1. Overview
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 two 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.
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
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
Strimzi works on all kinds of clusters - from public and private clouds down 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.
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.
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
-
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
-
Deploy the Cluster Operator
kubectl create -f examples/install/cluster-operator
-
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
-
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
-
Deploy the Cluster Operator
oc create -f examples/install/cluster-operator oc create -f examples/templates/cluster-operator
-
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
-
Helm has to be installed on the local machine.
-
Helm has to be installed in the OpenShift or Kubernetes cluster.
-
Deploy the Cluster Operator using the Helm command line tool:
helm install strimzi-kafka-operator-0.6.0.tgz
-
Verify whether the Cluster Operator has been deployed successfully using the Helm command line tool:
helm ls
-
For more information about Helm, see the Helm website.
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 anemptyDir
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. ThePersistentVolume
is acquired using aPersistentVolumeClaim
to make it independent of the actual type of thePersistentVolume
. For example, it can use HostPath volumes on Minikube or Amazon EBS volumes in Amazon AWS deployments without any changes in the YAML files. ThePersistentVolumeClaim
can use aStorageClass
to trigger automatic volume provisioning.
2.3.1. Deploying the Kafka cluster to Kubernetes
-
Before deploying a Kafka cluster, the Cluster Operator must be deployed.
-
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
-
If you are planning to use the Kafka cluster in production, create a persistent cluster
kubectl apply -f examples/kafka/kafka-persistent.yaml
-
For more information about deploying the Cluster Operator, see Cluster Operator
-
For more information about the different configuration options supported by the
Kafka
resource, see Kafka cluster configuration
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.
-
Before deploying a Kafka cluster, the Cluster Operator must be deployed.
-
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
-
If you are planning to use the Kafka cluster in production, create a persistent cluster
oc apply -f examples/kafka/kafka-persistent.yaml
-
For more information about deploying the Cluster Operator, see Cluster Operator For more information about the different configuration options supported by the
Kafka
resource, see Kafka cluster configuration
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
-
Before deploying Kafka Connect, the Cluster Operator must be deployed.
-
Deploy Kafka Connect on Kubernetes by creating the corresponding
KafkaConnect
resource.kubectl apply -f examples/kafka-connect/kafka-connect.yaml
-
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.
-
Before deploying Kafka Connect, the Cluster Operator must be deployed.
-
Create a Kafka Connect cluster from the command-line:
oc apply -f examples/kafka-connect/kafka-connect.yaml
-
For more information about deploying the Cluster Operator, see Cluster Operator
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:0.6.0
.
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.
-
Create a new
Dockerfile
usingstrimzi/kafka-connect:0.6.0
as the base image:FROM strimzi/kafka-connect:0.6.0 USER root:root COPY ./<my-plugins>/ /opt/kafka/plugins/ USER kafka:kafka
-
Build the container image and upload it to the appropriate container image repository.
-
Set the
KafkaConnect.spec.image
property of the KafkaConnect custom resource or theSTRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE
variable to point to the new container image.
-
For more information about the
STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGE
variable, see Cluster Operator Configuration. -
For more information about the
KafkaConnect.spec.image property
, see Container images.
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:0.6.0
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.
-
Create a Kafka Connect S2I cluster from the command-line
oc apply -f examples/kafka-connect/kafka-connect-s2i.yaml
-
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
-
Start a new image build using the prepared directory:
oc start-build <my-connect-cluster-connect> --from-dir ./<my-plugins>/
NoteThe name of the build will be changed according to the cluster name of the deployed Kafka Connect cluster. -
Once the build is finished, the new image will be used automatically by the Kafka Connect deployment.
2.5. Deploying example clients
-
An existing Kafka cluster for the client to connect to.
-
Deploy the producer.
oc run kafka-producer -ti --image=strimzi/kafka:0.6.0 --restart=Never \-- bin/kafka-console-producer.sh --broker-list <cluster-name>-kafka-bootstrap:9092 --topic <my-topic>
-
Type your message into the console where the producer is running.
-
Press Enter to send the message.
-
Deploy the consumer.
oc run kafka-consumer -ti --image=strimzi/kafka:0.6.0 --restart=Never \-- bin/kafka-console-consumer.sh --bootstrap-server <cluster-name>-kafka-bootstrap:9092 --topic <my-topic> --from-beginning
-
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.
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
-
A running Cluster Operator
-
A
Kafka
resource to be created or updated
-
Topic Operator can be included in the Entity Operator. Edit the
Kafka
resource ensuring it has aKafka.spec.entityOperator
object that configures the Entity Operator. -
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>
-
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, seeEntityOperatorSpec
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
-
A running Cluster Operator
-
A
Kafka
resource to be created or updated.
