using the PLAIN mechanism

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
  # ...
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

3.2.6. 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.zookeeper.tlsSidecar

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.entityOperator.tlsSidecar

  • 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

3.2.7. Logging

This section provides information on loggers and how to configure log levels.

You can set the log levels by specifying the loggers and their levels directly (inline) or use a custom (external) config map.

Kafka Connect loggers

Kafka Connect has its own configurable loggers:

  • connect.root.logger.level

  • log4j.logger.org.apache.zookeeper

  • log4j.logger.org.I0Itec.zkclient

  • log4j.logger.org.reflections

Specifying inline logging
Procedure

  1. Edit the YAML file to specify the loggers and logging level for the required components.

    For example, the logging level here is set to INFO:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
spec:
  # ...
  logging:
    type: inline
    loggers:
      logger.name: "INFO"
  # ...

    You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.

    For more information about the log levels, see the log4j manual.

  2. Create or update the Kafka resource in Kubernetes.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Specifying an external ConfigMap for logging
Procedure

  1. Edit the YAML file to specify the name of the ConfigMap to use for the required components. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
spec:
  # ...
  logging:
    type: external
    name: customConfigMap
  # ...

    Remember to place your custom ConfigMap under the log4j.properties or log4j2.properties key.

  2. Create or update the Kafka resource in Kubernetes.

    This can be done using kubectl apply:

    kubectl apply -f your-file

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

3.2.8. 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.zookeeper.tlsSidecar

  • Kafka.spec.entityOperator.tlsSidecar

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaBridge.spec

Both livenessProbe and readinessProbe support two additional options:

  • initialDelaySeconds

  • timeoutSeconds

The initialDelaySeconds property defines the initial delay before the probe is tried for the first time. Default is 15 seconds.

The timeoutSeconds property defines timeout of the probe. Default is 5 seconds.

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

3.2.9. 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 configuring Prometheus and Grafana, see Metrics.

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

3.2.10. JVM Options

Apache Kafka and Apache Zookeeper run inside a Java Virtual Machine (JVM). 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

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 -Xms.

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 enabled by default. To disable it, set the gcLoggingEnabled property as follows:

Example of disabling GC logging
# ...
jvmOptions:
  gcLoggingEnabled: false
# ...
Configuring JVM options
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the jvmOptions property in the Kafka, KafkaConnect or KafkaConnectS2I 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

3.2.11. 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

Container image which should be used for given components can be specified using the image property in:

  • Kafka.spec.kafka

  • Kafka.spec.kafka.tlsSidecar

  • Kafka.spec.zookeeper

  • Kafka.spec.zookeeper.tlsSidecar

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.entityOperator.tlsSidecar

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaBridge.spec

Configuring the Kafka.spec.kafka.image property

The Kafka.spec.kafka.image property functions differently from the others, because Strimzi supports multiple versions of Kafka, each requiring the own image. The STRIMZI_KAFKA_IMAGES environment variable of the Cluster Operator configuration is used to provide a mapping between Kafka versions and the corresponding images. This is used in combination with the Kafka.spec.kafka.image and Kafka.spec.kafka.version properties as follows:

  • If neither Kafka.spec.kafka.image nor Kafka.spec.kafka.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 STRIMZI_KAFKA_IMAGES.

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

  • If Kafka.spec.kafka.version is given but Kafka.spec.kafka.image is not then image will be the one corresponding to this version in the STRIMZI_KAFKA_IMAGES.

  • Both Kafka.spec.kafka.version and Kafka.spec.kafka.image are given the given image will be used, and it will be assumed to contain a Kafka broker with the given version.

Warning
 It is best to provide just Kafka.spec.kafka.version and leave the Kafka.spec.kafka.image property unspecified. This reduces the chances of making a mistake in configuring the Kafka resource. If you need to change the images used for different versions of Kafka, it is better to configure the Cluster Operator’s STRIMZI_KAFKA_IMAGES environment variable.
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:0.13.0-kafka-2.3.0 container image.

  • For Zookeeper nodes:

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

    2. strimzi/kafka:0.13.0-kafka-2.3.0 container image.

  • 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:0.13.0-kafka-2.3.0 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:0.13.0 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:0.13.0 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:0.13.0-kafka-2.3.0 container image.

  • For Kafka Connect:

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

    2. strimzi/kafka:0.13.0-kafka-2.3.0 container image.

  • For Kafka Connect with Source2image support:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_CONNECT_S2I_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.13.0-kafka-2.3.0 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

3.2.12. Configuring pod scheduling

Important
 When two application 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

3.2.13. Using external configuration and secrets

Kafka Connect connectors are configured using an HTTP REST interface. The connector configuration is passed to Kafka Connect as part of an HTTP request and stored within Kafka itself.

Some parts of the configuration of a Kafka Connect connector can be externalized using ConfigMaps or Secrets. 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.

ConfigMaps and Secrets are standard Kubernetes resources used for storing of configurations and confidential data.

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. 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}",
      # ...
   }
}
Additional resources

3.2.14. 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 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 run multiple of 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 absolutely 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 currently supported authentication types are:

  • TLS client authentication

  • SASL-based authentication using the SCRAM-SHA-512 mechanism

  • SASL-based authentication using the PLAIN mechanism

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
  # ...
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

3.3.6. 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.zookeeper.tlsSidecar

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.entityOperator.tlsSidecar

  • 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

3.3.7. Logging

This section provides information on loggers and how to configure log levels.

You can set the log levels by specifying the loggers and their levels directly (inline) or use a custom (external) config map.

Kafka Connect with Source2Image loggers

Kafka Connect with Source2Image support has its own configurable loggers:

  • connect.root.logger.level

  • log4j.logger.org.apache.zookeeper

  • log4j.logger.org.I0Itec.zkclient

  • log4j.logger.org.reflections

Specifying inline logging
Procedure

  1. Edit the YAML file to specify the loggers and logging level for the required components.

    For example, the logging level here is set to INFO:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnectS2I
spec:
  # ...
  logging:
    type: inline
    loggers:
      logger.name: "INFO"
  # ...

    You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.

    For more information about the log levels, see the log4j manual.

  2. Create or update the Kafka resource in Kubernetes.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Specifying an external ConfigMap for logging
Procedure

  1. Edit the YAML file to specify the name of the ConfigMap to use for the required components. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnectS2I
spec:
  # ...
  logging:
    type: external
    name: customConfigMap
  # ...

    Remember to place your custom ConfigMap under the log4j.properties or log4j2.properties key.

  2. Create or update the Kafka resource in Kubernetes.

    This can be done using kubectl apply:

    kubectl apply -f your-file

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

3.3.8. 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.zookeeper.tlsSidecar

  • Kafka.spec.entityOperator.tlsSidecar

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaBridge.spec

Both livenessProbe and readinessProbe support two additional options:

  • initialDelaySeconds

  • timeoutSeconds

The initialDelaySeconds property defines the initial delay before the probe is tried for the first time. Default is 15 seconds.

The timeoutSeconds property defines timeout of the probe. Default is 5 seconds.

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

3.3.9. 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 configuring Prometheus and Grafana, see Metrics.

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

3.3.10. JVM Options

Apache Kafka and Apache Zookeeper run inside a Java Virtual Machine (JVM). 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

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 -Xms.

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 enabled by default. To disable it, set the gcLoggingEnabled property as follows:

Example of disabling GC logging
# ...
jvmOptions:
  gcLoggingEnabled: false
# ...
Configuring JVM options
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the jvmOptions property in the Kafka, KafkaConnect or KafkaConnectS2I 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

3.3.11. 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

Container image which should be used for given components can be specified using the image property in:

  • Kafka.spec.kafka

  • Kafka.spec.kafka.tlsSidecar

  • Kafka.spec.zookeeper

  • Kafka.spec.zookeeper.tlsSidecar

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.entityOperator.tlsSidecar

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaBridge.spec

Configuring the Kafka.spec.kafka.image property

The Kafka.spec.kafka.image property functions differently from the others, because Strimzi supports multiple versions of Kafka, each requiring the own image. The STRIMZI_KAFKA_IMAGES environment variable of the Cluster Operator configuration is used to provide a mapping between Kafka versions and the corresponding images. This is used in combination with the Kafka.spec.kafka.image and Kafka.spec.kafka.version properties as follows:

  • If neither Kafka.spec.kafka.image nor Kafka.spec.kafka.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 STRIMZI_KAFKA_IMAGES.

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

  • If Kafka.spec.kafka.version is given but Kafka.spec.kafka.image is not then image will be the one corresponding to this version in the STRIMZI_KAFKA_IMAGES.

  • Both Kafka.spec.kafka.version and Kafka.spec.kafka.image are given the given image will be used, and it will be assumed to contain a Kafka broker with the given version.

Warning
 It is best to provide just Kafka.spec.kafka.version and leave the Kafka.spec.kafka.image property unspecified. This reduces the chances of making a mistake in configuring the Kafka resource. If you need to change the images used for different versions of Kafka, it is better to configure the Cluster Operator’s STRIMZI_KAFKA_IMAGES environment variable.
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:0.13.0-kafka-2.3.0 container image.

  • For Zookeeper nodes:

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

    2. strimzi/kafka:0.13.0-kafka-2.3.0 container image.

  • 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:0.13.0-kafka-2.3.0 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:0.13.0 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:0.13.0 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:0.13.0-kafka-2.3.0 container image.

  • For Kafka Connect:

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

    2. strimzi/kafka:0.13.0-kafka-2.3.0 container image.

  • For Kafka Connect with Source2image support:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_CONNECT_S2I_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.13.0-kafka-2.3.0 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

3.3.12. Configuring pod scheduling

Important
 When two application 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

3.3.13. Using external configuration and secrets

Kafka Connect connectors are configured using an HTTP REST interface. The connector configuration is passed to Kafka Connect as part of an HTTP request and stored within Kafka itself.

Some parts of the configuration of a Kafka Connect connector can be externalized using ConfigMaps or Secrets. 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.

ConfigMaps and Secrets are standard Kubernetes resources used for storing of configurations and confidential data.

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. 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}",
      # ...
   }
}
Additional resources

3.3.14. 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.

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

You can use OpenShift builds and the Source-to-Image (S2I) framework to create new container images. 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:0.13.0-kafka-2.3.0 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. Once the build has finished, the new image is used automatically by the Kafka Connect deployment.

3.4. Kafka Mirror Maker configuration

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

3.4.1. Replicas

It is possible to run multiple Mirror Maker replicas. The number of replicas is defined in the KafkaMirrorMaker resource. You can run multiple Mirror Maker replicas to provide better availability and scalability. However, when running Kafka Mirror Maker on Kubernetes it is not absolutely necessary to run multiple replicas of the Kafka Mirror Maker for high availability. When the node where the Kafka Mirror Maker has deployed crashes, Kubernetes will automatically reschedule the Kafka Mirror Maker pod to a different node. However, running Kafka Mirror Maker with multiple replicas can provide faster failover times as the other nodes will be up and running.

Configuring the number of replicas

The number of Kafka Mirror Maker replicas can be configured using the replicas property in KafkaMirrorMaker.spec.

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

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

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  replicas: 3
  # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f <your-file>

3.4.2. Bootstrap servers

Kafka Mirror Maker 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. The bootstrap server lists can refer to Kafka clusters which do not need to be deployed in the same Kubernetes cluster. They can even refer to any Kafka cluster not deployed by Strimzi or even deployed by Strimzi but on a different Kubernetes cluster and accessible from 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 way used for exposing the clusters (routes, nodeports or loadbalancers).

The list of bootstrap servers can be configured in the KafkaMirrorMaker.spec.consumer.bootstrapServers and KafkaMirrorMaker.spec.producer.bootstrapServers properties. The servers should be a comma-separated list containing one or more Kafka brokers or a Service pointing to Kafka brokers specified as a <hostname>:<port> pairs.

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

Configuring bootstrap servers
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the KafkaMirrorMaker.spec.consumer.bootstrapServers and KafkaMirrorMaker.spec.producer.bootstrapServers properties. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  consumer:
    bootstrapServers: my-source-cluster-kafka-bootstrap:9092
  # ...
  producer:
    bootstrapServers: my-target-cluster-kafka-bootstrap:9092

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f <your-file>

3.4.3. Whitelist

You specify the list topics that the Kafka Mirror Maker has to mirror from the source to the target Kafka cluster in the KafkaMirrorMaker resource using the whitelist option. It 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 Mirror Maker.

Configuring the topics whitelist

Specify the list topics that have to be mirrored by the Kafka Mirror Maker from source to target Kafka cluster using the whitelist property in KafkaMirrorMaker.spec.

