1. Overview of Strimzi
Strimzi is based on Apache Kafka, a popular platform for streaming data delivery and processing. Strimzi makes it easy to run Apache Kafka on Kubernetes.
Strimzi provides three operators:
- Cluster Operator
-
Responsible for deploying and managing Apache Kafka clusters within a Kubernetes cluster.
- Topic Operator
-
Responsible for managing Kafka topics within a Kafka cluster running within a Kubernetes cluster.
- User Operator
-
Responsible for managing Kafka users within a Kafka cluster running within a Kubernetes cluster.
Note
|
The Cluster Operator can deploy the Topic Operator and User Operator (as part of an Entity Operator configuration) at the same time as a Kafka cluster. |
1.1. Kafka Key Features
-
Designed for horizontal scalability
-
Message ordering guarantee at the partition level
-
Message rewind/replay
-
"Long term" storage allows the reconstruction of an application state by replaying the messages
-
Combines with compacted topics to use Kafka as a key-value store
-
-
For more information about Apache Kafka, see the Apache Kafka website.
1.2. Document Conventions
In this document, replaceable text is styled in monospace and italics.
For example, in the following code, you will want to replace my-namespace
with the name of your namespace:
sed -i 's/namespace: .*/namespace: my-namespace/' install/cluster-operator/*RoleBinding*.yaml
2. Getting started with Strimzi
Strimzi is designed to work on all types of Kubernetes cluster regardless of distribution, from public and private clouds to local deployments intended for development. Strimzi supports a few features which are specific to OpenShift, where such integration benefits OpenShift users and cannot be implemented equivalently using standard Kubernetes.
This guide assumes that a Kubernetes cluster is available and the kubectl
command-line tool is installed and configured to connect to the running cluster.
When no existing Kubernetes cluster is available, minikube
can be used to create a local cluster.
More details can be found in Installing Kubernetes clusters.
If you want to use OpenShift-specific features and have no OpenShift cluster minishift
can be used to create a local cluster.
More details can be found in Installing OpenShift clusters.
Note
|
To run the commands in this guide, your cluster user must have the rights to manage role-based access control (RBAC) and CRDs. |
2.1. Installing Strimzi and deploying components
To install Strimzi, download the release artefacts from GitHub.
The folder contains several YAML files to help you deploy the components of Strimzi to Kubernetes, perform common operations, and configure your Kafka cluster. The YAML files are referenced throughout this documentation.
Additionally, a Helm Chart is provided for deploying the Cluster Operator using Helm. The container images are available through the Docker Hub.
The remainder of this chapter provides an overview of each component and instructions for deploying the components to Kubernetes using the YAML files provided.
Note
|
Although container images for Strimzi are available in the Docker Hub, we recommend that you use the YAML files provided instead. |
2.2. Custom resources
Custom resource definitions (CRDs) extend the Kubernetes API, providing definitions to create and modify custom resources to a Kubernetes cluster. Custom resources are created as instances of CRDs.
In Strimzi, CRDs introduce custom resources specific to Strimzi to a Kubernetes cluster, such as Kafka, Kafka Connect, Kafka Mirror Maker, and users and topics custom resources. CRDs provide configuration instructions, defining the schemas used to instantiate and manage the Strimzi-specific resources. CRDs also allow Strimzi resources to benefit from native Kubernetes features like CLI accessibility and configuration validation.
CRDs require a one-time installation in a cluster. Depending on the cluster setup, installation typically requires cluster admin privileges.
Note
|
Access to manage custom resources is limited to Strimzi administrators. |
CRDs and custom resources are defined as YAML files.
A CRD defines a new kind
of resource, such as kind:Kafka
, within a Kubernetes cluster.
The Kubernetes API server allows custom resources to be created based on the kind
and understands from the CRD how to validate and store the custom resource when it is added to the Kubernetes cluster.
Warning
|
When CRDs are deleted, custom resources of that type are also deleted. Additionally, the resources created by the custom resource, such as pods and statefulsets are also deleted. |
2.2.1. Strimzi custom resource example
Each Strimzi-specific custom resource conforms to the schema defined by the CRD for the resource’s kind
.
To understand the relationship between a CRD and a custom resource, let’s look at a sample of the CRD for a Kafka topic.
apiVersion: kafka.strimzi.io/v1beta1
kind: CustomResourceDefinition
metadata: (1)
name: kafkatopics.kafka.strimzi.io
labels:
app: strimzi
spec: (2)
group: kafka.strimzi.io
versions:
v1beta1
scope: Namespaced
names:
# ...
singular: kafkatopic
plural: kafkatopics
shortNames:
- kt (3)
additionalPrinterColumns: (4)
# ...
subresources:
status: {} (5)
validation: (6)
openAPIV3Schema:
properties:
spec:
type: object
properties:
partitions:
type: integer
minimum: 1
replicas:
type: integer
minimum: 1
maximum: 32767
# ...
-
The metadata for the topic CRD, its name and a label to identify the CRD.
-
The specification for this CRD, including the group (domain) name, the plural name and the supported schema version, which are used in the URL to access the API of the topic. The other names are used to identify instance resources in the CLI. For example,
kubectl get kafkatopic my-topic
orkubectl get kafkatopics
. -
The shortname can be used in CLI commands. For example,
kubectl get kt
can be used as an abbreviation instead ofkubectl get kafkatopic
. -
The information presented when using a
get
command on the custom resource. -
The current status of the CRD as described in the schema reference for the resource.
-
openAPIV3Schema validation provides validation for the creation of topic custom resources. For example, a topic requires at least one partition and one replica.
Note
|
You can identify the CRD YAML files supplied with the Strimzi installation files, because the file names contain an index number followed by ‘Crd’. |
Here is a corresponding example of a KafkaTopic
custom resource.
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaTopic (1)
metadata:
name: my-topic
labels:
strimzi.io/cluster: my-cluster (2)
spec: (3)
partitions: 1
replicas: 1
config:
retention.ms: 7200000
segment.bytes: 1073741824
status:
conditions: (4)
lastTransitionTime: "2019-08-20T11:37:00.706Z"
status: "True"
type: Ready
observedGeneration: 1
/ ...
-
The
kind
andapiVersion
identify the CRD of which the custom resource is an instance. -
A label, applicable only to
KafkaTopic
andKafkaUser
resources, that defines the name of the Kafka cluster (which is same as the name of theKafka
resource) to which a topic or user belongs.The name is used by the Topic Operator and User Operator to identify the Kafka cluster when creating a topic or user.
-
The spec shows the number of partitions and replicas for the topic as well as the configuration parameters for the topic itself. In this example, the retention period for a message to remain in the topic and the segment file size for the log are specified.
-
Status conditions for the
KafkaTopic
resource. Thetype
condition changed toReady
at thelastTransitionTime
.
Custom resources can be applied to a cluster through the platform CLI. When the custom resource is created, it uses the same validation as the built-in resources of the Kubernetes API.
After a KafkaTopic
custom resource is created, the Topic Operator is notified and corresponding Kafka topics are created in Strimzi.
2.2.2. Strimzi custom resource status
The status
property of a Strimzi custom resource publishes information about the resource to users and tools that need it.
Several resources have a status
property, as described in the following table.
Strimzi resource | Schema reference | Publishes status information on… |
---|---|---|
|
The Kafka cluster. |
|
|
The Kafka Connect cluster, if deployed. |
|
|
The Kafka Connect cluster with Source-to-Image support, if deployed. |
|
|
The Kafka MirrorMaker tool, if deployed. |
|
|
Kafka topics in your Kafka cluster. |
|
|
Kafka users in your Kafka cluster. |
|
|
The Strimzi Kafka Bridge, if deployed. |
The status
property of a resource provides information on the resource’s:
-
Current state, in the
status.conditions
property -
Last observed generation, in the
status.observedGeneration
property
The status
property also provides resource-specific information. For example:
-
KafkaConnectStatus
provides the REST API endpoint for Kafka Connect connectors. -
KafkaUserStatus
provides the user name of the Kafka user and theSecret
in which their credentials are stored. -
KafkaBridgeStatus
provides the HTTP address at which external client applications can access the Bridge service.
A resource’s current state is useful for tracking progress related to the resource achieving its desired state, as defined by the spec
property. The status conditions provide the time and reason the state of the resource changed and details of events preventing or delaying the operator from realizing the resource’s desired state.
The last observed generation is the generation of the resource that was last reconciled by the Cluster Operator. If the value of observedGeneration
is different from the value of metadata.generation
, the operator has not yet processed the latest update to the resource. If these values are the same, the status information reflects the most recent changes to the resource.
Strimzi creates and maintains the status of custom resources, periodically evaluating the current state of the custom resource and updating its status accordingly.
When performing an update on a custom resource using kubectl edit
, for example, its status
is not editable. Moreover, changing the status
would not affect the configuration of the Kafka cluster.
Here we see the status
property specified for a Kafka custom resource.
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
spec:
# ...
status:
conditions: (1)
- lastTransitionTime: 2019-07-23T23:46:57+0000
status: "True"
type: Ready (2)
observedGeneration: 4 (3)
listeners: (4)
- addresses:
- host: my-cluster-kafka-bootstrap.myproject.svc
port: 9092
type: plain
- addresses:
- host: my-cluster-kafka-bootstrap.myproject.svc
port: 9093
type: tls
- addresses:
- host: 172.29.49.180
port: 9094
type: external
# ...
-
Status
conditions
describe criteria related to the status that cannot be deduced from the existing resource information, or are specific to the instance of a resource. -
The
Ready
condition indicates whether the Cluster Operator currently considers the Kafka cluster able to handle traffic. -
The
observedGeneration
indicates the generation of theKafka
custom resource that was last reconciled by the Cluster Operator. -
The
listeners
describe the current Kafka bootstrap addresses by type.ImportantThe address in the custom resource status for external listeners with type nodeport
is currently not supported.
Note
|
The Kafka bootstrap addresses listed in the status do not signify that those endpoints or the Kafka cluster is in a ready state. |
You can access status information for a resource from the command line. For more information, see Checking the status of a custom resource.
2.3. Cluster Operator
The Cluster Operator is responsible for deploying and managing Apache Kafka clusters within a Kubernetes cluster.
2.3.1. Overview of the Cluster Operator component
Strimzi uses the Cluster Operator to deploy and manage clusters for:
-
Kafka (including ZooKeeper, Entity Operator and Kafka Exporter)
-
Kafka Connect
-
Kafka Mirror Maker
-
Kafka Bridge
Custom resources are used to deploy the clusters.
For example, to deploy a Kafka cluster:
-
A
Kafka
resource with the cluster configuration is created within the Kubernetes cluster. -
The Cluster Operator deploys a corresponding Kafka cluster, based on what is declared in the
Kafka
resource.
The Cluster Operator can also deploy (through Entity Operator configuration of the Kafka
resource):
-
A Topic Operator to provide operator-style topic management through
KafkaTopic
custom resources -
A User Operator to provide operator-style user management through
KafkaUser
custom resources
For more information on the configuration options supported by the Kafka
resource, see Kafka cluster configuration.
Note
|
On OpenShift, a Kafka Connect deployment can incorporate a Source2Image feature to provides a convenient way to include connectors. |
2.3.2. Watch options for a Cluster Operator deployment
When the Cluster Operator is running, it starts to watch for updates of Kafka resources.
Depending on the deployment, the Cluster Operator can watch Kafka resources from:
Note
|
Strimzi provides example YAML files to make the deployment process easier. |
The Cluster Operator watches the following resources:
-
Kafka
for the Kafka cluster. -
KafkaConnect
for the Kafka Connect cluster. -
KafkaConnectS2I
for the Kafka Connect cluster with Source2Image support. -
KafkaMirrorMaker
for the Kafka Mirror Maker instance. -
KafkaBridge
for the Kafka Bridge instance
When one of these resources is created in the Kubernetes cluster, the operator gets the cluster description from the resource and starts creating a new cluster for the resource by creating the necessary Kubernetes resources, such as StatefulSets, Services and ConfigMaps.
Each time a Kafka resource is updated, the operator performs corresponding updates on the Kubernetes resources that make up the cluster for the resource.
Resources are either patched or deleted, and then recreated in order to make the cluster for the resource reflect the desired state of the cluster. This operation might cause a rolling update that might lead to service disruption.
When a resource is deleted, the operator undeploys the cluster and deletes all related Kubernetes resources.
2.3.3. Deploying the Cluster Operator to watch a single namespace
-
This procedure requires use of a Kubernetes user account which is able to create
CustomResourceDefinitions
,ClusterRoles
andClusterRoleBindings
. Use of Role Base Access Control (RBAC) in the Kubernetes cluster usually means that permission to create, edit, and delete these resources is limited to Kubernetes cluster administrators, such assystem:admin
. -
Modify the installation files according to the namespace the Cluster Operator is going to be installed in.
On Linux, use:
sed -i 's/namespace: .*/namespace: my-namespace/' install/cluster-operator/*RoleBinding*.yaml
On MacOS, use:
sed -i '' 's/namespace: .*/namespace: my-namespace/' install/cluster-operator/*RoleBinding*.yaml
-
Deploy the Cluster Operator:
kubectl apply -f install/cluster-operator -n my-namespace
2.3.4. Deploying the Cluster Operator to watch multiple namespaces
-
This procedure requires use of a Kubernetes user account which is able to create
CustomResourceDefinitions
,ClusterRoles
andClusterRoleBindings
. Use of Role Base Access Control (RBAC) in the Kubernetes cluster usually means that permission to create, edit, and delete these resources is limited to Kubernetes cluster administrators, such assystem:admin
. -
Edit the installation files according to the namespace the Cluster Operator is going to be installed in.
On Linux, use:
sed -i 's/namespace: .*/namespace: my-namespace/' install/cluster-operator/*RoleBinding*.yaml
On MacOS, use:
sed -i '' 's/namespace: .*/namespace: my-namespace/' install/cluster-operator/*RoleBinding*.yaml
-
Edit the file
install/cluster-operator/050-Deployment-strimzi-cluster-operator.yaml
and in the environment variableSTRIMZI_NAMESPACE
list all the namespaces where Cluster Operator should watch for resources. For example:apiVersion: apps/v1 kind: Deployment spec: # ... template: spec: serviceAccountName: strimzi-cluster-operator containers: - name: strimzi-cluster-operator image: strimzi/operator:0.15.0 imagePullPolicy: IfNotPresent env: - name: STRIMZI_NAMESPACE value: watched-namespace-1,watched-namespace-2,watched-namespace-3
-
For all namespaces which should be watched by the Cluster Operator (
watched-namespace-1
,watched-namespace-2
,watched-namespace-3
in the above example), install theRoleBindings
. Replace thewatched-namespace
with the namespace used in the previous step.This can be done using
kubectl apply
:kubectl apply -f install/cluster-operator/020-RoleBinding-strimzi-cluster-operator.yaml -n watched-namespace kubectl apply -f install/cluster-operator/031-RoleBinding-strimzi-cluster-operator-entity-operator-delegation.yaml -n watched-namespace kubectl apply -f install/cluster-operator/032-RoleBinding-strimzi-cluster-operator-topic-operator-delegation.yaml -n watched-namespace
-
Deploy the Cluster Operator
This can be done using
kubectl apply
:kubectl apply -f install/cluster-operator -n my-namespace
2.3.5. Deploying the Cluster Operator to watch all namespaces
You can configure the Cluster Operator to watch Strimzi resources across all namespaces in your Kubernetes cluster. When running in this mode, the Cluster Operator automatically manages clusters in any new namespaces that are created.
-
This procedure requires use of a Kubernetes user account which is able to create
CustomResourceDefinitions
,ClusterRoles
andClusterRoleBindings
. Use of Role Base Access Control (RBAC) in the Kubernetes cluster usually means that permission to create, edit, and delete these resources is limited to Kubernetes cluster administrators, such assystem:admin
. -
Your Kubernetes cluster is running.