-
Edit the
Kafka
resource ensuring it has aKafka.spec.entityOperator.userOperator
object that configures the User Operator how you want. -
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>
-
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, seeEntityOperatorSpec
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.
|
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
.
size
# ...
storage:
type: persistent-claim
size: 1000Gi
# ...
The following example demonstrates the use of a 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.
# ...
storage:
type: persistent-claim
size: 1Gi
selector:
matchLabels:
"hdd-type": "ssd"
deleteClaim: true
# ...
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
-
For more information about ephemeral storage, see ephemeral storage schema reference.
-
For more information about persistent storage, see persistent storage schema reference.
-
For more information about the schema for
Kafka
, seeKafka
schema reference.
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
.
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
replicas: 3
# ...
zookeeper:
# ...
Changing number of replicas
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
replicas
property in theKafka
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... replicas: 3 # ... zookeeper: # ...
-
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.
|
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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
config
property in theKafka
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: # ...
-
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.
listeners
property with all listeners enabled# ...
listeners:
plain: {}
tls: {}
# ...
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.
tls
listener with TLS client authentication enabled# ...
listeners:
tls:
authentication:
type: tls
# ...
Configuring Kafka listeners
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
listeners
property in theKafka.spec.kafka
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... listeners: plain: {} # ... zookeeper: # ...
-
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>
-
For more information about the schema for
Kafka
, seeKafka
schema reference.
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.
authentication
with type tls
# ...
authentication:
type: tls
# ...
Configuring authentication in Kafka brokers
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
listeners
property in theKafka.spec.kafka
resource. Add theauthentication
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: # ...
-
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>
-
For more information about the supported authentication mechanisms, see authentication reference.
-
For more information about the schema for
Kafka
, seeKafka
schema reference.
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
.
# ...
authorization:
type: simple
# ...
Configuring authorization in Kafka brokers
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Add or edit the
authorization
property in theKafka.spec.kafka
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... authorization: type: simple # ... zookeeper: # ...
-
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>
-
For more information about the supported authorization methods, see authorization reference.
-
For more information about the schema for
Kafka
, seeKafka
schema reference.
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
.
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
zookeeper:
# ...
replicas: 3
# ...
Changing number of replicas
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
replicas
property in theKafka
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... replicas: 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>
3.1.7. 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 value2000
-
initLimit
with default value5
-
syncLimit
with default value2
-
autopurge.purgeInterval
with default value1
These options will be automatically configured when they are not present in the Kafka.spec.zookeeper.config
property.
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
spec:
kafka:
# ...
zookeeper:
# ...
config:
autopurge.snapRetainCount: 3
autopurge.purgeInterval: 1
# ...
Configuring Zookeeper
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
config
property in theKafka
resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... zookeeper: # ... config: autopurge.snapRetainCount: 3 autopurge.purgeInterval: 1 # ...
-
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.
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.
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.
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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
entityOperator
property in theKafka
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
-
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
.
# ...
resources:
requests:
cpu: 12
memory: 64Gi
# ...
It is also possible to specify a resource request just for one of the resources:
# ...
resources:
requests:
memory: 64Gi
# ...
Or:
# ...
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
.
# ...
resources:
limits:
cpu: 12
memory: 64Gi
# ...
It is also possible to specify the resource limit just for one of the resources:
# ...
resources:
limits:
memory: 64Gi
# ...
Or:
# ...
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 same1
CPU core.
# ...
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 example1000M
. -
To specify memory in gigabytes, use the
G
suffix. For example1G
. -
To specify memory in mebibytes, use the
Mi
suffix. For example1000Mi
. -
To specify memory in gibibytes, use the
Gi
suffix. For example1Gi
.
# ...
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
-
For more information about managing computing resources on OpenShift or Kubernetes, see Managing Compute Resources for Containers.
Configuring resource requests and limits
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
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: # ...
-
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
-
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.
-
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
-
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
eventuallylog4j2.properties
key. -
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.
Kafka
resource with the rack
feature enabledapiVersion: 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):
-
Container image specified in
STRIMZI_DEFAULT_KAFKA_INIT_IMAGE
environment variable in Cluster Operator configuration. -
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. |
brokerRackInitImage
configurationapiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
brokerRackInitImage: my-org/my-image:latest
# ...
Configuring rack awareness in Kafka brokers
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Find out which node labels represent the zone / rack into which the node is deployed.
-
Edit the
rack
property in theKafka
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 # ...
-
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.
