Strimzi

Strimzi Documentation (Master)

Table of Contents

1. Overview of Strimzi

Strimzi makes it easy to run Apache Kafka on OpenShift or Kubernetes. Apache Kafka is a popular platform for streaming data delivery and processing. For more information about Apache Kafka, see the Apache Kafka website.

Strimzi is based on Apache Kafka 2.0.0 and consists of three main components:

Cluster Operator

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

Topic Operator

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

User Operator

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

This guide describes how to install and use Strimzi.

1.1. Kafka Key Features

  • Scalability and performance

    • Designed for horizontal scalability

  • Message ordering guarantee

    • At partition level

  • Message rewind/replay

    • "Long term" storage

    • Allows to reconstruct application state by replaying the messages

    • Combined with compacted topics allows to use Kafka as key-value store

1.2. Document Conventions

Replaceables

In this document, replaceable text is styled in monospace and surrounded by angle brackets.

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>/' examples/install/cluster-operator/*ClusterRoleBinding*.yaml

2. Getting started with Strimzi

Strimzi works on all types of clusters, from public and private clouds on to local deployments intended for development. This guide expects that an OpenShift or Kubernetes cluster is available and the kubectl and oc command-line tools are installed and configured to connect to the running cluster.

Table 1. Supported Versions
Product Version

Kubernetes

1.9 and later

OpenShift Origin

3.9 and later

Apache Kafka

2.0.0

When no existing OpenShift or Kubernetes cluster is available, Minikube or Minishift can be used to create a local cluster. More details can be found in Installing Kubernetes and OpenShift clusters.

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

2.1. Strimzi Downloads

Strimzi releases are available for download from GitHub. The release artifacts contain documentation, example YAML files for deployment on OpenShift or Kubernetes, and a Helm Chart for deployment using Helm. The example files are used throughout this documentation and can be used to install Strimzi. The container images are available on Docker Hub.

2.2. Cluster Operator

Strimzi uses the Cluster Operator to deploy and manage Kafka (including Zookeeper) and Kafka Connect clusters. The Cluster Operator is deployed inside of the Kubernetes or OpenShift cluster. To deploy a Kafka cluster, a Kafka resource with the cluster configuration has to be created within the Kubernetes or OpenShift cluster. Based on what is declared inside of the Kafka resource, the Cluster Operator deploys a corresponding Kafka cluster. For more information about the different configuration options supported by the Kafka resource, see Kafka cluster configuration

Note
Strimzi contains example YAML files, which make deploying a Cluster Operator easier.

2.2.1. What the Cluster Operator does

The Cluster Operator is in charge of deploying a Kafka cluster alongside a Zookeeper ensemble. As part of the Kafka cluster, it can also deploy the topic operator which provides operator-style topic management via KafkaTopic custom resources. The Cluster Operator is also able to deploy a Kafka Connect cluster which connects to an existing Kafka cluster. On OpenShift such a cluster can be deployed using the Source2Image feature, providing an easy way of including more connectors.

Cluster Operator
Figure 1. Example Architecture diagram of the Cluster Operator.

When the Cluster Operator is up, it starts to watch for certain OpenShift or Kubernetes resources containing the desired Kafka or Kafka Connect cluster configuration. A Kafka resource is used for Kafka cluster configuration, and a KafkaConnect resource is used for Kafka Connect cluster configuration.

When a new desired resource (that is, a Kafka or KafkaConnect resource) is created in the OpenShift or Kubernetes cluster, the operator gets the cluster configuration from the desired resource and starts creating a new Kafka or Kafka Connect cluster by creating the necessary other OpenShift or Kubernetes resources, such as StatefulSets, Services, ConfigMaps, and so on.

Every time the desired resource is updated by the user, the operator performs corresponding updates on the OpenShift or Kubernetes resources which make up the Kafka or Kafka Connect cluster. Resources are either patched or deleted and then re-created in order to make the Kafka or Kafka Connect cluster reflect the state of the desired cluster resource. This might cause a rolling update which might lead to service disruption.

Finally, when the desired resource is deleted, the operator starts to undeploy the cluster and delete all the related OpenShift or Kubernetes resources.

2.2.2. Deploying the Cluster Operator to Kubernetes

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

    sed -i 's/namespace: .*/namespace: <my-namespace>/' examples/install/cluster-operator/*ClusterRoleBinding*.yaml
Procedure
  1. Deploy the Cluster Operator

    kubectl create -f examples/install/cluster-operator
  2. Verify whether the Cluster Operator has been deployed successfully, the Kubernetes Dashboard or the command line:

    kubectl describe all

2.2.3. Deploying the Cluster Operator to OpenShift

Prerequisites
  • A user with cluster-admin role needs to be used, for example, system:admin.

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

    sed -i 's/namespace: .*/namespace: <my-namespace>/' examples/install/cluster-operator/*ClusterRoleBinding*.yaml
Procedure
  1. Deploy the Cluster Operator

    oc create -f examples/install/cluster-operator
    oc create -f examples/templates/cluster-operator
  2. Verify whether the Cluster Operator has been deployed successfully, the OpenShift console or the command line:

    oc describe all

2.2.4. Deploying the Cluster Operator using Helm Chart

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

  • Helm has to be installed in the OpenShift or Kubernetes cluster.

Procedure
  1. Deploy the Cluster Operator using the Helm command line tool:

    helm install strimzi-kafka-operator-latest.tgz
  2. Verify whether the Cluster Operator has been deployed successfully using the Helm command line tool:

    helm ls
Additional resources

2.3. Kafka cluster

When installing Kafka, Strimzi also installs a Zookeeper cluster and adds the necessary configuration to connect Kafka with Zookeeper.

Strimzi provides two options for Kafka cluster deployment:

Ephemeral

is suitable only for development and testing purposes and not for production. This deployment uses emptyDir volumes for storing broker information (Zookeeper) and topics or partitions (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

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 HostPath volumes on Minikube or 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.

2.3.1. Deploying the Kafka cluster to Kubernetes

Prerequisites
  • Before deploying a Kafka cluster, the Cluster Operator must be deployed.

Procedure
  1. If you are planning to use the Kafka broker for development or testing, create an ephemeral cluster

    kubectl apply -f examples/kafka/kafka-ephemeral.yaml
  2. If you are planning to use the Kafka cluster in production, create a persistent cluster

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

2.3.2. Deploying the Kafka cluster to OpenShift

OpenShift provides a template for deploying the Kafka cluster either in the OpenShift console or on the command-line.

Prerequisites
  • Before deploying a Kafka cluster, the Cluster Operator must be deployed.

Procedure
  1. If you are planning to use the Kafka cluster for development or testing, create an ephemeral cluster

    oc apply -f examples/kafka/kafka-ephemeral.yaml
  2. If you are planning to use the Kafka cluster in production, create a persistent cluster

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

2.4. Kafka Connect

The Cluster Operator deploys a Kafka Connect cluster, which can be used with your Kafka broker deployment. It is implemented as a Deployment with a configurable number of workers. The default image currently contains only the FileStreamSinkConnector and FileStreamSourceConnector connectors. The REST interface for managing the Kafka Connect cluster is exposed internally within the OpenShift or Kubernetes cluster as a kafka-connect service on port 8083.

Example KafkaConnect resources and the details about the KafkaConnect format for deploying Kafka Connect can be found in Kafka Connect cluster configuration and Kafka Connect cluster with Source2Image support.

2.4.1. Deploying Kafka Connect to Kubernetes

Prerequisites
  • Before deploying Kafka Connect, the Cluster Operator must be deployed.

Procedure
  • Deploy Kafka Connect on Kubernetes by creating the corresponding KafkaConnect resource.

    kubectl apply -f examples/kafka-connect/kafka-connect.yaml
Additional resources
  • For more information about deploying the Cluster Operator, see Cluster Operator

2.4.2. Deploying Kafka Connect to OpenShift

On OpenShift, Kafka Connect is provided in the form of a template. It can be deployed from the template using the command-line or through the OpenShift console.

Prerequisites
  • Before deploying Kafka Connect, the Cluster Operator must be deployed.

Procedure
  • Create a Kafka Connect cluster from the command-line:

    oc apply -f examples/kafka-connect/kafka-connect.yaml
Additional resources
  • For more information about deploying the Cluster Operator, see Cluster Operator

2.4.3. Using Kafka Connect with plugins

Strimzi container images for Kafka Connect contain, by default, only the FileStreamSinkConnector and FileStreamSourceConnector connectors which are part of Apache Kafka.

To facilitate deployment with 3rd party connectors, Kafka Connect is configured to automatically load all plugins or connectors that are present in the /opt/kafka/plugins directory during startup.

There are two ways of adding custom plugins into this directory:

  • Using a custom Docker image

  • Using the OpenShift build system with the Strimzi S2I

Create a new image based on our base image

Strimzi provides its own Docker image for running Kafka Connect, which can be found on Docker Hub as strimzi/kafka-connect:latest. This image can be used as a base image for building a new custom image with additional plugins.

The following procedure describes the process for creating such a custom image.

Procedure
  1. Create a new Dockerfile using strimzi/kafka-connect:latest as the base image:

    FROM strimzi/kafka-connect:latest
    USER root:root
    COPY ./<my-plugins>/ /opt/kafka/plugins/
    USER kafka:kafka
  2. Build the container image and upload it to the appropriate container image repository.

  3. Set the KafkaConnect.spec.image property of the KafkaConnect custom resource or the STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE variable to point to the new container image.

Additional resources
Using OpenShift builds and S2I to create new images

OpenShift supports builds, which can be used together with 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. The newly created container image is stored in OpenShift’s local container image repository and can be used in deployments. Strimzi provides a Kafka Connect builder image, which can be found on Docker Hub as strimzi/kafka-connect-s2i:latest with this S2I support. It takes user-provided binaries (with plugins and connectors) and creates a new Kafka Connect image. This enhanced Kafka Connect image can be used with the Kafka Connect deployment.

The S2I deployment provided as an OpenShift template. It can be deployed from the template using the command-line or the OpenShift console.

Procedure
  1. Create a Kafka Connect S2I cluster from the command-line

    oc apply -f examples/kafka-connect/kafka-connect-s2i.yaml
  2. Once the cluster is deployed, a new build can be triggered from the command-line by creating a directory with Kafka Connect plugins:

    $ 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. Start a new image build using the prepared directory:

    oc start-build <my-connect-cluster-connect> --from-dir ./<my-plugins>/
    Note
    The name of the build will be changed according to the cluster name of the deployed Kafka Connect cluster.
  4. Once the build is finished, the new image will be used automatically by the Kafka Connect deployment.

2.5. Deploying example clients

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

Procedure
  1. Deploy the producer.

    oc run kafka-producer -ti --image=strimzi/kafka:latest --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.

    oc run kafka-consumer -ti --image=strimzi/kafka:latest --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.

2.6. Topic Operator

2.6.1. What the Topic Operator does

The Topic Operator provides a way of managing topics in a Kafka cluster via OpenShift or Kubernetes resources.

Topic Operator

The role of the Topic Operator is to keep a set of KafkaTopic OpenShift or 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

  • if a KafkaTopic is deleted, the operator will delete the topic it describes

  • if a KafkaTopic is 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

  • if a topic is deleted from the Kafka cluster, the operator will create the KafkaTopic describing it

  • if a topic in the Kafka cluster is changed, 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 be 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.

2.6.2. Deploying the Topic Operator using the Cluster Operator

Prerequisites
  • A running Cluster Operator

  • A Kafka resource to be created or updated

Procedure
  1. Topic Operator can be included in the Entity Operator. Edit the Kafka resource ensuring it has a Kafka.spec.entityOperator object that configures the Entity Operator.

  2. Create or update the Kafka resource in OpenShift or Kubernetes.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc 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.

2.7. User Operator

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

2.7.1. What the User Operator does

The User Operator manages Kafka users for a Kafka cluster by watching for KafkaUser OpenShift or Kubernetes 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 OpenShift or 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 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.

2.7.2. 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 OpenShift or Kubernetes.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc 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.

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

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 OpenShift or Kubernetes resources making up the Kafka cluster. This provides a convenient mechanism for those resources to be labelled in whatever way the user requires.

3.1.1. Kafka and Zookeeper storage

Kafka brokers and Zookeeper are stateful applications. They need to store data on disks. Strimzi allows you to configure the type of storage, which they want to use for Kafka and Zookeeper. Storage configuration is mandatory and has to be specified in every Kafka resource.

Important
Once the Kafka cluster is deployed, the storage cannot be changed.
Storage

Storage can be configured using the storage property in following resources:

  • Kafka.spec.kafka

  • Kafka.spec.zookeeper

Strimzi supports two types of storage:

  • Ephemeral

  • Persistent

The type of storage is specified in the type field.

Ephemeral storage

Ephemeral storage is using the `emptyDir` volumes to store tha 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/v1alpha1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    storage:
      type: ephemeral
    # ...
  zookeeper:
    # ...
    storage:
      type: ephemeral
    # ...
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:

size (required)

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

class (optional)

The OpenShift or Kubernetes Storage Class to use for dynamic volume provisioning.

selector (optional)

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

delete-claim (optional)

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

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 in order to select a specific labelled persistent volume which provides some needed features like an SSD.