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the whitelist property in the KafkaMirrorMaker resource. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  whitelist: "my-topic|other-topic"
  # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f <your-file>

3.4.4. Consumer group identifier

The Kafka Mirror Maker uses Kafka consumer to consume messages and it behaves like any other Kafka consumer client. It is in charge to consume the messages from the source Kafka cluster which will be mirrored to the target Kafka cluster. The consumer needs to be part of a consumer group for being assigned partitions.

Configuring the consumer group identifier

The consumer group identifier can be configured in the KafkaMirrorMaker.spec.consumer.groupId property.

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the KafkaMirrorMaker.spec.consumer.groupId property. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  consumer:
    groupId: "my-group"
  # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f <your-file>

3.4.5. Number of consumer streams

You can increase the throughput in mirroring topics by increase the number of consumer threads. More consumer threads will belong to the same configured consumer group. The topic partitions will be assigned across these consumer threads which will consume messages in parallel.

Configuring the number of consumer streams

The number of consumer streams can be configured using the KafkaMirrorMaker.spec.consumer.numStreams property.

Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the KafkaMirrorMaker.spec.consumer.numStreams property. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  consumer:
    numStreams: 2
  # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f <your-file>

3.4.6. Offset auto-commit interval

You can specify the regular time interval at which an offset is committed after Kafka Mirror Maker has consumed data from the source Kafka cluster. The time interval is set in milliseconds, with a default value of 60,000.

Configuring the offset auto-commit interval

Configure the offset auto-commit interval using the KafkaMirrorMaker.spec.consumer.offsetCommitInterval property.

Prerequisites

  • An {ProductPlatformName} cluster

  • The Cluster Operator is running

Procedure

  1. Edit the KafkaMirrorMaker.spec.consumer.offsetCommitInterval property. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  consumer:
    offsetCommitInterval: 120000
  # ...

  2. Create or update the resource.

    On {OpenShiftName} use oc apply:

    oc apply -f <your-file>

3.4.7. Abort Mirror Maker if message sending fails

You can control whether the Mirror Maker container is terminated if an error occurs when sending a message. After the Mirror Maker container has been terminated, it is recreated on {ProductPlatformName}. If the abortOnSendFailure option is set to false, message sending errors are ignored.

Enabling Mirror Maker to continue on message failure

By default, if an error occurs when sending a message from Kafka Mirror Maker to a Kafka cluster:

  • The Kafka Mirror Maker container is terminated in {ProductPlatformName}.

  • The container is then recreated.

To ignore the sending error and attempt to send the next message in the topic, you can set the KafkaMirrorMaker.spec.producer.abortOnSendFailure property to false. WARNING: Message might be lost when setting this value to false, as there is no attempt to resend a failed message.

Prerequisites

  • An {ProductPlatformName} cluster

  • The Cluster Operator is running

Procedure

  1. Edit the KafkaMirrorMaker.spec.producer.abortOnSendFailure property. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  producer:
    abortOnSendFailure: false
  # ...

  2. Create or update the resource.

    On {OpenShiftName} use oc apply:

    oc apply -f <your-file>

3.4.8. Connecting to Kafka brokers using TLS

By default, Kafka Mirror Maker will try to connect to Kafka brokers, in the source and target clusters, using a plain text connection. You must make additional configurations to use TLS.

TLS support in Kafka Mirror Maker

TLS support is configured in the tls sub-property of consumer and producer properties in KafkaMirrorMaker.spec. The tls property contains a list of secrets with key names under which the certificates are stored. The certificates should be stored in X.509 format.

An example showing TLS configuration with multiple certificates
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  consumer:
    tls:
      trustedCertificates:
        - secretName: my-source-secret
          certificate: ca.crt
        - secretName: my-other-source-secret
          certificate: certificate.crt
  # ...
  producer:
    tls:
      trustedCertificates:
        - secretName: my-target-secret
          certificate: ca.crt
        - secretName: my-other-target-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: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  consumer:
    tls:
      trustedCertificates:
        - secretName: my-source-secret
          certificate: ca.crt
        - secretName: my-source-secret
          certificate: ca2.crt
  # ...
  producer:
    tls:
      trustedCertificates:
        - secretName: my-target-secret
          certificate: ca.crt
        - secretName: my-target-secret
          certificate: ca2.crt
  # ...
Configuring TLS encryption in Kafka Mirror Maker
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

As the Kafka Mirror Maker connects to two Kafka clusters (source and target), you can choose to configure TLS for one or both the clusters. The following steps describe how to configure TLS on the consumer side for connecting to the source Kafka cluster:

  1. (Optional) If they do not already exist, prepare the TLS certificate used for 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 KafkaMirrorMaker.spec.consumer.tls property. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  consumer:
    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>

Repeat the above steps for configuring TLS on the target Kafka cluster. In this case, the secret containing the certificate has to be configured in the KafkaMirrorMaker.spec.producer.tls property.

3.4.9. Connecting to Kafka brokers with Authentication

By default, Kafka Mirror Maker will try to connect to Kafka brokers without any authentication. Authentication is enabled through the KafkaMirrorMaker resource.

Authentication support in Kafka Mirror Maker

Authentication can be configured in the KafkaMirrorMaker.spec.consumer.authentication and KafkaMirrorMaker.spec.producer.authentication properties. The authentication property specifies the type of the authentication method which should be used and additional configuration details depending on the mechanism. The currently supported authentication types are:

  • TLS client authentication

  • SASL-based authentication using the SCRAM-SHA-512 mechanism

  • SASL-based authentication using the PLAIN mechanism

You can use different authentication mechanisms for the Kafka Mirror Maker producer and consumer.

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 Mirror Maker see Connecting to Kafka brokers using TLS.
An example TLS client authentication configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  consumer:
    authentication:
      type: tls
      certificateAndKey:
        secretName: my-source-secret
        certificate: public.crt
        key: private.key
  # ...
  producer:
    authentication:
      type: tls
      certificateAndKey:
        secretName: my-target-secret
        certificate: public.crt
        key: private.key
  # ...
SCRAM-SHA-512 authentication

To configure Kafka Mirror Maker to use SCRAM-SHA-512 authentication, set the type property to scram-sha-512. The broker listener to which clients will connect must also be configured to use SCRAM-SHA-512 SASL authentication. 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 SCRAM-SHA-512 client authentication configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  consumer:
    authentication:
      type: scram-sha-512
      username: my-source-user
      passwordSecret:
        secretName: my-source-user
        password: my-source-password-key
  # ...
  producer:
    authentication:
      type: scram-sha-512
      username: my-producer-user
      passwordSecret:
        secretName: my-producer-user
        password: my-producer-password-key
  # ...
PLAIN authentication

To configure Kafka Mirror Maker to use PLAIN authentication, set the type property to plain. The broker listener to which clients will connect must also be configured to use SASL PLAIN authentication. 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 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 PLAIN client authentication configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  consumer:
    authentication:
      type: plain
      username: my-source-user
      passwordSecret:
        secretName: my-source-user
        password: my-source-password-key
  # ...
  producer:
    authentication:
      type: plain
      username: my-producer-user
      passwordSecret:
        secretName: my-producer-user
        password: my-producer-password-key
  # ...
Configuring TLS client authentication in Kafka Mirror Maker
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator with a tls listener with tls authentication enabled

  • 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

As the Kafka Mirror Maker connects to two Kafka clusters (source and target), you can choose to configure TLS client authentication for one or both the clusters. The following steps describe how to configure TLS client authentication on the consumer side for connecting to the source Kafka cluster:

  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 KafkaMirrorMaker.spec.consumer.authentication property. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  consumer:
    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>

Repeat the above steps for configuring TLS client authentication on the target Kafka cluster. In this case, the secret containing the certificate has to be configured in the KafkaMirrorMaker.spec.producer.authentication property.

Configuring SCRAM-SHA-512 authentication in Kafka Mirror Maker
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator with a listener configured for SCRAM-SHA-512 authentication

  • Username to be used for authentication

  • If they exist, the name of the Secret with the password used for authentication, and the key under which it is stored in the Secret

Procedure

As the Kafka Mirror Maker connects to two Kafka clusters (source and target), you can choose to configure SCRAM-SHA-512 authentication for one or both the clusters. The following steps describe how to configure SCRAM-SHA-512 authentication on the consumer side for connecting to the source Kafka cluster:

  1. (Optional) If they do not already exist, prepare a file with the password used for 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 KafkaMirrorMaker.spec.consumer.authentication property. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  consumer:
    authentication:
      type: scram-sha-512
      username: _<my-username>_
      passwordSecret:
        secretName: _<my-secret>_
        password: _<my-password.txt>_
  # ...

  3. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f <your-file>

Repeat the above steps for configuring SCRAM-SHA-512 authentication on the target Kafka cluster. In this case, the secret containing the certificate has to be configured in the KafkaMirrorMaker.spec.producer.authentication property.

3.4.10. Kafka Mirror Maker configuration

Strimzi allows you to customize the configuration of the Kafka Mirror Maker by editing most of the options for the related consumer and producer. Producer options are listed in Apache Kafka documentation. Consumer options are listed in Apache Kafka documentation.

The only options which cannot be configured are those related to the following areas:

  • Kafka cluster bootstrap address

  • Security (Encryption, Authentication, and Authorization)

  • Consumer group identifier

These options are automatically configured by Strimzi.

Kafka Mirror Maker configuration

Kafka Mirror Maker can be configured using the config sub-property in KafkaMirrorMaker.spec.consumer and KafkaMirrorMaker.spec.producer. This property should contain the Kafka Mirror Maker consumer and producer configuration options as keys. The values could be in one of the following JSON types:

  • String

  • Number

  • Boolean

Users can specify and configure the options listed in the Apache Kafka documentation and Apache Kafka 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:

  • 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 Mirror Maker.

Important
 The Cluster Operator does not validate keys or values in the provided config object. When an invalid configuration is provided, the Kafka Mirror Maker 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 Mirror Maker.
An example showing Kafka Mirror Maker configuration
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirroMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  consumer:
    config:
      max.poll.records: 100
      receive.buffer.bytes: 32768
  producer:
    config:
      compression.type: gzip
      batch.size: 8192
  # ...
Configuring Kafka Mirror Maker
Prerequisites

  • Two running Kafka clusters (source and target)

  • A running Cluster Operator

Procedure

  1. Edit the KafkaMirrorMaker.spec.consumer.config and KafkaMirrorMaker.spec.producer.config properties. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirroMaker
metadata:
  name: my-mirror-maker
spec:
  # ...
  consumer:
    config:
      max.poll.records: 100
      receive.buffer.bytes: 32768
  producer:
    config:
      compression.type: gzip
      batch.size: 8192
  # ...

  2. Create or update the resource.

    This can be done using kubectl apply:

    kubectl apply -f <your-file>

3.4.11. 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.zookeeper.tlsSidecar

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.entityOperator.tlsSidecar

  • 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

3.4.12. Logging

This section provides information on loggers and how to configure log levels.

You can set the log levels by specifying the loggers and their levels directly (inline) or use a custom (external) config map.

Kafka Mirror Maker loggers

Kafka Mirror Maker has its own configurable logger:

  • mirrormaker.root.logger

Specifying inline logging
Procedure

  1. Edit the YAML file to specify the loggers and logging level for the required components.

    For example, the logging level here is set to INFO:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
spec:
  # ...
  logging:
    type: inline
    loggers:
      logger.name: "INFO"
  # ...

    You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.

    For more information about the log levels, see the log4j manual.

  2. Create or update the Kafka resource in Kubernetes.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Specifying an external ConfigMap for logging
Procedure

  1. Edit the YAML file to specify the name of the ConfigMap to use for the required components. For example:

    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
spec:
  # ...
  logging:
    type: external
    name: customConfigMap
  # ...

    Remember to place your custom ConfigMap under the log4j.properties or log4j2.properties key.

  2. Create or update the Kafka resource in Kubernetes.

    This can be done using kubectl apply:

    kubectl apply -f your-file

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

3.4.13. 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 configuring Prometheus and Grafana, see Metrics.

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

3.4.14. JVM Options

Apache Kafka and Apache Zookeeper run inside a Java Virtual Machine (JVM). 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

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 -Xms.