# ...
readinessProbe:
initialDelaySeconds: 15
timeoutSeconds: 5
livenessProbe:
initialDelaySeconds: 15
timeoutSeconds: 5
# ...
Configuring healthchecks
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
livenessProbe
orreadinessProbe
property in theKafka
,KafkaConnect
orKafkaConnectS2I
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: # ...
-
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 ({}
).
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.
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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
metrics
property in theKafka
,KafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... metrics: lowercaseOutputName: true # ...
-
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
|
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. |
-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.
-Xms
# ...
jvmOptions:
"-Xmx": "2g"
"-Xms": "2g"
# ...
-server
-server
enables the server JVM. This option can be set to true or false.
-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.
-XX
objectjvmOptions:
"-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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
jvmOptions
property in theKafka
,KafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... jvmOptions: "-Xmx": "8g" "-Xms": "8g" # ... zookeeper: # ...
-
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. |
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
image: my-org/my-image:latest
# ...
zookeeper:
# ...
Configuring container images
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
image
property in theKafka
,KafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... image: my-org/my-image:latest # ... zookeeper: # ...
-
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.
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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
tlsSidecar
property in theKafka
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: # ...
-
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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
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. ThetopologyKey
should be set tokubernetes.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: # ...
-
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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
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.
-
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: # ...
-
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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Select the nodes which should be used as dedicated
-
Make sure there are no workloads scheduled on these nodes
-
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
-
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
-
Edit the
affinity
andtolerations
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: # ...
-
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
.
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaConnectS2I
metadata:
name: my-cluster
spec:
# ...
replicas: 3
# ...
Changing number of replicas
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
replicas
property in theKafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaConnectS2I metadata: name: my-cluster spec: # ... replicas: 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>
3.2.2. 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.
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaConnect
metadata:
name: my-cluster
spec:
# ...
bootstrapServers: my-cluster-kafka-bootstrap:9092
# ...
Configuring bootstrap servers
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
bootstrapServers
property in theKafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaConnect metadata: name: my-cluster spec: # ... bootstrapServers: my-cluster-kafka-bootstrap:9092 # ...
-
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.
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.
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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
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>
-
Edit the
tls
property in theKafkaConnect
orKafkaConnectS2I
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 # ...
-
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. |
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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
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>
-
Edit the
tls
property in theKafkaConnect
orKafkaConnectS2I
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 # ...
-
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 valueconnect-cluster
-
offset.storage.topic
with default valueconnect-cluster-offsets
-
config.storage.topic
with default valueconnect-cluster-configs
-
status.storage.topic
with default valueconnect-cluster-status
-
key.converter
with default valueorg.apache.kafka.connect.json.JsonConverter
-
value.converter
with default valueorg.apache.kafka.connect.json.JsonConverter
-
internal.key.converter
with default valueorg.apache.kafka.connect.json.JsonConverter
-
internal.value.converter
with default valueorg.apache.kafka.connect.json.JsonConverter
-
internal.key.converter.schemas.enable
with default valuefalse
-
internal.value.converter.schemas.enable
with default valuefalse
These options will be automatically configured in case they are not present in the KafkaConnect.spec.config
or KafkaConnectS2I.spec.config
properties.
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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
config
property in theKafkaConnect
orKafkaConnectS2I
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 # ...
-
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
.
# ...
resources:
requests:
cpu: 12
memory: 64Gi
# ...
It is also possible to specify a resource request just for one of the resources:
# ...
resources:
requests:
memory: 64Gi
# ...
Or:
# ...
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
.
# ...
resources:
limits:
cpu: 12
memory: 64Gi
# ...
It is also possible to specify the resource limit just for one of the resources:
# ...
resources:
limits:
memory: 64Gi
# ...
Or:
# ...
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 same1
CPU core.
# ...
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 example1000M
. -
To specify memory in gigabytes, use the
G
suffix. For example1G
. -
To specify memory in mebibytes, use the
Mi
suffix. For example1000Mi
. -
To specify memory in gibibytes, use the
Gi
suffix. For example1Gi
.
# ...
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
-
For more information about managing computing resources on OpenShift or Kubernetes, see Managing Compute Resources for Containers.
Configuring resource requests and limits
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
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: # ...
-
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
-
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.
-
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
-
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
eventuallylog4j2.properties
key. -
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.
# ...
readinessProbe:
initialDelaySeconds: 15
timeoutSeconds: 5
livenessProbe:
initialDelaySeconds: 15
timeoutSeconds: 5
# ...
Configuring healthchecks
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
livenessProbe
orreadinessProbe
property in theKafka
,KafkaConnect
orKafkaConnectS2I
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: # ...