-
Configure the Cluster Operator to watch all namespaces:
-
Edit the
050-Deployment-strimzi-cluster-operator.yaml
file. -
Set the value of the
STRIMZI_NAMESPACE
environment variable to*
.apiVersion: apps/v1 kind: Deployment spec: # ... template: spec: # ... serviceAccountName: strimzi-cluster-operator containers: - name: strimzi-cluster-operator image: strimzi/operator:0.15.0 imagePullPolicy: IfNotPresent env: - name: STRIMZI_NAMESPACE value: "*" # ...
-
-
Create
ClusterRoleBindings
that grant cluster-wide access to all namespaces to the Cluster Operator.Use the
kubectl create clusterrolebinding
command:kubectl create clusterrolebinding strimzi-cluster-operator-namespaced --clusterrole=strimzi-cluster-operator-namespaced --serviceaccount my-namespace:strimzi-cluster-operator kubectl create clusterrolebinding strimzi-cluster-operator-entity-operator-delegation --clusterrole=strimzi-entity-operator --serviceaccount my-namespace:strimzi-cluster-operator kubectl create clusterrolebinding strimzi-cluster-operator-topic-operator-delegation --clusterrole=strimzi-topic-operator --serviceaccount my-namespace:strimzi-cluster-operator
Replace
my-namespace
with the namespace in which you want to install the Cluster Operator. -
Deploy the Cluster Operator to your Kubernetes cluster.
Use the
kubectl apply
command:kubectl apply -f install/cluster-operator -n my-namespace
2.3.6. Deploying the Cluster Operator using Helm Chart
-
Helm client has to be installed on the local machine.
-
Helm has to be installed in the Kubernetes cluster.
-
Add the Strimzi Helm Chart repository:
helm repo add strimzi https://strimzi.io/charts/
-
Deploy the Cluster Operator using the Helm command line tool:
helm install strimzi/strimzi-kafka-operator
-
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.
2.3.7. Deploying the Cluster Operator from OperatorHub.io
OperatorHub.io is a catalog of Kubernetes Operators sourced from multiple providers. It offers you an alternative way to install stable versions of Strimzi using the Strimzi Kafka Operator.
The Operator Lifecycle Manager is used for the installation and management of all Operators published on OperatorHub.io.
To install Strimzi from OperatorHub.io, locate the Strimzi Kafka Operator and follow the instructions provided.
2.4. Kafka cluster
You can use Strimzi to deploy an ephemeral or persistent Kafka cluster to Kubernetes. When installing Kafka, Strimzi also installs a ZooKeeper cluster and adds the necessary configuration to connect Kafka with ZooKeeper.
You can also use it to deploy Kafka Exporter.
- Ephemeral cluster
-
In general, an ephemeral (that is, temporary) Kafka cluster is suitable for development and testing purposes, not for production. This deployment uses
emptyDir
volumes for storing broker information (for ZooKeeper) and topics or partitions (for Kafka). Using anemptyDir
volume means that its content is strictly related to the pod life cycle and is deleted when the pod goes down. - Persistent cluster
-
A persistent Kafka cluster uses
PersistentVolumes
to store ZooKeeper and Kafka data. ThePersistentVolume
is acquired using aPersistentVolumeClaim
to make it independent of the actual type of thePersistentVolume
. For example, it can use Amazon EBS volumes in Amazon AWS deployments without any changes in the YAML files. ThePersistentVolumeClaim
can use aStorageClass
to trigger automatic volume provisioning.
Strimzi includes several examples for deploying a Kafka cluster.
-
kafka-persistent.yaml
deploys a persistent cluster with three ZooKeeper and three Kafka nodes. -
kafka-jbod.yaml
deploys a persistent cluster with three ZooKeeper and three Kafka nodes (each using multiple persistent volumes). -
kafka-persistent-single.yaml
deploys a persistent cluster with a single ZooKeeper node and a single Kafka node. -
kafka-ephemeral.yaml
deploys an ephemeral cluster with three ZooKeeper and three Kafka nodes. -
kafka-ephemeral-single.yaml
deploys an ephemeral cluster with three ZooKeeper nodes and a single Kafka node.
The example clusters are named my-cluster
by default.
The cluster name is defined by the name of the resource and cannot be changed after the cluster has been deployed. To change the cluster name before you deploy the cluster, edit the Kafka.metadata.name
property of the resource in the relevant YAML file.
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
name: my-cluster
# ...
2.4.1. Deploying the Kafka cluster
You can deploy an ephemeral or persistent Kafka cluster to Kubernetes on the command line.
-
The Cluster Operator is deployed.
-
If you plan to use the cluster for development or testing purposes, you can create and deploy an ephemeral cluster using
kubectl apply
.kubectl apply -f examples/kafka/kafka-ephemeral.yaml
-
If you plan to use the cluster in production, create and deploy a persistent cluster using
kubectl apply
.kubectl apply -f examples/kafka/kafka-persistent.yaml
-
For more information on deploying the Cluster Operator, see Cluster Operator.
-
For more information on the different configuration options supported by the
Kafka
resource, see Kafka cluster configuration.
2.5. Kafka Connect
Kafka Connect is a tool for streaming data between Apache Kafka and external systems. It provides a framework for moving large amounts of data into and out of your Kafka cluster while maintaining scalability and reliability. Kafka Connect is typically used to integrate Kafka with external databases and storage and messaging systems.
You can use Kafka Connect to:
-
Build connector plug-ins (as JAR files) for your Kafka cluster
-
Run connectors
Kafka Connect includes the following built-in connectors for moving file-based data into and out of your Kafka cluster.
File Connector | Description |
---|---|
|
Transfers data to your Kafka cluster from a file (the source). |
|
Transfers data from your Kafka cluster to a file (the sink). |
In Strimzi, you can use the Cluster Operator to deploy a Kafka Connect to your Kubernetes cluster. OpenShift also supports Kafka Connect Source-2-Image (S2I) clusters.
A Kafka Connect cluster is implemented as a Deployment
with a configurable number of workers. The Kafka Connect REST API is available on port 8083, as the <connect-cluster-name>-connect-api
service.
For more information on deploying a Kafka Connect S2I cluster on OpenShift, see Creating a container image using OpenShift builds and Source-to-Image.
2.5.1. Deploying Kafka Connect to your cluster
You can deploy a Kafka Connect cluster to your Kubernetes cluster by using the Cluster Operator.
-
Use the
kubectl apply
command to create aKafkaConnect
resource based on thekafka-connect.yaml
file:kubectl apply -f examples/kafka-connect/kafka-connect.yaml
2.5.2. Extending Kafka Connect with connector plug-ins
The Strimzi container images for Kafka Connect include the two built-in file connectors: FileStreamSourceConnector
and FileStreamSinkConnector
.
You can add your own connectors by:
-
Creating a container image from the Kafka Connect base image (manually or using your CI (continuous integration), for example).
-
Creating a container image using OpenShift builds and Source-to-Image (S2I) - available only on OpenShift.
Creating a Docker image from the Kafka Connect base image
You can use the Kafka container image on Docker Hub as a base image for creating your own custom image with additional connector plug-ins.
The following procedure explains how to create your custom image and add it to the /opt/kafka/plugins
directory. At startup, the Strimzi version of Kafka Connect loads any third-party connector plug-ins contained in the /opt/kafka/plugins
directory.
-
Create a new
Dockerfile
usingstrimzi/kafka:0.15.0-kafka-2.3.1
as the base image:FROM strimzi/kafka:0.15.0-kafka-2.3.1 USER root:root COPY ./my-plugins/ /opt/kafka/plugins/ USER 1001
-
Build the container image.
-
Push your custom image to your container registry.
-
Point to the new container image.
You can either:
-
Edit the
KafkaConnect.spec.image
property of theKafkaConnect
custom resource.If set, this property overrides the
STRIMZI_KAFKA_CONNECT_IMAGES
variable in the Cluster Operator.apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnect metadata: name: my-connect-cluster spec: #... image: my-new-container-image
or
-
In the
install/cluster-operator/050-Deployment-strimzi-cluster-operator.yaml
file, edit theSTRIMZI_KAFKA_CONNECT_IMAGES
variable to point to the new container image, and then reinstall the Cluster Operator.
-
-
For more information on the
KafkaConnect.spec.image property
, see Container images. -
For more information on the
STRIMZI_KAFKA_CONNECT_IMAGES
variable, see Cluster Operator Configuration.
Creating a container image using OpenShift builds and Source-to-Image
You can use OpenShift builds and the Source-to-Image (S2I) framework to create new container images. An OpenShift build takes a builder image with S2I support, together with source code and binaries provided by the user, and uses them to build a new container image. Once built, container images are stored in OpenShift’s local container image repository and are available for use in deployments.
A Kafka Connect builder image with S2I support is provided on the Docker Hub as part of the strimzi/kafka:0.15.0-kafka-2.3.1
image. This S2I image takes your binaries (with plug-ins and connectors) and stores them in the /tmp/kafka-plugins/s2i
directory. It creates a new Kafka Connect image from this directory, which can then be used with the Kafka Connect deployment. When started using the enhanced image, Kafka Connect loads any third-party plug-ins from the /tmp/kafka-plugins/s2i
directory.
-
On the command line, use the
oc apply
command to create and deploy a Kafka Connect S2I cluster:oc apply -f examples/kafka-connect/kafka-connect-s2i.yaml
-
Create a directory with Kafka Connect plug-ins:
$ tree ./my-plugins/ ./my-plugins/ ├── debezium-connector-mongodb │ ├── bson-3.4.2.jar │ ├── CHANGELOG.md │ ├── CONTRIBUTE.md │ ├── COPYRIGHT.txt │ ├── debezium-connector-mongodb-0.7.1.jar │ ├── debezium-core-0.7.1.jar │ ├── LICENSE.txt │ ├── mongodb-driver-3.4.2.jar │ ├── mongodb-driver-core-3.4.2.jar │ └── README.md ├── debezium-connector-mysql │ ├── CHANGELOG.md │ ├── CONTRIBUTE.md │ ├── COPYRIGHT.txt │ ├── debezium-connector-mysql-0.7.1.jar │ ├── debezium-core-0.7.1.jar │ ├── LICENSE.txt │ ├── mysql-binlog-connector-java-0.13.0.jar │ ├── mysql-connector-java-5.1.40.jar │ ├── README.md │ └── wkb-1.0.2.jar └── debezium-connector-postgres ├── CHANGELOG.md ├── CONTRIBUTE.md ├── COPYRIGHT.txt ├── debezium-connector-postgres-0.7.1.jar ├── debezium-core-0.7.1.jar ├── LICENSE.txt ├── postgresql-42.0.0.jar ├── protobuf-java-2.6.1.jar └── README.md
-
Use the
oc start-build
command to start a new build of the image using the prepared directory:oc start-build my-connect-cluster-connect --from-dir ./my-plugins/
NoteThe name of the build is the same as the name of the deployed Kafka Connect cluster. -
Once the build has finished, the new image is used automatically by the Kafka Connect deployment.
2.6. Kafka Mirror Maker
The Cluster Operator deploys one or more Kafka Mirror Maker replicas to replicate data between Kafka clusters. This process is called mirroring to avoid confusion with the Kafka partitions replication concept. The Mirror Maker consumes messages from the source cluster and republishes those messages to the target cluster.
For information about example resources and the format for deploying Kafka Mirror Maker, see Kafka Mirror Maker configuration.
2.6.1. Deploying Kafka Mirror Maker
-
Before deploying Kafka Mirror Maker, the Cluster Operator must be deployed.
-
Create a Kafka Mirror Maker cluster from the command-line:
kubectl apply -f examples/kafka-mirror-maker/kafka-mirror-maker.yaml
-
For more information about deploying the Cluster Operator, see Cluster Operator
2.7. Kafka Bridge
The Cluster Operator deploys one or more Kafka bridge replicas to send data between Kafka clusters and clients via HTTP API.
For information about example resources and the format for deploying Kafka Bridge, see Kafka Bridge configuration.
2.7.1. Deploying Kafka Bridge to your Kubernetes cluster
You can deploy a Kafka Bridge cluster to your Kubernetes cluster by using the Cluster Operator.
-
Use the
kubectl apply
command to create aKafkaBridge
resource based on thekafka-bridge.yaml
file:kubectl apply -f examples/kafka-bridge/kafka-bridge.yaml
2.8. Deploying example clients
-
An existing Kafka cluster for the client to connect to.
-
Deploy the producer.
Use
kubectl run
:kubectl run kafka-producer -ti --image=strimzi/kafka:0.15.0-kafka-2.3.1 --rm=true --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.
Use
kubectl run
:kubectl run kafka-consumer -ti --image=strimzi/kafka:0.15.0-kafka-2.3.1 --rm=true --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.
2.9. Topic Operator
The Topic Operator is responsible for managing Kafka topics within a Kafka cluster running within a Kubernetes cluster.
2.9.1. Overview of the Topic Operator component
The Topic Operator provides a way of managing topics in a Kafka cluster via Kubernetes resources.
The role of the Topic Operator is to keep a set of KafkaTopic
Kubernetes resources describing Kafka topics in-sync with corresponding Kafka topics.
Specifically, if a KafkaTopic
is:
-
Created, the operator will create the topic it describes
-
Deleted, the operator will delete the topic it describes
-
Changed, the operator will update the topic it describes
And also, in the other direction, if a topic is:
-
Created within the Kafka cluster, the operator will create a
KafkaTopic
describing it -
Deleted from the Kafka cluster, the operator will delete the
KafkaTopic
describing it -
Changed in the Kafka cluster, the operator will update the
KafkaTopic
describing it
This allows you to declare a KafkaTopic
as part of your application’s deployment and the Topic Operator will take care of creating the topic for you.
Your application just needs to deal with producing or consuming from the necessary topics.
If the topic is reconfigured or reassigned to different Kafka nodes, the KafkaTopic
will always be up to date.
For more details about creating, modifying and deleting topics, see Using the Topic Operator.
2.9.2. Deploying the Topic Operator using the Cluster Operator
This procedure describes how to deploy the Topic Operator using the Cluster Operator. If you want to use the Topic Operator with a Kafka cluster that is not managed by Strimzi, you must deploy the Topic Operator as a standalone component. For more information, see Deploying the standalone Topic Operator.
-
A running Cluster Operator
-
A
Kafka
resource to be created or updated
-
Ensure that the
Kafka.spec.entityOperator
object exists in theKafka
resource. This configures the Entity Operator.apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: #... entityOperator: topicOperator: {} userOperator: {}
-
Configure the Topic Operator using the fields described in
EntityTopicOperatorSpec
schema reference. -
Create or update the Kafka resource in Kubernetes.
Use
kubectl apply
:kubectl 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.
2.10. User Operator
The User Operator is responsible for managing Kafka users within a Kafka cluster running within a Kubernetes cluster.
2.10.1. Overview of the User Operator component
The User Operator manages Kafka users for a Kafka cluster by watching for KafkaUser
resources that describe Kafka users and ensuring that they are configured properly in the Kafka cluster.
For example:
-
if a
KafkaUser
is created, the User Operator will create the user it describes -
if a
KafkaUser
is deleted, the User Operator will delete the user it describes -
if a
KafkaUser
is changed, the User Operator will update the user it describes
Unlike the Topic Operator, the User Operator does not sync any changes from the Kafka cluster with the Kubernetes resources. Unlike the Kafka topics which might be created by applications directly in Kafka, it is not expected that the users will be managed directly in the Kafka cluster in parallel with the User Operator, so this should not be needed.
The User Operator allows you to declare a KafkaUser
as part of your application’s deployment.
When the user is created, the user credentials will be created in a Secret
.
Your application needs to use the user and its credentials for authentication and to produce or consume messages.
In addition to managing credentials for authentication, the User Operator also manages authorization rules by including a description of the user’s rights in the KafkaUser
declaration.