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 enabled by default. To disable it, set the gcLoggingEnabled property as follows:

Example of disabling GC logging
# ...
jvmOptions:
  gcLoggingEnabled: false
# ...
Configuring JVM options
Prerequisites

  • A Kubernetes cluster

  • A running Cluster Operator

Procedure

  1. Edit the jvmOptions property in the Kafka, KafkaConnect or KafkaConnectS2I 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

3.4.15. 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

Container image which should be used for given components can be specified using the image property in:

  • Kafka.spec.kafka

  • Kafka.spec.kafka.tlsSidecar

  • Kafka.spec.zookeeper

  • Kafka.spec.zookeeper.tlsSidecar

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.entityOperator.tlsSidecar

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaBridge.spec

Configuring the Kafka.spec.kafka.image property

The Kafka.spec.kafka.image property functions differently from the others, because Strimzi supports multiple versions of Kafka, each requiring the own image. The STRIMZI_KAFKA_IMAGES environment variable of the Cluster Operator configuration is used to provide a mapping between Kafka versions and the corresponding images. This is used in combination with the Kafka.spec.kafka.image and Kafka.spec.kafka.version properties as follows:

  • If neither Kafka.spec.kafka.image nor Kafka.spec.kafka.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 STRIMZI_KAFKA_IMAGES.

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

  • If Kafka.spec.kafka.version is given but Kafka.spec.kafka.image is not then image will be the one corresponding to this version in the STRIMZI_KAFKA_IMAGES.

  • Both Kafka.spec.kafka.version and Kafka.spec.kafka.image are given the given image will be used, and it will be assumed to contain a Kafka broker with the given version.

Warning
 It is best to provide just Kafka.spec.kafka.version and leave the Kafka.spec.kafka.image property unspecified. This reduces the chances of making a mistake in configuring the Kafka resource. If you need to change the images used for different versions of Kafka, it is better to configure the Cluster Operator’s STRIMZI_KAFKA_IMAGES environment variable.
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:0.13.0-kafka-2.3.0 container image.

  • For Zookeeper nodes:

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

    2. strimzi/kafka:0.13.0-kafka-2.3.0 container image.

  • 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:0.13.0-kafka-2.3.0 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:0.13.0 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:0.13.0 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:0.13.0-kafka-2.3.0 container image.

  • For Kafka Connect:

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

    2. strimzi/kafka:0.13.0-kafka-2.3.0 container image.

  • For Kafka Connect with Source2image support:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_CONNECT_S2I_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.13.0-kafka-2.3.0 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

3.4.16. Configuring pod scheduling

Important
 When two application 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

3.4.17. List of resources created as part of Kafka Mirror Maker

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

<mirror-maker-name>-mirror-maker

Deployment which is in charge to create the Kafka Mirror Maker pods.

<mirror-maker-name>-config

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

<mirror-maker-name>-mirror-maker

Pod Disruption Budget configured for the Kafka Mirror Maker worker nodes.

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.kafka. 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.kafka. 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

  • SASL-based authentication using the SCRAM-SHA-512 mechanism

  • SASL-based authentication using the PLAIN mechanism

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 documentation and Apache Kafka documentation.

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 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:

  • 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 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:

  • 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. 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.zookeeper.tlsSidecar

  • Kafka.spec.entityOperator.tlsSidecar

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaBridge.spec

Both livenessProbe and readinessProbe support two additional options:

  • initialDelaySeconds

  • timeoutSeconds

The initialDelaySeconds property defines the initial delay before the probe is tried for the first time. Default is 15 seconds.

The timeoutSeconds property defines timeout of the probe. Default is 5 seconds.

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

3.5.7. 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

Container image which should be used for given components can be specified using the image property in:

  • Kafka.spec.kafka

  • Kafka.spec.kafka.tlsSidecar

  • Kafka.spec.zookeeper

  • Kafka.spec.zookeeper.tlsSidecar

  • Kafka.spec.entityOperator.topicOperator

  • Kafka.spec.entityOperator.userOperator

  • Kafka.spec.entityOperator.tlsSidecar

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaBridge.spec

Configuring the Kafka.spec.kafka.image property

The Kafka.spec.kafka.image property functions differently from the others, because Strimzi supports multiple versions of Kafka, each requiring the own image. The STRIMZI_KAFKA_IMAGES environment variable of the Cluster Operator configuration is used to provide a mapping between Kafka versions and the corresponding images. This is used in combination with the Kafka.spec.kafka.image and Kafka.spec.kafka.version properties as follows:

  • If neither Kafka.spec.kafka.image nor Kafka.spec.kafka.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 STRIMZI_KAFKA_IMAGES.

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

  • If Kafka.spec.kafka.version is given but Kafka.spec.kafka.image is not then image will be the one corresponding to this version in the STRIMZI_KAFKA_IMAGES.

  • Both Kafka.spec.kafka.version and Kafka.spec.kafka.image are given the given image will be used, and it will be assumed to contain a Kafka broker with the given version.

Warning
 It is best to provide just Kafka.spec.kafka.version and leave the Kafka.spec.kafka.image property unspecified. This reduces the chances of making a mistake in configuring the Kafka resource. If you need to change the images used for different versions of Kafka, it is better to configure the Cluster Operator’s STRIMZI_KAFKA_IMAGES environment variable.
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:0.13.0-kafka-2.3.0 container image.

  • For Zookeeper nodes:

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

    2. strimzi/kafka:0.13.0-kafka-2.3.0 container image.

  • 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:0.13.0-kafka-2.3.0 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:0.13.0 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:0.13.0 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:0.13.0-kafka-2.3.0 container image.

  • For Kafka Connect:

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

    2. strimzi/kafka:0.13.0-kafka-2.3.0 container image.

  • For Kafka Connect with Source2image support:

    1. Container image specified in the STRIMZI_DEFAULT_KAFKA_CONNECT_S2I_IMAGE environment variable from the Cluster Operator configuration.

    2. strimzi/kafka:0.13.0-kafka-2.3.0 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

3.5.8. Configuring pod scheduling

Important
 When two application 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

3.5.9. 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. 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.

3.6.1. 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

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

  • KafkaMirrorMakerSpec

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 resources in Kafka cluster

When defined in a Kafka cluster, the template object can have the following fields:

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.

Supported resources in Zookeeper cluster

When defined in a Zookeeper cluster, the template object can have the following fields:

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.

Supported resources in Entity Operator

When defined in an Entity Operator , the template object can have the following fields:

deployment

Configures the Deployment used by the Entity Operator.

pod

Configures the Entity Operator Pod created by the Deployment.

Supported resources in Kafka Connect and Kafka Connect with Source2Image support

When used with Kafka Connect and Kafka Connect with Source2Image support , the template object can have the following fields:

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.

Supported resource in Kafka Mirror Maker

When used with Kafka Mirror Maker , the template object can have the following fields:

deployment

Configures the Kafka Mirror Maker Deployment.

pod

Configures the Kafka Mirror Maker Pods created by the Deployment.

podDisruptionBudget

Configures the Pod Disruption Budget for Kafka Mirror Maker Deployment.

3.6.2. 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.

3.6.3. 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.

These fields are effective on each type of cluster (Kafka and Zookeeper; Kafka Connect and Kafka Connect with S2I support; and Kafka Mirror Maker).

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

3.6.4. 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 Mirror Maker clusters at once. Changing the policy will result in a rolling update of all your Kafka, Kafka Connect, and Kafka Mirror Maker clusters.

Additional resources

  • For more information about Cluster Operator configuration, see Cluster Operator.

  • For more information about Image Pull Policies, see Disruptions.

3.6.5. 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 Mirror Maker).

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

3.6.6. 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

4. Operators

4.1. Cluster Operator

4.1.1. Overview of the Cluster Operator component

Strimzi uses the Cluster Operator to deploy and manage clusters for:

  • Kafka (including Zookeeper)

  • Kafka Connect

  • Kafka Mirror Maker

  • 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 Entity Operator 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 topic management through KafkaUser custom resources

For more information on the configuration options supported by the Kafka resource, see Kafka cluster configuration.

Note
 On OpenShift, a Kafka Connect deployment can incorporate a Source2Image feature to provides a convenient way to include connectors.
Example architecture for the Cluster Operator

Cluster Operator

4.1.2. Watch options for a Cluster Operator deployment

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

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

Note
 Strimzi provides example YAML files to make the deployment process easier.

The Cluster Operator watches the following resources:

  • Kafka for the Kafka cluster.

  • KafkaConnect for the Kafka Connect cluster.

  • KafkaConnectS2I for the Kafka Connect cluster with Source2Image support.

  • KafkaMirrorMaker for the Kafka Mirror Maker 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.

4.1.3. Deploying the Cluster Operator to watch a single namespace

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.

  • Modify the installation files according to the namespace the Cluster Operator is going to be installed in.

    On Linux, use:

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

    On MacOS, use:

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

  • Deploy the Cluster Operator:

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

4.1.4. Deploying the Cluster Operator to watch multiple namespaces

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.

  • Edit the installation files according to the namespace the Cluster Operator is going to be installed in.

    On Linux, use:

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

    On MacOS, use:

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

  1. Edit the file install/cluster-operator/050-Deployment-strimzi-cluster-operator.yaml and in the environment variable STRIMZI_NAMESPACE list all the namespaces where Cluster Operator should watch for resources. For example:

    apiVersion: extensions/v1beta1
kind: Deployment
spec:
  template:
    spec:
      serviceAccountName: strimzi-cluster-operator
      containers:
      - name: strimzi-cluster-operator
        image: strimzi/operator:0.13.0
        imagePullPolicy: IfNotPresent
        env:
        - name: STRIMZI_NAMESPACE
          value: watched-namespace-1,watched-namespace-2,watched-namespace-3

  2. For all namespaces which should be watched by the Cluster Operator (watched-namespace-1, watched-namespace-2, watched-namespace-3 in the above example), install the RoleBindings. Replace the watched-namespace with the namespace used in the previous step.

    This can be done using kubectl apply:

    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

  3. Deploy the Cluster Operator

    This can be done using kubectl apply:

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

4.1.5. Deploying the Cluster Operator to watch all namespaces

You can configure 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.

  • Your Kubernetes cluster is running.

Procedure

  1. Configure the Cluster Operator to watch all namespaces:

    1. Edit the 050-Deployment-strimzi-cluster-operator.yaml file.

    2. Set the value of the STRIMZI_NAMESPACE environment variable to *.

      apiVersion: extensions/v1beta1
kind: Deployment
spec:
  template:
    spec:
      # ...
      serviceAccountName: strimzi-cluster-operator
      containers:
      - name: strimzi-cluster-operator
        image: strimzi/operator:0.13.0
        imagePullPolicy: IfNotPresent
        env:
        - name: STRIMZI_NAMESPACE
          value: "*"
        # ...

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

    Use the kubectl create clusterrolebinding command:

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

    Replace my-namespace with the namespace in which you want to install the Cluster Operator.

  3. Deploy the Cluster Operator to your Kubernetes cluster.

    Use the kubectl apply command:

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

4.1.6. Deploying the Cluster Operator using Helm Chart

Prerequisites

  • Helm client has to be installed on the local machine.

  • Helm has to be installed in the Kubernetes cluster.

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 whether the Cluster Operator has been deployed successfully using the Helm command line tool:

    helm ls
Additional resources

4.1.7. 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.

4.1.8. 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.

4.1.9. 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 2.2.1=strimzi/kafka:0.13.0-kafka-2.2.1, 2.3.0=strimzi/kafka:0.13.0-kafka-2.3.0. 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:0.13.0. 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:0.13.0-kafka-2.3.0. 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_DEFAULT_ZOOKEEPER_IMAGE

Optional, default strimzi/kafka:0.13.0-kafka-2.3.0. The image name to use as the default when deploying Zookeeper, if no image is specified as the Kafka.spec.zookeeper.image in the Container images.

STRIMZI_DEFAULT_TLS_SIDECAR_ZOOKEEPER_IMAGE

Optional, default strimzi/kafka:0.13.0-kafka-2.3.0. The image name to use as the default when deploying the sidecar container which provides TLS support for Zookeeper, if no image is specified as the Kafka.spec.zookeeper.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 2.2.1=strimzi/kafka:0.13.0-kafka-2.2.1, 2.3.0=strimzi/kafka:0.13.0-kafka-2.3.0. 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 2.2.1=strimzi/kafka:0.13.0-kafka-2.2.1, 2.3.0=strimzi/kafka:0.13.0-kafka-2.3.0. 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 2.2.1=strimzi/kafka:0.13.0-kafka-2.2.1, 2.3.0=strimzi/kafka:0.13.0-kafka-2.3.0. 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:0.13.0. 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:0.13.0. 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:0.13.0-kafka-2.3.0. 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 Mirror Maker 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 Mirror Maker clusters.

4.1.10. 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 the default ServiceAccount, as they do not need access to the Kubernetes resources.

  • The Topic Operator pod uses a ServiceAccount called cluster-name-topic-operator

    • The Topic Operator produces Kubernetes events with status information, so the ServiceAccount is bound to a ClusterRole called strimzi-topic-operator which grants this access via the strimzi-topic-operator-role-binding RoleBinding.