Example fragment of persistent storage configuration with selector
# ...
storage:
  type: persistent-claim
  size: 1Gi
  selector:
    matchLabels:
      "hdd-type": "ssd"
  deleteClaim: true
# ...
Persistent Volume Claim naming

When the persistent storage is used, it will create Persistent Volume Claims with 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].

Additional resources

3.1.2. Replicas

Kafka cluster can run with many brokers and Kafka brokers can run with various numbers of nodes. The number of brokers used for the Kafka cluster is defined in the Kafka resource. The best number of brokers for your cluster has to be determined based on your specific use case.

Number of Kafka broker nodes

Number of Kafka broker nodes can be configured using the replicas property in Kafka.spec.kafka.

An example showing replicas configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    replicas: 3
    # ...
  zookeeper:
    # ...
Changing number of replicas
Prerequisites
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

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

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        replicas: 3
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.1.3. Kafka broker configuration

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

The only options which cannot be configured are those 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

Kafka broker can be configured using the config property in Kafka.spec.kafka.

This property should contain the Kafka broker 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 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:

  • 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

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 Cluster Operator log file. All other options will be passed to Kafka.

Important
The Cluster Operator does not validate keys or values in the provided config object. When invalid configuration is provided, the Kafka cluster might not start or might become unstable. In such cases, the configuration in the Kafka.spec.kafka.config object should be fixed and the cluster operator will roll out the new configuration to all Kafka brokers.
An example showing Kafka broker configuration
apiVersion: kafka.strimzi.io/v1alpha1
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
Prerequisites
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

Procedure
  1. Edit the config property in the Kafka resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    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:
        # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.1.4. Kafka broker listeners

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

  • Plain listener on port 9092 (without encryption)

  • TLS listener on port 9093 (with encryption)

Kafka listeners

Kafka broker listeners can be configured using the listeners property in the Kafka.spec.kafka resource. The listeners property contains two sub-properties:

  • plain

  • tls

When these sub-properties are not defined, the listener will be disabled.

An example of listeners property with all listeners enabled
# ...
listeners:
  plain: {}
  tls: {}
# ...
An example of listeners property with only the plain listener enables
# ...
listeners:
  plain: {}
# ...

The listener sub-properties might also contain additional configuration. Currently, the only supported configuration is authentication on the tls listener.

An example of tls listener with TLS client authentication enabled
# ...
listeners:
  tls:
    authentication:
      type: tls
# ...
Configuring Kafka listeners
Prerequisites
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

Procedure
  1. Edit the listeners property in the Kafka.spec.kafka resource. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        listeners:
          plain: {}
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>
Additional resources

3.1.5. 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. When the authentication property is missing, no authentication will be enabled on given listener. The authentication mechanism which will be used is defined by the type field.

Currently, the only supported authentication mechanism is the TLS client authentication.

TLS Client Authentication

TLS Client authentication can be 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
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

Procedure
  1. Edit the listeners property in the Kafka.spec.kafka resource. Add the authentication field to the listeners where you want to enable authentication. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        listeners:
          tls:
            authentication:
              type: tls
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>
Additional resources
Authorization

Authorization can be configured using the authorization property in the Kafka.spec.kafka resource. When the authorization property is missing, no authorization will be enabled. When authorization is enabled it will be applied for all enabled listeners. The authorization method is defined by the type field.

Currently, the only supported authorization method is the Simple authorization.

Simple authorization

Simple authorization is using the SimpleAclAuthorizer plugin. SimpleAclAuthorizer is the default authorization plugin which is part of Apache Kafka. To enable simple authorization, the type field should be set to simple.

An example of Simple authorization
# ...
authorization:
  type: simple
# ...
Configuring authorization in Kafka brokers
Prerequisites
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

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

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        authorization:
          type: simple
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>
Additional resources

3.1.6. Replicas

Zookeeper clusters or ensembles usually run with an odd number of nodes and always requires the majority of the nodes to be available in order to maintain a quorum. Maintaining a quorum is important because when the Zookeeper cluster loses a quorum, it will stop responding to clients. As a result, a Zookeeper cluster without a quorum will cause the Kafka brokers to stop working as well. This is why having a stable and highly available Zookeeper cluster is very important for Strimzi.

A Zookeeper cluster is usually deployed with three, five, or seven nodes.

Three nodes

Zookeeper cluster consisting of three nodes 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 nodes

Zookeeper cluster consisting of five nodes requires at least three nodes to be up and running in order to maintain the quorum. It can tolerate two nodes being unavailable.

Seven nodes

Zookeeper cluster consisting of seven nodes 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/v1alpha1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
    replicas: 3
    # ...
Changing number of replicas
Prerequisites
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

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

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
      zookeeper:
        # ...
        replicas: 3
        # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.1.7. Zookeeper configuration

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

The only 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 can be configured using the config property in Kafka.spec.zookeeper. This property should contain the Zookeeper 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 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 will be ignored and a warning message will be printed to the Custer Operator log file. All other options will be 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/v1alpha1
kind: Kafka
spec:
  kafka:
    # ...
  zookeeper:
    # ...
    config:
      autopurge.snapRetainCount: 3
      autopurge.purgeInterval: 1
    # ...
Configuring Zookeeper
Prerequisites
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

Procedure
  1. Edit the config property in the Kafka resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
      zookeeper:
        # ...
        config:
          autopurge.snapRetainCount: 3
          autopurge.purgeInterval: 1
        # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.1.8. Entity Operator

The Entity Operator is responsible for managing different entities in a running Kafka cluster. The currently supported entities are:

Kafka topics

managed by the Topic Operator.

Kafka users

managed by the User Operator

Both Topic and User Operators can be deployed on their own. But the easiest way to deploy them is together with the Kafka cluster as part of the Entity Operator. The Entity Operator can include either one or both of them depending on the configuration. They will be automatically configured to manage the topics and users of the Kafka cluster with which they are deployed.

For more information about Topic Operator, see Topic Operator. For more information about how to use Topic Operator to create or delete topics, see Using the Topic Operator.

Configuration

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

The entityOperator property supports several sub-properties:

  • tlsSidecar

  • affinity

  • tolerations

  • topicOperator

  • userOperator

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 affinity and tolerations properties can be used to configure how OpenShift or Kubernetes schedules the Entity Operator pod. For more details about pod scheduling, see Configuring pod scheduling.

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

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

Example of basic configuration enabling both operators
apiVersion: kafka.strimzi.io/v1alpha1
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. Following options are supported:

watchedNamespace

The OpenShift or 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 is 90.

zookeeperSessionTimeoutSeconds

The Zookeeper session timeout in seconds. Default is 20 seconds.

topicMetadataMaxAttempts

The number of attempts for getting topics metadata from Kafka. The time between each attempt is defined as an exponential back-off. You might want to increase this value when topic creation could take more time due to its many partitions or replicas. Default is 6.

image

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

resources

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

logging

The logging property configures the logging of the Topic Operator For more details about logging configuration, see Logging.

Example of Topic Operator configuration
apiVersion: kafka.strimzi.io/v1alpha1
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. Following options are supported:

watchedNamespace

The OpenShift or 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 is 120.

zookeeperSessionTimeoutSeconds

The Zookeeper session timeout in seconds. Default is 6 seconds.

image

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

resources

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

logging

The logging property configures the logging of the User Operator. For more details about logging configuration, see Logging.

Example of Topic Operator configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
  zookeeper:
    # ...
  entityOperator:
    # ...
    userOperator:
      watchedNamespace: my-user-namespace
      reconciliationIntervalSeconds: 60
    # ...
Configuring Entity Operator
Prerequisites
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

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

    apiVersion: kafka.strimzi.io/v1alpha1
    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.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.1.9. CPU and memory resources

For every deployed container, Strimzi allows you to specify the resources which should be reserved for it and the maximum resources that can be consumed by it. Strimzi supports two types of resources:

  • Memory

  • CPU

Strimzi is using the OpenShift or Kubernetes syntax for specifying CPU and memory resources.

Resource limits and requests

Resource limits and requests can be configured using the resources property in 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

Resource requests

Requests specify the resources that will be reserved for a given container. Reserving the resources will ensure that they are always available.

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

Resource requests can be specified in the request property. The resource requests currently supported by Strimzi are memory and CPU. Memory is specified under the property memory. CPU is specified under the property cpu.

An example showing resource request configuration
# ...
resources:
  requests:
    cpu: 12
    memory: 64Gi
# ...

It is also possible to specify a resource request just for one of the resources:

An example showing resource request configuration with memory request only
# ...
resources:
  requests:
    memory: 64Gi
# ...

Or:

An example showing resource request configuration with CPU request only
# ...
resources:
  requests:
    cpu: 12
# ...
Resource limits

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

Resource limits can be specified in the limits property. The resource limits currently supported by Strimzi are memory and CPU. Memory is specified under the property memory. CPU is specified under the property cpu.

An example showing resource limits configuration
# ...
resources:
  limits:
    cpu: 12
    memory: 64Gi
# ...

It is also possible to specify the resource limit just for one of the resources:

An example showing resource limit configuration with memory request only
# ...
resources:
  limits:
    memory: 64Gi
# ...

Or:

An example showing resource limits configuration with CPU request only
# ...
resources:
  requests:
    cpu: 12
# ...
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.

An example of using different CPU units
# ...
resources:
  requests:
    cpu: 500m
  limits:
    cpu: 2.5
# ...
Note
The amount of computing power of 1 CPU core might differ depending on the platform where the OpenShift or Kubernetes is deployed.

For more details about the CPU specification, see the Meaning of CPU website.

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
# ...

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

Additional resources
Configuring resource requests and limits
Prerequisites
  • An OpenShift or 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        resources:
          requests:
            cpu: 8
            memory: 64Gi
          limits:
            cpu: 12
            memory: 128Gi
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.1.10. Logging

Logging enables you to diagnose error and performance issues of Strimzi. For the logging, various logger implementations are used. Kafka and Zookeeper use log4j logger and Topic Operator, User Operator, and other components use log4j2 logger.

This section provides information about different loggers and describes how to configure log levels.

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

Using inline logging setting
Procedure
  1. Edit the YAML file to specify the loggers and their level for the required components. For example:

      logging:
        type: inline
        loggers:
          logger.name: "INFO"

    In the above example, the log level is set to INFO. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. For more information about the log levels, see link: log4j manual.

  2. Create or update the Kafka resource in OpenShift or Kubernetes.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>
Using external ConfigMap for logging setting
Procedure
  1. Edit the YAML file to specify the name of the ConfigMap which should be used for the required components. For example:

      logging:
        type: external
        name: customConfigMap

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

  2. Create or update the Kafka resource in OpenShift or Kubernetes.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>
Loggers

Strimzi consists of several components. Each component has its own loggers and is configurable. This section provides information about loggers of various components.

Components and their loggers are listed below.

  • Kafka

    • 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

  • Kafka Connect

    • connect.root.logger.level

    • log4j.logger.org.apache.zookeeper

    • log4j.logger.org.I0Itec.zkclient

    • log4j.logger.org.reflections

  • Topic Operator

    • rootLogger.level

3.1.11. Kafka rack awareness

Strimzi allows you to enable Kafka rack awareness. The rack awareness feature 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 actual rack in your data center.
Kafka rack awareness

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 OpenShift or Kubernetes cluster nodes. The label is used by OpenShift or Kubernetes when scheduling the Kafka broker pods to nodes. If the OpenShift or Kubernetes cluster is running on a cloud provider platform, that label should represent the availability zone where the node is running. If the OpenShift or 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 labelled with failure-domain.beta.kubernetes.io/zone that can be easily used as the topologyKey value. This will have the effect of spreading the broker pods across zones, and also setting the brokers' broker.rack configuration parameter inside Kafka broker.

Example of a Kafka resource with the rack feature enabled
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    rack:
      topologyKey: failure-domain.beta.kubernetes.io/zone
    # ...

In the above example, the failure-domain.beta.kubernetes.io/zone node label will be used for scheduling Kafka broker Pods. Consult your OpenShift or Kubernetes administrator about the label which should be used.

Configuring init container image

When the Kafka rack feature is enabled, Kafka broker pods will use init container which will collect the labels from the OpenShift or Kubernetes cluster nodes. The container image which will be used for this container can be configured using the brokerRackInitImage property in Kafka.spec.kafka. When the brokerRackInitImage field is missing, the following images will be used (in the order of priority):

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

  2. strimzi/kafka-init:latest container image.

Warning
Overriding container images is recommended only in 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 brokerRackInitImage configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    brokerRackInitImage: my-org/my-image:latest
    # ...
Configuring rack awareness in Kafka brokers
Prerequisites
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

Procedure
  1. Find out which node labels represent the zone / rack into which the node is deployed.