-
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 ({}
).
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.
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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
metrics
property in theKafka
,KafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... metrics: lowercaseOutputName: true # ...
-
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
|
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. |
-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.
-Xms
# ...
jvmOptions:
"-Xmx": "2g"
"-Xms": "2g"
# ...
-server
-server
enables the server JVM. This option can be set to true or false.
-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.
-XX
objectjvmOptions:
"-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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
jvmOptions
property in theKafka
,KafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... jvmOptions: "-Xmx": "8g" "-Xms": "8g" # ... zookeeper: # ...
-
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. |
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
image: my-org/my-image:latest
# ...
zookeeper:
# ...
Configuring container images
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
image
property in theKafka
,KafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... image: my-org/my-image:latest # ... zookeeper: # ...
-
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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
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. ThetopologyKey
should be set tokubernetes.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: # ...
-
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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
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.
-
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: # ...
-
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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Select the nodes which should be used as dedicated
-
Make sure there are no workloads scheduled on these nodes
-
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
-
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
-
Edit the
affinity
andtolerations
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: # ...
-
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
.
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaConnectS2I
metadata:
name: my-cluster
spec:
# ...
replicas: 3
# ...
Changing number of replicas
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
replicas
property in theKafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaConnectS2I metadata: name: my-cluster spec: # ... replicas: 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>
3.3.2. 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.
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaConnect
metadata:
name: my-cluster
spec:
# ...
bootstrapServers: my-cluster-kafka-bootstrap:9092
# ...
Configuring bootstrap servers
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
bootstrapServers
property in theKafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaConnect metadata: name: my-cluster spec: # ... bootstrapServers: my-cluster-kafka-bootstrap:9092 # ...
-
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.
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.
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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
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>
-
Edit the
tls
property in theKafkaConnect
orKafkaConnectS2I
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 # ...
-
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. |
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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
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>
-
Edit the
tls
property in theKafkaConnect
orKafkaConnectS2I
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 # ...
-
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 valueconnect-cluster
-
offset.storage.topic
with default valueconnect-cluster-offsets
-
config.storage.topic
with default valueconnect-cluster-configs
-
status.storage.topic
with default valueconnect-cluster-status
-
key.converter
with default valueorg.apache.kafka.connect.json.JsonConverter
-
value.converter
with default valueorg.apache.kafka.connect.json.JsonConverter
-
internal.key.converter
with default valueorg.apache.kafka.connect.json.JsonConverter
-
internal.value.converter
with default valueorg.apache.kafka.connect.json.JsonConverter
-
internal.key.converter.schemas.enable
with default valuefalse
-
internal.value.converter.schemas.enable
with default valuefalse
These options will be automatically configured in case they are not present in the KafkaConnect.spec.config
or KafkaConnectS2I.spec.config
properties.
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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
config
property in theKafkaConnect
orKafkaConnectS2I
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 # ...
-
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
.
# ...
resources:
requests:
cpu: 12
memory: 64Gi
# ...
It is also possible to specify a resource request just for one of the resources:
# ...
resources:
requests:
memory: 64Gi
# ...
Or:
# ...
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
.
# ...
resources:
limits:
cpu: 12
memory: 64Gi
# ...
It is also possible to specify the resource limit just for one of the resources:
# ...
resources:
limits:
memory: 64Gi
# ...
Or:
# ...
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 same1
CPU core.
# ...
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 example1000M
. -
To specify memory in gigabytes, use the
G
suffix. For example1G
. -
To specify memory in mebibytes, use the
Mi
suffix. For example1000Mi
. -
To specify memory in gibibytes, use the
Gi
suffix. For example1Gi
.
# ...
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
-
For more information about managing computing resources on OpenShift or Kubernetes, see Managing Compute Resources for Containers.
Configuring resource requests and limits
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
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: # ...
-
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
-
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.
-
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
-
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
eventuallylog4j2.properties
key. -
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.
# ...
readinessProbe:
initialDelaySeconds: 15
timeoutSeconds: 5
livenessProbe:
initialDelaySeconds: 15
timeoutSeconds: 5
# ...
Configuring healthchecks
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
livenessProbe
orreadinessProbe
property in theKafka
,KafkaConnect
orKafkaConnectS2I
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: # ...
-
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 ({}
).
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.
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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
metrics
property in theKafka
,KafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... metrics: lowercaseOutputName: true # ...
-
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
|
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. |
-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.