2.10.2. Deploying the User Operator using the Cluster Operator
-
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 Kubernetes.
This can be done using
kubectl apply
:kubectl 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.
2.11. Strimzi Administrators
Strimzi includes several custom resources. By default, permission to create, edit, and delete these resources is limited to Kubernetes cluster administrators. If you want to allow non-cluster administators to manage Strimzi resources, you must assign them the Strimzi Administrator role.
2.11.1. Designating Strimzi Administrators
-
Strimzi
CustomResourceDefinitions
are installed.
-
Create the
strimzi-admin
cluster role in Kubernetes.Use
kubectl apply
:kubectl apply -f install/strimzi-admin
-
Assign the
strimzi-admin
ClusterRole
to one or more existing users in the Kubernetes cluster.Use
kubectl create
:kubectl create clusterrolebinding strimzi-admin --clusterrole=strimzi-admin --user=user1 --user=user2
2.12. Container images
Container images for Strimzi are available in the Docker Hub. The installation YAML files provided by Strimzi will pull the images directly from the Docker Hub.
If you do not have access to the Docker Hub or want to use your own container repository:
-
Pull all container images listed here
-
Push them into your own registry
-
Update the image names in the installation YAML files
Note
|
Each Kafka version supported for the release has a separate image. |
Container image | Namespace/Repository | Description |
---|---|---|
Kafka |
|
Strimzi image for running Kafka, including:
|
Operator |
|
Strimzi image for running the operators:
|
Kafka Bridge |
|
Strimzi image for running the Strimzi kafka Bridge |
3. Deployment configuration
This chapter describes how to configure different aspects of the supported deployments:
-
Kafka clusters
-
Kafka Connect clusters
-
Kafka Connect clusters with Source2Image support
-
Kafka Mirror Maker
-
Kafka Bridge
-
OAuth 2.0 token based authentication
3.1. Kafka cluster configuration
The full schema of the Kafka
resource is described in the Kafka
schema reference.
All labels that are applied to the desired Kafka
resource will also be applied to the Kubernetes resources making up the Kafka cluster.
This provides a convenient mechanism for resources to be labeled as required.
3.1.1. Sample Kafka YAML configuration
For help in understanding the configuration options available for your Kafka deployment, refer to sample YAML file provided here.
The sample shows only some of the possible configuration options, but those that are particularly important include:
-
Resource requests (CPU / Memory)
-
JVM options for maximum and minimum memory allocation
-
Listeners (and authentication)
-
Authentication
-
Storage
-
Rack awareness
-
Metrics
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
replicas: 3 (1)
version: 0.15.0 (2)
resources: (3)
requests:
memory: 64Gi
cpu: "8"
limits: (4)
memory: 64Gi
cpu: "12"
jvmOptions: (5)
-Xms: 8192m
-Xmx: 8192m
listeners: (6)
tls:
authentication:(7)
type: tls
external: (8)
type: route
authentication:
type: tls
authorization: (9)
type: simple
config: (10)
auto.create.topics.enable: "false"
offsets.topic.replication.factor: 3
transaction.state.log.replication.factor: 3
transaction.state.log.min.isr: 2
storage: (11)
type: persistent-claim (12)
size: 10000Gi (13)
rack: (14)
topologyKey: failure-domain.beta.kubernetes.io/zone
metrics: (15)
lowercaseOutputName: true
rules: (16)
# Special cases and very specific rules
- pattern : kafka.server<type=(.+), name=(.+), clientId=(.+), topic=(.+), partition=(.*)><>Value
name: kafka_server_$1_$2
type: GAUGE
labels:
clientId: "$3"
topic: "$4"
partition: "$5"
# ...
zookeeper: (17)
replicas: 3
resources:
requests:
memory: 8Gi
cpu: "2"
limits:
memory: 8Gi
cpu: "2"
jvmOptions:
-Xms: 4096m
-Xmx: 4096m
storage:
type: persistent-claim
size: 1000Gi
metrics:
# ...
entityOperator: (18)
topicOperator:
resources:
requests:
memory: 512Mi
cpu: "1"
limits:
memory: 512Mi
cpu: "1"
userOperator:
resources:
requests:
memory: 512Mi
cpu: "1"
limits:
memory: 512Mi
cpu: "1"
kafkaExporter: (19)
# ...
-
Replicas specifies the number of broker nodes.
-
Kafka version, which can be changed by following the upgrade procedure.
-
Resource requests specify the resources to reserve for a given container.
-
Resource limits specify the maximum resources that can be consumed by a container.
-
JVM options can specify the minimum (
-Xms
) and maximum (-Xmx
) memory allocation for JVM. -
Listeners configure how clients connect to the Kafka cluster via bootstrap addresses. Listeners are configured as
plain
(without encryption),tls
orexternal
. -
Listener authentication mechanisms may be configured for each listener, and specified as mutual TLS or SCRAM-SHA.
-
External listener configuration specifies how the Kafka cluster is exposed outside Kubernetes, such as through a
route
,loadbalancer
ornodeport
. -
Authorization enables
simple
authorization on the Kafka broker using theSimpleAclAuthorizer
Kafka plugin. -
Config specifies the broker configuration. Standard Apache Kafka configuration may be provided, restricted to those properties not managed directly by Strimzi.
-
Storage is configured as
ephemeral
,persistent-claim
orjbod
. -
Storage size for persistent volumes may be increased and additional volumes may be added to JBOD storage.
-
Persistent storage has additional configuration options, such as a storage
id
andclass
for dynamic volume provisioning. -
Rack awareness is configured to spread replicas across different racks. A
topology
key must match the label of a cluster node. -
Kafka rules for exporting metrics to a Grafana dashboard through the JMX Exporter. A set of rules provided with Strimzi may be copied to your Kafka resource configuration.
-
ZooKeeper-specific configuration, which contains properties similar to the Kafka configuration.
-
Entity Operator configuration, which specifies the configuration for the Topic Operator and User Operator.
-
Kafka Exporter configuration, which is used to expose data as Prometheus metrics.
3.1.2. Data storage considerations
An efficient data storage infrastructure is essential to the optimal performance of Strimzi.
Strimzi requires block storage and is designed to work optimally with cloud-based block storage solutions, including Amazon Elastic Block Store (EBS). The use of file storage (for example, NFS) is not recommended.
Choose local storage (local persistent volumes) when possible. If local storage is not available, you can use a Storage Area Network (SAN) accessed by a protocol such as Fibre Channel or iSCSI.
Apache Kafka and ZooKeeper storage
Use separate disks for Apache Kafka and ZooKeeper.
Three types of data storage are supported:
-
Ephemeral (Recommended for development only)
-
Persistent
-
JBOD (Just a Bunch of Disks, suitable for Kafka only)
For more information, see Kafka and ZooKeeper storage.
Solid-state drives (SSDs), though not essential, can improve the performance of Kafka in large clusters where data is sent to and received from multiple topics asynchronously. SSDs are particularly effective with ZooKeeper, which requires fast, low latency data access.
Note
|
You do not need to provision replicated storage because Kafka and ZooKeeper both have built-in data replication. |
File systems
It is recommended that you configure your storage system to use the XFS file system. Strimzi is also compatible with the ext4 file system, but this might require additional configuration for best results.
3.1.3. Kafka and ZooKeeper storage types
As stateful applications, Kafka and ZooKeeper need to store data on disk. Strimzi supports three storage types for this data:
-
Ephemeral
-
Persistent
-
JBOD storage
Note
|
JBOD storage is supported only for Kafka, not for ZooKeeper. |
When configuring a Kafka
resource, you can specify the type of storage used by the Kafka broker and its corresponding ZooKeeper node. You configure the storage type using the storage
property in the following resources:
-
Kafka.spec.kafka
-
Kafka.spec.zookeeper
The storage type is configured in the type
field.
Warning
|
The storage type cannot be changed after a Kafka cluster is deployed. |
-
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 JBOD storage, see JBOD schema reference.
-
For more information about the schema for
Kafka
, seeKafka
schema reference.
Ephemeral storage
Ephemeral storage uses the `emptyDir` volumes to store data.
To use ephemeral storage, the type
field should be set to ephemeral
.
Important
|
EmptyDir volumes are not persistent and the data stored in them will be lost when the Pod is restarted.
After the new pod is started, it has to recover all data from other nodes of the cluster.
Ephemeral storage is not suitable for use with single node ZooKeeper clusters and for Kafka topics with replication factor 1, because it will lead to data loss.
|
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
storage:
type: ephemeral
# ...
zookeeper:
# ...
storage:
type: ephemeral
# ...
Log directories
The ephemeral volume will be used by the Kafka brokers as log directories mounted into the following path:
/var/lib/kafka/data/kafka-log_idx_
-
Where
idx
is the Kafka broker pod index. For example/var/lib/kafka/data/kafka-log0
.
Persistent storage
Persistent storage uses Persistent Volume Claims to provision persistent volumes for storing data. Persistent Volume Claims can be used to provision volumes of many different types, depending on the Storage Class which will provision the volume. The data types which can be used with persistent volume claims include many types of SAN storage as well as Local persistent volumes.
To use persistent storage, the type
has to be set to persistent-claim
.
Persistent storage supports additional configuration options:
id
(optional)-
Storage identification number. This option is mandatory for storage volumes defined in a JBOD storage declaration. Default is
0
. size
(required)-
Defines the size of the persistent volume claim, for example, "1000Gi".
class
(optional)-
The Kubernetes Storage Class to use for dynamic volume provisioning.
selector
(optional)-
Allows selecting a specific persistent volume to use. It contains key:value pairs representing labels for selecting such a volume.
deleteClaim
(optional)-
Boolean value which specifies if the Persistent Volume Claim has to be deleted when the cluster is undeployed. Default is
false
.
Warning
|
Increasing the size of persistent volumes in an existing Strimzi cluster is only supported in Kubernetes versions that support persistent volume resizing. The persistent volume to be resized must use a storage class that supports volume expansion. For other versions of Kubernetes and storage classes which do not support volume expansion, you must decide the necessary storage size before deploying the cluster. Decreasing the size of existing persistent volumes is not possible. |
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 to select a specific labeled persistent volume to provide needed features such as an SSD.
# ...
storage:
type: persistent-claim
size: 1Gi
selector:
hdd-type: ssd
deleteClaim: true
# ...
Storage class overrides
You can specify a different storage class for one or more Kafka brokers, instead of using the default storage class.
This is useful if, for example, storage classes are restricted to different availability zones or data centers.
You can use the overrides
field for this purpose.
In this example, the default storage class is named my-storage-class
:
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
labels:
app: my-cluster
name: my-cluster
namespace: myproject
spec:
# ...
kafka:
replicas: 3
storage:
deleteClaim: true
size: 100Gi
type: persistent-claim
class: my-storage-class
overrides:
- broker: 0
class: my-storage-class-zone-1a
- broker: 1
class: my-storage-class-zone-1b
- broker: 2
class: my-storage-class-zone-1c
# ...
As a result of the configured overrides
property, the broker volumes use the following storage classes:
-
The persistent volumes of broker 0 will use
my-storage-class-zone-1a
. -
The persistent volumes of broker 1 will use
my-storage-class-zone-1b
. -
The persistent volumes of broker 2 will use
my-storage-class-zone-1c
.
The overrides
property is currently used only to override storage class configurations. Overriding other storage configuration fields is not currently supported.
Other fields from the storage configuration are currently not supported.
Persistent Volume Claim naming
When persistent storage is used, it creates Persistent Volume Claims with the following names:
data-cluster-name-kafka-idx
-
Persistent Volume Claim for the volume used for storing data for the Kafka broker pod
idx
. data-cluster-name-zookeeper-idx
-
Persistent Volume Claim for the volume used for storing data for the ZooKeeper node pod
idx
.
Log directories
The persistent volume will be used by the Kafka brokers as log directories mounted into the following path:
/var/lib/kafka/data/kafka-log_idx_
-
Where
idx
is the Kafka broker pod index. For example/var/lib/kafka/data/kafka-log0
.
Resizing persistent volumes
You can provision increased storage capacity by increasing the size of the persistent volumes used by an existing Strimzi cluster. Resizing persistent volumes is supported in clusters that use either a single persistent volume or multiple persistent volumes in a JBOD storage configuration.
Note
|
You can increase but not decrease the size of persistent volumes. Decreasing the size of persistent volumes is not currently supported in Kubernetes. |
-
A Kubernetes cluster with support for volume resizing.
-
The Cluster Operator is running.
-
A Kafka cluster using persistent volumes created using a storage class that supports volume expansion.
-
In a
Kafka
resource, increase the size of the persistent volume allocated to the Kafka cluster, the ZooKeeper cluster, or both.-
To increase the volume size allocated to the Kafka cluster, edit the
spec.kafka.storage
property. -
To increase the volume size allocated to the ZooKeeper cluster, edit the
spec.zookeeper.storage
property.For example, to increase the volume size from
1000Gi
to2000Gi
:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... storage: type: persistent-claim size: 2000Gi class: my-storage-class # ... zookeeper: # ...
-
-
Create or update the resource.
Use
kubectl apply
:kubectl apply -f your-file
Kubernetes increases the capacity of the selected persistent volumes in response to a request from the Cluster Operator. When the resizing is complete, the Cluster Operator restarts all pods that use the resized persistent volumes. This happens automatically.
For more information about resizing persistent volumes in Kubernetes, see Resizing Persistent Volumes using Kubernetes.
JBOD storage overview
You can configure Strimzi to use JBOD, a data storage configuration of multiple disks or volumes. JBOD is one approach to providing increased data storage for Kafka brokers. It can also improve performance.
A JBOD configuration is described by one or more volumes, each of which can be either ephemeral or persistent. The rules and constraints for JBOD volume declarations are the same as those for ephemeral and persistent storage. For example, you cannot change the size of a persistent storage volume after it has been provisioned.
JBOD configuration
To use JBOD with Strimzi, the storage type
must be set to jbod
. The volumes
property allows you to describe the disks that make up your JBOD storage array or configuration. The following fragment shows an example JBOD configuration:
# ...
storage:
type: jbod
volumes:
- id: 0
type: persistent-claim
size: 100Gi
deleteClaim: false
- id: 1
type: persistent-claim
size: 100Gi
deleteClaim: false
# ...
The ids cannot be changed once the JBOD volumes are created.
Users can add or remove volumes from the JBOD configuration.
JBOD and Persistent Volume Claims
When persistent storage is used to declare JBOD volumes, the naming scheme of the resulting Persistent Volume Claims is as follows:
data-id-cluster-name-kafka-idx
-
Where
id
is the ID of the volume used for storing data for Kafka broker podidx
.
Log directories
The JBOD volumes will be used by the Kafka brokers as log directories mounted into the following path:
/var/lib/kafka/data-id/kafka-log_idx_
-
Where
id
is the ID of the volume used for storing data for Kafka broker podidx
. For example/var/lib/kafka/data-0/kafka-log0
.
Adding volumes to JBOD storage
This procedure describes how to add volumes to a Kafka cluster configured to use JBOD storage. It cannot be applied to Kafka clusters configured to use any other storage type.
Note
|
When adding a new volume under an id which was already used in the past and removed, you have to make sure that the previously used PersistentVolumeClaims have been deleted.
|
-
A Kubernetes cluster
-
A running Cluster Operator
-
A Kafka cluster with JBOD storage
-
Edit the
spec.kafka.storage.volumes
property in theKafka
resource. Add the new volumes to thevolumes
array. For example, add the new volume with id2
:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... storage: type: jbod volumes: - id: 0 type: persistent-claim size: 100Gi deleteClaim: false - id: 1 type: persistent-claim size: 100Gi deleteClaim: false - id: 2 type: persistent-claim size: 100Gi deleteClaim: false # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
-
Create new topics or reassign existing partitions to the new disks.
For more information about reassigning topics, see Partition reassignment.