  2. Edit the rack property in the Kafka resource. Use the label as the topology key. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        rack:
          topologyKey: failure-domain.beta.kubernetes.io/zone
        # ...
  3. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.1.12. Healthchecks

Healthchecks are periodical tests which verify that the application’s health. When the Healthcheck fails, OpenShift or Kubernetes can assume that the application is not healthy and attempt to fix it. OpenShift or 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.zookeeper

  • KafkaConnect.spec

  • KafkaConnectS2I.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
  • An OpenShift or 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/v1alpha1
    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.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.1.13. 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 can be 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/v1alpha1
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/v1alpha1
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
  • An OpenShift or 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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
      zookeeper:
        # ...
        metrics:
          lowercaseOutputName: true
        # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.1.14. JVM Options

Apache Kafka and Apache Zookeeper are running inside of a Java Virtual Machine (JVM). JVM has many configuration options to optimize the performance for different platforms and architectures. Strimzi allows configuring 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:

-Xmx

-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 OpenShift or Kubernetes convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes

The default value used for -Xmx depends on whether there is a memory request configured for the container:

  • If there is a memory request, the JVM’s maximum memory will be set to a value corresponding to the limit.

  • When there is no memory request, the JVM’s maximum memory will be set according to the RAM available to the container.

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 OpenShift or Kubernetes memory limit, it is possible that the container will be killed should the OpenShift or Kubernetes node experience memory pressure (from other Pods running on it).

  • If -Xmx is set without also setting an appropriate OpenShift or 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
# ...
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.

-Xms

-Xms configures the initial 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 OpenShift or Kubernetes convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes

Setting the same value for initial 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.

Example fragment configuring -Xms
# ...
jvmOptions:
  "-Xmx": "2g"
  "-Xms": "2g"
# ...
-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.
Configuring JVM options
Prerequisites
  • An OpenShift or 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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        jvmOptions:
          "-Xmx": "8g"
          "-Xms": "8g"
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.1.15. 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.

Configuring container images

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

The image property can be used to configure the container image which will be used. When the image field is missing, the following images will be used (in the order of priority):

  • For Kafka brokers:

    • Container image specified in the STRIMZI_DEFAULT_KAFKA_IMAGE environment variable from the Cluster Operator configuration.

    • strimzi/kafka:latest container image.

  • For Kafka broker TLS sidecar:

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

    • strimzi/kafka-stunnel:latest container image.

  • For Zookeeper nodes:

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

    • strimzi/zookeeper:latest container image.

  • For Zookeeper node TLS sidecar:

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

    • strimzi/zookeeper-stunnel:latest container image.

  • For Topic Operator:

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

    • strimzi/topic-operator:latest container image.

  • For User Operator:

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

    • strimzi/user-operator:latest container image.

  • For Entity Operator TLS sidecar:

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

    • strimzi/entity-operator-stunnel:latest container image.

  • For Kafka Connect:

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

    • strimzi/kafka-connect:latest container image.

  • For Kafka Connect with Source2image support:

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

    • strimzi/kafka-connect-s2i:latest 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 TLS sidecar configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    image: my-org/my-image:latest
    # ...
  zookeeper:
    # ...
Configuring container images
Prerequisites
  • An OpenShift or 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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        image: my-org/my-image:latest
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.1.16. TLS sidecar

Sidecar is a container which is running in a pod and serves an auxiliary purpose. The purpose of the TLS sidecar is to encrypt or decrypt the communication between Strimzi components and Zookeeper since Zookeeper does not support TLS encryption natively. Zookeeper does not support TLS encryption natively. Therefore Strimzi uses the sidecar to add the TLS support.

The TLS sidecar is currrently being used in:

  • Kafka brokers

  • Zookeeper

  • 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 two additional options:

  • image

  • resources

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.

Example of TLS sidecar configuration
apiVersion: kafka.strimzi.io/v1alpha1
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
    # ...
  zookeeper:
    # ...
Configuring TLS sidecar
Prerequisites
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

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

    apiVersion: kafka.strimzi.io/v1alpha1
    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.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.1.17. Configuring pod scheduling

Important
When two application are scheduled to the same OpenShift or 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

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

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

Configuring pod anti-affinity in Kafka components
Prerequisites
  • An OpenShift or 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        affinity:
          podAntiAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              - labelSelector:
                  matchExpressions:
                    - key: application
                      operator: In
                      values:
                        - postgresql
                        - mongodb
                topologyKey: "kubernetes.io/hostname"
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>
Scheduling pods to specific nodes
Node scheduling

The OpenShift or 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.

OpenShift or 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

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

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

Configuring node affinity in Kafka components
Prerequisites
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

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

    On Kubernetes this can be done using kubectl label:

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

    On OpenShift this can be done using oc label:

    oc 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        affinity:
          nodeAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
                - matchExpressions:
                  - key: node-type
                    operator: In
                    values:
                    - fast-network
        # ...
      zookeeper:
        # ...
  3. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>
Using dedicated nodes
Dedicated nodes

Cluster administrators can mark selected OpenShift or 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

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

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

Tolerations

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

  • Kafka.spec.kafka

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

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

Setting up dedicated nodes and scheduling pods on them
Prerequisites
  • An OpenShift or 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

    On Kubernetes this can be done using kubectl taint:

    kubectl taint node <your-node> -l dedicated=Kafka:NoSchedule

    On OpenShift this can be done using oc adm taint:

    oc adm taint node <your-node> -l dedicated=Kafka:NoSchedule
  4. Additionally, add a label to the selected nodes as well.

    On Kubernetes this can be done using kubectl label:

    kubectl label node <your-node> -l dedicated=Kafka

    On OpenShift this can be done using oc label:

    oc label node <your-node> -l dedicated=Kafka
  5. Edit the affinity and tolerations properties in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        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.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.1.18. List of resources created as part of Kafka cluster

The following resources will created by the Cluster Operator in the OpenShift or 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-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>-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>-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>-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.

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-<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 OpenShift or Kubernetes resources making up the Kafka Connect cluster. This provides a convenient mechanism for those resources to be labelled in whatever way the user requires.

3.2.1. Replicas

Kafka Connect clusters can run with a different number of nodes. The number of nodes is defined in the KafkaConnect and KafkaConnectS2I resources. Running Kafka Connect cluster with multiple nodes can provide better availability and scalability. However, when running Kafka Connect on OpenShift or Kubernetes it is not absolutely necessary to run multiple nodes of Kafka Connect for high availability. When the node where Kafka Connect is deployed to crashes, OpenShift or Kubernetes will automatically take care of rescheduling 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 already up and running.

Number of Kafka Connect nodes

Number of Kafka broker nodes can be configured using the replicas property in KafkaConnect.spec and KafkaConnectS2I.spec.

An example showing replicas configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaConnectS2I
metadata:
  name: my-cluster
spec:
  # ...
  replicas: 3
  # ...
Changing number of replicas
Prerequisites
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

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

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: KafkaConnectS2I
    metadata:
      name: my-cluster
    spec:
      # ...
      replicas: 3
      # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.2.2. Bootstrap servers

Kafka Connect cluster always works together with a Kafka cluster. The Kafka cluster is specified in the form of a list of bootstrap servers. On OpenShift or Kubernetes, the 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.

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

Bootstrap servers

List of bootstrap servers can be configured in the bootstrapServers property in KafkaConnect.spec and KafkaConnectS2I.spec. bootstrapServers should be a comma separated list containing one or mode Kafka brokers or a service pointing to Kafka brokers. The bootstrap servers should be specified as a <hostname>:<port> pairs.

An example showing bootstrap servers configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  bootstrapServers: my-cluster-kafka-bootstrap:9092
  # ...
Configuring bootstrap servers
Prerequisites
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

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

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: KafkaConnect
    metadata:
      name: my-cluster
    spec:
      # ...
      bootstrapServers: my-cluster-kafka-bootstrap:9092
      # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.2.3. Connecting to Kafka brokers using TLS

By default, Kafka Connect will try to connect to Kafka brokers using a plain text connection. If you would prefer to use TLS additional configuration will be necessary.

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 should be stored in X509 format.

An example showing TLS configuration with multiple certificates
apiVersion: kafka.strimzi.io/v1alpha1
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/v1alpha1
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
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

Procedure
  1. Find out the name of the secret with the certificate which should be used for TLS Server Authentication and the key under which the certificate is stored in the secret. If such secret does not exist yet, prepare the certificate in a file and create the secret.

    On Kubernetes this can be done using kubectl create:

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

    On OpenShift this can be done using oc create:

    oc 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/v1alpha1
    kind: KafkaConnect
    metadata:
      name: my-connect
    spec:
      # ...
      tls:
        trustedCertificates:
          - secretName: my-cluster-cluster-cert
            certificate: ca.crt
      # ...
  3. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc 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 any authentication. Authentication can be enabled in the KafkaConnect and KafkaConnectS2I resources.

Authentication support in Kafka Connect

Authentication can be configured in 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. Currently, the only supported authentication type is TLS client authentication.

TLS Client Authentication

To use the TLS client authentication, set the type property to the value tls. TLS client authentication is using TLS certificate to authenticate. The certificate has to be specified in the certificateAndKey property. It is always loaded from an OpenShift or Kubernetes secret. Inside the secret, it has to be stored in the X509 format under two different keys: for public and private keys.

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 showing TLS client authentication configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  authentication:
    type: tls
    certificateAndKey:
      secretName: my-secret
      certificate: public.crt
      key: private.key
  # ...
Configuring TLS client authentication in Kafka Connect
Prerequisites
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

Procedure
  1. Find out the name of the secret with the public and private keys which should be used for TLS Client Authentication and the keys under which they are stored in the secret. If such secret does not exist yet, prepare the keys in a file and create the secret.

    On Kubernetes this can be done using kubectl create:

    kubectl create secret generic <my-secret> --from-file=<my-public.crt> --from-file=<my-private.key>

    On OpenShift this can be done using oc create:

    oc create secret generic <my-secret> --from-file=<my-public.crt> --from-file=<my-private.key>
  2. Edit the tls property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    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.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.2.5. Kafka Connect configuration

Strimzi allows you to customize the configuration of Apache Kafka Connect nodes by editing most of the options 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)

  • Listener / REST interface configuration

  • Plugin path configuration

These options are automatically configured by Strimzi.

Kafka Connect configuration

Kafka Connect can be configured using the config property in KafkaConnect.spec and KafkaConnectS2I.spec. This property should contain the Kafka Connect 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 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.

  • listeners

  • plugin.path

  • rest.

  • bootstrap.servers

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 Connect.

Important
The Cluster Operator does not validate keys or values in the provided config object. When an invalid configuration is provided, the Kafka Connect cluster might not start or might become unstable. In such cases, the configuration in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config object should be fixed and the cluster operator will roll out the new configuration to all Kafka Connect nodes.

Selected 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

  • internal.key.converter with default value org.apache.kafka.connect.json.JsonConverter

  • internal.value.converter with default value org.apache.kafka.connect.json.JsonConverter

  • internal.key.converter.schemas.enable with default value false

  • internal.value.converter.schemas.enable with default value false

These options will be automatically configured in case they are not present in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config properties.

An example showing Kafka Connect configuration
apiVersion: kafka.strimzi.io/v1alpha1
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
    internal.key.converter: org.apache.kafka.connect.json.JsonConverter
    internal.value.converter: org.apache.kafka.connect.json.JsonConverter
    internal.key.converter.schemas.enable: false
    internal.value.converter.schemas.enable: false
    config.storage.replication.factor: 3
    offset.storage.replication.factor: 3
    status.storage.replication.factor: 3
  # ...
Configuring Kafka Connect
Prerequisites
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

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

    apiVersion: kafka.strimzi.io/v1alpha1
    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
        internal.key.converter: org.apache.kafka.connect.json.JsonConverter
        internal.value.converter: org.apache.kafka.connect.json.JsonConverter
        internal.key.converter.schemas.enable: false
        internal.value.converter.schemas.enable: false
        config.storage.replication.factor: 3
        offset.storage.replication.factor: 3
        status.storage.replication.factor: 3
      # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.2.6. CPU and memory resources

For every deployed container, Strimzi allows you to specify the resources which should be reserved for it and the maximum resources that can be consumed by it. Strimzi supports two types of resources:

  • Memory

  • CPU

Strimzi is using the OpenShift or Kubernetes syntax for specifying CPU and memory resources.

Resource limits and requests

Resource limits and requests can be configured using the resources property in 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

Resource requests

Requests specify the resources that will be reserved for a given container. Reserving the resources will ensure that they are always available.

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

Resource requests can be specified in the request property. The resource requests currently supported by Strimzi are memory and CPU. Memory is specified under the property memory. CPU is specified under the property cpu.

An example showing resource request configuration
# ...
resources:
  requests:
    cpu: 12
    memory: 64Gi
# ...

It is also possible to specify a resource request just for one of the resources:

An example showing resource request configuration with memory request only
# ...
resources:
  requests:
    memory: 64Gi
# ...

Or:

An example showing resource request configuration with CPU request only
# ...
resources:
  requests:
    cpu: 12
# ...
Resource limits

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

Resource limits can be specified in the limits property. The resource limits currently supported by Strimzi are memory and CPU. Memory is specified under the property memory. CPU is specified under the property cpu.