The pods for KafkaConnect and KafkaConnectS2I resources use the default ServiceAccount, as they do not require access to the Kubernetes resources.

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: extensions/v1beta1
kind: Deployment
metadata:
  name: strimzi-cluster-operator
  labels:
    app: strimzi
spec:
  replicas: 1
  template:
    metadata:
      labels:
        name: strimzi-cluster-operator
        strimzi.io/kind: cluster-operator
      # ...

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:
  - serviceaccounts
  verbs:
  - get
  - create
  - delete
  - patch
  - update
- apiGroups:
  - rbac.authorization.k8s.io
  resources:
  - rolebindings
  verbs:
  - get
  - create
  - delete
  - patch
  - update
- apiGroups:
  - ""
  resources:
  - configmaps
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - kafka.strimzi.io
  resources:
  - kafkas
  - kafkas/status
  - kafkaconnects
  - kafkaconnects/status
  - kafkaconnects2is
  - kafkaconnects2is/status
  - kafkamirrormakers
  - kafkamirrormakers/status
  - kafkabridges
  - kafkabridges/status
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - ""
  resources:
  - pods
  verbs:
  - get
  - list
  - watch
  - delete
- apiGroups:
  - ""
  resources:
  - services
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - ""
  resources:
  - endpoints
  verbs:
  - get
  - list
  - watch
- apiGroups:
  - extensions
  resources:
  - deployments
  - deployments/scale
  - replicasets
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - apps
  resources:
  - deployments
  - deployments/scale
  - deployments/status
  - statefulsets
  - replicasets
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - ""
  resources:
  - events
  verbs:
  - create
- apiGroups:
  - extensions
  resources:
  - replicationcontrollers
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - apps.openshift.io
  resources:
  - deploymentconfigs
  - deploymentconfigs/scale
  - deploymentconfigs/status
  - deploymentconfigs/finalizers
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - build.openshift.io
  resources:
  - buildconfigs
  - builds
  verbs:
  - create
  - delete
  - get
  - list
  - patch
  - watch
  - update
- apiGroups:
  - image.openshift.io
  resources:
  - imagestreams
  - imagestreams/status
  verbs:
  - create
  - delete
  - get
  - list
  - watch
  - patch
  - update
- apiGroups:
  - ""
  resources:
  - replicationcontrollers
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - ""
  resources:
  - secrets
  verbs:
  - get
  - list
  - create
  - delete
  - patch
  - update
- apiGroups:
  - extensions
  resources:
  - networkpolicies
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - networking.k8s.io
  resources:
  - networkpolicies
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - route.openshift.io
  resources:
  - routes
  - routes/custom-host
  verbs:
  - get
  - list
  - create
  - delete
  - patch
  - update
- apiGroups:
  - ""
  resources:
  - persistentvolumeclaims
  verbs:
  - get
  - list
  - create
  - delete
  - patch
  - update
- apiGroups:
  - policy
  resources:
  - poddisruptionbudgets
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - extensions
  resources:
  - ingresses
  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:
  - clusterrolebindings
  verbs:
  - get
  - create
  - delete
  - patch
  - update
- apiGroups:
  - storage.k8s.io
  resources:
  - storageclasses
  verbs:
  - get

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:
  - 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:
  - kafkatopics
  verbs:
  - get
  - list
  - watch
  - create
  - patch
  - update
  - delete
- apiGroups:
  - ""
  resources:
  - events
  verbs:
  - create
- apiGroups:
  - kafka.strimzi.io
  resources:
  - kafkausers
  - kafkausers/status
  verbs:
  - get
  - list
  - watch
  - create
  - patch
  - update
  - delete
- apiGroups:
  - ""
  resources:
  - 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
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
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

4.2.1. Overview of the Topic Operator component

The Topic Operator provides a way of managing topics in a Kafka cluster via 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 operator will create the topic it describes

  • Deleted, the operator will delete the topic it describes

  • Changed, the operator will update the topic it describes

And also, in the other direction, if a topic is:

  • Created within the Kafka cluster, the operator will create a KafkaTopic describing it

  • Deleted from the Kafka cluster, the operator will delete the KafkaTopic describing it

  • Changed in the Kafka cluster, the operator will update the KafkaTopic describing it

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.

For more details about creating, modifying and deleting topics, see Using the Topic Operator.

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. Deploying the Topic Operator using the Cluster Operator

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

Prerequisites

  • A running Cluster Operator

  • A Kafka resource to be created or updated

Procedure

  1. Ensure that the Kafka.spec.entityOperator object exists in the Kafka resource. This configures the Entity Operator.

    apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
  name: my-cluster
spec:
  #...
  entityOperator:
    topicOperator: {}
    userOperator: {}

  2. Configure the Topic Operator using the fields described in EntityTopicOperatorSpec schema reference.

  3. Create or update the Kafka resource in Kubernetes.

    Use kubectl apply:

    kubectl apply -f your-file
Additional resources

  • For more information about deploying the Cluster Operator, see Cluster Operator.

  • For more information about deploying the Entity Operator, see Entity Operator.

  • For more information about the Kafka.spec.entityOperator object used to configure the Topic Operator when deployed by the Cluster Operator, see EntityOperatorSpec schema reference.

4.2.5. 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

4.2.6. Deploying the standalone Topic Operator

Deploying the Topic Operator as a standalone component is more complicated than installing it using the Cluster Operator, but it is more flexible. For instance, it can operate with any Kafka cluster, not necessarily one deployed by the Cluster Operator.

Prerequisites

  • An existing Kafka cluster for the Topic Operator to connect to.

Procedure

  1. Edit the install/topic-operator/05-Deployment-strimzi-topic-operator.yaml resource. You will need to change the following

    1. The STRIMZI_KAFKA_BOOTSTRAP_SERVERS environment variable in Deployment.spec.template.spec.containers[0].env should be set to a list of bootstrap brokers in your Kafka cluster, given as a comma-separated list of hostname:‍port pairs.

    2. The STRIMZI_ZOOKEEPER_CONNECT environment variable in Deployment.spec.template.spec.containers[0].env should be set to a list of 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. The STRIMZI_NAMESPACE environment variable in Deployment.spec.template.spec.containers[0].env should be set to the Kubernetes namespace in which you want the operator to watch for KafkaTopic resources.

  2. Deploy the Topic Operator.

    This can be done using kubectl apply:

    kubectl apply -f install/topic-operator

  3. Verify that the Topic Operator has been deployed successfully. This can be done using kubectl describe:

    kubectl describe deployment strimzi-topic-operator

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

    Note
    		 This could take some time if you have a slow connection to the Kubernetes and the images have not been downloaded before.
Additional resources

4.2.7. Topic Operator environment

When deployed standalone the Topic Operator can be configured using environment variables.

Note
 The Topic Operator should be configured using the Kafka.spec.entityOperator.topicOperator property when deployed by the Cluster Operator.
STRIMZI_RESOURCE_LABELS

The label selector used to identify KafkaTopics to be managed by the operator.

STRIMZI_ZOOKEEPER_SESSION_TIMEOUT_MS

The Zookeeper session timeout, in milliseconds. For example, 10000. Default 20000 (20 seconds).

STRIMZI_KAFKA_BOOTSTRAP_SERVERS

The list of Kafka bootstrap servers. This variable is mandatory.

STRIMZI_ZOOKEEPER_CONNECT

The Zookeeper connection information. This variable is mandatory.

STRIMZI_FULL_RECONCILIATION_INTERVAL_MS

The interval between periodic reconciliations, in milliseconds.

STRIMZI_TOPIC_METADATA_MAX_ATTEMPTS

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.

STRIMZI_LOG_LEVEL

The level for printing logging messages. The value can be set to: ERROR, WARNING, INFO, DEBUG, and TRACE. Default INFO.

STRIMZI_TLS_ENABLED

For enabling the TLS support so encrypting the communication with Kafka brokers. Default true.

STRIMZI_TRUSTSTORE_LOCATION

The path to the truststore containing certificates for enabling TLS based communication. This variable is mandatory only if TLS is enabled through STRIMZI_TLS_ENABLED.

STRIMZI_TRUSTSTORE_PASSWORD

The password for accessing the truststore defined by STRIMZI_TRUSTSTORE_LOCATION. This variable is mandatory only if TLS is enabled through STRIMZI_TLS_ENABLED.

STRIMZI_KEYSTORE_LOCATION

The path to the keystore containing private keys for enabling TLS based communication. This variable is mandatory only if TLS is enabled through STRIMZI_TLS_ENABLED.

STRIMZI_KEYSTORE_PASSWORD

The password for accessing the keystore defined by STRIMZI_KEYSTORE_LOCATION. This variable is mandatory only if TLS is enabled through STRIMZI_TLS_ENABLED.

4.3. User Operator

The User Operator manages Kafka users through custom resources.

4.3.1. Overview of the User Operator component

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 will create the user it describes

  • if a KafkaUser is deleted, the User Operator will delete the user it describes

  • if a KafkaUser is changed, the User Operator will update the user it describes

Unlike the Topic Operator, the User Operator does not sync any changes from the Kafka cluster with the Kubernetes resources. Unlike the Kafka topics which might be created by applications directly in Kafka, it is not expected that the users will be managed directly in the Kafka cluster in parallel with the User Operator, so this should not be needed.

The User Operator allows you to declare a KafkaUser as part of your application’s deployment. When the user is created, the user credentials will be 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 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. Deploying the User Operator using the Cluster Operator

Prerequisites

  • A running Cluster Operator

  • A Kafka resource to be created or updated.

Procedure

  1. Edit the Kafka resource ensuring it has a Kafka.spec.entityOperator.userOperator object that configures the User Operator how you want.

  2. Create or update the Kafka resource in Kubernetes.

    This can be done using kubectl apply:

    kubectl apply -f your-file
Additional resources

  • For more information about deploying the Cluster Operator, see Cluster Operator.

  • For more information about the Kafka.spec.entityOperator object used to configure the User Operator when deployed by the Cluster Operator, see EntityOperatorSpec schema reference.

4.3.4. 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

4.3.5. Deploying the standalone User Operator

Deploying the User Operator as a standalone component is more complicated than installing it using the Cluster Operator, but it is more flexible. For instance, it can operate with any Kafka cluster, not only the one deployed by the Cluster Operator.

Prerequisites

  • An existing Kafka cluster for the User Operator to connect to.

Procedure

  1. Edit the install/user-operator/05-Deployment-strimzi-user-operator.yaml resource. You will need to change the following

    1. The STRIMZI_CA_CERT_NAME environment variable in Deployment.spec.template.spec.containers[0].env should be set to point to a Kubernetes Secret which should contain the public key of the Certificate Authority for signing new user certificates for TLS Client Authentication. The Secret should contain the public key of the Certificate Authority under the key ca.crt.

    2. The STRIMZI_CA_KEY_NAME environment variable in Deployment.spec.template.spec.containers[0].env should be set to point to a Kubernetes Secret which should contain the private key of the Certificate Authority for signing new user certificates for TLS Client Authentication. The Secret should contain the private key of the Certificate Authority under the key ca.key.

    3. The STRIMZI_ZOOKEEPER_CONNECT environment variable in Deployment.spec.template.spec.containers[0].env should be set to a list of 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.

    4. The STRIMZI_NAMESPACE environment variable in Deployment.spec.template.spec.containers[0].env should be set to the Kubernetes namespace in which you want the operator to watch for KafkaUser resources.

  2. Deploy the User Operator.

    This can be done using kubectl apply:

    kubectl apply -f install/user-operator

  3. Verify that the User Operator has been deployed successfully. This can be done using kubectl describe:

    kubectl describe deployment strimzi-user-operator

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

    Note
    		 This could take some time if you have a slow connection to the Kubernetes and the images have not been downloaded before.
Additional resources

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 {ProductPlatformName}.

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 Kubernetes resource.

Prerequisites
Procedure

  1. Prepare a file containing the desired KafkaTopic

    An example KafkaTopic
    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.
    Note
    		 Changing topic names using the KafkaTopic.spec.topicName variable and decreasing partition size using the KafkaTopic.spec.partitions variable is not supported by Kafka.
    Caution
    		 Increasing spec.partitions for topics with keys will change how records are partitioned, which can be particularly problematic when the topic uses semantic partitioning.

  2. Update the KafkaTopic resource in Kubernetes.

    This can be done using kubectl apply:

    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

The User Operator provides a way of managing Kafka users via Kubernetes resources.

6.1. Overview of the User Operator component

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 will create the user it describes

  • if a KafkaUser is deleted, the User Operator will delete the user it describes

  • if a KafkaUser is changed, the User Operator will update the user it describes

Unlike the Topic Operator, the User Operator does not sync any changes from the Kafka cluster with the Kubernetes resources. Unlike the Kafka topics which might be created by applications directly in Kafka, it is not expected that the users will be managed directly in the Kafka cluster in parallel with the User Operator, so this should not be needed.