-Xms
# ...
jvmOptions:
"-Xmx": "2g"
"-Xms": "2g"
# ...
-server
-server
enables the server JVM. This option can be set to true or false.
-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.
-XX
objectjvmOptions:
"-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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
jvmOptions
property in theKafka
,KafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... jvmOptions: "-Xmx": "8g" "-Xms": "8g" # ... zookeeper: # ...
-
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. |
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
image: my-org/my-image:latest
# ...
zookeeper:
# ...
Configuring container images
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Edit the
image
property in theKafka
,KafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... image: my-org/my-image:latest # ... zookeeper: # ...
-
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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
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. ThetopologyKey
should be set tokubernetes.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: # ...
-
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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
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.
-
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: # ...
-
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
-
An OpenShift or Kubernetes cluster
-
A running Cluster Operator
-
Select the nodes which should be used as dedicated
-
Make sure there are no workloads scheduled on these nodes
-
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
-
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
-
Edit the
affinity
andtolerations
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: # ...
-
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:0.6.0
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.
-
Create a Kafka Connect S2I cluster from the command-line
oc apply -f examples/kafka-connect/kafka-connect-s2i.yaml
-
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
-
Start a new image build using the prepared directory:
oc start-build <my-connect-cluster-connect> --from-dir ./<my-plugins>/
NoteThe name of the build will be changed according to the cluster name of the deployed Kafka Connect cluster. -
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.
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
-
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
-
Deploy the Cluster Operator
kubectl create -f examples/install/cluster-operator
-
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
-
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
-
Deploy the Cluster Operator
oc create -f examples/install/cluster-operator oc create -f examples/templates/cluster-operator
-
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
-
Helm has to be installed on the local machine.
-
Helm has to be installed in the OpenShift or Kubernetes cluster.
-
Deploy the Cluster Operator using the Helm command line tool:
helm install strimzi-kafka-operator-0.6.0.tgz
-
Verify whether the Cluster Operator has been deployed successfully using the Helm command line tool:
helm ls
-
For more information about Helm, see the Helm website.
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
, andTRACE
. 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 theKafka.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 thekafka-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 theKafka.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 theKafka.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 theKafka.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 theimage
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 theimage
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 theKafka.spec.entityOperator.topicOperator.image
in the Container images of theKafka
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 theKafka.spec.entityOperator.userOperator.image
in the Container images of theKafka
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 theKafka.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
andClusterRole
, -
RoleBinding
andClusterRoleBinding
.
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 thisServiceAccount
access to the nodes within the cluster via aClusterRole
calledstrimzi-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 aClusterRole
calledstrimzi-topic-operator
which grants this access via thestrimzi-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
:
ServiceAccount
for the Cluster OperatorapiVersion: 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
:
Deployment
for the Cluster OperatorapiVersion: 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
.
Role
for the Cluster OperatorapiVersion: 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 podsapiVersion: 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 OperatorapiVersion: 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
:
RoleBinding
for the Cluster OperatorapiVersion: 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:
RoleBinding
for the Cluster OperatorapiVersion: 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.
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
-
A running Cluster Operator
-
A
Kafka
resource to be created or updated
-
Topic Operator can be included in the Entity Operator. Edit the
Kafka
resource ensuring it has aKafka.spec.entityOperator
object that configures the Entity Operator. -
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>
-
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, seeEntityOperatorSpec
schema reference.
4.2.4. Configuring the Topic Operator with resource requests and limits
-
A running Cluster Operator
-
Edit the
Kafka
resource specifying in theKafka.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
-
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>
-
For more information about the schema of the resources object, see
Resources
schema reference.
4.2.5. Deploying the Topic Operator standalone
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.
-
An existing Kafka cluster for the Topic Operator to connect to.
-
Edit the
examples/install/topic-operator/05-Deployment-strimzi-topic-operator.yaml
resource. You will need to change the following-
The
STRIMZI_KAFKA_BOOTSTRAP_SERVERS
environment variable inDeployment.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. -
The
STRIMZI_ZOOKEEPER_CONNECT
environment variable inDeployment.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. -
The
STRIMZI_NAMESPACE
environment variable inDeployment.spec.template.spec.containers[0].env
should be set to the OpenShift or Kubernetes namespace in which you want the operator to watch forKafkaTopic
resources.
-
-
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
-
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 shows1 available
.NoteThis could take some time if you have a slow connection to the OpenShift or Kubernetes and the images have not been downloaded before.
-
For more information about the environment variables used to configure the Topic Operator, see Topic Operator environment.
-
For more information about getting the Cluster Operator to deploy the Topic Operator for you, see Deploying the Topic Operator using the Cluster Operator.