Removing volumes from JBOD storage
This procedure describes how to remove volumes from Kafka cluster configured to use JBOD storage. It cannot be applied to Kafka clusters configured to use any other storage type. The JBOD storage always has to contain at least one volume.
Important
|
To avoid data loss, you have to move all partitions before removing the volumes. |
-
A Kubernetes cluster
-
A running Cluster Operator
-
A Kafka cluster with JBOD storage with two or more volumes
-
Reassign all partitions from the disks which are you going to remove. Any data in partitions still assigned to the disks which are going to be removed might be lost.
-
Edit the
spec.kafka.storage.volumes
property in theKafka
resource. Remove one or more volumes from thevolumes
array. For example, remove the volumes with ids1
and2
:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... storage: type: jbod volumes: - id: 0 type: persistent-claim size: 100Gi deleteClaim: false # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
For more information about reassigning topics, see Partition reassignment.
3.1.4. Kafka broker replicas
A Kafka cluster can run with many brokers.
You can configure the number of brokers used for the Kafka cluster in Kafka.spec.kafka.replicas
.
The best number of brokers for your cluster has to be determined based on your specific use case.
Configuring the number of broker nodes
This procedure describes how to configure the number of Kafka broker nodes in a new cluster. It only applies to new clusters with no partitions. If your cluster already has topics defined, see Scaling clusters.
-
A Kubernetes cluster
-
A running Cluster Operator
-
A Kafka cluster with no topics defined yet
-
Edit the
replicas
property in theKafka
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... replicas: 3 # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
If your cluster already has topics defined, see Scaling clusters.
3.1.5. Kafka broker configuration
Strimzi allows you to customize the configuration of the Kafka brokers in your Kafka cluster. You can specify and configure most of the options listed in the "Broker Configs" section of the Apache Kafka documentation. You cannot configure options that are related to the following areas:
-
Security (Encryption, Authentication, and Authorization)
-
Listener configuration
-
Broker ID configuration
-
Configuration of log data directories
-
Inter-broker communication
-
ZooKeeper connectivity
These options are automatically configured by Strimzi.
Kafka broker configuration
The config
property in Kafka.spec.kafka
contains Kafka broker configuration options as keys with values in one of the following JSON types:
-
String
-
Number
-
Boolean
You can specify and configure all of the options in the "Broker Configs" section of the Apache Kafka documentation apart from those managed directly by Strimzi. Specifically, you are prevented from modifying all configuration options with keys equal to or starting with one of the following strings:
-
listeners
-
advertised.
-
broker.
-
listener.
-
host.name
-
port
-
inter.broker.listener.name
-
sasl.
-
ssl.
-
security.
-
password.
-
principal.builder.class
-
log.dir
-
zookeeper.connect
-
zookeeper.set.acl
-
authorizer.
-
super.user
If the config
property specifies a restricted option, it is ignored and a warning message is printed to the Cluster Operator log file.
All other supported options are passed to Kafka.
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
config:
num.partitions: 1
num.recovery.threads.per.data.dir: 1
default.replication.factor: 3
offsets.topic.replication.factor: 3
transaction.state.log.replication.factor: 3
transaction.state.log.min.isr: 1
log.retention.hours: 168
log.segment.bytes: 1073741824
log.retention.check.interval.ms: 300000
num.network.threads: 3
num.io.threads: 8
socket.send.buffer.bytes: 102400
socket.receive.buffer.bytes: 102400
socket.request.max.bytes: 104857600
group.initial.rebalance.delay.ms: 0
# ...
Configuring Kafka brokers
You can configure an existing Kafka broker, or create a new Kafka broker with a specified configuration.
-
A Kubernetes cluster is available.
-
The Cluster Operator is running.
-
Open the YAML configuration file that contains the
Kafka
resource specifying the cluster deployment. -
In the
spec.kafka.config
property in theKafka
resource, enter one or more Kafka configuration settings. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... config: default.replication.factor: 3 offsets.topic.replication.factor: 3 transaction.state.log.replication.factor: 3 transaction.state.log.min.isr: 1 # ... zookeeper: # ...
-
Apply the new configuration to create or update the resource.
Use
kubectl apply
:kubectl apply -f kafka.yaml
where
kafka.yaml
is the YAML configuration file for the resource that you want to configure; for example,kafka-persistent.yaml
.
3.1.6. Kafka broker listeners
You can configure the listeners enabled in Kafka brokers. The following types of listener are supported:
-
Plain listener on port 9092 (without encryption)
-
TLS listener on port 9093 (with encryption)
-
External listener on port 9094 for access from outside of Kubernetes
If you are using OAuth 2.0 token based authentication, you can configure the listeners to connect to your authorization server. For more information, see Using OAuth 2.0 token based authentication.
Kafka listeners
You can configure Kafka broker listeners using the listeners
property in the Kafka.spec.kafka
resource.
The listeners
property contains three sub-properties:
-
plain
-
tls
-
external
Each listener will only be defined when the listeners
object has the given property.
listeners
property with all listeners enabled# ...
listeners:
plain: {}
tls: {}
external:
type: loadbalancer
# ...
listeners
property with only the plain listener enabled# ...
listeners:
plain: {}
# ...
Configuring Kafka listeners
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
listeners
property in theKafka.spec.kafka
resource.An example configuration of the plain (unencrypted) listener without authentication:
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... listeners: plain: {} # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
-
For more information about the schema, see
KafkaListeners
schema reference.
Listener authentication
The listener authentication
property is used to specify an authentication mechanism specific to that listener:
-
Mutual TLS authentication (only on the listeners with TLS encryption)
-
SCRAM-SHA authentication
If no authentication
property is specified then the listener does not authenticate clients which connect through that listener.
Authentication must be configured when using the User Operator to manage KafkaUsers
.
Authentication configuration for a listener
The following example shows:
-
A
plain
listener configured for SCRAM-SHA authentication -
A
tls
listener with mutual TLS authentication -
An
external
listener with mutual TLS authentication
# ...
listeners:
plain:
authentication:
type: scram-sha-512
tls:
authentication:
type: tls
external:
type: loadbalancer
tls: true
authentication:
type: tls
# ...
Mutual TLS authentication
Mutual TLS authentication is always used for the communication between Kafka brokers and ZooKeeper pods.
Mutual authentication or two-way authentication is when both the server and the client present certificates. Strimzi can configure Kafka to use TLS (Transport Layer Security) to provide encrypted communication between Kafka brokers and clients either with or without mutual authentication. When you configure mutual authentication, the broker authenticates the client and the client authenticates the broker.
Note
|
TLS authentication is more commonly one-way, with one party authenticating the identity of another. For example, when HTTPS is used between a web browser and a web server, the server obtains proof of the identity of the browser. |
Mutual TLS authentication is recommended for authenticating Kafka clients when:
-
The client supports authentication using mutual TLS authentication
-
It is necessary to use the TLS certificates rather than passwords
-
You can reconfigure and restart client applications periodically so that they do not use expired certificates.
SCRAM-SHA authentication
SCRAM (Salted Challenge Response Authentication Mechanism) is an authentication protocol that can establish mutual authentication using passwords. Strimzi can configure Kafka to use SASL (Simple Authentication and Security Layer) SCRAM-SHA-512 to provide authentication on both unencrypted and TLS-encrypted client connections. TLS authentication is always used internally between Kafka brokers and ZooKeeper nodes. When used with a TLS client connection, the TLS protocol provides encryption, but is not used for authentication.
The following properties of SCRAM make it safe to use SCRAM-SHA even on unencrypted connections:
-
The passwords are not sent in the clear over the communication channel. Instead the client and the server are each challenged by the other to offer proof that they know the password of the authenticating user.
-
The server and client each generate a new challenge for each authentication exchange. This means that the exchange is resilient against replay attacks.
Strimzi supports SCRAM-SHA-512 only.
When a KafkaUser.spec.authentication.type
is configured with scram-sha-512
the User Operator will generate a random 12 character password consisting of upper and lowercase ASCII letters and numbers.
SCRAM-SHA is recommended for authenticating Kafka clients when:
-
The client supports authentication using SCRAM-SHA-512
-
It is necessary to use passwords rather than the TLS certificates
-
Authentication for unencrypted communication is required
External listeners
Use an external listener to expose your Strimzi Kafka cluster to a client outside a Kubernetes environment.
Customizing advertised addresses on external listeners
By default, Strimzi tries to automatically determine the hostnames and ports that your Kafka cluster advertises to its clients.
This is not sufficient in all situations, because the infrastructure on which Strimzi is running might not provide the right hostname or port through which Kafka can be accessed.
You can customize the advertised hostname and port in the overrides
property of the external listener.
Strimzi will then automatically configure the advertised address in the Kafka brokers and add it to the broker certificates so it can be used for TLS hostname verification.
Overriding the advertised host and ports is available for all types of external listeners.
# ...
listeners:
external:
type: route
authentication:
type: tls
overrides:
brokers:
- broker: 0
advertisedHost: example.hostname.0
advertisedPort: 12340
- broker: 1
advertisedHost: example.hostname.1
advertisedPort: 12341
- broker: 2
advertisedHost: example.hostname.2
advertisedPort: 12342
# ...
Additionally, you can specify the name of the bootstrap service. This name will be added to the broker certificates and can be used for TLS hostname verification. Adding the additional bootstrap address is available for all types of external listeners.
# ...
listeners:
external:
type: route
authentication:
type: tls
overrides:
bootstrap:
address: example.hostname
# ...
Route external listeners
An external listener of type route
exposes Kafka using OpenShift Routes
and the HAProxy router.
Note
|
route is only supported on OpenShift
|
Routes
When exposing Kafka using OpenShift Routes
and the HAProxy router, a dedicated Route
is created for every Kafka broker pod.
An additional Route
is created to serve as a Kafka bootstrap address.
Kafka clients can use these Routes
to connect to Kafka on port 443.
TLS encryption is always used with Routes
.
By default, the route hosts are automatically assigned by OpenShift.
However, you can override the assigned route hosts by specifying the requested hosts in the overrides
property.
Strimzi will not perform any validation that the requested hosts are available; you must ensure that they are free and can be used.
routes
configured with overrides for OpenShift route hosts# ...
listeners:
external:
type: route
authentication:
type: tls
overrides:
bootstrap:
host: bootstrap.myrouter.com
brokers:
- broker: 0
host: broker-0.myrouter.com
- broker: 1
host: broker-1.myrouter.com
- broker: 2
host: broker-2.myrouter.com
# ...
For more information on using Routes
to access Kafka, see Accessing Kafka using OpenShift routes.
-
An OpenShift cluster
-
A running Cluster Operator
-
Deploy Kafka cluster with an external listener enabled and configured to the type
route
.An example configuration with an external listener configured to use
Routes
:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... listeners: external: type: route # ... # ... zookeeper: # ...
-
Create or update the resource.
oc apply -f your-file
-
Find the address of the bootstrap
Route
.oc get routes _cluster-name_-kafka-bootstrap -o=jsonpath='{.status.ingress[0].host}{"\n"}'
Use the address together with port 443 in your Kafka client as the bootstrap address.
-
Extract the public certificate of the broker certification authority
kubectl get secret _<cluster-name>_-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
Use the extracted certificate in your Kafka client to configure TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication.
-
For more information about the schema, see
KafkaListeners
schema reference.
Loadbalancer external listeners
External listeners of type loadbalancer
expose Kafka by using Loadbalancer
type Services
.
When exposing Kafka using Loadbalancer
type Services
, a new loadbalancer service is created for every Kafka broker pod.
An additional loadbalancer is created to serve as a Kafka bootstrap address.
Loadbalancers listen to connections on port 9094.
By default, TLS encryption is enabled.
To disable it, set the tls
field to false
.
loadbalancer
# ...
listeners:
external:
type: loadbalancer
authentication:
type: tls
# ...
For more information on using loadbalancers to access Kafka, see Accessing Kafka using loadbalancers.
On loadbalancer
listeners, you can use the dnsAnnotations
property to add additional annotations to the loadbalancer services.
You can use these annotations to instrument DNS tooling such as External DNS, which automatically assigns DNS names to the loadbalancer services.
loadbalancer
using dnsAnnotations
# ...
listeners:
external:
type: loadbalancer
authentication:
type: tls
overrides:
bootstrap:
dnsAnnotations:
external-dns.alpha.kubernetes.io/hostname: kafka-bootstrap.mydomain.com.
external-dns.alpha.kubernetes.io/ttl: "60"
brokers:
- broker: 0
dnsAnnotations:
external-dns.alpha.kubernetes.io/hostname: kafka-broker-0.mydomain.com.
external-dns.alpha.kubernetes.io/ttl: "60"
- broker: 1
dnsAnnotations:
external-dns.alpha.kubernetes.io/hostname: kafka-broker-1.mydomain.com.
external-dns.alpha.kubernetes.io/ttl: "60"
- broker: 2
dnsAnnotations:
external-dns.alpha.kubernetes.io/hostname: kafka-broker-2.mydomain.com.
external-dns.alpha.kubernetes.io/ttl: "60"
# ...
On loadbalancer
listeners, you can use the loadBalancerIP
property to request a specific IP address when creating a loadbalancer.
Use this property when you need to use a loadbalancer with a specific IP address.
The loadBalancerIP
field is ignored if the cloud provider does not support the feature.
loadbalancer
with specific loadbalancer IP address requests# ...
listeners:
external:
type: loadbalancer
authentication:
type: tls
overrides:
bootstrap:
loadBalancerIP: 172.29.3.10
brokers:
- broker: 0
loadBalancerIP: 172.29.3.1
- broker: 1
loadBalancerIP: 172.29.3.2
- broker: 2
loadBalancerIP: 172.29.3.3
# ...
-
A Kubernetes cluster
-
A running Cluster Operator
-
Deploy Kafka cluster with an external listener enabled and configured to the type
loadbalancer
.An example configuration with an external listener configured to use loadbalancers:
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... listeners: external: type: loadbalancer authentication: type: tls # ... # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
-
Find the hostname of the bootstrap loadbalancer.
This can be done using
kubectl get
:kubectl get service cluster-name-kafka-external-bootstrap -o=jsonpath='{.status.loadBalancer.ingress[0].hostname}{"\n"}'
If no hostname was found (nothing was returned by the command), use the loadbalancer IP address.
This can be done using
kubectl get
:kubectl get service cluster-name-kafka-external-bootstrap -o=jsonpath='{.status.loadBalancer.ingress[0].ip}{"\n"}'
Use the hostname or IP address together with port 9094 in your Kafka client as the bootstrap address.
-
Unless TLS encryption was disabled, extract the public certificate of the broker certification authority.
This can be done using
kubectl get
:kubectl get secret cluster-name-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
Use the extracted certificate in your Kafka client to configure TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication.
-
For more information about the schema, see
KafkaListeners
schema reference.
Node Port external listeners
External listeners of type nodeport
expose Kafka by using NodePort
type Services
.
When exposing Kafka using NodePort
type Services
, Kafka clients connect directly to the nodes of Kubernetes.
You must enable access to the ports on the Kubernetes nodes for each client (for example, in firewalls or security groups).
Each Kafka broker pod is then accessible on a separate port.
Additional NodePort
type Service
is created to serve as a Kafka bootstrap address.
When configuring the advertised addresses for the Kafka broker pods, Strimzi uses the address of the node on which the given pod is running. When selecting the node address, the different address types are used with the following priority:
-
ExternalDNS
-
ExternalIP
-
Hostname
-
InternalDNS
-
InternalIP
By default, TLS encryption is enabled.
To disable it, set the tls
field to false
.
Note
|
TLS hostname verification is not currently supported when exposing Kafka clusters using node ports. |
By default, the port numbers used for the bootstrap and broker services are automatically assigned by Kubernetes.
However, you can override the assigned node ports by specifying the requested port numbers in the overrides
property.
Strimzi does not perform any validation on the requested ports; you must ensure that they are free and available for use.