The User Operator allows you to declare a KafkaUser as part of your application’s deployment. When the user is created, the user credentials will be 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 rights in the KafkaUser declaration.

6.2. 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.

6.2.1. 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.

6.3. Creating a Kafka user with mutual TLS authentication

Prerequisites
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:
    type: tls
  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: literal
        operation: Read

  2. Create the KafkaUser resource in Kubernetes.

    This can be done using kubectl apply:

    kubectl apply -f your-file

  3. Use the credentials from the secret my-user in your application

Additional resources

6.4. 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.

6.4.1. 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.

6.4.2. 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

6.5. Creating a Kafka user with SCRAM SHA authentication

Prerequisites
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:
    type: scram-sha-512
  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: literal
        operation: Read

  2. Create the KafkaUser resource in Kubernetes.

    This can be done using kubectl apply:

    kubectl apply -f your-file

  3. Use the credentials from the secret my-user in your application

Additional resources

6.6. Editing a Kafka user

This procedure describes how to change the configuration of an existing Kafka user by using a KafkaUser Kubernetes resource.

Prerequisites
Procedure

  1. Prepare a YAML file containing the desired KafkaUser.

    An example KafkaUser
    apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaUser
metadata:
  name: my-user
  labels:
    strimzi.io/cluster: my-cluster
spec:
  authentication:
    type: tls
  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: literal
        operation: Read

  2. Update the KafkaUser resource in Kubernetes.

    This can be done using kubectl apply:

    kubectl apply -f your-file

  3. Use the updated credentials from the my-user secret in your application.

Additional resources

6.7. Deleting a Kafka user

This procedure describes how to delete a Kafka user created with KafkaUser Kubernetes resource.

Prerequisites
Procedure

  • Delete the KafkaUser resource in Kubernetes.

    This can be done using kubectl delete:

    kubectl delete kafkauser your-user-name
Additional resources

6.8. Kafka User resource

The KafkaUser resource is used to declare a user with its authentication mechanism, authorization mechanism, and access rights.

6.8.1. Authentication

Authentication is configured using the authentication property in KafkaUser.spec. The authentication mechanism enabled for this user will be specified using the type field. Currently, the only supported authentication mechanisms are the TLS Client Authentication mechanism and the SCRAM-SHA-512 mechanism.

When no authentication mechanism is specified, User Operator will not create the user or its credentials.

TLS Client Authentication

To use TLS client authentication, set the type field to tls.

An example of KafkaUser with enabled TLS Client Authentication
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 will create a new secret with the same name as the KafkaUser resource. The secret will contain a public and private key which should be used for the TLS Client Authentication. Bundled with them will be the public key of the client certification authority which was used to sign the user certificate. All keys will be in X509 format.

An example of the 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 Clients CA
  user.crt: # Public key of the user
  user.key: # Private key of the user
SCRAM-SHA-512 Authentication

To use SCRAM-SHA-512 authentication mechanism, set the type field to scram-sha-512.

An example of KafkaUser with enabled SCRAM-SHA-512 authentication
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, the User Operator will create 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 of the 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

For decode the generated password:

echo "Z2VuZXJhdGVkcGFzc3dvcmQ=" | base64 --decode

6.8.2. Authorization

Authorization is configured using the authorization property in KafkaUser.spec. The authorization type enabled for this user will be specified using the type field. Currently, the only supported authorization type is the Simple authorization.

When no authorization is specified, the User Operator will not provision any access rights for the user.

Simple Authorization

To use Simple Authorization, set the type property to simple. Simple authorization uses the SimpleAclAuthorizer plugin. SimpleAclAuthorizer is the default authorization plugin which is part of Apache Kafka. Simple Authorization allows you to specify list of ACL rules in the acls property.

The acls property should contain a list of AclRule objects. AclRule specifies the access rights whcih will be granted to the user. The AclRule object contains following properties:

type

Specifies the type of the ACL rule. The type can be either allow or deny. The type field is optional and when not specified, the ACL rule will be treated as allow rule.

operation

Specifies the operation which will be allowed or denied. Following operations are supported:

  • Read

  • Write

  • Delete

  • Alter

  • Describe

  • All

  • IdempotentWrite

  • ClusterAction

  • Create

  • AlterConfigs

  • DescribeConfigs

    Note
    		 Not every operation can be combined with every resource.
host

Specifies a remote host from which is the rule allowed or denied. Use * to allow or deny the operation from all hosts. The host field is optional and when not specified, the value * will be used as default.

resource

Specifies the resource for which the rule applies. Simple Authorization supports four different resource types:

  • Topics

  • Consumer Groups

  • Clusters

  • Transactional IDs

    The resource type can be specified in the type property. Use topic for Topics, group for Consumer Groups, cluster for clusters, and transactionalId for Transactional IDs.

    Additionally, Topic, Group, and Transactional ID resources allow you to specify the name of the resource for which the rule applies. The name can be specified in the name property. The name can be either specified as literal or as a prefix. To specify the name as literal, set the patternType property to the value literal. Literal names will be taken exactly as they are specified in the name field. To specify the name as a prefix, set the patternType property to the value prefix. Prefix type names will use the value from the name only a prefix and will apply the rule to all resources with names starting with the value. The cluster type resources have no name.

For more details about SimpleAclAuthorizer, its ACL rules and the allowed combinations of resources and operations, see Authorization and ACLs.

For more information about the AclRule object, see AclRule schema reference.

An example KafkaUser
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

6.8.3. Additional resources

7. Using the Strimzi 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.

Note
 For the full list of REST API endpoints and descriptions, including example requests and responses, see Kafka Bridge API reference. For information on how to deploy and configure the Kafka Bridge, see Kafka Bridge.

7.1. Overview of the Strimzi Kafka Bridge

The Strimzi Kafka Bridge provides an API for integrating HTTP-based clients with a Kafka cluster running on Kubernetes. The API enables such clients to produce and consume messages without the requirement to use the native 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.

You can:

  • Send messages to a topic.

  • Create and delete consumers.

  • Subscribe consumers to topics, so that they start receiving messages from those topics.

  • Unsubscribe consumers from topics.

  • Assign partitions to consumers.

  • Retrieve messages from topics.

  • 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.

Similar to a Kafka Connect cluster, you can deploy the Kafka Bridge into your Kubernetes cluster using the Cluster Operator. For deployment instructions, see Kafka Bridge.

After the Kafka Bridge is deployed, the Cluster Operator creates a Deployment, Service, and Pod in your Kubernetes cluster, each named strimzi-kafka-bridge by default.

7.2. Supported clients for the Strimzi Kafka Bridge

You can use the Kafka Bridge to integrate both internal and external HTTP client applications with your Kafka cluster.

  • Internal clients are container-based HTTP clients running in the same Kubernetes cluster as the Kafka Bridge itself.

  • External clients are HTTP clients running outside the Kubernetes cluster in which the Kafka Bridge is deployed and running.

Internal clients can access the Kafka Bridge on the host and port defined in the KafkaBridge custom resource. External clients can access the Kafka Bridge through an OpenShift Route, a LoadBalancer Service, or a Ingress.

Additional resources

7.3. Securing the Strimzi 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.

7.4. Accessing the Strimzi Kafka Bridge from 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.

7.5. Requests to the Strimzi Kafka Bridge

7.5.1. Data formats and headers

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:

    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.

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

7.6. Strimzi Kafka Bridge API resources

For the full list of REST API endpoints and descriptions, including example requests and responses, see Kafka Bridge API reference.

8. Introducing Metrics

This section describes how to monitor Strimzi Kafka, Zookeeper and Kafka Connect clusters using Prometheus to provide monitoring data for example Grafana dashboards.

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

  1. Add metrics configuration to your Kafka cluster resource

  2. Deploy Prometheus and Prometheus Alertmanager

  3. Deploy Grafana

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

8.1. Example Metrics files

You can find the example metrics configuration files in the examples/metrics directory.

metrics
├── grafana-install
│   ├── grafana.yaml (1)
├── grafana-dashboards (2)
│   ├── strimzi-kafka-connect.json
│   ├── strimzi-kafka.json
│   └── strimzi-zookeeper.json
├── kafka-connect-metrics.yaml (3)
├── kafka-metrics.yaml (4)
├── prometheus-additional-properties
│   └── prometheus-additional.yaml (5)
├── prometheus-alertmanager-config
│   └── alert-manager-config.yaml (6)
└── prometheus-install
    ├── alert-manager.yaml (7)
    ├── prometheus-rules.yaml (8)
    ├── prometheus.yaml (9)
    └── strimzi-service-monitor.yaml (10)

  1. Installation file for the Grafana image

  2. Grafana dashboard configuration

  3. Metrics configuration that defines Prometheus JMX Exporter relabeling rules for Kafka Connect

  4. Metrics configuration that defines Prometheus JMX Exporter relabeling rules for Kafka and Zookeeper

  5. Configuration to add roles for service monitoring

  6. Hook definitions for sending notifications through Alertmanager

  7. Resources for deploying and configuring Alertmanager

  8. Alerting rules examples for use with Prometheus Alertmanager (deployed with Prometheus)

  9. Installation file for the Prometheus image

  10. Prometheus job definitions to scrape metrics data

8.2. Prometheus metrics

Strimzi uses the Prometheus JMX Exporter to expose JMX metrics from Kafka and Zookeeper using an HTTP endpoint, which is then scraped by the Prometheus server.

8.2.1. Prometheus metrics configuration

Strimzi provides example configuration files for Grafana.

Grafana dashboards are dependent on Prometheus JMX Exporter relabeling rules, which are defined for:

  • Kafka and Zookeeper as a Kafka resource configuration in the example kafka-metrics.yaml file

  • Kafka Connect as KafkaConnect and KafkaConnectS2I resources in the example kafka-connect-metrics.yaml file

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

Note
 We show metrics configuration using kafka-metrics.yaml in this section, but the process is the same when configuring Kafka Connect using the kafka-connect-metrics.yaml file.
Additional resources

For more information on the use of relabeling, see Configuration in the Prometheus documentation.

8.2.2. Prometheus metrics deployment options

To apply the example metrics configuration of relabeling rules to your Kafka cluster, do one of the following:

8.2.3. Copying Prometheus metrics configuration to a Kafka resource

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

Procedure

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

  1. Update the Kafka resource in an editor.

    kubectl edit kafka my-cluster

  2. Copy the example configuration in kafka-metrics.yaml to your own Kafka resource definition.

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

8.2.4. Deploying a Kafka cluster with Prometheus metrics configuration

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

Procedure

  • Deploy the Kafka cluster with the metrics configuration:

    kubectl apply -f kafka-metrics.yaml

8.3. Prometheus

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

We describe here how you can use the CoreOS Prometheus Operator to run and manage a Prometheus server that is suitable for use in production environments, but with the correct configuration you can run any Prometheus server.

Note

The Prometheus server configuration uses service discovery to discover the pods in the cluster from which it gets metrics. For this feature to work correctly, the service account used for running the Prometheus service pod must have access to the API server so it can retrieve the pod list.

For more information, see Discovering services.

8.3.1. Prometheus configuration

Strimzi provides example configuration files for the Prometheus server.

A Prometheus image is provided for deployment:

  • prometheus.yaml

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

  • prometheus-additional.yaml

  • prometheus-rules.yaml

  • strimzi-service-monitor.yaml

For Prometheus to obtain monitoring data:

Then use the configuration files to:

Alerting rules

The prometheus-rules.yaml file provides example alerting rule examples for use with Alertmanager.

8.3.2. Prometheus resources

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

  • A ClusterRole that grants permissions to Prometheus to read the health endpoints exposed by the Kafka and Zookeeper pods, cAdvisor and the kubelet for container metrics.

  • A ServiceAccount for the Prometheus pods to run under.

  • A ClusterRoleBinding which binds the ClusterRole to the ServiceAccount.

  • A Deployment to manage the Prometheus Operator pod.

  • A ServiceMonitor to manage the configuration of the Prometheus pod.

  • A Prometheus to manage the configuration of the Prometheus pod.

  • A PrometheusRule to manage alerting rules for the Prometheus pod.

  • A Secret to manage additional Prometheus settings.

8.3.3. Deploying the Prometheus Operator

To deploy the Prometheus Operator to your Kafka cluster, apply the YAML resource files from the Prometheus CoreOS repository.