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
, andTRACE
. DefaultINFO
. 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 throughSTRIMZI_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 throughSTRIMZI_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
-
A running Cluster Operator
-
A
Kafka
resource to be created or updated.
-
Edit the
Kafka
resource ensuring it has aKafka.spec.entityOperator.userOperator
object that configures the User Operator how you want. -
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>
-
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, seeEntityOperatorSpec
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.
-
An existing Kafka cluster for the User Operator to connect to.
-
Edit the
examples/install/user-operator/05-Deployment-strimzi-user-operator.yaml
resource. You will need to change the following-
The
STRIMZI_CA_NAME
environment variable inDeployment.spec.template.spec.containers[0].env
should be set to point to an OpenShift or KubernetesSecret
which should contain the Certificate Authority for signing new user certificates for TLS Client Authentication. TheSecret
should contain the public key of the Certificate Authority under the keyclients-ca.crt
and the private key underclients-ca.key
. -
The
STRIMZI_ZOOKEEPER_CONNECT
environment variable inDeployment.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. -
The
STRIMZI_NAMESPACE
environment variable inDeployment.spec.template.spec.containers[0].env
should be set to the OpenShift or Kubernetes namespace in which you want the operator to watch forKafkaUser
resources.
-
-
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
-
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 shows1 available
.NoteThis could take some time if you have a slow connection to the OpenShift or Kubernetes and the images have not been downloaded before.
-
For more information about getting the Cluster Operator to deploy the User Operator for you, see Deploying the User Operator using the Cluster Operator.
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 itsspec.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.
-
A running Kafka cluster.
-
A running Topic Operator.
-
Prepare a file containing the
KafkaTopic
to be createdAn exampleKafkaTopic
apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaTopic metadata: name: orders labels: strimzi.io/cluster: my-cluster spec: partitions: 10 replicas: 2
NoteIt 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 theKafkaTopic.spec.topicName
cannot be changed after creation.NoteThe KafkaTopic.spec.partitions
cannot be decreased. -
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>
-
For more information about the schema for
KafkaTopics
, seeKafkaTopic
schema reference. -
For more information about deploying a Kafka cluster using the Cluster Operator, see Cluster Operator.
-
For more information about deploying the Topic Operator using the Cluster Operator, see Deploying the Topic Operator using the Cluster Operator.
-
For more information about deploying the Topic Operator standalone, see Deploying the Topic Operator standalone.
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.
-
A running Kafka cluster.
-
A running Topic Operator.
-
An existing
KafkaTopic
to be changed.
-
Prepare a file containing the desired
KafkaTopic
An exampleKafkaTopic
apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaTopic metadata: name: orders labels: strimzi.io/cluster: my-cluster spec: partitions: 16 replicas: 2
TipYou can get the current version of the resource using oc get kafkatopic orders -o yaml
.NoteChanging topic names using the KafkaTopic.spec.topicName
variable and decreasing partition size using theKafkaTopic.spec.partitions
variable is not supported by Kafka.CautionIncreasing spec.partitions
for topics with keys will change how records are partitioned, which can be particularly problematic when the topic uses semantic partitioning. -
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>
-
For more information about the schema for
KafkaTopics
, seeKafkaTopic
schema reference. -
For more information about deploying a Kafka cluster, see Cluster Operator.
-
For more information about deploying the Topic Operator using the Cluster Operator, see Deploying the Topic Operator using the Cluster Operator.
-
For more information about creating a topic using the Topic Operator, see Creating a topic.
5.4. Deleting a topic
This procedure describes how to delete a Kafka topic using a KafkaTopic
OpenShift or Kubernetes resource.
-
A running Kafka cluster.
-
A running Topic Operator.
-
An existing
KafkaTopic
to be deleted.
-
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>
NoteWhether the topic can actually be deleted depends on the value of the delete.topic.enable
Kafka broker configuration, specified in theKafka.spec.kafka.config
property.
-
For more information about deploying a Kafka cluster using the Cluster Operator, see Cluster Operator.
-
For more information about deploying the Topic Operator using the Cluster Operator, see Deploying the Topic Operator using the Cluster Operator.
-
For more information about creating a topic using the Topic Operator, see Creating a topic.
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
-
A running Kafka cluster.
-
A running User Operator.
-
Prepare a YAML file containing the
KafkaUser
to be created.An exampleKafkaUser
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
-
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>
-
Use the credentials from the secret
my-user
in your application
-
For more information about deploying the Cluster Operator, see Cluster Operator.