An example showing resource limits configuration
# ...
resources:
  limits:
    cpu: 12
    memory: 64Gi
# ...

It is also possible to specify the resource limit just for one of the resources:

An example showing resource limit configuration with memory request only
# ...
resources:
  limits:
    memory: 64Gi
# ...

Or:

An example showing resource limits configuration with CPU request only
# ...
resources:
  requests:
    cpu: 12
# ...
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.

An example of using different CPU units
# ...
resources:
  requests:
    cpu: 500m
  limits:
    cpu: 2.5
# ...
Note
The amount of computing power of 1 CPU core might differ depending on the platform where the OpenShift or Kubernetes is deployed.

For more details about the CPU specification, see the Meaning of CPU website.

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
# ...

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

Additional resources
Configuring resource requests and limits
Prerequisites
  • An OpenShift or 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        resources:
          requests:
            cpu: 8
            memory: 64Gi
          limits:
            cpu: 12
            memory: 128Gi
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.2.7. Logging

Logging enables you to diagnose error and performance issues of Strimzi. For the logging, various logger implementations are used. Kafka and Zookeeper use log4j logger and Topic Operator, User Operator, and other components use log4j2 logger.

This section provides information about different loggers and describes how to configure log levels.

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

Using inline logging setting
Procedure
  1. Edit the YAML file to specify the loggers and their level for the required components. For example:

      logging:
        type: inline
        loggers:
          logger.name: "INFO"

    In the above example, the log level is set to INFO. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. For more information about the log levels, see link: log4j manual.

  2. Create or update the Kafka resource in OpenShift or Kubernetes.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>
Using external ConfigMap for logging setting
Procedure
  1. Edit the YAML file to specify the name of the ConfigMap which should be used for the required components. For example:

      logging:
        type: external
        name: customConfigMap

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

  2. Create or update the Kafka resource in OpenShift or Kubernetes.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>
Loggers

Strimzi consists of several components. Each component has its own loggers and is configurable. This section provides information about loggers of various components.

Components and their loggers are listed below.

  • Kafka

    • 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

  • Kafka Connect

    • connect.root.logger.level

    • log4j.logger.org.apache.zookeeper

    • log4j.logger.org.I0Itec.zkclient

    • log4j.logger.org.reflections

  • Topic Operator

    • rootLogger.level

3.2.8. Healthchecks

Healthchecks are periodical tests which verify that the application’s health. When the Healthcheck fails, OpenShift or Kubernetes can assume that the application is not healthy and attempt to fix it. OpenShift or 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.zookeeper

  • KafkaConnect.spec

  • KafkaConnectS2I.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
  • An OpenShift or 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/v1alpha1
    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.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc 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 can be 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/v1alpha1
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/v1alpha1
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
  • An OpenShift or 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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
      zookeeper:
        # ...
        metrics:
          lowercaseOutputName: true
        # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.2.10. JVM Options

Apache Kafka and Apache Zookeeper are running inside of a Java Virtual Machine (JVM). JVM has many configuration options to optimize the performance for different platforms and architectures. Strimzi allows configuring 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:

-Xmx

-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 OpenShift or Kubernetes convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes

The default value used for -Xmx depends on whether there is a memory request configured for the container:

  • If there is a memory request, the JVM’s maximum memory will be set to a value corresponding to the limit.

  • When there is no memory request, the JVM’s maximum memory will be set according to the RAM available to the container.

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 OpenShift or Kubernetes memory limit, it is possible that the container will be killed should the OpenShift or Kubernetes node experience memory pressure (from other Pods running on it).

  • If -Xmx is set without also setting an appropriate OpenShift or 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
# ...
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.

-Xms

-Xms configures the initial 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 OpenShift or Kubernetes convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes

Setting the same value for initial 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.

Example fragment configuring -Xms
# ...
jvmOptions:
  "-Xmx": "2g"
  "-Xms": "2g"
# ...
-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.
Configuring JVM options
Prerequisites
  • An OpenShift or 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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        jvmOptions:
          "-Xmx": "8g"
          "-Xms": "8g"
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc 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.

Configuring container images

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

The image property can be used to configure the container image which will be used. When the image field is missing, the following images will be used (in the order of priority):

  • For Kafka brokers:

    • Container image specified in the STRIMZI_DEFAULT_KAFKA_IMAGE environment variable from the Cluster Operator configuration.

    • strimzi/kafka:latest container image.

  • For Kafka broker TLS sidecar:

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

    • strimzi/kafka-stunnel:latest container image.

  • For Zookeeper nodes:

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

    • strimzi/zookeeper:latest container image.

  • For Zookeeper node TLS sidecar:

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

    • strimzi/zookeeper-stunnel:latest container image.

  • For Topic Operator:

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

    • strimzi/topic-operator:latest container image.

  • For User Operator:

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

    • strimzi/user-operator:latest container image.

  • For Entity Operator TLS sidecar:

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

    • strimzi/entity-operator-stunnel:latest container image.

  • For Kafka Connect:

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

    • strimzi/kafka-connect:latest container image.

  • For Kafka Connect with Source2image support:

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

    • strimzi/kafka-connect-s2i:latest 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 TLS sidecar configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    image: my-org/my-image:latest
    # ...
  zookeeper:
    # ...
Configuring container images
Prerequisites
  • An OpenShift or 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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        image: my-org/my-image:latest
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.2.12. Configuring pod scheduling

Important
When two application are scheduled to the same OpenShift or 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

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

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

Configuring pod anti-affinity in Kafka components
Prerequisites
  • An OpenShift or 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        affinity:
          podAntiAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              - labelSelector:
                  matchExpressions:
                    - key: application
                      operator: In
                      values:
                        - postgresql
                        - mongodb
                topologyKey: "kubernetes.io/hostname"
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>
Scheduling pods to specific nodes
Node scheduling

The OpenShift or 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.

OpenShift or 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

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

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

Configuring node affinity in Kafka components
Prerequisites
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

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

    On Kubernetes this can be done using kubectl label:

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

    On OpenShift this can be done using oc label:

    oc 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        affinity:
          nodeAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
                - matchExpressions:
                  - key: node-type
                    operator: In
                    values:
                    - fast-network
        # ...
      zookeeper:
        # ...
  3. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>
Using dedicated nodes
Dedicated nodes

Cluster administrators can mark selected OpenShift or 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

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

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

Tolerations

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

  • Kafka.spec.kafka

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

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

Setting up dedicated nodes and scheduling pods on them
Prerequisites
  • An OpenShift or 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

    On Kubernetes this can be done using kubectl taint:

    kubectl taint node <your-node> -l dedicated=Kafka:NoSchedule

    On OpenShift this can be done using oc adm taint:

    oc adm taint node <your-node> -l dedicated=Kafka:NoSchedule
  4. Additionally, add a label to the selected nodes as well.

    On Kubernetes this can be done using kubectl label:

    kubectl label node <your-node> -l dedicated=Kafka

    On OpenShift this can be done using oc label:

    oc label node <your-node> -l dedicated=Kafka
  5. Edit the affinity and tolerations properties in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        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.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.2.13. List of resources created as part of Kafka Connect cluster

The following resources will created by the Cluster Operator in the OpenShift or 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.

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 OpenShift or Kubernetes resources making up the Kafka Connect cluster with Source2Image support. This provides a convenient mechanism for those resources to be labelled in whatever way the user requires.

3.3.1. Replicas

Kafka Connect clusters can run with a different number of nodes. The number of nodes is defined in the KafkaConnect and KafkaConnectS2I resources. Running Kafka Connect cluster with multiple nodes can provide better availability and scalability. However, when running Kafka Connect on OpenShift or Kubernetes it is not absolutely necessary to run multiple nodes of Kafka Connect for high availability. When the node where Kafka Connect is deployed to crashes, OpenShift or Kubernetes will automatically take care of rescheduling 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 already up and running.

Number of Kafka Connect nodes

Number of Kafka broker nodes can be configured using the replicas property in KafkaConnect.spec and KafkaConnectS2I.spec.

An example showing replicas configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaConnectS2I
metadata:
  name: my-cluster
spec:
  # ...
  replicas: 3
  # ...
Changing number of replicas
Prerequisites
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

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

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: KafkaConnectS2I
    metadata:
      name: my-cluster
    spec:
      # ...
      replicas: 3
      # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.3.2. Bootstrap servers

Kafka Connect cluster always works together with a Kafka cluster. The Kafka cluster is specified in the form of a list of bootstrap servers. On OpenShift or Kubernetes, the 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.

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

Bootstrap servers

List of bootstrap servers can be configured in the bootstrapServers property in KafkaConnect.spec and KafkaConnectS2I.spec. bootstrapServers should be a comma separated list containing one or mode Kafka brokers or a service pointing to Kafka brokers. The bootstrap servers should be specified as a <hostname>:<port> pairs.

An example showing bootstrap servers configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  bootstrapServers: my-cluster-kafka-bootstrap:9092
  # ...
Configuring bootstrap servers
Prerequisites
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

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

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: KafkaConnect
    metadata:
      name: my-cluster
    spec:
      # ...
      bootstrapServers: my-cluster-kafka-bootstrap:9092
      # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.3.3. Connecting to Kafka brokers using TLS

By default, Kafka Connect will try to connect to Kafka brokers using a plain text connection. If you would prefer to use TLS additional configuration will be necessary.

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 should be stored in X509 format.

An example showing TLS configuration with multiple certificates
apiVersion: kafka.strimzi.io/v1alpha1
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/v1alpha1
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
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

Procedure
  1. Find out the name of the secret with the certificate which should be used for TLS Server Authentication and the key under which the certificate is stored in the secret. If such secret does not exist yet, prepare the certificate in a file and create the secret.

    On Kubernetes this can be done using kubectl create:

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

    On OpenShift this can be done using oc create:

    oc 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/v1alpha1
    kind: KafkaConnect
    metadata:
      name: my-connect
    spec:
      # ...
      tls:
        trustedCertificates:
          - secretName: my-cluster-cluster-cert
            certificate: ca.crt
      # ...
  3. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc 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 any authentication. Authentication can be enabled in the KafkaConnect and KafkaConnectS2I resources.

Authentication support in Kafka Connect

Authentication can be configured in 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. Currently, the only supported authentication type is TLS client authentication.

TLS Client Authentication

To use the TLS client authentication, set the type property to the value tls. TLS client authentication is using TLS certificate to authenticate. The certificate has to be specified in the certificateAndKey property. It is always loaded from an OpenShift or Kubernetes secret. Inside the secret, it has to be stored in the X509 format under two different keys: for public and private keys.

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 showing TLS client authentication configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaConnect
metadata:
  name: my-cluster
spec:
  # ...
  authentication:
    type: tls
    certificateAndKey:
      secretName: my-secret
      certificate: public.crt
      key: private.key
  # ...
Configuring TLS client authentication in Kafka Connect
Prerequisites
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

Procedure
  1. Find out the name of the secret with the public and private keys which should be used for TLS Client Authentication and the keys under which they are stored in the secret. If such secret does not exist yet, prepare the keys in a file and create the secret.

    On Kubernetes this can be done using kubectl create:

    kubectl create secret generic <my-secret> --from-file=<my-public.crt> --from-file=<my-private.key>

    On OpenShift this can be done using oc create:

    oc create secret generic <my-secret> --from-file=<my-public.crt> --from-file=<my-private.key>
  2. Edit the tls property in the KafkaConnect or KafkaConnectS2I resource. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    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.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.3.5. Kafka Connect configuration

Strimzi allows you to customize the configuration of Apache Kafka Connect nodes by editing most of the options 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)

  • Listener / REST interface configuration

  • Plugin path configuration

These options are automatically configured by Strimzi.

Kafka Connect configuration

Kafka Connect can be configured using the config property in KafkaConnect.spec and KafkaConnectS2I.spec. This property should contain the Kafka Connect 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 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.

  • listeners

  • plugin.path

  • rest.

  • bootstrap.servers

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 Connect.

Important
The Cluster Operator does not validate keys or values in the provided config object. When an invalid configuration is provided, the Kafka Connect cluster might not start or might become unstable. In such cases, the configuration in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config object should be fixed and the cluster operator will roll out the new configuration to all Kafka Connect nodes.

Selected 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

  • internal.key.converter with default value org.apache.kafka.connect.json.JsonConverter

  • internal.value.converter with default value org.apache.kafka.connect.json.JsonConverter

  • internal.key.converter.schemas.enable with default value false

  • internal.value.converter.schemas.enable with default value false

These options will be automatically configured in case they are not present in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config properties.