Procedure

  1. Download the resource files from the repository and replace the example namespace with your own:

    On Linux, use:

    curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-deployment.yaml | sed -e 's/namespace: .\*/namespace: my-namespace/' > prometheus-operator-deployment.yaml
    curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-cluster-role.yaml > prometheus-operator-cluster-role.yaml
    curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-cluster-role-binding.yaml | sed -e 's/namespace: .*/namespace: my-namespace/' > prometheus-operator-cluster-role-binding.yaml
    curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-service-account.yaml | sed -e 's/namespace: .*/namespace: my-namespace/' > prometheus-operator-service-account.yaml

    On MacOS, use:

    curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-deployment.yaml | sed -e '' 's/namespace: .\*/namespace: my-namespace/' > prometheus-operator-deployment.yaml
    curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-cluster-role.yaml > prometheus-operator-cluster-role.yaml
    curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-cluster-role-binding.yaml | sed -e '' 's/namespace: .*/namespace: my-namespace/' > prometheus-operator-cluster-role-binding.yaml
    curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-service-account.yaml | sed -e '' 's/namespace: .*/namespace: my-namespace/' > prometheus-operator-service-account.yaml
    Note
    		 If it is not required, you can manually remove the spec.template.spec.securityContext property from the prometheus-operator-deployment.yaml file.

  2. Deploy the Prometheus Operator:

    kubectl apply -f prometheus-operator-deployment.yaml
kubectl apply -f prometheus-operator-cluster-role.yaml
kubectl apply -f prometheus-operator-cluster-role-binding.yaml
kubectl apply -f prometheus-operator-service-account.yaml

8.3.4. Deploying Prometheus

To deploy Prometheus to your Kafka cluster to obtain monitoring data, apply the example resource file for the Prometheus docker image and the YAML files for Prometheus-related resources.

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

Note
 By default, the Prometheus Operator only supports jobs that include an endpoints role for service discovery. Targets are discovered and scraped for each endpoint port address. For endpoint discovery, the port address may be derived from service (role: service) or pod (role: pod) discovery.
Prerequisites
Procedure

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

    On Linux, use:

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

    On MacOS, use:

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

  2. Edit the ServiceMonitor resource in strimzi-service-monitor.yaml to define Prometheus jobs that will scrape the metrics data.

  3. To use another role:

    1. Create a Secret resource:

      oc create secret generic additional-scrape-configs --from-file=prometheus-additional.yaml

    2. Edit the additionalScrapeConfigs property in the prometheus.yaml file to include the name of the Secret and the YAML file (prometheus-additional.yaml) that contains the additional configuration.

  4. Edit the prometheus-rules.yaml file that creates sample alert notification rules:

    On Linux, use:

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

    On MacOS, use:

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

  5. Deploy the Prometheus resources:

    kubectl apply -f strimzi-service-monitor.yaml
kubectl apply -f prometheus-rules.yaml
kubectl apply -f prometheus.yaml

8.4. Prometheus Alertmanager

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

8.4.1. Alertmanager configuration

Strimzi provides example configuration files for Prometheus Alertmanager.

A configuration file defines the resources for deploying Alertmanager:

  • alert-manager.yaml

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

  • alert-manager-config.yaml

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

8.4.2. Alerting rules

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

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

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

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

Additional resources

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

8.4.3. Alerting rule examples

Example alerting rules for Kafka and Zookeeper metrics are provided with Strimzi for use in a Prometheus deployment.

General points about the alerting rule definitions:

  • A for property is used with the rules to determine the period of time a condition must persist before an alert is triggered.

  • A tick is a basic Zookeeper time unit, which is measured in milliseconds and configured using the tickTime parameter of Kafka.spec.zookeeper.config. For example, if Zookeeper tickTime=3000, 3 ticks (3 x 3000) equals 9000 milliseconds.

  • The availability of the ZookeeperRunningOutOfSpace metric and alert is dependent on the Kubernetes configuration and storage implementation used. Storage implementations for certain platforms may not be able to supply the information on available space required for the metric to provide an alert.

Kafka alerting rules

  • UnderReplicatedPartitions: gives the number of partitions for which the current broker is the lead replica but which have fewer replicas than the min.insync.replicas configured for their topic. This metric provides insights about brokers that host the follower replicas. Those followers are not keeping up with the leader. Reasons for this could include being (or having been) offline, and over-throttled interbroker replication. An alert is raised when this value is greater than zero, providing information on the under-replicated partitions for each broker.

  • AbnormalControllerState: indicates whether the current broker is the controller for the cluster. The metric can be 0 or 1. During the life of a cluster, only one broker should be the controller and the cluster always needs to have an active controller. Having two or more brokers saying that they are controllers indicates a problem. If the condition persists, an alert is raised when the sum of all the values for this metric on all brokers is not equal to 1, meaning that there is no active controller (the sum is 0) or more than one controller (the sum is greater than 1).

  • UnderMinIsrPartitionCount: indicates that the minimum number of in-sync replicas (ISRs) for a lead Kafka broker, specified using min.insync.replicas, that must acknowledge a write operation has not been reached. The metric defines the number of partitions that the broker leads for which the in-sync replicas count is less than the minimum in-sync. An alert is raised when this value is greater than zero, providing information on the partition count for each broker that did not achieve the minimum number of acknowledgments.

  • OfflineLogDirectoryCount: indicates the number of log directories which are offline (for example, due to a hardware failure) so that the broker cannot store incoming messages anymore. An alert is raised when this value is greater than zero, providing information on the number of offline log directories for each broker.

  • KafkaRunningOutOfSpace: indicates the remaining amount of disk space that can be used for writing data. An alert is raised when this value is lower than 5GiB, providing information on the disk that is running out of space for each persistent volume claim. The threshold value may be changed in prometheus-rules.yaml.

Zookeeper alerting rules

  • AvgRequestLatency: indicates the amount of time it takes for the server to respond to a client request. An alert is raised when this value is greater than 10 (ticks), providing the actual value of the average request latency for each server.

  • OutstandingRequests: indicates the number of queued requests in the server. This value goes up when the server receives more requests than it can process. An alert is raised when this value is greater than 10, providing the actual number of outstanding requests for each server.

  • ZookeeperRunningOutOfSpace: indicates the remaining amount of disk space that can be used for writing data to Zookeeper. An alert is raised when this value is lower than 5GiB., providing information on the disk that is running out of space for each persistent volume claim.

8.4.4. Deploying Alertmanager

To deploy Alertmanager, apply the example configuration files.

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

The following resources are defined on deployment:

  • An Alertmanager to manage the Alertmanager pod.

  • A Secret to manage the configuration of the Alertmanager.

  • A Service to provide an easy to reference hostname for other services to connect to Alertmanager (such as Prometheus).

Prerequisites
Procedure

  1. Create a Secret resource from the Alertmanager configuration file (alert-manager-config.yaml):

    kubectl create secret generic alertmanager-alertmanager --from-file=alert-manager-config.yaml

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

    • slack_api_url property with the actual value of the Slack API URL related to the application for the Slack workspace

    • channel property with the actual Slack channel on which to send notifications

  3. Deploy Alertmanager:

    kubectl apply -f alert-manager.yaml

8.5. Grafana

Grafana provides visualizations of Prometheus metrics.

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

8.5.1. Grafana configuration

Strimzi provides example configuration files for Grafana.

A Grafana docker image is provided for deployment:

  • grafana.yaml

Example dashboards are also provided as JSON files:

  • strimzi-kafka.json

  • strimzi-kafka-connect.json

  • strimzi-zookeeper.json

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

For Grafana to present the dashboards, use the configuration files to:

8.5.2. Deploying Grafana

To deploy Grafana to provide visualizations of Prometheus metrics, apply the example configuration file.

Prerequisites
Procedure

  1. Deploy Grafana:

    kubectl apply -f grafana.yaml

  2. Enable the Grafana dashboards.

8.5.3. Enabling the example Grafana dashboards

Set up a Prometheus data source and example dashboards to enable Grafana for monitoring.

Note
 No alert notification rules are defined.

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

For example, you can access the Grafana user interface by:

  1. Running kubectl port-forward grafana-1-fbl7s 3000:3000

  2. Pointing a browser to http://localhost:3000

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

  1. Access the Grafana user interface using admin/admin credentials.

    On the initial view choose to reset the password.

    Grafana login

  2. Click the Add data source button.

    Grafana home

  3. Add Prometheus as a data source.

  4. Click Add to test the connection to the data source.

    Add Prometheus data source

  5. Click Dashboards, then Import to open the Import Dashboard window and import the example dashboards (or paste the JSON).

    Add Grafana dashboard

After importing the dashboards, the Grafana dashboard homepage presents Kafka and Zookeeper dashboards.

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

Kafka dashboard
Kafka dashboardKafka dashboard
Zookeeper dashboard
Zookeeper dashboardZookeeper dashboard

8.6. Using metrics with minikube or minishift

When adding Prometheus and Grafana servers to an Apache Kafka deployment using minikube or minishift, the memory available to the virtual machine should be increased (to 4 GB of RAM, for example, instead of the default 2 GB).

For information on how to increase the default amount of memory, see:

Additional resources

9. 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 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.

Secure Communication
Figure 1. Example architecture diagram of the communication secured by TLS.

9.1. Certificate Authorities

To support encryption, each Strimzi component needs its own private keys and public key certificates. All component certificates are signed by a Certificate Authority (CA) called the cluster CA.

Similarly, each Kafka client application connecting using TLS client authentication needs private keys and certificates. The clients CA is used to sign the certificates for the Kafka clients.

9.1.1. CA certificates

Each CA has a self-signed public key certificate.

Kafka brokers are configured to trust certificates signed by either the clients CA or the cluster CA. Components to which clients do not need to connect, such as Zookeeper, only trust certificates signed by the cluster CA. Client applications that perform mutual TLS authentication have to trust the certificates signed by the cluster CA.

By default, Strimzi generates and renews CA certificates automatically. You can configure the management of CA certificates in the Kafka.spec.clusterCa and Kafka.spec.clientsCa objects.

9.2. Certificates and Secrets

Strimzi stores CA, component and Kafka client private keys and certificates in Secrets. All keys are 2048 bits in size.

CA certificate validity periods, expressed as a number of days after certificate generation, can be configured in Kafka.spec.clusterCa.validityDays and Kafka.spec.clusterCa.validityDays.

9.2.1. Cluster CA Secrets

Table 1. 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.crt

The current certificate for the cluster CA.

<cluster>-kafka-brokers

<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>.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_.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.

9.2.2. Client CA Secrets

Table 2. 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.crt

The current certificate for the clients CA.

The certificates in <cluster>-clients-ca-cert are those which the Kafka brokers trust.

Note
 <cluster>-cluster-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.

9.2.3. User Secrets

Table 3. Secrets managed by the User Operator
Secret name Field within Secret Description

<user>

user.crt

Certificate for the user, signed by the clients CA

user.key

Private key for the user

9.3. 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 one
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) Run the following commands:

    # 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 labels strimzi.io/kind=Kafka and strimzi.io/cluster=<my-cluster>: Run the following commands:

    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

9.4. 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.

9.4.1. Renewal process with generated CAs

The Cluster Operator performs the following process to renew CA certificates:

  1. Generate a new CA certificate, but retaining 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.

9.4.2. Client applications

The Cluster Operator is not aware of all the client applications using the Kafka cluster.

Important
 Depending on how your applications are configured, you might need take action to ensure they continue working after certificate renewal.

Consider the following important points to ensure that client applications continue working.

  • When they connect to the cluster, client applications must trust the cluster CA certificate published in <cluster>-cluster-ca-cert.

  • When using the User Operator to provision client certificates, client applications must use the current user.crt and user.key published in their <user> Secret when they connect to the cluster. For workloads running inside the same Kubernetes cluster this can be achieved by mounting the secrets as a volume and having the client Pods construct their key- and truststores from the current state of the Secrets. For more details on this procedure, see Configuring internal clients to trust the cluster CA.

  • When renewing client certificates, 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.

9.5. 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

9.6. 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

9.7. TLS connections

9.7.1. 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.

9.7.2. Kafka interbroker communication

Communication between Kafka brokers is done through the REPLICATION listener on port 9091, which is encrypted by default.

Communication between Kafka brokers and Zookeeper nodes uses a TLS sidecar, as described above.

9.7.3. 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.

9.7.4. Kafka Client connections

Encrypted communication between Kafka brokers and clients running within the same Kubernetes cluster is provided through the CLIENTTLS listener on port 9093.

Encrypted communication between Kafka brokers and clients running outside the same Kubernetes cluster is provided through the EXTERNAL listener on port 9094.

Note
 You can use the CLIENT listener on port 9092 for unencrypted communication with brokers.

9.8. 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.

Prerequisites

  • The Cluster Operator is running.

  • A Kafka resource within the Kubernetes cluster.

  • A Kafka client application inside the Kubernetes cluster which will connect using TLS and needs to trust the cluster CA certificate.