# ...
listeners:
external:
type: nodeport
tls: true
authentication:
type: tls
overrides:
bootstrap:
nodePort: 32100
brokers:
- broker: 0
nodePort: 32000
- broker: 1
nodePort: 32001
- broker: 2
nodePort: 32002
# ...
For more information on using node ports to access Kafka, see Accessing Kafka using node ports.
On nodeport
listeners, you can use the dnsAnnotations
property to add additional annotations to the nodeport services.
You can use these annotations to instrument DNS tooling such as External DNS, which automatically assigns DNS names to the cluster nodes.
nodeport
using dnsAnnotations
# ...
listeners:
external:
type: nodeport
tls: true
authentication:
type: tls
overrides:
bootstrap:
dnsAnnotations:
external-dns.alpha.kubernetes.io/hostname: kafka-bootstrap.mydomain.com.
external-dns.alpha.kubernetes.io/ttl: "60"
brokers:
- broker: 0
dnsAnnotations:
external-dns.alpha.kubernetes.io/hostname: kafka-broker-0.mydomain.com.
external-dns.alpha.kubernetes.io/ttl: "60"
- broker: 1
dnsAnnotations:
external-dns.alpha.kubernetes.io/hostname: kafka-broker-1.mydomain.com.
external-dns.alpha.kubernetes.io/ttl: "60"
- broker: 2
dnsAnnotations:
external-dns.alpha.kubernetes.io/hostname: kafka-broker-2.mydomain.com.
external-dns.alpha.kubernetes.io/ttl: "60"
# ...
-
A Kubernetes cluster
-
A running Cluster Operator
-
Deploy Kafka cluster with an external listener enabled and configured to the type
nodeport
.An example configuration with an external listener configured to use node ports:
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... listeners: external: type: nodeport tls: true # ... # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
-
Find the port number of the bootstrap service.
This can be done using
kubectl get
:kubectl get service cluster-name-kafka-external-bootstrap -o=jsonpath='{.spec.ports[0].nodePort}{"\n"}'
The port should be used in the Kafka bootstrap address.
-
Find the address of the Kubernetes node.
This can be done using
kubectl get
:kubectl get node node-name -o=jsonpath='{range .status.addresses[*]}{.type}{"\t"}{.address}{"\n"}'
If several different addresses are returned, select the address type you want based on the following order:
-
ExternalDNS
-
ExternalIP
-
Hostname
-
InternalDNS
-
InternalIP
Use the address with the port found in the previous step in the Kafka bootstrap address.
-
-
Unless TLS encryption was disabled, extract the public certificate of the broker certification authority.
This can be done using
kubectl get
:kubectl get secret cluster-name-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
Use the extracted certificate in your Kafka client to configure TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication.
-
For more information about the schema, see
KafkaListeners
schema reference.
Kubernetes Ingress external listeners
External listeners of type ingress
exposes Kafka by using Kubernetes Ingress
and the NGINX Ingress Controller for Kubernetes.
Ingress
When exposing Kafka using using Kubernetes Ingress
and the NGINX Ingress Controller for Kubernetes, a dedicated Ingress
resource is created for every Kafka broker pod.
An additional Ingress
resource is created to serve as a Kafka bootstrap address.
Kafka clients can use these Ingress
resources to connect to Kafka on port 443.
Note
|
External listeners using Ingress have been currently tested only with the NGINX Ingress Controller for Kubernetes.
|
Strimzi uses the TLS passthrough feature of the NGINX Ingress Controller for Kubernetes.
Make sure TLS passthrough is enabled in your NGINX Ingress Controller for Kubernetes deployment.
For more information about enabling TLS passthrough see TLS passthrough documentation.
Because it is using the TLS passthrough functionality, TLS encryption cannot be disabled when exposing Kafka using Ingress
.
The Ingress controller does not assign any hostnames automatically.
You have to specify the hostnames which should be used by the bootstrap and per-broker services in the spec.kafka.listeners.external.configuration
section.
You also have to make sure that the hostnames resolve to the Ingress endpoints.
Strimzi will not perform any validation that the requested hosts are available and properly routed to the Ingress endpoints.
ingress
# ...
listeners:
external:
type: ingress
authentication:
type: tls
configuration:
bootstrap:
host: bootstrap.myingress.com
brokers:
- broker: 0
host: broker-0.myingress.com
- broker: 1
host: broker-1.myingress.com
- broker: 2
host: broker-2.myingress.com
# ...
For more information on using Ingress
to access Kafka, see Accessing Kafka using ingress.
Ingress
classBy default, the Ingress
class is set to nginx
.
You can change the Ingress
class using the class
property.
ingress
using Ingress
class nginx-internal
# ...
listeners:
external:
type: ingress
class: nginx-internal
# ...
# ...
On ingress
listeners, you can use the dnsAnnotations
property to add additional annotations to the ingress resources.
You can use these annotations to instrument DNS tooling such as External DNS, which automatically assigns DNS names to the ingress resources.
ingress
using dnsAnnotations
# ...
listeners:
external:
type: ingress
authentication:
type: tls
configuration:
bootstrap:
dnsAnnotations:
external-dns.alpha.kubernetes.io/hostname: bootstrap.myingress.com.
external-dns.alpha.kubernetes.io/ttl: "60"
host: bootstrap.myingress.com
brokers:
- broker: 0
dnsAnnotations:
external-dns.alpha.kubernetes.io/hostname: broker-0.myingress.com.
external-dns.alpha.kubernetes.io/ttl: "60"
host: broker-0.myingress.com
- broker: 1
dnsAnnotations:
external-dns.alpha.kubernetes.io/hostname: broker-1.myingress.com.
external-dns.alpha.kubernetes.io/ttl: "60"
host: broker-1.myingress.com
- broker: 2
dnsAnnotations:
external-dns.alpha.kubernetes.io/hostname: broker-2.myingress.com.
external-dns.alpha.kubernetes.io/ttl: "60"
host: broker-2.myingress.com
# ...
This procedure shows how to access Strimzi Kafka clusters from outside of Kubernetes using Ingress.
-
An Kubernetes cluster
-
Deployed NGINX Ingress Controller for Kubernetes with TLS passthrough enabled
-
A running Cluster Operator
-
Deploy Kafka cluster with an external listener enabled and configured to the type
ingress
.An example configuration with an external listener configured to use
Ingress
:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... listeners: external: type: ingress authentication: type: tls configuration: bootstrap: host: bootstrap.myingress.com brokers: - broker: 0 host: broker-0.myingress.com - broker: 1 host: broker-1.myingress.com - broker: 2 host: broker-2.myingress.com # ... zookeeper: # ...
-
Make sure the hosts in the
configuration
section properly resolve to the Ingress endpoints. -
Create or update the resource.
kubectl apply -f your-file
-
Extract the public certificate of the broker certificate authority
kubectl get secret cluster-name-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
-
Use the extracted certificate in your Kafka client to configure the TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication. Connect with your client to the host you specified in the configuration on port 443.
-
For more information about the schema, see
KafkaListeners
schema reference.
Network policies
Strimzi automatically creates a NetworkPolicy
resource for every listener that is enabled on a Kafka broker.
By default, a NetworkPolicy
grants access to a listener to all applications and namespaces.
If you want to restrict access to a listener at the network level to only selected applications or namespaces, use the networkPolicyPeers
field.
Use network policies in conjunction with authentication and authorization.
Each listener can have a different networkPolicyPeers
configuration.
Network policy configuration for a listener
The following example shows a networkPolicyPeers
configuration for a plain
and a tls
listener:
# ...
listeners:
plain:
authentication:
type: scram-sha-512
networkPolicyPeers:
- podSelector:
matchLabels:
app: kafka-sasl-consumer
- podSelector:
matchLabels:
app: kafka-sasl-producer
tls:
authentication:
type: tls
networkPolicyPeers:
- namespaceSelector:
matchLabels:
project: myproject
- namespaceSelector:
matchLabels:
project: myproject2
# ...
In the example:
-
Only application pods matching the labels
app: kafka-sasl-consumer
andapp: kafka-sasl-producer
can connect to theplain
listener. The application pods must be running in the same namespace as the Kafka broker. -
Only application pods running in namespaces matching the labels
project: myproject
andproject: myproject2
can connect to thetls
listener.
The syntax of the networkPolicyPeers
field is the same as the from
field in NetworkPolicy
resources.
For more information about the schema, see NetworkPolicyPeer API reference and the KafkaListeners
schema reference.
Note
|
Your configuration of Kubernetes must support ingress NetworkPolicies in order to use network policies in Strimzi. |
Restricting access to Kafka listeners using networkPolicyPeers
You can restrict access to a listener to only selected applications by using the networkPolicyPeers
field.
-
A Kubernetes cluster with support for Ingress NetworkPolicies.
-
The Cluster Operator is running.
-
Open the
Kafka
resource. -
In the
networkPolicyPeers
field, define the application pods or namespaces that will be allowed to access the Kafka cluster.For example, to configure a
tls
listener to allow connections only from application pods with the labelapp
set tokafka-client
:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... listeners: tls: networkPolicyPeers: - podSelector: matchLabels: app: kafka-client # ... zookeeper: # ...
-
Create or update the resource.
Use
kubectl apply
:kubectl apply -f your-file
-
For more information about the schema, see NetworkPolicyPeer API reference and the
KafkaListeners
schema reference.
3.1.7. Authentication and Authorization
Strimzi supports authentication and authorization. Authentication can be configured independently for each listener. Authorization is always configured for the whole Kafka cluster.
Authentication
Authentication is configured as part of the listener configuration in the authentication
property.
The authentication mechanism is defined by the type
field.
When the authentication
property is missing, no authentication is enabled on a given listener.
The listener will accept all connections without authentication.
Supported authentication mechanisms:
-
TLS client authentication
-
SASL SCRAM-SHA-512
TLS client authentication
TLS Client authentication is enabled by specifying the type
as tls
.
The TLS client authentication is supported only on the tls
listener.
authentication
with type tls
# ...
authentication:
type: tls
# ...
Configuring authentication in Kafka brokers
-
A Kubernetes cluster is available.
-
The Cluster Operator is running.
-
Open the YAML configuration file that contains the
Kafka
resource specifying the cluster deployment. -
In the
spec.kafka.listeners
property in theKafka
resource, add theauthentication
field to the listeners for which you want to enable authentication. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... listeners: tls: authentication: type: tls # ... zookeeper: # ...
-
Apply the new configuration to create or update the resource.
Use
kubectl apply
:kubectl apply -f kafka.yaml
where
kafka.yaml
is the YAML configuration file for the resource that you want to configure; for example,kafka-persistent.yaml
.
-
For more information about the supported authentication mechanisms, see authentication reference.
-
For more information about the schema for
Kafka
, seeKafka
schema reference.
Authorization
You configure authorization for Kafka brokers using the authorization
property in the Kafka.spec.kafka
resource.
If the authorization
property is missing, no authorization is enabled.
When enabled, authorization is applied to all enabled listeners.
The authorization method is defined in the type
field; only Simple authorization is currently supported.
You can optionally designate a list of super users in the superUsers
field.
Simple authorization
Simple authorization in Strimzi uses the SimpleAclAuthorizer
plugin, the default Access Control Lists (ACLs) authorization plugin provided with Apache Kafka. ACLs allow you to define which users have access to which resources at a granular level.
To enable simple authorization, set the type
field to simple
.
# ...
authorization:
type: simple
# ...
Super users
Super users can access all resources in your Kafka cluster regardless of any access restrictions defined in ACLs.
To designate super users for a Kafka cluster, enter a list of user principles in the superUsers
field.
If a user uses TLS Client Authentication, the username will be the common name from their certificate subject prefixed with CN=
.
# ...
authorization:
type: simple
superUsers:
- CN=fred
- sam
- CN=edward
# ...
Note
|
The super.user configuration option in the config property in Kafka.spec.kafka is ignored.
Designate super users in the authorization property instead.
For more information, see Kafka broker configuration.
|
Configuring authorization in Kafka brokers
Configure authorization and designate super users for a particular Kafka broker.
-
A Kubernetes cluster
-
The Cluster Operator is running
-
Add or edit the
authorization
property in theKafka.spec.kafka
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... authorization: type: simple superUsers: - CN=fred - sam - CN=edward # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl 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. -
For more information about configuring user authentication, see Kafka User resource.
3.1.8. ZooKeeper replicas
ZooKeeper clusters or ensembles usually run with an odd number of nodes, typically three, five, or seven.
The majority of nodes must be available in order to maintain an effective quorum. If the ZooKeeper cluster loses its quorum, it will stop responding to clients and the Kafka brokers will stop working. Having a stable and highly available ZooKeeper cluster is crucial for Strimzi.
- Three-node cluster
-
A three-node ZooKeeper cluster requires at least two nodes to be up and running in order to maintain the quorum. It can tolerate only one node being unavailable.
- Five-node cluster
-
A five-node ZooKeeper cluster requires at least three nodes to be up and running in order to maintain the quorum. It can tolerate two nodes being unavailable.
- Seven-node cluster
-
A seven-node ZooKeeper cluster requires at least four nodes to be up and running in order to maintain the quorum. It can tolerate three nodes being unavailable.
Note
|
For development purposes, it is also possible to run ZooKeeper with a single node. |
Having more nodes does not necessarily mean better performance, as the costs to maintain the quorum will rise with the number of nodes in the cluster. Depending on your availability requirements, you can decide for the number of nodes to use.
Number of ZooKeeper nodes
The number of ZooKeeper nodes can be configured using the replicas
property in Kafka.spec.zookeeper
.
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
zookeeper:
# ...
replicas: 3
# ...
Changing the number of ZooKeeper replicas
-
A Kubernetes cluster is available.
-
The Cluster Operator is running.
-
Open the YAML configuration file that contains the
Kafka
resource specifying the cluster deployment. -
In the
spec.zookeeper.replicas
property in theKafka
resource, enter the number of replicated ZooKeeper servers. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... replicas: 3 # ...
-
Apply the new configuration to create or update the resource.
Use
kubectl apply
:kubectl apply -f kafka.yaml
where
kafka.yaml
is the YAML configuration file for the resource that you want to configure; for example,kafka-persistent.yaml
.
3.1.9. ZooKeeper configuration
Strimzi allows you to customize the configuration of Apache ZooKeeper nodes. You can specify and configure most of the options listed in the ZooKeeper documentation.
Options which cannot be configured are those related to the following areas:
-
Security (Encryption, Authentication, and Authorization)
-
Listener configuration
-
Configuration of data directories
-
ZooKeeper cluster composition
These options are automatically configured by Strimzi.
ZooKeeper configuration
ZooKeeper nodes are configured using the config
property in Kafka.spec.zookeeper
.
This property contains the ZooKeeper configuration options as keys.
The values can be described using one of the following JSON types:
-
String
-
Number
-
Boolean
Users can specify and configure the options listed in ZooKeeper documentation with the exception of those options which are managed directly by Strimzi. Specifically, all configuration options with keys equal to or starting with one of the following strings are forbidden:
-
server.
-
dataDir
-
dataLogDir
-
clientPort
-
authProvider
-
quorum.auth
-
requireClientAuthScheme
When one of the forbidden options is present in the config
property, it is ignored and a warning message is printed to the Custer Operator log file.
All other options are passed to ZooKeeper.
Important
|
The Cluster Operator does not validate keys or values in the provided config object.
When invalid configuration is provided, the ZooKeeper cluster might not start or might become unstable.
In such cases, the configuration in the Kafka.spec.zookeeper.config object should be fixed and the Cluster Operator will roll out the new configuration to all ZooKeeper nodes.
|
Selected options have default values:
-
timeTick
with default 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/v1beta1
kind: Kafka
spec:
kafka:
# ...
zookeeper:
# ...
config:
autopurge.snapRetainCount: 3
autopurge.purgeInterval: 1
# ...
Configuring ZooKeeper
-
A Kubernetes cluster is available.