-
For more information about the
KafkaUser
object, seeKafkaUser
schema reference.
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.
-
A running Kafka cluster.
-
A running User Operator.
-
An existing
KafkaUser
to be changed
-
Prepare a YAML file containing the desired
KafkaUser
.An exampleKafkaUser
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 usingkubectl edit
:kubectl edit kafkauser <your-user-name>
On OpenShift this can be done using
oc edit
:oc edit kafkauser <your-user-name>
-
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>
-
Use the updated credentials from the secret
my-user
in your application
-
For more information about deploying the Cluster Operator, see Cluster Operator.
-
For more information about the
KafkaUser
object, seeKafkaUser
schema reference.
6.4. Deleting a Kafka user
This procedure describes how to delete a Kafka user created with KafkaUser
OpenShift or Kubernetes resource.
-
A running Kafka cluster.
-
A running User Operator.
-
An existing
KafkaUser
to be deleted.
-
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-
For more information about deploying the Cluster Operator, see Cluster Operator.
-
For more information about the
KafkaUser
object, seeKafkaUser
schema reference.
-
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
.
KafkaUser
with enabled TLS Client AuthenticationapiVersion: 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.
Secret
with user credentialsapiVersion: 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
ordeny
. Thetype
field is optional and when not specified, the ACL rule will be treated asallow
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
NoteNot 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. Thehost
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. Usetopic
for Topics,group
for Consumer Groups andcluster
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 thepatternType
property to the valueliteral
. Literal names will be taken exactly as they are specified in thename
field. To specify the name as a prefix, set thepatternType
property to the valueprefix
. Prefix type names will use the value from thename
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.
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
-
For more information about the
KafkaUser
object, seeKafkaUser
schema reference.
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.
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. |
C.2. KafkaSpec
schema reference
Used in: Kafka
Field | Description |
---|---|
kafka |
Configuration of the Kafka cluster. |
zookeeper |
Configuration of the Zookeeper cluster. |
topicOperator |
Configuration of the Topic Operator. |
entityOperator |
Configuration of the Entity Operator. |
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 |
listeners |
Configures listeners of Kafka brokers. |
authorization |
Authorization configuration for Kafka brokers. The type depends on the value of the |
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 |
brokerRackInitImage |
The image of the init container used for initializing the |
string |
|
affinity |
Pod affinity rules.See external documentation of core/v1 affinity. |
tolerations |
Pod’s tolerations.See external documentation of core/v1 tolerations. |
Toleration array |
|
livenessProbe |
Pod liveness checking. |
readinessProbe |
Pod readiness checking. |
jvmOptions |
JVM Options for pods. |
resources |
Resource constraints (limits and requests). |
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 |
tlsSidecar |
TLS sidecar configuration. |
C.4. EphemeralStorage
schema reference
Used in: KafkaClusterSpec
, ZookeeperClusterSpec
The type
property is a discriminator that distinguishes the use of the type EphemeralStorage
from PersistentClaimStorage
.
It must have the value ephemeral
for the type EphemeralStorage
.
Field | Description |
---|---|
type |
Must be |
string |
C.5. PersistentClaimStorage
schema reference
Used in: KafkaClusterSpec
, ZookeeperClusterSpec
The type
property is a discriminator that distinguishes the use of the type PersistentClaimStorage
from EphemeralStorage
.
It must have the value persistent-claim
for the type PersistentClaimStorage
.
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 |
string |
C.6. KafkaListeners
schema reference
Used in: KafkaClusterSpec
Field | Description |
---|---|
plain |
Configures plain listener on port 9092. |
tls |
Configures TLS listener on port 9093. |
C.8. KafkaListenerTls
schema reference
Used in: KafkaListeners
Field | Description |
---|---|
authentication |
Authorization configuration for Kafka brokers. The type depends on the value of the |
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 |
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 |
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 |
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
Used in: EntityTopicOperatorSpec
, EntityUserOperatorSpec
, KafkaClusterSpec
, KafkaConnectS2ISpec
, KafkaConnectSpec
, Sidecar
, TopicOperatorSpec
, ZookeeperClusterSpec
Field | Description |
---|---|
limits |
Resource limits applied at runtime. |
requests |
Resource requests applied during pod scheduling. |
C.15. CpuMemory
schema reference
Used in: Resources
Field | Description |
---|---|
cpu |
CPU. |
string |
|
memory |
Memory. |
string |
C.16. InlineLogging
schema reference
Used in: EntityTopicOperatorSpec
, EntityUserOperatorSpec
, KafkaClusterSpec
, KafkaConnectS2ISpec
, KafkaConnectSpec
, TopicOperatorSpec
, ZookeeperClusterSpec
The type
property is a discriminator that distinguishes the use of the type InlineLogging
from ExternalLogging
.