An example showing Kafka Connect configuration
apiVersion: kafka.strimzi.io/v1alpha1
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
    internal.key.converter: org.apache.kafka.connect.json.JsonConverter
    internal.value.converter: org.apache.kafka.connect.json.JsonConverter
    internal.key.converter.schemas.enable: false
    internal.value.converter.schemas.enable: false
    config.storage.replication.factor: 3
    offset.storage.replication.factor: 3
    status.storage.replication.factor: 3
  # ...
Configuring Kafka Connect
Prerequisites
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

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

    apiVersion: kafka.strimzi.io/v1alpha1
    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
        internal.key.converter: org.apache.kafka.connect.json.JsonConverter
        internal.value.converter: org.apache.kafka.connect.json.JsonConverter
        internal.key.converter.schemas.enable: false
        internal.value.converter.schemas.enable: false
        config.storage.replication.factor: 3
        offset.storage.replication.factor: 3
        status.storage.replication.factor: 3
      # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.3.6. CPU and memory resources

For every deployed container, Strimzi allows you to specify the resources which should be reserved for it and the maximum resources that can be consumed by it. Strimzi supports two types of resources:

  • Memory

  • CPU

Strimzi is using the OpenShift or Kubernetes syntax for specifying CPU and memory resources.

Resource limits and requests

Resource limits and requests can be configured using the resources property in 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

Resource requests

Requests specify the resources that will be reserved for a given container. Reserving the resources will ensure that they are always available.

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

Resource requests can be specified in the request property. The resource requests currently supported by Strimzi are memory and CPU. Memory is specified under the property memory. CPU is specified under the property cpu.

An example showing resource request configuration
# ...
resources:
  requests:
    cpu: 12
    memory: 64Gi
# ...

It is also possible to specify a resource request just for one of the resources:

An example showing resource request configuration with memory request only
# ...
resources:
  requests:
    memory: 64Gi
# ...

Or:

An example showing resource request configuration with CPU request only
# ...
resources:
  requests:
    cpu: 12
# ...
Resource limits

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

Resource limits can be specified in the limits property. The resource limits currently supported by Strimzi are memory and CPU. Memory is specified under the property memory. CPU is specified under the property cpu.

An example showing resource limits configuration
# ...
resources:
  limits:
    cpu: 12
    memory: 64Gi
# ...

It is also possible to specify the resource limit just for one of the resources:

An example showing resource limit configuration with memory request only
# ...
resources:
  limits:
    memory: 64Gi
# ...

Or:

An example showing resource limits configuration with CPU request only
# ...
resources:
  requests:
    cpu: 12
# ...
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.

An example of using different CPU units
# ...
resources:
  requests:
    cpu: 500m
  limits:
    cpu: 2.5
# ...
Note
The amount of computing power of 1 CPU core might differ depending on the platform where the OpenShift or Kubernetes is deployed.

For more details about the CPU specification, see the Meaning of CPU website.

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
# ...

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

Additional resources
Configuring resource requests and limits
Prerequisites
  • An OpenShift or 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        resources:
          requests:
            cpu: 8
            memory: 64Gi
          limits:
            cpu: 12
            memory: 128Gi
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.3.7. Logging

Logging enables you to diagnose error and performance issues of Strimzi. For the logging, various logger implementations are used. Kafka and Zookeeper use log4j logger and Topic Operator, User Operator, and other components use log4j2 logger.

This section provides information about different loggers and describes how to configure log levels.

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

Using inline logging setting
Procedure
  1. Edit the YAML file to specify the loggers and their level for the required components. For example:

      logging:
        type: inline
        loggers:
          logger.name: "INFO"

    In the above example, the log level is set to INFO. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. For more information about the log levels, see link: log4j manual.

  2. Create or update the Kafka resource in OpenShift or Kubernetes.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>
Using external ConfigMap for logging setting
Procedure
  1. Edit the YAML file to specify the name of the ConfigMap which should be used for the required components. For example:

      logging:
        type: external
        name: customConfigMap

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

  2. Create or update the Kafka resource in OpenShift or Kubernetes.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>
Loggers

Strimzi consists of several components. Each component has its own loggers and is configurable. This section provides information about loggers of various components.

Components and their loggers are listed below.

  • Kafka

    • 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

  • Kafka Connect

    • connect.root.logger.level

    • log4j.logger.org.apache.zookeeper

    • log4j.logger.org.I0Itec.zkclient

    • log4j.logger.org.reflections

  • Topic Operator

    • rootLogger.level

3.3.8. Healthchecks

Healthchecks are periodical tests which verify that the application’s health. When the Healthcheck fails, OpenShift or Kubernetes can assume that the application is not healthy and attempt to fix it. OpenShift or 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.zookeeper

  • KafkaConnect.spec

  • KafkaConnectS2I.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
  • An OpenShift or 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/v1alpha1
    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.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc 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 can be 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/v1alpha1
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/v1alpha1
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
  • An OpenShift or 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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
      zookeeper:
        # ...
        metrics:
          lowercaseOutputName: true
        # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.3.10. JVM Options

Apache Kafka and Apache Zookeeper are running inside of a Java Virtual Machine (JVM). JVM has many configuration options to optimize the performance for different platforms and architectures. Strimzi allows configuring 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:

-Xmx

-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 OpenShift or Kubernetes convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes

The default value used for -Xmx depends on whether there is a memory request configured for the container:

  • If there is a memory request, the JVM’s maximum memory will be set to a value corresponding to the limit.

  • When there is no memory request, the JVM’s maximum memory will be set according to the RAM available to the container.

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 OpenShift or Kubernetes memory limit, it is possible that the container will be killed should the OpenShift or Kubernetes node experience memory pressure (from other Pods running on it).

  • If -Xmx is set without also setting an appropriate OpenShift or 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
# ...
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.

-Xms

-Xms configures the initial 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 OpenShift or Kubernetes convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes

Setting the same value for initial 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.

Example fragment configuring -Xms
# ...
jvmOptions:
  "-Xmx": "2g"
  "-Xms": "2g"
# ...
-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.
Configuring JVM options
Prerequisites
  • An OpenShift or 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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        jvmOptions:
          "-Xmx": "8g"
          "-Xms": "8g"
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc 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.

Configuring container images

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

The image property can be used to configure the container image which will be used. When the image field is missing, the following images will be used (in the order of priority):

  • For Kafka brokers:

    • Container image specified in the STRIMZI_DEFAULT_KAFKA_IMAGE environment variable from the Cluster Operator configuration.

    • strimzi/kafka:latest container image.

  • For Kafka broker TLS sidecar:

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

    • strimzi/kafka-stunnel:latest container image.

  • For Zookeeper nodes:

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

    • strimzi/zookeeper:latest container image.

  • For Zookeeper node TLS sidecar:

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

    • strimzi/zookeeper-stunnel:latest container image.

  • For Topic Operator:

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

    • strimzi/topic-operator:latest container image.

  • For User Operator:

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

    • strimzi/user-operator:latest container image.

  • For Entity Operator TLS sidecar:

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

    • strimzi/entity-operator-stunnel:latest container image.

  • For Kafka Connect:

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

    • strimzi/kafka-connect:latest container image.

  • For Kafka Connect with Source2image support:

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

    • strimzi/kafka-connect-s2i:latest 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 TLS sidecar configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
  name: my-cluster
spec:
  kafka:
    # ...
    image: my-org/my-image:latest
    # ...
  zookeeper:
    # ...
Configuring container images
Prerequisites
  • An OpenShift or 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/v1alpha1
    kind: Kafka
    metadata:
      name: my-cluster
    spec:
      kafka:
        # ...
        image: my-org/my-image:latest
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.3.12. Configuring pod scheduling

Important
When two application are scheduled to the same OpenShift or 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

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

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

Configuring pod anti-affinity in Kafka components
Prerequisites
  • An OpenShift or 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        affinity:
          podAntiAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              - labelSelector:
                  matchExpressions:
                    - key: application
                      operator: In
                      values:
                        - postgresql
                        - mongodb
                topologyKey: "kubernetes.io/hostname"
        # ...
      zookeeper:
        # ...
  2. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>
Scheduling pods to specific nodes
Node scheduling

The OpenShift or 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.

OpenShift or 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

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

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

Configuring node affinity in Kafka components
Prerequisites
  • An OpenShift or Kubernetes cluster

  • A running Cluster Operator

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

    On Kubernetes this can be done using kubectl label:

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

    On OpenShift this can be done using oc label:

    oc 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/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        affinity:
          nodeAffinity:
            requiredDuringSchedulingIgnoredDuringExecution:
              nodeSelectorTerms:
                - matchExpressions:
                  - key: node-type
                    operator: In
                    values:
                    - fast-network
        # ...
      zookeeper:
        # ...
  3. Create or update the resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>
Using dedicated nodes
Dedicated nodes

Cluster administrators can mark selected OpenShift or 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

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

The affinity configuration can include different types of affinity:

  • Pod affinity and anti-affinity

  • Node affinity

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

Tolerations

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

  • Kafka.spec.kafka

  • Kafka.spec.zookeeper

  • Kafka.spec.entityOperator

  • KafkaConnect.spec

  • KafkaConnectS2I.spec

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

Setting up dedicated nodes and scheduling pods on them
Prerequisites
  • An OpenShift or 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

    On Kubernetes this can be done using kubectl taint:

    kubectl taint node <your-node> -l dedicated=Kafka:NoSchedule

    On OpenShift this can be done using oc adm taint:

    oc adm taint node <your-node> -l dedicated=Kafka:NoSchedule
  4. Additionally, add a label to the selected nodes as well.

    On Kubernetes this can be done using kubectl label:

    kubectl label node <your-node> -l dedicated=Kafka

    On OpenShift this can be done using oc label:

    oc label node <your-node> -l dedicated=Kafka
  5. Edit the affinity and tolerations properties in the resource specifying the cluster deployment. For example:

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: Kafka
    spec:
      kafka:
        # ...
        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.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>

3.3.13. List of resources created as part of Kafka Connect cluster with Source2Image support

The following resources will created by the Cluster Operator in the OpenShift or 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.

3.3.14. Using OpenShift builds and S2I to create new images

OpenShift supports builds, which can be used together with 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. The newly created container image is stored in OpenShift’s local container image repository and can be used in deployments. Strimzi provides a Kafka Connect builder image, which can be found on Docker Hub as strimzi/kafka-connect-s2i:latest with this S2I support. It takes user-provided binaries (with plugins and connectors) and creates a new Kafka Connect image. This enhanced Kafka Connect image can be used with the Kafka Connect deployment.

The S2I deployment provided as an OpenShift template. It can be deployed from the template using the command-line or the OpenShift console.

Procedure
  1. Create a Kafka Connect S2I cluster from the command-line

    oc apply -f examples/kafka-connect/kafka-connect-s2i.yaml
  2. Once the cluster is deployed, a new build can be triggered from the command-line by creating a directory with Kafka Connect plugins:

    $ 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. Start a new image build using the prepared directory:

    oc start-build <my-connect-cluster-connect> --from-dir ./<my-plugins>/
    Note
    The name of the build will be changed according to the cluster name of the deployed Kafka Connect cluster.
  4. Once the build is finished, the new image will be used automatically by the Kafka Connect deployment.

4. Operators

4.1. Cluster Operator

4.1.1. What the Cluster Operator does

The Cluster Operator is in charge of deploying a Kafka cluster alongside a Zookeeper ensemble. As part of the Kafka cluster, it can also deploy the topic operator which provides operator-style topic management via KafkaTopic custom resources. The Cluster Operator is also able to deploy a Kafka Connect cluster which connects to an existing Kafka cluster. On OpenShift such a cluster can be deployed using the Source2Image feature, providing an easy way of including more connectors.

Cluster Operator
Figure 2. Example Architecture diagram of the Cluster Operator.

When the Cluster Operator is up, it starts to watch for certain OpenShift or Kubernetes resources containing the desired Kafka or Kafka Connect cluster configuration. A Kafka resource is used for Kafka cluster configuration, and a KafkaConnect resource is used for Kafka Connect cluster configuration.

When a new desired resource (that is, a Kafka or KafkaConnect resource) is created in the OpenShift or Kubernetes cluster, the operator gets the cluster configuration from the desired resource and starts creating a new Kafka or Kafka Connect cluster by creating the necessary other OpenShift or Kubernetes resources, such as StatefulSets, Services, ConfigMaps, and so on.

Every time the desired resource is updated by the user, the operator performs corresponding updates on the OpenShift or Kubernetes resources which make up the Kafka or Kafka Connect cluster. Resources are either patched or deleted and then re-created in order to make the Kafka or Kafka Connect cluster reflect the state of the desired cluster resource. This might cause a rolling update which might lead to service disruption.

Finally, when the desired resource is deleted, the operator starts to undeploy the cluster and delete all the related OpenShift or Kubernetes resources.

4.1.2. Deploying the Cluster Operator to Kubernetes

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

    sed -i 's/namespace: .*/namespace: <my-namespace>/' examples/install/cluster-operator/*ClusterRoleBinding*.yaml
Procedure
  1. Deploy the Cluster Operator

    kubectl create -f examples/install/cluster-operator
  2. Verify whether the Cluster Operator has been deployed successfully, the Kubernetes Dashboard or the command line:

    kubectl describe all

4.1.3. Deploying the Cluster Operator to OpenShift

Prerequisites
  • A user with cluster-admin role needs to be used, for example, system:admin.