-
The Cluster Operator is running.
-
Open the YAML configuration file that contains the
Kafka
resource specifying the cluster deployment. -
In the
spec.zookeeper.config
property in theKafka
resource, enter one or more ZooKeeper configuration settings. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... zookeeper: # ... config: autopurge.snapRetainCount: 3 autopurge.purgeInterval: 1 # ...
-
Apply the new configuration to create or update the resource.
Use
kubectl apply
:kubectl apply -f kafka.yaml
where
kafka.yaml
is the YAML configuration file for the resource that you want to configure; for example,kafka-persistent.yaml
.
3.1.10. ZooKeeper connection
ZooKeeper services are secured with encryption and authentication and are not intended to be used by external applications that are not part of Strimzi.
However, if you want to use Kafka CLI tools that require a connection to ZooKeeper, such as the kafka-topics
tool, you can use a terminal inside a Kafka container and connect to the local end of the TLS tunnel to ZooKeeper by using localhost:2181
as the ZooKeeper address.
Connecting to ZooKeeper from a terminal
Open a terminal inside a Kafka container to use Kafka CLI tools that require a ZooKeeper connection.
-
A Kubernetes cluster is available.
-
A kafka cluster is running.
-
The Cluster Operator is running.
-
Open the terminal using the Kubernetes console or run the
exec
command from your CLI.For example:
kubectl exec -ti my-cluster-kafka-0 -- bin/kafka-topics.sh --list --zookeeper localhost:2181
Be sure to use
localhost:2181
.You can now run Kafka commands to ZooKeeper.
3.1.11. Entity Operator
The Entity Operator is responsible for managing Kafka-related entities in a running Kafka cluster.
The Entity Operator comprises the:
-
Topic Operator to manage Kafka topics
-
User Operator to manage Kafka users
Through Kafka
resource configuration, the Cluster Operator can deploy the Entity Operator, including one or both operators, when deploying a Kafka cluster.
Note
|
When deployed, the Entity Operator contains the operators according to the deployment configuration. |
The operators are automatically configured to manage the topics and users of the Kafka cluster.
Configuration
The Entity Operator can be configured using the entityOperator
property in Kafka.spec
The entityOperator
property supports several sub-properties:
-
tlsSidecar
-
topicOperator
-
userOperator
-
template
The tlsSidecar
property can be used to configure the TLS sidecar container which is used to communicate with ZooKeeper.
For more details about configuring the TLS sidecar, see TLS sidecar.
The template
property can be used to configure details of the Entity Operator pod, such as labels, annotations, affinity, tolerations and so on.
The topicOperator
property contains the configuration of the Topic Operator.
When this option is missing, the Entity Operator is deployed without the Topic Operator.
The userOperator
property contains the configuration of the User Operator.
When this option is missing, the Entity Operator is deployed without the User Operator.
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
zookeeper:
# ...
entityOperator:
topicOperator: {}
userOperator: {}
When both topicOperator
and userOperator
properties are missing, the Entity Operator will be not deployed.
Topic Operator
Topic Operator deployment can be configured using additional options inside the topicOperator
object.
The following options are supported:
watchedNamespace
-
The Kubernetes namespace in which the topic operator watches for
KafkaTopics
. Default is the namespace where the Kafka cluster is deployed. reconciliationIntervalSeconds
-
The interval between periodic reconciliations in seconds. Default
90
. zookeeperSessionTimeoutSeconds
-
The ZooKeeper session timeout in seconds. Default
20
. topicMetadataMaxAttempts
-
The number of attempts at getting topic metadata from Kafka. The time between each attempt is defined as an exponential back-off. Consider increasing this value when topic creation could take more time due to the number of partitions or replicas. Default
6
. image
-
The
image
property can be used to configure the container image which will be used. For more details about configuring custom container images, see Container images. resources
-
The
resources
property configures the amount of resources allocated to the Topic Operator. For more details about resource request and limit configuration, see CPU and memory resources. logging
-
The
logging
property configures the logging of the Topic Operator.The Topic Operator has its own configurable logger:
-
rootLogger.level
-
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
zookeeper:
# ...
entityOperator:
# ...
topicOperator:
watchedNamespace: my-topic-namespace
reconciliationIntervalSeconds: 60
# ...
User Operator
User Operator deployment can be configured using additional options inside the userOperator
object.
The following options are supported:
watchedNamespace
-
The Kubernetes namespace in which the topic operator watches for
KafkaUsers
. Default is the namespace where the Kafka cluster is deployed. reconciliationIntervalSeconds
-
The interval between periodic reconciliations in seconds. Default
120
. zookeeperSessionTimeoutSeconds
-
The ZooKeeper session timeout in seconds. Default
6
. image
-
The
image
property can be used to configure the container image which will be used. For more details about configuring custom container images, see Container images. resources
-
The
resources
property configures the amount of resources allocated to the User Operator. For more details about resource request and limit configuration, see CPU and memory resources. logging
-
The
logging
property configures the logging of the User Operator.The User Operator has its own configurable logger:
-
rootLogger.level
-
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
zookeeper:
# ...
entityOperator:
# ...
userOperator:
watchedNamespace: my-user-namespace
reconciliationIntervalSeconds: 60
# ...
Configuring Entity Operator
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
entityOperator
property in theKafka
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... entityOperator: topicOperator: watchedNamespace: my-topic-namespace reconciliationIntervalSeconds: 60 userOperator: watchedNamespace: my-user-namespace reconciliationIntervalSeconds: 60
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.1.12. CPU and memory resources
For every deployed container, Strimzi allows you to request specific resources and define the maximum consumption of those resources.
Strimzi supports two types of resources:
-
CPU
-
Memory
Strimzi uses the Kubernetes syntax for specifying CPU and memory resources.
Resource limits and requests
Resource limits and requests are configured using the resources
property in the following resources:
-
Kafka.spec.kafka
-
Kafka.spec.kafka.tlsSidecar
-
Kafka.spec.zookeeper
-
Kafka.spec.zookeeper.tlsSidecar
-
Kafka.spec.entityOperator.topicOperator
-
Kafka.spec.entityOperator.userOperator
-
Kafka.spec.entityOperator.tlsSidecar
-
Kafka.spec.KafkaExporter
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
-
KafkaBridge.spec
-
For more information about managing computing resources on Kubernetes, see Managing Compute Resources for Containers.
Resource requests
Requests specify the resources to reserve for a given container. Reserving the resources ensures that they are always available.
Important
|
If the resource request is for more than the available free resources in the Kubernetes cluster, the pod is not scheduled. |
Resources requests are specified in the requests
property.
Resources requests currently supported by Strimzi:
-
cpu
-
memory
A request may be configured for one or more supported resources.
# ...
resources:
requests:
cpu: 12
memory: 64Gi
# ...
Resource limits
Limits specify the maximum resources that can be consumed by a given container. The limit is not reserved and might not always be available. A container can use the resources up to the limit only when they are available. Resource limits should be always higher than the resource requests.
Resource limits are specified in the limits
property.
Resource limits currently supported by Strimzi:
-
cpu
-
memory
A resource may be configured for one or more supported limits.
# ...
resources:
limits:
cpu: 12
memory: 64Gi
# ...
Supported CPU formats
CPU requests and limits are supported in the following formats:
-
Number of CPU cores as integer (
5
CPU core) or decimal (2.5
CPU core). -
Number or millicpus / millicores (
100m
) where 1000 millicores is the same1
CPU core.
# ...
resources:
requests:
cpu: 500m
limits:
cpu: 2.5
# ...
Note
|
The computing power of 1 CPU core may differ depending on the platform where Kubernetes is deployed. |
-
For more information on CPU specification, see the Meaning of CPU.
Supported memory formats
Memory requests and limits are specified in megabytes, gigabytes, mebibytes, and gibibytes.
-
To specify memory in megabytes, use the
M
suffix. For 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 memory specification and additional supported units, see Meaning of memory.
Configuring resource requests and limits
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
resources
property in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... resources: requests: cpu: "8" memory: 64Gi limits: cpu: "12" memory: 128Gi # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
-
For more information about the schema, see
Resources
schema reference.
3.1.13. Logging
This section provides information on loggers and how to configure log levels.
You can set the log levels by specifying the loggers and their levels directly (inline) or use a custom (external) config map.
Kafka and ZooKeeper loggers
Kafka has its own configurable loggers:
-
kafka.root.logger.level
-
log4j.logger.org.I0Itec.zkclient.ZkClient
-
log4j.logger.org.apache.zookeeper
-
log4j.logger.kafka
-
log4j.logger.org.apache.kafka
-
log4j.logger.kafka.request.logger
-
log4j.logger.kafka.network.Processor
-
log4j.logger.kafka.server.KafkaApis
-
log4j.logger.kafka.network.RequestChannel$
-
log4j.logger.kafka.controller
-
log4j.logger.kafka.log.LogCleaner
-
log4j.logger.state.change.logger
-
log4j.logger.kafka.authorizer.logger
ZooKeeper has its own logger as well:
-
zookeeper.root.logger
Specifying inline logging
-
Edit the YAML file to specify the loggers and logging level for the required components.
For example, the logging level here is set to INFO:
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... logging: type: inline loggers: logger.name: "INFO" # ... zookeeper: # ... logging: type: inline loggers: logger.name: "INFO" # ... entityOperator: # ... topicOperator: # ... logging: type: inline loggers: logger.name: "INFO" # ... # ... userOperator: # ... logging: type: inline loggers: logger.name: "INFO" # ...
You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.
For more information about the log levels, see the log4j manual.
-
Create or update the Kafka resource in Kubernetes.
This can be done using
kubectl apply
:kubectl apply -f your-file
Specifying an external ConfigMap for logging
-
Edit the YAML file to specify the name of the
ConfigMap
to use for the required components. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... logging: type: external name: customConfigMap # ...
Remember to place your custom ConfigMap under the
log4j.properties
orlog4j2.properties
key. -
Create or update the Kafka resource in Kubernetes.
This can be done using
kubectl apply
:kubectl apply -f your-file
Garbage collector (GC) logging can also be enabled (or disabled). For more information on GC, see JVM configuration
3.1.14. Kafka rack awareness
The rack awareness feature in Strimzi helps to spread the Kafka broker pods and Kafka topic replicas across different racks. Enabling rack awareness helps to improve availability of Kafka brokers and the topics they are hosting.
Note
|
"Rack" might represent an availability zone, data center, or an actual rack in your data center. |
Configuring rack awareness in Kafka brokers
Kafka rack awareness can be configured in the rack
property of Kafka.spec.kafka
.
The rack
object has one mandatory field named topologyKey
.
This key needs to match one of the labels assigned to the Kubernetes cluster nodes.
The label is used by Kubernetes when scheduling the Kafka broker pods to nodes.
If the Kubernetes cluster is running on a cloud provider platform, that label should represent the availability zone where the node is running.
Usually, the nodes are labeled with failure-domain.beta.kubernetes.io/zone
that can be easily used as the topologyKey
value.
This has the effect of spreading the broker pods across zones, and also setting the brokers' broker.rack
configuration parameter inside Kafka broker.
-
A Kubernetes cluster
-
A running Cluster Operator
-
Consult your Kubernetes administrator regarding the node label that represents the zone / rack into which the node is deployed.
-
Edit the
rack
property in theKafka
resource using the label as the topology key.apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... rack: topologyKey: failure-domain.beta.kubernetes.io/zone # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
-
For information about Configuring init container image for Kafka rack awareness, see Container images.
3.1.15. Healthchecks
Healthchecks are periodical tests which verify the health of an application. When a Healthcheck probe fails, Kubernetes assumes that the application is not healthy and attempts to fix it.
Kubernetes supports two types of Healthcheck probes:
-
Liveness probes
-
Readiness probes
For more details about the probes, see Configure Liveness and Readiness Probes. Both types of probes are used in Strimzi components.
Users can configure selected options for liveness and readiness probes.
Healthcheck configurations
Liveness and readiness probes can be configured using the livenessProbe
and readinessProbe
properties in following resources:
-
Kafka.spec.kafka
-
Kafka.spec.kafka.tlsSidecar
-
Kafka.spec.zookeeper
-
Kafka.spec.zookeeper.tlsSidecar
-
Kafka.spec.entityOperator.tlsSidecar
-
Kafka.spec.entityOperator.topicOperator
-
Kafka.spec.entityOperator.userOperator
-
Kafka.spec.KafkaExporter
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
-
KafkaMirrorMaker.spec
-
KafkaBridge.spec
Both livenessProbe
and readinessProbe
support the following options:
-
initialDelaySeconds
-
timeoutSeconds
-
periodSeconds
-
successThreshold
-
failureThreshold
For more information about the livenessProbe
and readinessProbe
options, see Probe
schema reference.
# ...
readinessProbe:
initialDelaySeconds: 15
timeoutSeconds: 5
livenessProbe:
initialDelaySeconds: 15
timeoutSeconds: 5
# ...
Configuring healthchecks
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
livenessProbe
orreadinessProbe
property in theKafka
,KafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... readinessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.1.16. Prometheus metrics
Strimzi supports Prometheus metrics using Prometheus JMX exporter to convert the JMX metrics supported by Apache Kafka and ZooKeeper to Prometheus metrics. When metrics are enabled, they are exposed on port 9404.
For more information about configuring Prometheus and Grafana, see Metrics.
Metrics configuration
Prometheus metrics are enabled by configuring the metrics
property in following resources:
-
Kafka.spec.kafka
-
Kafka.spec.zookeeper
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
When the metrics
property is not defined in the resource, the Prometheus metrics will be disabled.
To enable Prometheus metrics export without any further configuration, you can set it to an empty object ({}
).
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
metrics: {}
# ...
zookeeper:
# ...
The metrics
property might contain additional configuration for the Prometheus JMX exporter.
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
metrics:
lowercaseOutputName: true
rules:
- pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*><>Count"
name: "kafka_server_$1_$2_total"
- pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*, topic=(.+)><>Count"
name: "kafka_server_$1_$2_total"
labels:
topic: "$3"
# ...
zookeeper:
# ...
Configuring Prometheus metrics
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
metrics
property in theKafka
,KafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... metrics: lowercaseOutputName: true # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.1.17. JVM Options
The following components of Strimzi run inside a Virtual Machine (VM):
-
Apache Kafka
-
Apache ZooKeeper
-
Apache Kafka Connect
-
Apache Kafka Mirror Maker
-
Strimzi Kafka Bridge
JVM configuration options optimize the performance for different platforms and architectures. Strimzi allows you to configure some of these options.
JVM configuration
JVM options can be configured using the jvmOptions
property in following resources:
-
Kafka.spec.kafka
-
Kafka.spec.zookeeper
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
-
KafkaMirrorMaker.spec
-
KafkaBridge.spec
Only a selected subset of available JVM options can be configured. The following options are supported:
-Xms
configures the minimum initial allocation heap size when the JVM starts.
-Xmx
configures the maximum heap size.
Note
|
The units accepted by JVM settings such as -Xmx and -Xms are those accepted by the JDK java binary in the corresponding image.
Accordingly, 1g or 1G means 1,073,741,824 bytes, and Gi is not a valid unit suffix.
This is in contrast to the units used for memory requests and limits, which follow the Kubernetes convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes
|
The default values used for -Xms
and -Xmx
depends on whether there is a memory request limit configured for the container:
-
If there is a memory limit then the JVM’s minimum and maximum memory will be set to a value corresponding to the limit.
-
If there is no memory limit then the JVM’s minimum memory will be set to
128M
and the JVM’s maximum memory will not be defined. This allows for the JVM’s memory to grow as-needed, which is ideal for single node environments in test and development.
Important
|
Setting
|
When setting -Xmx
explicitly, it is recommended to:
-
set the memory request and the memory limit to the same value,
-
use a memory request that is at least 4.5 × the
-Xmx
, -
consider setting
-Xms
to the same value as-Xmx
.