It must have the value inline
for the type InlineLogging
.
Field | Description |
---|---|
loggers |
A Map from logger name to logger level. |
map |
|
type |
Must be |
string |
C.17. ExternalLogging
schema reference
Used in: EntityTopicOperatorSpec
, EntityUserOperatorSpec
, KafkaClusterSpec
, KafkaConnectS2ISpec
, KafkaConnectSpec
, TopicOperatorSpec
, ZookeeperClusterSpec
The type
property is a discriminator that distinguishes the use of the type ExternalLogging
from InlineLogging
.
It must have the value external
for the type ExternalLogging
.
Field | Description |
---|---|
name |
The name of the |
string |
|
type |
Must be |
string |
C.18. Sidecar
schema reference
Field | Description |
---|---|
image |
The docker image for the container. |
string |
|
resources |
Resource constraints (limits and requests). |
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 |
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. |
tolerations |
Pod’s tolerations.See external documentation of core/v1 tolerations. |
Toleration array |
|
livenessProbe |
Pod liveness checking. |
readinessProbe |
Pod readiness checking. |
jvmOptions |
JVM Options for pods. |
resources |
Resource constraints (limits and requests). |
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 |
tlsSidecar |
TLS sidecar configuration. |
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. |
resources |
Resource constraints (limits and requests). |
topicMetadataMaxAttempts |
The number of attempts at getting topic metadata. |
integer |
|
tlsSidecar |
TLS sidecar configuration. |
logging |
Logging configuration. The type depends on the value of the |
C.21. EntityOperatorSpec
schema reference
Used in: KafkaSpec
Field | Description |
---|---|
topicOperator |
Configuration of the Topic Operator. |
userOperator |
Configuration of the User Operator. |
affinity |
Pod affinity rules.See external documentation of core/v1 affinity. |
tolerations |
Pod’s tolerations.See external documentation of core/v1 tolerations. |
Toleration array |
|
tlsSidecar |
TLS sidecar configuration. |
C.22. EntityTopicOperatorSpec
schema reference
Used in: EntityOperatorSpec
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). |
topicMetadataMaxAttempts |
The number of attempts at getting topic metadata. |
integer |
|
logging |
Logging configuration. The type depends on the value of the |
C.23. EntityUserOperatorSpec
schema reference
Used in: EntityOperatorSpec
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). |
logging |
Logging configuration. The type depends on the value of the |
C.24. KafkaConnect
schema reference
Field | Description |
---|---|
spec |
The specification of the Kafka Connect deployment. |
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. |
readinessProbe |
Pod readiness checking. |
jvmOptions |
JVM Options for pods. |
affinity |
Pod affinity rules.See external documentation of core/v1 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 |
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 |
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). |
tls |
TLS configuration. |
C.26. KafkaConnectAuthenticationTls
schema reference
Used in: KafkaConnectS2ISpec
, KafkaConnectSpec
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. |
type |
Must be |
string |
C.27. CertAndKeySecretSource
schema reference
Used in: KafkaConnectAuthenticationTls
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
Used in: KafkaConnectS2ISpec
, KafkaConnectSpec
Field | Description |
---|---|
trustedCertificates |
Trusted certificates for TLS connection. |
|
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. |
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. |
readinessProbe |
Pod readiness checking. |
jvmOptions |
JVM Options for pods. |
affinity |
Pod affinity rules.See external documentation of core/v1 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 |
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 |
resources |
Resource constraints (limits and requests). |
tls |
TLS configuration. |
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. |
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 |
integer |
|
replicas |
The number of replicas the topic should have. If unspecified this will default to the broker’s |
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. |
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 |
authorization |
Authorization rules for this Kafka user. The type depends on the value of the |
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 |
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. |
|
|
type |
Must be |
string |
C.38. AclRule
schema reference
Used in: KafkaUserAuthorizationSimple
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 |
|
|
type |
The type of the rule.Currently the only supported type is |
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 |
string |
|
patternType |
Describes the pattern used in the resource field. The supported types are |
string (one of [prefix, literal]) |
|
type |
Must be |
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 |
string |
|
patternType |
Describes the pattern used in the resource field. The supported types are |
string (one of [prefix, literal]) |
|
type |
Must be |
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 |
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 .
|
-
Access to the Grafana UI using
admin/admin
credentials. -
Click on the "Add data source" button from the Grafana home in order to add Prometheus as data source.
-
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. -
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. -
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.