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

    sed -i 's/namespace: .*/namespace: <my-namespace>/' examples/install/cluster-operator/*ClusterRoleBinding*.yaml
Procedure
  1. Deploy the Cluster Operator

    oc create -f examples/install/cluster-operator
    oc create -f examples/templates/cluster-operator
  2. Verify whether the Cluster Operator has been deployed successfully, the OpenShift console or the command line:

    oc describe all

4.1.4. Deploying the Cluster Operator using Helm Chart

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

  • Helm has to be installed in the OpenShift or Kubernetes cluster.

Procedure
  1. Deploy the Cluster Operator using the Helm command line tool:

    helm install strimzi-kafka-operator-latest.tgz
  2. Verify whether the Cluster Operator has been deployed successfully using the Helm command line tool:

    helm ls
Additional resources

4.1.5. Reconciliation

Although the operator reacts to all notifications about the desired cluster resources received from the OpenShift or 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 OpenShift or 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.

4.1.6. Watching multiple namespaces

The Cluster Operator is able to watch for Kafka, KafkaConnect, and KafkaConnectS2I resources in multiple namespaces without being installed on each namespace individually.

Note
The only reason to run multiple Cluster Operators in a single cluster is to use different versions of Strimzi.

The STRIMZI_NAMESPACE environment variable can be used to configure a single operator instance to operate in multiple namespaces. For each namespace given, the operator will watch for the Kafka, KafkaConnect, and KafkaConnectS2I and perform periodic reconciliation. For more details about the STIRMZI_NAMESPACE environment variable, see Cluster Operator Configuration.

4.1.7. Cluster Operator Configuration

The Cluster Operator can be configured through the following supported environment variables:

STRIMZI_NAMESPACE

Required. A comma-separated list of namespaces that the operator should operate in. 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: 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 OpenShift or Kubernetes operations take longer than usual (because of slow downloading of Docker images, for example).

STRIMZI_DEFAULT_KAFKA_IMAGE

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

STRIMZI_DEFAULT_KAFKA_INIT_IMAGE

Optional, default strimzi/kafka-init:latest. 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-stunnel:latest. 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/zookeeper:latest. 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/zookeeper-stunnel:latest. 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_DEFAULT_KAFKA_CONNECT_IMAGE

Optional, default strimzi/kafka-connect:latest. The image name to use as the default when deploying Kafka Connect, if no image is specified as the image in the Kafka Connect cluster ConfigMap

STRIMZI_DEFAULT_KAFKA_CONNECT_S2I_IMAGE

Optional, default strimzi/kafka-connect-s2i:latest. The image name to use as the default when deploying Kafka Connect S2I, if no image is specified as the image in the cluster ConfigMap.

STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE

Optional, default strimzi/topic-operator:latest. 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/user-operator:latest. 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/entity-operator-stunnel:latest. 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.

4.1.8. 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 OpenShift or 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 OpenShift or 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 OpenShift or Kubernetes resources.

OpenShift or 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 OpenShift or Kubernetes resources.

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

    • The Topic Operator produces OpenShift or 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 OpenShift or 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
    spec:
      # ...

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 OpenShift or 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 OpenShift or Kubernetes resources such as StatefulSets, Deployments, Pods, and ConfigMaps.

Example Role for the Cluster Operator
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRole
metadata:
  name: strimzi-cluster-operator
  labels:
    app: strimzi
rules:
- apiGroups:
  - ""
  resources:
  - serviceaccounts
  verbs:
  - get
  - create
  - delete
  - patch
  - update
- apiGroups:
  - rbac.authorization.k8s.io
  resources:
  - clusterrolebindings
  - rolebindings
  verbs:
  - get
  - create
  - delete
  - patch
  - update
- apiGroups:
  - ""
  resources:
  - configmaps
  verbs:
  - get
  - list
  - watch
  - create
  - delete
  - patch
  - update
- apiGroups:
  - kafka.strimzi.io
  resources:
  - kafkas
  - kafkaconnects
  - kafkaconnects2is
  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
  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

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 OpenShift or Kubernetes nodes to the Kafka broker pods
apiVersion: rbac.authorization.k8s.io/v1beta1
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/v1beta1
kind: ClusterRole
metadata:
  name: strimzi-topic-operator
  labels:
    app: strimzi
rules:
- apiGroups:
  - kafka.strimzi.io
  resources:
  - kafkatopics
  verbs:
  - get
  - list
  - watch
  - create
  - patch
  - update
  - delete
- apiGroups:
  - ""
  resources:
  - events
  verbs:
  - create
ClusterRoleBindings

The operator needs a ClusterRoleBinding which associates its ClusterRole with its ServiceAccount:

Example RoleBinding for the Cluster Operator
apiVersion: rbac.authorization.k8s.io/v1beta1
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
  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/v1beta1
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
apiVersion: rbac.authorization.k8s.io/v1beta1
kind: ClusterRoleBinding
metadata:
  name: strimzi-cluster-operator-topic-operator-delegation
  labels:
    app: strimzi
subjects:
- kind: ServiceAccount
  name: strimzi-cluster-operator
  namespace: myproject
roleRef:
  kind: ClusterRole
  name: strimzi-topic-operator
  apiGroup: rbac.authorization.k8s.io

4.2. Topic Operator

4.2.1. What the Topic Operator does

The Topic Operator provides a way of managing topics in a Kafka cluster via OpenShift or Kubernetes resources.

Topic Operator

The role of the Topic Operator is to keep a set of KafkaTopic OpenShift or 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

  • if a KafkaTopic is deleted, the operator will delete the topic it describes

  • if a KafkaTopic is 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

  • if a topic is deleted from the Kafka cluster, the operator will create the KafkaTopic describing it

  • if a topic in the Kafka cluster is changed, 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 be 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. How the Topic Operator works

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 OpenShift or 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 OpenShift or 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.

4.2.3. Deploying the Topic Operator using the Cluster Operator

Prerequisites
  • A running Cluster Operator

  • A Kafka resource to be created or updated

Procedure
  1. Topic Operator can be included in the Entity Operator. Edit the Kafka resource ensuring it has a Kafka.spec.entityOperator object that configures the Entity Operator.

  2. Create or update the Kafka resource in OpenShift or Kubernetes.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc 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.

4.2.4. Configuring the Topic Operator with resource requests and limits

Prerequisites
  • A running Cluster Operator

Procedure
  1. Edit the Kafka resource specifying in the Kafka.spec.entityOperator.topicOperator.resources property the resource requests and limits you want the Topic Operator to have.

    apiVersion: kafka.strimzi.io/v1alpha1
    kind: Kafka
    spec:
      # kafka and zookeeper sections...
      topicOperator:
        resources:
          request:
            cpu: "1"
            memory: 500Mi
          limit:
            cpu: "1"
            memory: 500Mi
  2. Create or update the Kafka resource.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>
Additional resources

4.2.5. Deploying the standalone Topic Operator

Deploying the Topic Operator as a standalone component is more complicated than installing it using the Cluster Operator, but is more flexible. For instance is 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 examples/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 OpenShift or Kubernetes namespace in which you want the operator to watch for KafkaTopic resources.

  2. Deploy the Cluster Operator.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f examples/install/topic-operator

    On OpenShift this can be done using oc apply:

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

    On Kubernetes this can be done using kubectl describe:

    kubectl describe deployment strimzi-topic-operator

    On OpenShift this can be done using oc describe:

    oc 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 OpenShift or Kubernetes and the images have not been downloaded before.
Additional resources

4.2.6. 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 for getting topics metadata from Kafka. The time between each attempt is defined as an exponential back-off. You might want to increase this value when topic creation could take more time due to its larger size (that is, many partitions/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 provides a way of managing Kafka users via OpenShift or Kubernetes resources.

4.3.1. What the User Operator does

The User Operator manages Kafka users for a Kafka cluster by watching for KafkaUser OpenShift or Kubernetes 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 OpenShift or 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 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. 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 OpenShift or Kubernetes.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc 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.

4.3.3. Deploying the standalone User Operator

Deploying the User Operator as a standalone component is more complicated than installing it using the Cluster Operator, but 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 examples/install/user-operator/05-Deployment-strimzi-user-operator.yaml resource. You will need to change the following

    1. The STRIMZI_CA_NAME environment variable in Deployment.spec.template.spec.containers[0].env should be set to point to an OpenShift or Kubernetes Secret which should contain 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 clients-ca.crt and the private key under clients-ca.key.

    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 OpenShift or Kubernetes namespace in which you want the operator to watch for KafkaUser resources.

  2. Deploy the Cluster Operator.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f examples/install/user-operator

    On OpenShift this can be done using oc apply:

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

    On Kubernetes this can be done using kubectl describe:

    kubectl describe deployment strimzi-user-operator

    On OpenShift this can be done using oc describe:

    oc 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 OpenShift or Kubernetes and the images have not been downloaded before.
Additional resources

5. Using the Topic Operator

5.1. Topic Operator usage recommendations

  • Be consistent and always operate on KafkaTopic resources or always operate on topics directly. Avoid routinely using both methods for a given topic.

  • When creating a KafkaTopic resource:

    • Remember that the name cannot be changed later.

    • Choose a name for the KafkaTopic resource that reflects the name of the topic it describes.

    • Ideally the KafkaTopic.metadata.name should be the same as its spec.topicName. To do this, the topic name will have to be a valid Kubernetes resource name.

  • When creating a topic:

    • Remember that the name cannot be changed later.

    • It is best to use a name that is a valid Kubernetes resource name, otherwise the operator will have to modify the name when creating the corresponding KafkaTopic.

5.2. Creating a topic

This procedure describes how to create a Kafka topic using a KafkaTopic OpenShift or Kubernetes resource.

Prerequisites
  • A running Kafka cluster.

  • A running Topic Operator.

Procedure
  1. Prepare a file containing the KafkaTopic to be created

    An example KafkaTopic
    apiVersion: kafka.strimzi.io/v1alpha1
    kind: KafkaTopic
    metadata:
      name: orders
      labels:
        strimzi.io/cluster: my-cluster
    spec:
      partitions: 10
      replicas: 2
    Note
    It is recommended to use a topic name that is a valid OpenShift or Kubernetes resource name. Doing this means that it is not necessary to set the KafkaTopic.spec.topicName property. In any case the KafkaTopic.spec.topicName cannot be changed after creation.
    Note
    The KafkaTopic.spec.partitions cannot be decreased.
  2. Create the KafkaTopic resource in OpenShift or Kubernetes.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc 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 OpenShift or Kubernetes resource.

Prerequisites
  • A running Kafka cluster.

  • A running Topic Operator.

  • An existing KafkaTopic to be changed.

Procedure
  1. Prepare a file containing the desired KafkaTopic

    An example KafkaTopic
    apiVersion: kafka.strimzi.io/v1alpha1
    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 oc 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 OpenShift or Kubernetes.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>
Additional resources

5.4. Deleting a topic

This procedure describes how to delete a Kafka topic using a KafkaTopic OpenShift or Kubernetes resource.

Prerequisites
  • A running Kafka cluster.

  • A running Topic Operator.

  • An existing KafkaTopic to be deleted.

Procedure
  1. Delete the KafkaTopic resource in OpenShift or Kubernetes.

    On Kubernetes this can be done using kubectl:

    kubectl delete kafkatopic <your-topic-name>

    On OpenShift this can be done using oc:

    oc delete kafkatopic <your-topic-name>
    Note
    Whether the topic can actually be deleted depends on the value of the delete.topic.enable Kafka broker configuration, specified in the Kafka.spec.kafka.config property.
Additional resources

6. Using the User Operator

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

6.1. What the User Operator does

The User Operator manages Kafka users for a Kafka cluster by watching for KafkaUser OpenShift or Kubernetes 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 OpenShift or 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 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. Creating a Kafka user

Prerequisites
  • A running Kafka cluster.

  • A running User Operator.

Procedure
  1. Prepare a YAML file containing the KafkaUser to be created.

    An example KafkaUser
    apiVersion: kafka.strimzi.io/v1alpha1
    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 OpenShift or Kubernetes.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>
  3. Use the credentials from the secret my-user in your application

Additional resources

6.3. Editing a Kafka user

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

Prerequisites
  • A running Kafka cluster.

  • A running User Operator.

  • An existing KafkaUser to be changed

Procedure
  1. Prepare a YAML file containing the desired KafkaUser.

    An example KafkaUser
    apiVersion: kafka.strimzi.io/v1alpha1
    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

    Or edit the KafkaUser resource using OpenShift or Kubernetes tools. On Kubernetes this can be done using kubectl edit:

    kubectl edit kafkauser <your-user-name>

    On OpenShift this can be done using oc edit:

    oc edit kafkauser <your-user-name>
  2. Update the KafkaUser resource in OpenShift or Kubernetes.

    On Kubernetes this can be done using kubectl apply:

    kubectl apply -f <your-file>

    On OpenShift this can be done using oc apply:

    oc apply -f <your-file>
  3. Use the updated credentials from the secret my-user in your application

Additional resources

6.4. Deleting a Kafka user

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

Prerequisites
  • A running Kafka cluster.

  • A running User Operator.

  • An existing KafkaUser to be deleted.