Procedure

  1. When defining the client Pod

  2. The Kafka client has to be configured to trust certificates signed by this CA. For the Java-based Kafka Producer, Consumer, and Streams APIs, you can do this by importing the CA certificate into the JVM’s truststore using the following keytool command:

    keytool -keystore client.truststore.jks -alias CARoot -import -file ca.crt

  3. To configure the Kafka client, specify the following properties:

    • security.protocol: SSL when using TLS for encryption (with or without TLS authentication), or security.protocol: SASL_SSL when using SCRAM-SHA authentication over TLS.

    • ssl.truststore.location: the truststore location where the certificates were imported.

    • ssl.truststore.password: the password for accessing the truststore. This property can be omitted if it is not needed by the truststore.

Additional resources

9.9. 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.

You can use the same procedure to configure clients inside Kubernetes, which connect to the tls listener on port 9093, but it is usually more convenient to access the Secrets using a volume mount in the client Pod.

Follow this procedure when setting up the client and during the renewal period, when the old clients CA certificate is replaced.

Important
 The <cluster-name>-cluster-ca-cert Secret will contain more than one CA certificate during CA certificate renewal. Clients must add all of them to their truststores.
Prerequisites

  • The Cluster Operator is running.

  • A Kafka resource within the {ProductPlatformName} cluster.

  • A Kafka client application outside the {ProductPlatformName} cluster which will connect using TLS and needs to trust the cluster CA certificate.

Procedure

  1. Extract the cluster CA certificate from the generated <cluster-name>-cluster-ca-cert Secret.

    Run the following command to extract the certificates:

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

  2. The Kafka client has to be configured to trust certificates signed by this CA. For the Java-based Kafka Producer, Consumer, and Streams APIs, you can do this by importing the CA certificates into the JVM’s truststore using the following keytool command:

    keytool -keystore client.truststore.jks -alias CARoot -import -file ca.crt

  3. To configure the Kafka client, specify the following properties:

    • security.protocol: SSL when using TLS for encryption (with or without TLS authentication), or security.protocol: SASL_SSL when using SCRAM-SHA authentication over TLS.

    • ssl.truststore.location: the truststore location where the certificates were imported.

    • ssl.truststore.password: the password for accessing the truststore. This property can be omitted if it is not needed by the truststore.

Additional resources

10. Strimzi and Kafka upgrades

Strimzi can be upgraded with no cluster downtime. Each version of Strimzi supports one or more versions of Apache Kafka: you can upgrade to a higher Kafka version as long as it is supported by your version of Strimzi. In some cases, you can also downgrade to a lower supported Kafka version.

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

Important
 Resource upgrades must be performed after upgrading Strimzi and Kafka.

10.1. Upgrade process

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

  1. Update your Cluster Operator to the latest Strimzi version.

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

10.2. Kafka versions

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

The following table shows the differences between Kafka versions:

Kafka version Interbroker protocol version Log message format version Zookeeper version

2.1.0

2.1

2.1

3.4.13

2.1.1

2.1

2.1

3.4.13

2.2.0

2.2

2.2

3.4.13

2.2.1

2.2

2.2

3.4.13

2.3.0

2.3

2.3

3.4.13
Message format version

When a producer sends a message to a Kafka broker, the message is encoded using a specific format. The format can change between Kafka releases, so messages include a version identifying which version of the format they were encoded with. You can configure a Kafka broker to convert messages from newer format versions to a given older format version before the broker appends the message to the log.

In Kafka, there are two different methods for setting the message format version:

  • The message.format.version property is set on topics.

  • The log.message.format.version property is set on Kafka brokers.

The default value of message.format.version for a topic is defined by the log.message.format.version that is set on the Kafka broker. You can manually set the message.format.version of a topic by modifying its topic configuration.

The upgrade tasks in this section assume that the message format version is defined by the log.message.format.version.

10.3. Upgrading the Cluster Operator

The steps to upgrade your Cluster Operator deployment to use Strimzi 0.13.0 are outlined in this section.

The availability of Kafka clusters managed by the Cluster Operator is not affected by the upgrade operation.

Note
 Refer to the documentation supporting a specific version of Strimzi for information on how to upgrade to that version.

10.3.1. Upgrading the Cluster Operator to a later version

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

Prerequisites

  • An existing Cluster Operator deployment.

Procedure

  1. Backup the existing Cluster Operator resources:

    kubectl get all -l app=strimzi -o yaml > strimzi-backup.yaml

  2. Update the Cluster Operator.

    Modify the installation files according to the namespace the Cluster Operator is running in.

    On Linux, use:

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

    On MacOS, use:

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

    If you modified one or more environment variables in your existing Cluster Operator Deployment, edit the install/cluster-operator/050-Deployment-cluster-operator.yaml file to reflect the changes that you made in the new version of the Cluster Operator.

  3. When you have an updated configuration, deploy it along with the rest of the install resources:

    kubectl apply -f install/cluster-operator

    Wait for the rolling updates to complete.

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

    kubectl get po my-cluster-kafka-0 -o jsonpath='{.spec.containers[0].image}'

    The image tag shows the new Strimzi version followed by the Kafka version. For example, <New Strimzi version>-kafka-<Current Kafka version>.

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

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

What to do next

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

10.4. Upgrading Kafka

After you have upgraded your Cluster Operator, you can upgrade your brokers to a higher supported version of Kafka.

Kafka upgrades are performed using the Cluster Operator. How the Cluster Operator performs an upgrade depends on the differences between versions of:

  • Interbroker protocol

  • Log message format

  • Zookeeper

When the versions are the same for the current and target Kafka version, as is typically the case for a patch level upgrade, the Cluster Operator can upgrade through a single rolling update of the Kafka brokers.

When one or more of these versions differ, the Cluster Operator requires two or three rolling updates of the Kafka brokers to perform the upgrade.

Additional resources

10.4.1. Kafka version and image mappings

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

  • Each Kafka resource can be configured with a Kafka.spec.kafka.version.

  • The Cluster Operator’s STRIMZI_KAFKA_IMAGES environment variable provides a mapping between the Kafka version and the image to be used when that version is requested in a given Kafka resource.

    • If Kafka.spec.kafka.image is not configured, the default image for the given version is used.

    • If Kafka.spec.kafka.image is configured, the default image is overridden.

Warning
 The Cluster Operator cannot validate that an image actually contains a Kafka broker of the expected version. Take care to ensure that the given image corresponds to the given Kafka version.

10.4.2. Strategies for upgrading clients

The best approach to upgrading your client applications (including Kafka Connect connectors) depends on your particular circumstances.

Consuming applications need to receive messages in a message format that they understand. You can ensure that this is the case in one of two ways:

  • By upgrading all the consumers for a topic before upgrading any of the producers.

  • By having the brokers down-convert messages to an older format.

Using broker down-conversion puts extra load on the brokers, so it is not ideal to rely on down-conversion for all topics for a prolonged period of time. For brokers to perform optimally they should not be down converting messages at all.

Broker down-conversion is configured in two ways:

  • The topic-level message.format.version configures it for a single topic.

  • The broker-level log.message.format.version is the default for topics that do not have the topic-level message.format.version configured.

Messages published to a topic in a new-version format will be visible to consumers, because brokers perform down-conversion when they receive messages from producers, not when they are sent to consumers.

There are a number of strategies you can use to upgrade your clients:

Consumers first

  1. Upgrade all the consuming applications.

  2. Change the broker-level log.message.format.version to the new version.

  3. Upgrade all the producing applications.

    This strategy is straightforward, and avoids any broker down-conversion. However, it assumes that all consumers in your organization can be upgraded in a coordinated way, and it does not work for applications that are both consumers and producers. There is also a risk that, if there is a problem with the upgraded clients, new-format messages might get added to the message log so that you cannot revert to the previous consumer version.

Per-topic consumers first

For each topic:

  1. Upgrade all the consuming applications.

  2. Change the topic-level message.format.version to the new version.

  3. Upgrade all the producing applications.

    This strategy avoids any broker down-conversion, and means you can proceed on a topic-by-topic basis. It does not work for applications that are both consumers and producers of the same topic. Again, it has the risk that, if there is a problem with the upgraded clients, new-format messages might get added to the message log.

Per-topic consumers first, with down conversion

For each topic:

  1. Change the topic-level message.format.version to the old version (or rely on the topic defaulting to the broker-level log.message.format.version).

  2. Upgrade all the consuming and producing applications.

  3. Verify that the upgraded applications function correctly.

  4. Change the topic-level message.format.version to the new version.

    This strategy requires broker down-conversion, but the load on the brokers is minimized because it is only required for a single topic (or small group of topics) at a time. It also works for applications that are both consumers and producers of the same topic. This approach ensures that the upgraded producers and consumers are working correctly before you commit to using the new message format version.

    The main drawback of this approach is that it can be complicated to manage in a cluster with many topics and applications.

Other strategies for upgrading client applications are also possible.

Note
 It is also possible to apply multiple strategies. For example, for the first few applications and topics the "per-topic consumers first, with down conversion" strategy can be used. When this has proved successful another, more efficient strategy can be considered acceptable to use instead.

10.4.3. Upgrading Kafka brokers and client applications

This procedure describes how to upgrade a Strimzi Kafka cluster to a higher version of Kafka.

Prerequisites

For the Kafka resource to be upgraded, check:

  • The Cluster Operator, which supports both versions of Kafka, is up and running.

  • The Kafka.spec.kafka.config does not contain options that are not supported in the version of Kafka that you are upgrading to.

  • Whether the log.message.format.version for the current Kafka version needs to be updated for the new version.

    Consult the Kafka versions table.

Procedure

  1. Update the Kafka cluster configuration in an editor, as required:

    kubectl edit kafka my-cluster

    1. If the log.message.format.version of the current Kafka version is the same as that of the new Kafka version, proceed to the next step.

      Otherwise, ensure that Kafka.spec.kafka.config has the log.message.format.version configured to the default for the current version.

      For example, if upgrading from Kafka 2.2.1:

      kind: Kafka
spec:
  # ...
  kafka:
    version: 2.2.1
    config:
      log.message.format.version: "2.2"
      # ...

      If the log.message.format.version is unset, set it to the current version.

      Note
      		 The value of log.message.format.version must be a string to prevent it from being interpreted as a floating point number.

    2. Change the Kafka.spec.kafka.version to specify the new version (leaving the log.message.format.version as the current version).

      For example, if upgrading from Kafka 2.2.1 to 2.3.0:

      apiVersion: v1alpha1
kind: Kafka
spec:
  # ...
  kafka:
    version: 2.3.0 (1)
    config:
      log.message.format.version: "2.2" (2)
      # ...

      1. This is changed to the new version

      2. This remains at the current version

    3. If the image for the Kafka version is different from the image defined in STRIMZI_KAFKA_IMAGES for the Cluster Operator, update Kafka.spec.kafka.image.

      See Kafka version and image mappings

  2. Save and exit the editor, then wait for rolling updates to complete.

    Check the update in the logs or by watching the pod state transitions:

    kubectl logs -f <cluster-operator-pod-name> | grep -E "Kafka version upgrade from [0-9.]+ to [0-9.]+, phase ([0-9]+) of \1 completed"
    kubectl get po -w

    If the current and new versions of Kafka have different interbroker protocol versions, check the Cluster Operator logs for an INFO level message:

    Reconciliation #<num>(watch) Kafka(<namespace>/<name>): Kafka version upgrade from <from-version> to <to-version>, phase 2 of 2 completed

    Alternatively, if the current and new versions of Kafka have the same interbroker protocol version, check for:

    Reconciliation #<num>(watch) Kafka(<namespace>/<name>): Kafka version upgrade from <from-version> to <to-version>, phase 1 of 1 completed

    The rolling updates:

    • Ensure each pod is using the broker binaries for the new version of Kafka

    • Configure the brokers to send messages using the interbroker protocol of the new version of Kafka

      Note
      		 Clients are still using the old version, so brokers will convert messages to the old version before sending them to the clients. To minimize this additional load, updates the clients as quickly as possible.

  3. Depending on your chosen strategy for upgrading clients, upgrade all client applications to use the new version of the client binaries.

    See Strategies for upgrading clients

    Warning
    		 You cannot downgrade after completing this step. If you need to revert the update at this point, follow the procedure Downgrading Kafka brokers and client applications.

    If required, set the version property for Kafka Connect and Mirror Maker as the new version of Kafka:

    1. For Kafka Connect, update KafkaConnect.spec.version

    2. For MIrror Maker, update KafkaMirrorMaker.spec.version

  4. If the log.message.format.version identified in step 1 is the same as the new version proceed to the next step.

    Otherwise change the log.message.format.version in Kafka.spec.kafka.config to the default version for the new version of Kafka now being used.