Important
|
Containers doing lots of disk I/O (such as Kafka broker containers) will need to leave some memory available for use as operating system page cache. On such containers, the requested memory should be significantly higher than the memory used by the JVM. |
-Xmx
and -Xms
# ...
jvmOptions:
"-Xmx": "2g"
"-Xms": "2g"
# ...
In the above example, the JVM will use 2 GiB (=2,147,483,648 bytes) for its heap. Its total memory usage will be approximately 8GiB.
Setting the same value for initial (-Xms
) and maximum (-Xmx
) heap sizes avoids the JVM having to allocate memory after startup, at the cost of possibly allocating more heap than is really needed.
For Kafka and ZooKeeper pods such allocation could cause unwanted latency.
For Kafka Connect avoiding over allocation may be the most important concern, especially in distributed mode where the effects of over-allocation will be multiplied by the number of consumers.
-server
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
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.
|
Garbage collector logging
The jvmOptions
section also allows you to enable and disable garbage collector (GC) logging.
GC logging is disabled by default.
To enable it, set the gcLoggingEnabled
property as follows:
# ...
jvmOptions:
gcLoggingEnabled: true
# ...
Configuring JVM options
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
jvmOptions
property in theKafka
,KafkaConnect
,KafkaConnectS2I
,KafkaMirrorMaker
, orKafkaBridge
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... jvmOptions: "-Xmx": "8g" "-Xms": "8g" # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.1.18. Container images
Strimzi allows you to configure container images which will be used for its components. Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by Strimzi. In such a case, you should either copy the Strimzi images or build them from the source. If the configured image is not compatible with Strimzi images, it might not work properly.
Container image configurations
You can specify which container image to use for each component using the image
property in the following resources:
-
Kafka.spec.kafka
-
Kafka.spec.kafka.tlsSidecar
-
Kafka.spec.zookeeper
-
Kafka.spec.zookeeper.tlsSidecar
-
Kafka.spec.entityOperator.topicOperator
-
Kafka.spec.entityOperator.userOperator
-
Kafka.spec.entityOperator.tlsSidecar
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
-
KafkaBridge.spec
Configuring the image
property for Kafka, Kafka Connect, and Kafka Mirror Maker
Kafka, Kafka Connect (including Kafka Connect with S2I support), and Kafka Mirror Maker support multiple versions of Kafka. Each component requires its own image. The default images for the different Kafka versions are configured in the following environment variables:
-
STRIMZI_KAFKA_IMAGES
-
STRIMZI_KAFKA_CONNECT_IMAGES
-
STRIMZI_KAFKA_CONNECT_S2I_IMAGES
-
STRIMZI_KAFKA_MIRROR_MAKER_IMAGES
These environment variables contain mappings between the Kafka versions and their corresponding images.
The mappings are used together with the image
and version
properties:
-
If neither
image
norversion
are given in the custom resource then theversion
will default to the Cluster Operator’s default Kafka version, and the image will be the one corresponding to this version in the environment variable. -
If
image
is given butversion
is not, then the given image is used and theversion
is assumed to be the Cluster Operator’s default Kafka version. -
If
version
is given butimage
is not, then the image that corresponds to the given version in the environment variable is used. -
If both
version
andimage
are given, then the given image is used. The image is assumed to contain a Kafka image with the given version.
The image
and version
for the different components can be configured in the following properties:
-
For Kafka in
spec.kafka.image
andspec.kafka.version
. -
For Kafka Connect, Kafka Connect S2I, and Kafka Mirror Maker in
spec.image
andspec.version
.
Warning
|
It is recommended to provide only the version and leave the image property unspecified.
This reduces the chance of making a mistake when configuring the custom resource.
If you need to change the images used for different versions of Kafka, it is preferable to configure the Cluster Operator’s environment variables.
|
Configuring the image
property in other resources
For the image
property in the other custom resources, the given value will be used during deployment.
If the image
property is missing, the image
specified in the Cluster Operator configuration will be used.
If the image
name is not defined in the Cluster Operator configuration, then the default value will be used.
-
For Kafka broker TLS sidecar:
-
Container image specified in the
STRIMZI_DEFAULT_TLS_SIDECAR_KAFKA_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/kafka:0.15.0-kafka-2.3.1
container image.
-
-
For ZooKeeper nodes:
-
Container image specified in the
STRIMZI_DEFAULT_ZOOKEEPER_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/kafka:0.15.0-kafka-2.3.1
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/kafka:0.15.0-kafka-2.3.1
container image.
-
-
For Topic Operator:
-
Container image specified in the
STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/operator:0.15.0
container image.
-
-
For User Operator:
-
Container image specified in the
STRIMZI_DEFAULT_USER_OPERATOR_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/operator:0.15.0
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/kafka:0.15.0-kafka-2.3.1
container image.
-
-
For Kafka Exporter:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_EXPORTER_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/kafka:0.15.0-kafka-2.3.1
container image.
-
-
For Kafka Bridge:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_BRIDGE_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/kafka-bridge:0.15.0
container image.
-
-
For Kafka broker initializer:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_INIT_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/operator:0.15.0
container image.
-
Warning
|
Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by Strimzi. In such case, you should either copy the Strimzi images or build them from source. In case the configured image is not compatible with Strimzi images, it might not work properly. |
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
image: my-org/my-image:latest
# ...
zookeeper:
# ...
Configuring container images
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
image
property in theKafka
,KafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... image: my-org/my-image:latest # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.1.19. TLS sidecar
A sidecar is a container that runs in a pod but serves a supporting purpose. In Strimzi, the TLS sidecar uses TLS to encrypt and decrypt all communication between the various components and ZooKeeper. ZooKeeper does not have native TLS support.
The TLS sidecar is used in:
-
Kafka brokers
-
ZooKeeper nodes
-
Entity Operator
TLS sidecar configuration
The TLS sidecar can be configured using the tlsSidecar
property in:
-
Kafka.spec.kafka
-
Kafka.spec.zookeeper
-
Kafka.spec.entityOperator
The TLS sidecar supports the following additional options:
-
image
-
resources
-
logLevel
-
readinessProbe
-
livenessProbe
The resources
property can be used to specify the memory and CPU resources allocated for the TLS sidecar.
The image
property can be used to configure the container image which will be used.
For more details about configuring custom container images, see Container images.
The logLevel
property is used to specify the logging level.
Following logging levels are supported:
-
emerg
-
alert
-
crit
-
err
-
warning
-
notice
-
info
-
debug
The default value is notice.
For more information about configuring the readinessProbe
and livenessProbe
properties for the healthchecks, see Healthcheck configurations.
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
tlsSidecar:
image: my-org/my-image:latest
resources:
requests:
cpu: 200m
memory: 64Mi
limits:
cpu: 500m
memory: 128Mi
logLevel: debug
readinessProbe:
initialDelaySeconds: 15
timeoutSeconds: 5
livenessProbe:
initialDelaySeconds: 15
timeoutSeconds: 5
# ...
zookeeper:
# ...
Configuring TLS sidecar
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
tlsSidecar
property in theKafka
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... tlsSidecar: resources: requests: cpu: 200m memory: 64Mi limits: cpu: 500m memory: 128Mi # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.1.20. Configuring pod scheduling
Important
|
When two applications are scheduled to the same Kubernetes node, both applications might use the same resources like disk I/O and impact performance. That can lead to performance degradation. Scheduling Kafka pods in a way that avoids sharing nodes with other critical workloads, using the right nodes or dedicated a set of nodes only for Kafka are the best ways how to avoid such problems. |
Scheduling pods based on other applications
Avoid critical applications to share the node
Pod anti-affinity can be used to ensure that critical applications are never scheduled on the same disk. When running Kafka cluster, it is recommended to use pod anti-affinity to ensure that the Kafka brokers do not share the nodes with other workloads like databases.
Affinity
Affinity can be configured using the affinity
property in following resources:
-
Kafka.spec.kafka.template.pod
-
Kafka.spec.zookeeper.template.pod
-
Kafka.spec.entityOperator.template.pod
-
KafkaConnect.spec.template.pod
-
KafkaConnectS2I.spec.template.pod
-
KafkaBridge.spec.template.pod
The affinity configuration can include different types of affinity:
-
Pod affinity and anti-affinity
-
Node affinity
The format of the affinity
property follows the Kubernetes specification.
For more details, see the Kubernetes node and pod affinity documentation.
Configuring pod anti-affinity in Kafka components
-
A 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/v1beta1 kind: Kafka spec: kafka: # ... template: pod: affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: application operator: In values: - postgresql - mongodb topologyKey: "kubernetes.io/hostname" # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
Scheduling pods to specific nodes
Node scheduling
The Kubernetes cluster usually consists of many different types of worker nodes. Some are optimized for CPU heavy workloads, some for memory, while other might be optimized for storage (fast local SSDs) or network. Using different nodes helps to optimize both costs and performance. To achieve the best possible performance, it is important to allow scheduling of Strimzi components to use the right nodes.
Kubernetes uses node affinity to schedule workloads onto specific nodes.
Node affinity allows you to create a scheduling constraint for the node on which the pod will be scheduled.
The constraint is specified as a label selector.
You can specify the label using either the built-in node label like beta.kubernetes.io/instance-type
or custom labels to select the right node.
Affinity
Affinity can be configured using the affinity
property in following resources:
-
Kafka.spec.kafka.template.pod
-
Kafka.spec.zookeeper.template.pod
-
Kafka.spec.entityOperator.template.pod
-
KafkaConnect.spec.template.pod
-
KafkaConnectS2I.spec.template.pod
-
KafkaBridge.spec.template.pod
The affinity configuration can include different types of affinity:
-
Pod affinity and anti-affinity
-
Node affinity
The format of the affinity
property follows the Kubernetes specification.
For more details, see the Kubernetes node and pod affinity documentation.
Configuring node affinity in Kafka components
-
A Kubernetes cluster
-
A running Cluster Operator
-
Label the nodes where Strimzi components should be scheduled.
This can be done using
kubectl label
:kubectl label node your-node node-type=fast-network
Alternatively, some of the existing labels might be reused.
-
Edit the
affinity
property in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... template: pod: affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: node-type operator: In values: - fast-network # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
Using dedicated nodes
Dedicated nodes
Cluster administrators can mark selected Kubernetes nodes as tainted. Nodes with taints are excluded from regular scheduling and normal pods will not be scheduled to run on them. Only services which can tolerate the taint set on the node can be scheduled on it. The only other services running on such nodes will be system services such as log collectors or software defined networks.
Taints can be used to create dedicated nodes. Running Kafka and its components on dedicated nodes can have many advantages. There will be no other applications running on the same nodes which could cause disturbance or consume the resources needed for Kafka. That can lead to improved performance and stability.
To schedule Kafka pods on the dedicated nodes, configure node affinity and tolerations.
Affinity
Affinity can be configured using the affinity
property in following resources:
-
Kafka.spec.kafka.template.pod
-
Kafka.spec.zookeeper.template.pod
-
Kafka.spec.entityOperator.template.pod
-
KafkaConnect.spec.template.pod
-
KafkaConnectS2I.spec.template.pod
-
KafkaBridge.spec.template.pod
The affinity configuration can include different types of affinity:
-
Pod affinity and anti-affinity
-
Node affinity
The format of the affinity
property follows the Kubernetes specification.
For more details, see the Kubernetes node and pod affinity documentation.
Tolerations
Tolerations can be configured using the tolerations
property in following resources:
-
Kafka.spec.kafka.template.pod
-
Kafka.spec.zookeeper.template.pod
-
Kafka.spec.entityOperator.template.pod
-
KafkaConnect.spec.template.pod
-
KafkaConnectS2I.spec.template.pod
-
KafkaBridge.spec.template.pod
The format of the tolerations
property follows the Kubernetes specification.
For more details, see the Kubernetes taints and tolerations.
Setting up dedicated nodes and scheduling pods on them
-
A 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:
This can be done using
kubectl taint
:kubectl taint node your-node dedicated=Kafka:NoSchedule
-
Additionally, add a label to the selected nodes as well.
This can be done using
kubectl label
:kubectl label node your-node dedicated=Kafka
-
Edit the
affinity
andtolerations
properties in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... template: pod: tolerations: - key: "dedicated" operator: "Equal" value: "Kafka" effect: "NoSchedule" affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: dedicated operator: In values: - Kafka # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.1.21. Kafka Exporter
You can configure the Kafka
resource to automatically deploy Kafka Exporter in your cluster.
Kafka Exporter extracts data for analysis as Prometheus metrics, primarily data relating to offsets, consumer groups, consumer lag and topics.
For information on Kafka Exporter and why it is important to monitor consumer lag for performance, see Kafka Exporter.
Configuring Kafka Exporter
Configure Kafka Exporter in the Kafka
resource through KafkaExporter
properties.
Refer to the sample Kafka YAML configuration for an overview of the Kafka
resource and its properties.
The properties relevant to the Kafka Exporter configuration are shown in this procedure.
You can configure these properties as part of a deployment or redeployment of the Kafka cluster.
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
KafkaExporter
properties for theKafka
resource.The properties you can configure are shown in this example configuration:
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: # ... kafkaExporter: image: my-org/my-image:latest (1) groupRegex: ".*" (2) topicRegex: ".*" (3) resources: (4) requests: cpu: 200m memory: 64Mi limits: cpu: 500m memory: 128Mi logging: debug (5) enableSaramaLogging: true (6) template: (7) pod: metadata: labels: label1: value1 imagePullSecrets: - name: my-docker-credentials securityContext: runAsUser: 1000001 fsGroup: 0 terminationGracePeriodSeconds: 120 readinessProbe: (8) initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: (9) initialDelaySeconds: 15 timeoutSeconds: 5 # ...
-
ADVANCED OPTION: Container image configuration, which is recommended only in special situations.
-
A regular expression to specify the consumer groups to include in the metrics.
-
A regular expression to specify the topics to include in the metrics.
-
Logging configuration, to log messages with a given severity (debug, info, warn, error, fatal) or above.
-
Boolean to enable Sarama logging, a Go client library used by Kafka Exporter.
-
-
Create or update the resource:
kubectl apply -f kafka.yaml
After configuring and deploying Kafka Exporter, you can enable Grafana to present the Kafka Exporter dashboards.
3.1.22. Performing a rolling update of a Kafka cluster
This procedure describes how to manually trigger a rolling update of an existing Kafka cluster by using a Kubernetes annotation.
-
A running Kafka cluster.
-
A running Cluster Operator.
-
Find the name of the
StatefulSet
that controls the Kafka pods you want to manually update.For example, if your Kafka cluster is named my-cluster, the corresponding
StatefulSet
is named my-cluster-kafka. -
Annotate the
StatefulSet
resource in Kubernetes. For example, usingkubectl annotate
:kubectl annotate statefulset cluster-name-kafka strimzi.io/manual-rolling-update=true
-
Wait for the next reconciliation to occur (every two minutes by default). A rolling update of all pods within the annotated
StatefulSet
is triggered, as long as the annotation was detected by the reconciliation process. When the rolling update of all the pods is complete, the annotation is removed from theStatefulSet
.
-
For more information about deploying the Cluster Operator, see Cluster Operator.
-
For more information about deploying the Kafka cluster, see Deploying the Kafka cluster.
3.1.23. Performing a rolling update of a ZooKeeper cluster
This procedure describes how to manually trigger a rolling update of an existing ZooKeeper cluster by using a Kubernetes annotation.
-
A running ZooKeeper cluster.
-
A running Cluster Operator.
-
Find the name of the
StatefulSet
that controls the ZooKeeper pods you want to manually update.For example, if your Kafka cluster is named my-cluster, the corresponding
StatefulSet
is named my-cluster-zookeeper. -
Annotate the
StatefulSet
resource in Kubernetes. For example, usingkubectl annotate
:kubectl annotate statefulset cluster-name-zookeeper strimzi.io/manual-rolling-update=true
-
Wait for the next reconciliation to occur (every two minutes by default). A rolling update of all pods within the annotated
StatefulSet
is triggered, as long as the annotation was detected by the reconciliation process. When the rolling update of all the pods is complete, the annotation is removed from theStatefulSet
.