Procedure
  1. Delete the KafkaUser resource in OpenShift or Kubernetes.

    On Kubernetes this can be done using kubectl:

    kubectl delete kafkauser <your-user-name>

    On OpenShift this can be done using oc:

    oc delete kafkauser <your-user-name>
    Additional resources

6.5. Kafka User resource

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

6.5.1. 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 mechanism is the TLS Client Authentication 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/v1alpha1
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

6.5.2. 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 is using 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 does the rule apply. Simple Authorization supports 3 different resource types:

  • Topics

  • Consumer Groups

  • Clusters

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

    Topic and Group resources additionally allow 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/v1alpha1
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

6.5.3. Additional resources

7. Security

The Strimzi project supports data encryption of communication between the different Kafka and Strimzi components by means of the SSL/TLS protocol. This makes it possible to encrypt data transferred between Kafka brokers (interbroker communication), between Zookeeper nodes (internodal communication), and between clients and Kafka brokers. For the Kafka and Strimzi components, TLS certificates are also used for authentication.

The Cluster Operator sets up the SSL/TLS certificates to provide this encryption and authentication.

Secure Communication
Figure 3. Example Architecture diagram of the communication secured by TLS.

7.1. Certificates

Each component needs its own private and public keys in order to support encryption. The public key has to be signed by a certificate authority (CA) in order to have a related X.509 certificate for providing server authentication and encrypting the communication channel with the client (which could be another broker as well). All component certificates are signed by a Certification Authority (CA) called cluster CA. Another CA is used for authentication of Kafka clients connecting to Kafka brokers. This CA is called clients CA. The CAs themselves use self-signed certificates.

All of the generated certificates are saved as Secrets in the OpenShift or Kubernetes cluster, named as follows:

<cluster-name>-cluster-ca

Contains the private and public keys of the cluster CA which is used for signing server certificates for the Kafka and Strimzi components (Kafka brokers, Zookeeper nodes, and so on).

<cluster-name>-cluster-ca-cert

Contains only the public key of the cluster CA. The public key used by Kafka clients to verify the identity of the Kafka brokers they are connecting to (TLS server authentication).

<cluster-name>-clients-ca

Contains the private and public keys of the clients CA. The clients CA is used for TLS client authentication of Kafka clients when connecting to Kafka brokers.

<cluster-name>-clients-ca-cert

Contains only the public key of the client CA. The public key is used by the Kafka brokers to verify the identity of Kafka clients when TLS client authentication is used.

<cluster-name>-kafka-brokers

Contains all the Kafka broker private and public keys (certificates signed with the cluster CA).

<cluster-name>-zookeeper-nodes

Contains all the Zookeeper node private and public keys (certificates signed with the cluster CA).

<cluster-name>-topic-operator-certs

Contains the private and public keys (certificates signed with the cluster CA) used for encrypting communication between the Topic Operator and Kafka or Zookeeper.

All the keys are 2048 bits in size and are valid for 365 days from initial generation.

Note
"certificates rotation" for generating new ones on their expiration will be supported in future releases.

7.2. Interbroker Kafka Communication

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

7.3. Zookeeper Communication

By deploying an stunnel sidecar within every Zookeeper pod, the Cluster Operator is able to provide data encryption and authentication between Zookeeper nodes in a cluster. The stunnel sidecar 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.

7.4. Kafka Client connections via TLS

Encrypted communication between Kafka brokers and clients is provided through the CLIENTTLS listener on port 9093.

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

If a Kafka client wants to connect to the encrypted listener (CLIENTTLS) on port 9093, it needs to trust the cluster CA certificate in order to verify the broker certificate received during the SSL/TLS handshake. The cluster CA certificate can be extracted from the generated <cluster-name>-cluster-ca-cert Secret.

On Kubernetes, the certificate can be extracted with the following command:

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

On OpenShift, the certificate can be extracted with the following command:

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

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

Finally, in order to configure the Kafka client, following properties should be specified:

  • security.protocol: SSL is the value for using encryption.

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

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

The current implementation does not support Subject Alternative Names (SAN) so the hostname verification should be disabled on the client side. For doing so the ssl.endpoint.identification.algorithm property needs to be set as empty.

Appendix A: Frequently Asked Questions

A.1. Cluster Operator

A.1.1. 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.

Appendix B: Installing OpenShift or Kubernetes cluster

The easiest way to get started with OpenShift or Kubernetes is using the Minikube, Minishift or oc cluster up utilities. This section provides basic guidance on how to use them. More details are provided on the websites of the tools themselves.

B.1. Kubernetes

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

B.2. OpenShift

In order to interact with an OpenShift cluster, the oc utility is needed.

An OpenShift cluster can be started in two different ways. The oc utility can start a cluster locally 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

Appendix C: Custom Resource API Reference

C.1. Kafka schema reference

Field Description

spec

The specification of the Kafka and Zookeeper clusters, and Topic Operator.

KafkaSpec

C.2. KafkaSpec schema reference

Used in: Kafka

Field Description

kafka

Configuration of the Kafka cluster.

KafkaClusterSpec

zookeeper

Configuration of the Zookeeper cluster.

ZookeeperClusterSpec

topicOperator

Configuration of the Topic Operator.

TopicOperatorSpec

entityOperator

Configuration of the Entity Operator.

EntityOperatorSpec

C.3. KafkaClusterSpec schema reference

Used in: KafkaSpec

Field Description

replicas

The number of pods in the cluster.

integer

image

The docker image for the pods.

string

storage

Storage configuration (disk). Cannot be updated. The type depends on the value of the storage.type property within the given object, which must be one of [ephemeral, persistent-claim].

EphemeralStorage, PersistentClaimStorage

listeners

Configures listeners of Kafka brokers.

KafkaListeners

authorization

Authorization configuration for Kafka brokers. The type depends on the value of the authorization.type property within the given object, which must be one of [simple].

KafkaAuthorizationSimple

config

The kafka broker config. Properties with the following prefixes cannot be set: listeners, advertised., broker., listener., host.name, port, inter.broker.listener.name, sasl., ssl., security., password., principal.builder.class, log.dir, zookeeper.connect, zookeeper.set.acl, authorizer., super.user.

map

rack

Configuration of the broker.rack broker config.

Rack

brokerRackInitImage

The image of the init container used for initializing the broker.rack.

string

affinity

Pod affinity rules.See external documentation of core/v1 affinity.

Affinity

tolerations

Pod’s tolerations.See external documentation of core/v1 tolerations.

Toleration array

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

jvmOptions

JVM Options for pods.

JvmOptions

resources

Resource constraints (limits and requests).

Resources

metrics

The Prometheus JMX Exporter configuration. See https://github.com/prometheus/jmx_exporter for details of the structure of this configuration.

map

logging

Logging configuration for Kafka. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

tlsSidecar

TLS sidecar configuration.

Sidecar

C.4. EphemeralStorage schema reference

The type property is a discriminator that distinguishes the use of the type EphemeralStorage from PersistentClaimStorage. It must have the value ephemeral for the type EphemeralStorage.

Field Description

type

Must be ephemeral.

string

C.5. PersistentClaimStorage schema reference

The type property is a discriminator that distinguishes the use of the type PersistentClaimStorage from EphemeralStorage. It must have the value persistent-claim for the type PersistentClaimStorage.

Field Description

class

The storage class to use for dynamic volume allocation.

string

deleteClaim

Specifies if the persistent volume claim has to be deleted when the cluster is un-deployed.

boolean

selector

Specifies a specific persistent volume to use. It contains a matchLabels field which defines an inner JSON object with key:value representing labels for selecting such a volume.

map

size

When type=persistent-claim, defines the size of the persistent volume claim (i.e 1Gi). Mandatory when type=persistent-claim.

string

type

Must be persistent-claim.

string

C.6. KafkaListeners schema reference

Used in: KafkaClusterSpec

Field Description

plain

Configures plain listener on port 9092.

KafkaListenerPlain

tls

Configures TLS listener on port 9093.

KafkaListenerTls

C.7. KafkaListenerPlain schema reference

Used in: KafkaListeners

Field Description

C.8. KafkaListenerTls schema reference

Used in: KafkaListeners

Field Description

authentication

Authorization configuration for Kafka brokers. The type depends on the value of the authentication.type property within the given object, which must be one of [tls].

KafkaListenerAuthenticationTls

C.9. KafkaListenerAuthenticationTls schema reference

Used in: KafkaListenerTls

The type property is a discriminator that distinguishes the use of the type KafkaListenerAuthenticationTls from other subtypes which may be added in the future. It must have the value tls for the type KafkaListenerAuthenticationTls.

Field Description

type

Must be tls.

string

C.10. KafkaAuthorizationSimple schema reference

Used in: KafkaClusterSpec

The type property is a discriminator that distinguishes the use of the type KafkaAuthorizationSimple from other subtypes which may be added in the future. It must have the value simple for the type KafkaAuthorizationSimple.

Field Description

superUsers

List of super users. Should contain list of user principals which should get unlimited access rights.

string array

type

Must be simple.

string

C.11. Rack schema reference

Used in: KafkaClusterSpec

Field Description

topologyKey

A key that matches labels assigned to the OpenShift or Kubernetes cluster nodes. The value of the label is used to set the broker’s broker.rack config.

string

C.12. Probe schema reference

Field Description

initialDelaySeconds

The initial delay before first the health is first checked.

integer

timeoutSeconds

The timeout for each attempted health check.

integer

C.13. JvmOptions schema reference

Field Description

-XX

A map of -XX options to the JVM.

map

-Xms

-Xms option to to the JVM.

string

-Xmx

-Xmx option to to the JVM.

string

C.14. Resources schema reference

Field Description

limits

Resource limits applied at runtime.

CpuMemory

requests

Resource requests applied during pod scheduling.

CpuMemory

C.15. CpuMemory schema reference

Used in: Resources

Field Description

cpu

CPU.

string

memory

Memory.

string

C.16. InlineLogging schema reference

The type property is a discriminator that distinguishes the use of the type InlineLogging from ExternalLogging. It must have the value inline for the type InlineLogging.

Field Description

loggers

A Map from logger name to logger level.

map

type

Must be inline.

string

C.17. ExternalLogging schema reference

The type property is a discriminator that distinguishes the use of the type ExternalLogging from InlineLogging. It must have the value external for the type ExternalLogging.

Field Description

name

The name of the ConfigMap from which to get the logging configuration.

string

type

Must be external.

string

C.18. Sidecar schema reference

Field Description

image

The docker image for the container.

string

resources

Resource constraints (limits and requests).

Resources

C.19. ZookeeperClusterSpec schema reference

Used in: KafkaSpec

Field Description

replicas

The number of pods in the cluster.

integer

image

The docker image for the pods.

string

storage

Storage configuration (disk). Cannot be updated. The type depends on the value of the storage.type property within the given object, which must be one of [ephemeral, persistent-claim].

EphemeralStorage, PersistentClaimStorage

config

The zookeeper broker config. Properties with the following prefixes cannot be set: server., dataDir, dataLogDir, clientPort, authProvider, quorum.auth, requireClientAuthScheme.

map

affinity

Pod affinity rules.See external documentation of core/v1 affinity.

Affinity

tolerations

Pod’s tolerations.See external documentation of core/v1 tolerations.

Toleration array

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

jvmOptions

JVM Options for pods.

JvmOptions

resources

Resource constraints (limits and requests).

Resources

metrics

The Prometheus JMX Exporter configuration. See https://github.com/prometheus/jmx_exporter for details of the structure of this configuration.

map

logging

Logging configuration for Zookeeper. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

tlsSidecar

TLS sidecar configuration.

Sidecar

C.20. TopicOperatorSpec schema reference

Used in: KafkaSpec

Field Description

watchedNamespace

The namespace the Topic Operator should watch.

string

image

The image to use for the Topic Operator.

string

reconciliationIntervalSeconds

Interval between periodic reconciliations.

integer

zookeeperSessionTimeoutSeconds

Timeout for the Zookeeper session.

integer

affinity

Pod affinity rules.See external documentation of core/v1 affinity.

Affinity

resources

Resource constraints (limits and requests).

Resources

topicMetadataMaxAttempts

The number of attempts at getting topic metadata.

integer

tlsSidecar

TLS sidecar configuration.

Sidecar

logging

Logging configuration. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

C.21. EntityOperatorSpec schema reference

Used in: KafkaSpec

Field Description

topicOperator

Configuration of the Topic Operator.

EntityTopicOperatorSpec

userOperator

Configuration of the User Operator.

EntityUserOperatorSpec

affinity

Pod affinity rules.See external documentation of core/v1 affinity.

Affinity

tolerations

Pod’s tolerations.See external documentation of core/v1 tolerations.

Toleration array

tlsSidecar

TLS sidecar configuration.

Sidecar

C.22. EntityTopicOperatorSpec schema reference

Field Description

watchedNamespace

The namespace the Topic Operator should watch.

string

image

The image to use for the Topic Operator.

string

reconciliationIntervalSeconds

Interval between periodic reconciliations.

integer

zookeeperSessionTimeoutSeconds

Timeout for the Zookeeper session.

integer

resources

Resource constraints (limits and requests).