    For example, if upgrading to 2.3.0:

    apiVersion: v1alpha1
kind: Kafka
spec:
  # ...
  kafka:
    version: 2.3.0
    config:
      log.message.format.version: "2.3"
      # ...

  5. Wait for the Cluster Operator to update the cluster.

    The Kafka cluster and clients are now using the new Kafka version.

Additional resources

10.5. Downgrading Kafka

Kafka version downgrades are performed using the Cluster Operator.

Whether and how the Cluster Operator performs a downgrade depends on the differences between versions of:

  • Interbroker protocol

  • Log message format

  • Zookeeper

10.5.1. Target downgrade version

How the Cluster Operator handles a downgrade operation depends on the log.message.format.version.

  • If the target downgrade version of Kafka has the same log.message.format.version as the current version, the Cluster Operator downgrades by performing a single rolling restart of the brokers.

  • If the target downgrade version of Kafka has a different log.message.format.version, downgrading is only possible if the running cluster has always had log.message.format.version set to the version used by the downgraded version.

    This is typically only the case if the upgrade procedure was aborted before the log.message.format.version was changed. In this case, the downgrade requires:

    • Two rolling restarts of the brokers if the interbroker protocol of the two versions is different

    • A single rolling restart if they are the same

10.5.2. Downgrading Kafka brokers and client applications

This procedure describes how you can downgrade a Strimzi Kafka cluster to a lower (previous) version of Kafka, such as downgrading from 2.3.0 to 2.2.1.

Important

Downgrading is not possible if the new version has ever used a log.message.format.version that is not supported by the previous version, including when the default value for log.message.format.version is used. For example, this resource can be downgraded to Kafka version 2.2.1 because the log.message.format.version has not been changed:

apiVersion: v1alpha1
kind: Kafka
spec:
  # ...
  kafka:
    version: 2.3.0
    config:
      log.message.format.version: "2.2"
      # ...

The downgrade would not be possible if the log.message.format.version was set at "2.3" or a value was absent (so that the parameter took the default value for a 2.3.0 broker of 2.3).
Prerequisites

For the Kafka resource to be downgraded, check:

  • The Cluster Operator, which supports both versions of Kafka, is up and running.

  • The Kafka.spec.kafka.config does not contain options that are not supported in the version of Kafka you are downgrading to.

  • The Kafka.spec.kafka.config has a log.message.format.version that is supported by the version being downgraded to.

Procedure

  1. Update the Kafka cluster configuration in an editor, as required:

    Use kubectl edit:

    kubectl edit kafka my-cluster

    1. Change the Kafka.spec.kafka.version to specify the previous version.

      For example, if downgrading from Kafka 2.3.0 to 2.2.1:

      apiVersion: v1alpha1
kind: Kafka
spec:
  # ...
  kafka:
    version: 2.2.1 (1)
    config:
      log.message.format.version: "2.2" (2)
      # ...

      1. This is changed to the previous version

      2. This is unchanged

      Note
      		 You must format the value of log.message.format.version as a string to prevent it from being interpreted as a floating point number.

    2. If the image for the Kafka version is different from the image defined in STRIMZI_KAFKA_IMAGES for the Cluster Operator, update Kafka.spec.kafka.image.

      See Kafka version and image mappings

  2. Save and exit the editor, then wait for rolling updates to complete.

    Check the update in the logs or by watching the pod state transitions:

    kubectl logs -f <cluster-operator-pod-name> | grep -E "Kafka version downgrade from [0-9.]+ to [0-9.]+, phase ([0-9]+) of \1 completed"
    kubectl get po -w

    If the previous and current versions of Kafka have different interbroker protocol versions, check the Cluster Operator logs for an INFO level message:

    Reconciliation #<num>(watch) Kafka(<namespace>/<name>): Kafka version downgrade from <from-version> to <to-version>, phase 2 of 2 completed

    Alternatively, if the previous and current versions of Kafka have the same interbroker protocol version, check for:

    Reconciliation #<num>(watch) Kafka(<namespace>/<name>): Kafka version downgrade from <from-version> to <to-version>, phase 1 of 1 completed

  3. Downgrade all client applications (consumers) to use the previous version of the client binaries.

    The Kafka cluster and clients are now using the previous Kafka version.

11. Strimzi resource upgrades

For this release of Strimzi, resources that use the API version kafka.strimzi.io/v1alpha1 must be updated to use kafka.strimzi.io/v1beta1.

The kafka.strimzi.io/v1alpha1 API version is deprecated in release 0.13.0.

This section describes the upgrade steps for the resources.

Important
 The upgrade of resources must be performed after upgrading the Cluster Operator, so the Cluster Operator can understand the resources.
What if the resource upgrade does not take effect?

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

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

Example annotation:

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

11.1. Upgrading Kafka resources

Prerequisites

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

Procedure

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

  1. Update the Kafka resource in an editor.

    kubectl edit kafka my-cluster

  2. Replace:

    apiVersion: kafka.strimzi.io/v1alpha1

    with:

    apiVersion:kafka.strimzi.io/v1beta1

  3. If the Kafka resource has:

    Kafka.spec.topicOperator

    Replace it with:

    Kafka.spec.entityOperator.topicOperator

    For example, replace:

    spec:
  # ...
  topicOperator: {}

    with:

    spec:
  # ...
  entityOperator:
    topicOperator: {}

  4. If present, move:

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

    to:

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

    For example, move:

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

    to:

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

  5. If present, move:

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

    to:

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

    For example, move:

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

    to:

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

  6. If present, move:

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

    to:

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

    For example, move:

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

    to:

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

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

11.2. Upgrading Kafka Connect resources

Prerequisites

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

Procedure

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

  1. Update the KafkaConnect resource in an editor.

    kubectl edit kafkaconnect my-connect

  2. Replace:

    apiVersion: kafka.strimzi.io/v1alpha1

    with:

    apiVersion:kafka.strimzi.io/v1beta1

  3. If present, move:

    KafkaConnect.spec.affinity
    KafkaConnect.spec.tolerations

    to:

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

    For example, move:

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

    to:

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

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

11.3. Upgrading Kafka Connect S2I resources

Prerequisites

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

Procedure

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

  1. Update the KafkaConnectS2I resource in an editor.

    kubectl edit kafkaconnects2i my-connect

  2. Replace:

    apiVersion: kafka.strimzi.io/v1alpha1

    with:

    apiVersion:kafka.strimzi.io/v1beta1

  3. If present, move:

    KafkaConnectS2I.spec.affinity
    KafkaConnectS2I.spec.tolerations

    to:

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

    For example, move:

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

    to:

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

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

11.4. Upgrading Kafka Mirror Maker resources

Prerequisites

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

Procedure

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

  1. Update the KafkaMirrorMaker resource in an editor.

    kubectl edit kafkamirrormaker my-connect

  2. Replace:

    apiVersion: kafka.strimzi.io/v1alpha1

    with:

    apiVersion:kafka.strimzi.io/v1beta1

  3. If present, move:

    KafkaConnectMirrorMaker.spec.affinity
    KafkaConnectMirrorMaker.spec.tolerations

    to:

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

    For example, move:

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

    to:

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

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

11.5. Upgrading Kafka Topic resources

Prerequisites

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

Procedure

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

  1. Update the KafkaTopic resource in an editor.

    kubectl edit kafkatopic my-topic

  2. Replace:

    apiVersion: kafka.strimzi.io/v1alpha1

    with:

    apiVersion:kafka.strimzi.io/v1beta1

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

11.6. Upgrading Kafka User resources

Prerequisites

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

Procedure

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

  1. Update the KafkaUser resource in an editor.

    kubectl edit kafkauser my-user

  2. Replace:

    apiVersion: kafka.strimzi.io/v1alpha1

    with:

    apiVersion:kafka.strimzi.io/v1beta1

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

12. 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.

13. Checking 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

Appendix A: Configurable loggers

Logging allows you to diagnose error and performance issues for Strimzi.

The following logger implementations are used in Strimzi:

  • log4j logger for Kafka and Zookeeper

  • log4j2 logger for Topic Operator, User Operator, and other components

Strimzi components have their own configurable loggers.

Appendix B: Frequently Asked Questions

B.1. Cluster Operator

B.1.1. Why do I need cluster admin privileges to install Strimzi?

To install Strimzi, you must have the ability to create Custom Resource Definitions (CRDs). CRDs instruct Kubernetes about resources that are specific to Strimzi, such as Kafka, KafkaConnect, and so on. Because CRDs are a cluster-scoped resource rather than being scoped to a particular Kubernetes namespace, they typically require cluster admin privileges to install.

In addition, you must also have the ability to create ClusterRoles and ClusterRoleBindings. Like CRDs, these are cluster-scoped resources that typically require cluster admin privileges.

The cluster administrator can inspect all the resources being installed (in the /install/ directory) to assure themselves that the ClusterRoles do not grant unnecessary privileges. For more information about why the Cluster Operator installation resources grant the ability to create ClusterRoleBindings see the following question.

After installation, the Cluster Operator will run as a regular Deployment; any non-admin user with privileges to access the Deployment can configure it.

By default, normal users will not have the privileges necessary to manipulate the custom resources, such as Kafka, KafkaConnect and so on, which the Cluster Operator deals with. These privileges can be granted using normal RBAC resources by the cluster administrator. See this procedure for more details of how to do this.

B.1.2. Why does the Cluster Operator require the ability to create ClusterRoleBindings? Is that not a security risk?

Kubernetes has built-in privilege escalation prevention. That means that the Cluster Operator cannot grant privileges it does not have itself. Which in turn means that the Cluster Operator needs to have the privileges necessary for all the components it orchestrates.

In the context of this question there are two places where the Cluster Operator needs to create bindings to ClusterRoleBindings to ServiceAccounts:

  1. The Topic Operator and User Operator need to be able to manipulate KafkaTopics and KafkaUsers, respectively. The Cluster Operator therefore needs to be able to grant them this access, which it does by creating a Role and RoleBinding. For this reason the Cluster Operator itself needs to be able to create Roles and RoleBindings in the namespace that those operators will run in. However, because of the privilege escalation prevention, the Cluster Operator cannot grant privileges it does not have itself (in particular it cannot grant such privileges in namespace it cannot access).

  2. When using rack-aware partition assignment, Strimzi needs to be able to discover the failure domain (for example, the Availability Zone in AWS) of the node on which a broker pod is assigned. To do this the broker pod needs to be able to get information about the Node it is running on. A Node is a cluster-scoped resource, so access to it can only be granted via a ClusterRoleBinding (not a namespace-scoped RoleBinding). Therefore the Cluster Operator needs to be able to create ClusterRoleBindings. But again, because of privilege escalation prevention, the Cluster Operator cannot grant privileges it does not have itself (so it cannot, for example, create a ClusterRoleBinding to a ClusterRole to grant privileges that the Cluster Operator does not not already have).

B.1.3. Why can standard Kubernetes users not create the custom resource (Kafka, KafkaTopic, and so on)?

Because, when they installed Strimzi, the Kubernetes cluster administrator did not grant the necessary privileges to standard users.

See this FAQ answer for more details.

B.1.4. Log contains warnings about failing to acquire lock

For each cluster, the Cluster Operator always 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. In case an operation requires more time to complete, other operations will wait until it is completed and the lock is released.

INFO

Examples of cluster operations are cluster creation, rolling update, scale down or scale up and so on.

If the wait for the lock takes too long, the operation times out and the following warning message will be 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 may appear regularly without indicating any problems. The operations which time out will be picked up by the next periodic reconciliation. It will try to acquire the lock again and execute.

Should this message appear periodically even in situations when there should be no other operations running for a given cluster, it might indicate that due to some error the lock was not properly released. In such cases it is recommended to restart the Cluster Operator.

B.1.5. Hostname verification fails 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 C: Installing a 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.

The easiest way to get a running Kubernetes cluster is using Minikube. Minikube can be downloaded and installed 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, it could be worth running Minikube at least with 4 GB of RAM instead of the default 2 GB. Once installed, it can be started using:

minikube start --memory 4096

Appendix D: Installing an 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.

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. More information about this way can be found here.

Another option is to use 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, it could be worth running minishift at least with 4 GB of RAM instead of the default 2 GB. Once installed, minishift can be started using the following command:

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.

D.1. oc and kubectl

The oc command also 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

kubectl apply -f your-file

when using OpenShift you could alternatively use

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

Appendix E: Custom Resource API Reference

E.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

E.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.entityOerator.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

maintenanceTimeWindows

A list of time windows for the maintenance tasks (that is, certificates renewal). Each time window is defined by a cron expression.

string array

E.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.