Resources

topicMetadataMaxAttempts

The number of attempts at getting topic metadata.

integer

logging

Logging configuration. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

C.23. EntityUserOperatorSpec schema reference

Field Description

watchedNamespace

The namespace the User Operator should watch.

string

image

The image to use for the User Operator.

string

reconciliationIntervalSeconds

Interval between periodic reconciliations.

integer

zookeeperSessionTimeoutSeconds

Timeout for the Zookeeper session.

integer

resources

Resource constraints (limits and requests).

Resources

logging

Logging configuration. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

C.24. KafkaConnect schema reference

Field Description

spec

The specification of the Kafka Connect deployment.

KafkaConnectSpec

C.25. KafkaConnectSpec schema reference

Used in: KafkaConnect

Field Description

replicas

The number of pods in the Kafka Connect group.

integer

image

The docker image for the pods.

string

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

jvmOptions

JVM Options for pods.

JvmOptions

affinity

Pod affinity rules.See external documentation of core/v1 affinity.

Affinity

tolerations

Pod’s tolerations.See external documentation of core/v1 tolerations.

Toleration array

logging

Logging configuration for Kafka Connect. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

metrics

The Prometheus JMX Exporter configuration. See https://github.com/prometheus/jmx_exporter for details of the structure of this configuration.

map

authentication

Authentication configuration for Kafka Connect. The type depends on the value of the authentication.type property within the given object, which must be one of [tls].

KafkaConnectAuthenticationTls

bootstrapServers

Bootstrap servers to connect to. This should be given as a comma separated list of <hostname>:‍<port> pairs.

string

config

The Kafka Connect configuration. Properties with the following prefixes cannot be set: ssl., sasl., security., listeners, plugin.path, rest., bootstrap.servers.

map

resources

Resource constraints (limits and requests).

Resources

tls

TLS configuration.

KafkaConnectTls

C.26. KafkaConnectAuthenticationTls schema reference

The type property is a discriminator that distinguishes the use of the type KafkaConnectAuthenticationTls from other subtypes which may be added in the future. It must have the value tls for the type KafkaConnectAuthenticationTls.

Field Description

certificateAndKey

Certificate and private key pair for TLS authentication.

CertAndKeySecretSource

type

Must be tls.

string

C.27. CertAndKeySecretSource schema reference

Field Description

certificate

The name of the file certificate in the Secret.

string

key

The name of the private key in the Secret.

string

secretName

The name of the Secret containing the certificate.

string

C.28. KafkaConnectTls schema reference

Field Description

trustedCertificates

Trusted certificates for TLS connection.

CertSecretSource array

C.29. CertSecretSource schema reference

Used in: KafkaConnectTls

Field Description

certificate

The name of the file certificate in the Secret.

string

secretName

The name of the Secret containing the certificate.

string

C.30. KafkaConnectS2I schema reference

Field Description

spec

The specification of the Kafka Connect deployment.

KafkaConnectS2ISpec

C.31. KafkaConnectS2ISpec schema reference

Used in: KafkaConnectS2I

Field Description

replicas

The number of pods in the Kafka Connect group.

integer

image

The docker image for the pods.

string

livenessProbe

Pod liveness checking.

Probe

readinessProbe

Pod readiness checking.

Probe

jvmOptions

JVM Options for pods.

JvmOptions

affinity

Pod affinity rules.See external documentation of core/v1 affinity.

Affinity

metrics

The Prometheus JMX Exporter configuration. See https://github.com/prometheus/jmx_exporter for details of the structure of this configuration.

map

authentication

Authentication configuration for Kafka Connect. The type depends on the value of the authentication.type property within the given object, which must be one of [tls].

KafkaConnectAuthenticationTls

bootstrapServers

Bootstrap servers to connect to. This should be given as a comma separated list of <hostname>:‍<port> pairs.

string

config

The Kafka Connect configuration. Properties with the following prefixes cannot be set: ssl., sasl., security., listeners, plugin.path, rest., bootstrap.servers.

map

insecureSourceRepository

When true this configures the source repository with the 'Local' reference policy and an import policy that accepts insecure source tags.

boolean

logging

Logging configuration for Kafka Connect. The type depends on the value of the logging.type property within the given object, which must be one of [inline, external].

InlineLogging, ExternalLogging

resources

Resource constraints (limits and requests).

Resources

tls

TLS configuration.

KafkaConnectTls

tolerations

Pod’s tolerations.See external documentation of core/v1 tolerations.

Toleration array

C.32. KafkaTopic schema reference

Field Description

spec

The specification of the topic.

KafkaTopicSpec

C.33. KafkaTopicSpec schema reference

Used in: KafkaTopic

Field Description

partitions

The number of partitions the topic should have. This cannot be decreased after topic creation. It can be increased after topic creation, but it is important to understand the consequences that has, especially for topics with semantic partitioning. If unspecified this will default to the broker’s num.partitions config.

integer

replicas

The number of replicas the topic should have. If unspecified this will default to the broker’s default.replication.factor config.

integer

config

The topic configuration.

map

topicName

The name of the topic. When absent this will default to the metadata.name of the topic. It is recommended to not set this unless the topic name is not a valid Kubernetes resource name.

string

C.34. KafkaUser schema reference

Field Description

spec

The specification of the user.

KafkaUserSpec

C.35. KafkaUserSpec schema reference

Used in: KafkaUser

Field Description

authentication

Authentication mechanism enabled for this Kafka user. The type depends on the value of the authentication.type property within the given object, which must be one of [tls].

KafkaUserTlsClientAuthentication

authorization

Authorization rules for this Kafka user. The type depends on the value of the authorization.type property within the given object, which must be one of [simple].

KafkaUserAuthorizationSimple

C.36. KafkaUserTlsClientAuthentication schema reference

Used in: KafkaUserSpec

The type property is a discriminator that distinguishes the use of the type KafkaUserTlsClientAuthentication from other subtypes which may be added in the future. It must have the value tls for the type KafkaUserTlsClientAuthentication.

Field Description

type

Must be tls.

string

C.37. KafkaUserAuthorizationSimple schema reference

Used in: KafkaUserSpec

The type property is a discriminator that distinguishes the use of the type KafkaUserAuthorizationSimple from other subtypes which may be added in the future. It must have the value simple for the type KafkaUserAuthorizationSimple.

Field Description

acls

List of ACL rules which should be applied to this user.

AclRule array

type

Must be simple.

string

C.38. AclRule schema reference

Field Description

host

The host from which the action described in the ACL rule is allowed or denied.

string

operation

Operation which will be allowed or denied. Supported operations are: Read, Write, Create, Delete, Alter, Describe, ClusterAction, AlterConfigs, DescribeConfigs, IdempotentWrite and All.

string (one of [Read, Write, Delete, Alter, Describe, All, IdempotentWrite, ClusterAction, Create, AlterConfigs, DescribeConfigs])

resource

Indicates the resource for which given ACL rule applies. The type depends on the value of the resource.type property within the given object, which must be one of [topic, group, cluster].

AclRuleTopicResource, AclRuleGroupResource, AclRuleClusterResource

type

The type of the rule.Currently the only supported type is allow.ACL rules with type allow are used to allow user to execute the specified operations. Default value is allow.

string (one of [allow, deny])

C.39. AclRuleTopicResource schema reference

Used in: AclRule

The type property is a discriminator that distinguishes the use of the type AclRuleTopicResource from AclRuleGroupResource, AclRuleClusterResource. It must have the value topic for the type AclRuleTopicResource.

Field Description

name

Name of resource for which given ACL rule applies. Can be combined with patternType field to use prefix pattern.

string

patternType

Describes the pattern used in the resource field. The supported types are literal and prefix. With literal pattern type, the resource field will be used as a definition of a full topic name. With prefix pattern type, the resource name will be used only as a prefix. Default value is literal.

string (one of [prefix, literal])

type

Must be topic.

string

C.40. AclRuleGroupResource schema reference

Used in: AclRule

The type property is a discriminator that distinguishes the use of the type AclRuleGroupResource from AclRuleTopicResource, AclRuleClusterResource. It must have the value group for the type AclRuleGroupResource.

Field Description

name

Name of resource for which given ACL rule applies. Can be combined with patternType field to use prefix pattern.

string

patternType

Describes the pattern used in the resource field. The supported types are literal and prefix. With literal pattern type, the resource field will be used as a definition of a full topic name. With prefix pattern type, the resource name will be used only as a prefix. Default value is literal.

string (one of [prefix, literal])

type

Must be group.

string

C.41. AclRuleClusterResource schema reference

Used in: AclRule

The type property is a discriminator that distinguishes the use of the type AclRuleClusterResource from AclRuleTopicResource, AclRuleGroupResource. It must have the value cluster for the type AclRuleClusterResource.

Field Description

type

Must be cluster.

string

Appendix D: Metrics

This section describes how to deploy a Prometheus server for scraping metrics from the Kafka cluster and showing them using a Grafana dashboard. The resources provided are examples to show how Kafka metrics can be stored in Prometheus: They are not a recommended configuration, and further support should be available from the Prometheus and Grafana communities.

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). Information on how to increase the default amount of memory can be found in the following section Installing OpenShift or Kubernetes cluster.

D.1. Deploying on OpenShift

D.1.1. Prometheus

The Prometheus server configuration uses a service discovery feature in order to discover the pods in the cluster from which it gets metrics. In order to have this feature working, it is necessary for the service account used for running the Prometheus service pod to have access to the API server to get the pod list. By default the service account prometheus-server is used.

export NAMESPACE=[namespace]
oc login -u system:admin
oc create sa prometheus-server
oc adm policy add-cluster-role-to-user cluster-reader system:serviceaccount:${NAMESPACE}:prometheus-server
oc login -u developer

where [namespace] is the namespace/project where the Apache Kafka cluster was deployed.

Finally, create the Prometheus service by running

oc create -f https://raw.githubusercontent.com/strimzi/strimzi-kafka-operator/master/metrics/examples/prometheus/kubernetes.yaml

D.1.2. Grafana

A Grafana server is necessary only to get a visualisation of the Prometheus metrics.

To deploy Grafana on OpenShift, the following commands should be executed:

oc create -f https://raw.githubusercontent.com/strimzi/strimzi-kafka-operator/master/metrics/examples/grafana/kubernetes.yaml

D.2. Deploying on Kubernetes

D.2.1. Prometheus

The Prometheus server configuration uses a service discovery feature in order to discover the pods in the cluster from which it gets metrics. If the RBAC is enabled in your Kubernetes deployment then in order to have this feature working, it is necessary for the service account used for running the Prometheus service pod to have access to the API server to get the pod list. By default the service account prometheus-server is used.

export NAMESPACE=[namespace]
kubectl create sa prometheus-server
kubectl create -f https://raw.githubusercontent.com/strimzi/strimzi-kafka-operator/master/metrics/examples/prometheus/cluster-reader.yaml
kubectl create clusterrolebinding read-pods-binding --clusterrole=cluster-reader --serviceaccount=${NAMESPACE}:prometheus-server

where [namespace] is the namespace/project where the Apache Kafka cluster was deployed.

Finally, create the Prometheus service by running

kubectl apply -f https://raw.githubusercontent.com/strimzi/strimzi-kafka-operator/master/metrics/examples/prometheus/kubernetes.yaml

D.2.2. Grafana

A Grafana server is necessary only to get a visualisation of Prometheus metrics.

To deploy Grafana on Kubernetes, the following commands should be executed:

kubectl apply -f https://raw.githubusercontent.com/strimzi/strimzi-kafka-operator/master/metrics/examples/grafana/kubernetes.yaml

D.3. Grafana dashboard

As an example, and in order to visualize the exported metrics in Grafana, the simple dashboard kafka-dashboard.json file is provided. The Prometheus data source, and the above dashboard, can be set up in Grafana by following these steps.

Note
For accessing the dashboard, you can use the port-forward command for forwarding traffic from the Grafana pod to the host. For example, you can access the Grafana UI by running oc port-forward grafana-1-fbl7s 3000:3000 (or using kubectl instead of oc) and then pointing a browser to http://localhost:3000.
  1. Access to the Grafana UI using admin/admin credentials.

    Grafana login
  2. Click on the "Add data source" button from the Grafana home in order to add Prometheus as data source.

    Grafana home
  3. Fill in the information about the Prometheus data source, specifying a name and "Prometheus" as type. In the URL field, the connection string to the Prometheus server (that is, http://prometheus:9090) should be specified. After "Add" is clicked, Grafana will test the connection to the data source.

    Add Prometheus data source
  4. From the top left menu, click on "Dashboards" and then "Import" to open the "Import Dashboard" window where the provided kafka-dashboard.json file can be imported or its content pasted.

    Add Grafana dashboard
  5. After importing the dashboard, the Grafana home should show with some initial metrics about CPU and JVM memory usage. When the Kafka cluster is used (creating topics and exchanging messages) the other metrics, like messages in and bytes in/out per topic, will be shown.

    Kafka dashboard