-
For more information about deploying the Cluster Operator, see Cluster Operator.
-
For more information about deploying the ZooKeeper cluster, see Deploying the Kafka cluster.
3.1.24. Scaling clusters
Scaling Kafka clusters
Adding brokers to a cluster
The primary way of increasing throughput for a topic is to increase the number of partitions for that topic. That works because the extra partitions allow the load of the topic to be shared between the different brokers in the cluster. However, in situations where every broker is constrained by a particular resource (typically I/O) using more partitions will not result in increased throughput. Instead, you need to add brokers to the cluster.
When you add an extra broker to the cluster, Kafka does not assign any partitions to it automatically. You must decide which partitions to move from the existing brokers to the new broker.
Once the partitions have been redistributed between all the brokers, the resource utilization of each broker should be reduced.
Removing brokers from a cluster
Because Strimzi uses StatefulSets
to manage broker pods, you cannot remove any pod from the cluster.
You can only remove one or more of the highest numbered pods from the cluster.
For example, in a cluster of 12 brokers the pods are named cluster-name-kafka-0
up to cluster-name-kafka-11
.
If you decide to scale down by one broker, the cluster-name-kafka-11
will be removed.
Before you remove a broker from a cluster, ensure that it is not assigned to any partitions. You should also decide which of the remaining brokers will be responsible for each of the partitions on the broker being decommissioned. Once the broker has no assigned partitions, you can scale the cluster down safely.
Partition reassignment
The Topic Operator does not currently support reassigning replicas to different brokers, so it is necessary to connect directly to broker pods to reassign replicas to brokers.
Within a broker pod, the kafka-reassign-partitions.sh
utility allows you to reassign partitions to different brokers.
It has three different modes:
--generate
-
Takes a set of topics and brokers and generates a reassignment JSON file which will result in the partitions of those topics being assigned to those brokers. Because this operates on whole topics, it cannot be used when you just need to reassign some of the partitions of some topics.
--execute
-
Takes a reassignment JSON file and applies it to the partitions and brokers in the cluster. Brokers that gain partitions as a result become followers of the partition leader. For a given partition, once the new broker has caught up and joined the ISR (in-sync replicas) the old broker will stop being a follower and will delete its replica.
--verify
-
Using the same reassignment JSON file as the
--execute
step,--verify
checks whether all of the partitions in the file have been moved to their intended brokers. If the reassignment is complete, --verify also removes any throttles that are in effect. Unless removed, throttles will continue to affect the cluster even after the reassignment has finished.
It is only possible to have one reassignment running in a cluster at any given time, and it is not possible to cancel a running reassignment.
If you need to cancel a reassignment, wait for it to complete and then perform another reassignment to revert the effects of the first reassignment.
The kafka-reassign-partitions.sh
will print the reassignment JSON for this reversion as part of its output.
Very large reassignments should be broken down into a number of smaller reassignments in case there is a need to stop in-progress reassignment.
Reassignment JSON file
The reassignment JSON file has a specific structure:
{
"version": 1,
"partitions": [
<PartitionObjects>
]
}
Where <PartitionObjects> is a comma-separated list of objects like:
{
"topic": <TopicName>,
"partition": <Partition>,
"replicas": [ <AssignedBrokerIds> ]
}
Note
|
Although Kafka also supports a "log_dirs" property this should not be used in Strimzi.
|
The following is an example reassignment JSON file that assigns topic topic-a
, partition 4
to brokers 2
, 4
and 7
, and topic topic-b
partition 2
to brokers 1
, 5
and 7
:
{
"version": 1,
"partitions": [
{
"topic": "topic-a",
"partition": 4,
"replicas": [2,4,7]
},
{
"topic": "topic-b",
"partition": 2,
"replicas": [1,5,7]
}
]
}
Partitions not included in the JSON are not changed.
Reassigning partitions between JBOD volumes
When using JBOD storage in your Kafka cluster, you can choose to reassign the partitions between specific volumes and their log directories (each volume has a single log directory).
To reassign a partition to a specific volume, add the log_dirs
option to <PartitionObjects> in the reassignment JSON file.
{
"topic": <TopicName>,
"partition": <Partition>,
"replicas": [ <AssignedBrokerIds> ],
"log_dirs": [ <AssignedLogDirs> ]
}
The log_dirs
object should contain the same number of log directories as the number of replicas specified in the replicas
object.
The value should be either an absolute path to the log directory, or the any
keyword.
For example:
{
"topic": "topic-a",
"partition": 4,
"replicas": [2,4,7].
"log_dirs": [ "/var/lib/kafka/data-0/kafka-log2", "/var/lib/kafka/data-0/kafka-log4", "/var/lib/kafka/data-0/kafka-log7" ]
}
Generating reassignment JSON files
This procedure describes how to generate a reassignment JSON file that reassigns all the partitions for a given set of topics using the kafka-reassign-partitions.sh
tool.
-
A running Cluster Operator
-
A
Kafka
resource -
A set of topics to reassign the partitions of
-
Prepare a JSON file named
topics.json
that lists the topics to move. It must have the following structure:{ "version": 1, "topics": [ <TopicObjects> ] }
where <TopicObjects> is a comma-separated list of objects like:
{ "topic": <TopicName> }
For example if you want to reassign all the partitions of
topic-a
andtopic-b
, you would need to prepare atopics.json
file like this:{ "version": 1, "topics": [ { "topic": "topic-a"}, { "topic": "topic-b"} ] }
-
Copy the
topics.json
file to one of the broker pods:cat topics.json | kubectl exec -c kafka <BrokerPod> -i -- \ /bin/bash -c \ 'cat > /tmp/topics.json'
-
Use the
kafka-reassign-partitions.sh`
command to generate the reassignment JSON.kubectl exec <BrokerPod> -c kafka -it -- \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --topics-to-move-json-file /tmp/topics.json \ --broker-list <BrokerList> \ --generate
For example, to move all the partitions of
topic-a
andtopic-b
to brokers4
and7
kubectl exec <BrokerPod> -c kafka -it -- \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --topics-to-move-json-file /tmp/topics.json \ --broker-list 4,7 \ --generate
Creating reassignment JSON files manually
You can manually create the reassignment JSON file if you want to move specific partitions.
Reassignment throttles
Partition reassignment can be a slow process because it involves transferring large amounts of data between brokers. To avoid a detrimental impact on clients, you can throttle the reassignment process. This might cause the reassignment to take longer to complete.
-
If the throttle is too low then the newly assigned brokers will not be able to keep up with records being published and the reassignment will never complete.
-
If the throttle is too high then clients will be impacted.
For example, for producers, this could manifest as higher than normal latency waiting for acknowledgement. For consumers, this could manifest as a drop in throughput caused by higher latency between polls.
Scaling up a Kafka cluster
This procedure describes how to increase the number of brokers in a Kafka cluster.
-
An existing Kafka cluster.
-
A reassignment JSON file named
reassignment.json
that describes how partitions should be reassigned to brokers in the enlarged cluster.
-
Add as many new brokers as you need by increasing the
Kafka.spec.kafka.replicas
configuration option. -
Verify that the new broker pods have started.
-
Copy the
reassignment.json
file to the broker pod on which you will later execute the commands:cat reassignment.json | \ kubectl exec broker-pod -c kafka -i -- /bin/bash -c \ 'cat > /tmp/reassignment.json'
For example:
cat reassignment.json | \ kubectl exec my-cluster-kafka-0 -c kafka -i -- /bin/bash -c \ 'cat > /tmp/reassignment.json'
-
Execute the partition reassignment using the
kafka-reassign-partitions.sh
command line tool from the same broker pod.kubectl exec broker-pod -c kafka -it -- \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --reassignment-json-file /tmp/reassignment.json \ --execute
If you are going to throttle replication you can also pass the
--throttle
option with an inter-broker throttled rate in bytes per second. For example:kubectl exec my-cluster-kafka-0 -c kafka -it -- \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --reassignment-json-file /tmp/reassignment.json \ --throttle 5000000 \ --execute
This command will print out two reassignment JSON objects. The first records the current assignment for the partitions being moved. You should save this to a local file (not a file in the pod) in case you need to revert the reassignment later on. The second JSON object is the target reassignment you have passed in your reassignment JSON file.
-
If you need to change the throttle during reassignment you can use the same command line with a different throttled rate. For example:
kubectl exec my-cluster-kafka-0 -c kafka -it -- \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --reassignment-json-file /tmp/reassignment.json \ --throttle 10000000 \ --execute
-
Periodically verify whether the reassignment has completed using the
kafka-reassign-partitions.sh
command line tool from any of the broker pods. This is the same command as the previous step but with the--verify
option instead of the--execute
option.kubectl exec broker-pod -c kafka -it -- \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --reassignment-json-file /tmp/reassignment.json \ --verify
For example,
kubectl exec my-cluster-kafka-0 -c kafka -it -- \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --reassignment-json-file /tmp/reassignment.json \ --verify
-
The reassignment has finished when the
--verify
command reports each of the partitions being moved as completed successfully. This final--verify
will also have the effect of removing any reassignment throttles. You can now delete the revert file if you saved the JSON for reverting the assignment to their original brokers.
Scaling down a Kafka cluster
This procedure describes how to decrease the number of brokers in a Kafka cluster.
-
An existing Kafka cluster.
-
A reassignment JSON file named
reassignment.json
describing how partitions should be reassigned to brokers in the cluster once the broker(s) in the highest numberedPod(s)
have been removed.
-
Copy the
reassignment.json
file to the broker pod on which you will later execute the commands:cat reassignment.json | \ kubectl exec broker-pod -c kafka -i -- /bin/bash -c \ 'cat > /tmp/reassignment.json'
For example:
cat reassignment.json | \ kubectl exec my-cluster-kafka-0 -c kafka -i -- /bin/bash -c \ 'cat > /tmp/reassignment.json'
-
Execute the partition reassignment using the
kafka-reassign-partitions.sh
command line tool from the same broker pod.kubectl exec broker-pod -c kafka -it -- \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --reassignment-json-file /tmp/reassignment.json \ --execute
If you are going to throttle replication you can also pass the
--throttle
option with an inter-broker throttled rate in bytes per second. For example:kubectl exec my-cluster-kafka-0 -c kafka -it -- \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --reassignment-json-file /tmp/reassignment.json \ --throttle 5000000 \ --execute
This command will print out two reassignment JSON objects. The first records the current assignment for the partitions being moved. You should save this to a local file (not a file in the pod) in case you need to revert the reassignment later on. The second JSON object is the target reassignment you have passed in your reassignment JSON file.
-
If you need to change the throttle during reassignment you can use the same command line with a different throttled rate. For example:
kubectl exec my-cluster-kafka-0 -c kafka -it -- \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --reassignment-json-file /tmp/reassignment.json \ --throttle 10000000 \ --execute
-
Periodically verify whether the reassignment has completed using the
kafka-reassign-partitions.sh
command line tool from any of the broker pods. This is the same command as the previous step but with the--verify
option instead of the--execute
option.kubectl exec broker-pod -c kafka -it -- \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --reassignment-json-file /tmp/reassignment.json \ --verify
For example,
kubectl exec my-cluster-kafka-0 -c kafka -it -- \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --reassignment-json-file /tmp/reassignment.json \ --verify
-
The reassignment has finished when the
--verify
command reports each of the partitions being moved as completed successfully. This final--verify
will also have the effect of removing any reassignment throttles. You can now delete the revert file if you saved the JSON for reverting the assignment to their original brokers. -
Once all the partition reassignments have finished, the broker(s) being removed should not have responsibility for any of the partitions in the cluster. You can verify this by checking that the broker’s data log directory does not contain any live partition logs. If the log directory on the broker contains a directory that does not match the extended regular expression
[a-zA-Z0-9.-]+\.[a-z0-9]+-delete$
then the broker still has live partitions and it should not be stopped.You can check this by executing the command:
kubectl exec my-cluster-kafka-0 -c kafka -it -- \ /bin/bash -c \ "ls -l /var/lib/kafka/kafka-log_<N>_ | grep -E '^d' | grep -vE '[a-zA-Z0-9.-]+\.[a-z0-9]+-delete$'"
where N is the number of the
Pod(s)
being deleted.If the above command prints any output then the broker still has live partitions. In this case, either the reassignment has not finished, or the reassignment JSON file was incorrect.
-
Once you have confirmed that the broker has no live partitions you can edit the
Kafka.spec.kafka.replicas
of yourKafka
resource, which will scale down theStatefulSet
, deleting the highest numbered brokerPod(s)
.
3.1.25. Deleting Kafka nodes manually
This procedure describes how to delete an existing Kafka node by using a Kubernetes annotation.
Deleting a Kafka node consists of deleting both the Pod
on which the Kafka broker is running and the related PersistentVolumeClaim
(if the cluster was deployed with persistent storage).
After deletion, the Pod
and its related PersistentVolumeClaim
are recreated automatically.
Warning
|
Deleting a PersistentVolumeClaim can cause permanent data loss. The following procedure should only be performed if you have encountered storage issues.
|
-
A running Kafka cluster.
-
A running Cluster Operator.
-
Find the name of the
Pod
that you want to delete.For example, if the cluster is named cluster-name, the pods are named cluster-name-kafka-index, where index starts at zero and ends at the total number of replicas.
-
Annotate the
Pod
resource in Kubernetes.Use
kubectl annotate
:kubectl annotate pod cluster-name-kafka-index strimzi.io/delete-pod-and-pvc=true
-
Wait for the next reconciliation, when the annotated pod with the underlying persistent volume claim will be deleted and then recreated.
-
For more information about deploying the Cluster Operator, see Cluster Operator.
-
For more information about deploying the Kafka cluster, see Deploying the Kafka cluster.
3.1.26. Deleting ZooKeeper nodes manually
This procedure describes how to delete an existing ZooKeeper node by using a Kubernetes annotation.
Deleting a ZooKeeper node consists of deleting both the Pod
on which ZooKeeper is running and the related PersistentVolumeClaim
(if the cluster was deployed with persistent storage).
After deletion, the Pod
and its related PersistentVolumeClaim
are recreated automatically.
Warning
|
Deleting a PersistentVolumeClaim can cause permanent data loss. The following procedure should only be performed if you have encountered storage issues.
|
-
A running ZooKeeper cluster.
-
A running Cluster Operator.
-
Find the name of the
Pod
that you want to delete.For example, if the cluster is named cluster-name, the pods are named cluster-name-zookeeper-index, where index starts at zero and ends at the total number of replicas.
-
Annotate the
Pod
resource in Kubernetes.Use
kubectl annotate
:kubectl annotate pod cluster-name-zookeeper-index strimzi.io/delete-pod-and-pvc=true
-
Wait for the next reconciliation, when the annotated pod with the underlying persistent volume claim will be deleted and then recreated.
-
For more information about deploying the Cluster Operator, see Cluster Operator.
-
For more information about deploying the ZooKeeper cluster, see Deploying the Kafka cluster.
3.1.27. Maintenance time windows for rolling updates
Maintenance time windows allow you to schedule certain rolling updates of your Kafka and ZooKeeper clusters to start at a convenient time.
Maintenance time windows overview
In most cases, the Cluster Operator only updates your Kafka or ZooKeeper clusters in response to changes to the corresponding Kafka
resource.
This enables you to plan when to apply changes to a Kafka
resource to minimize the impact on Kafka client applications.
However, some updates to your Kafka and ZooKeeper clusters can happen without any corresponding change to the Kafka
resource.
For example, the Cluster Operator will need to perform a rolling restart if a CA (Certificate Authority) certificate that it manages is close to expiry.
While a rolling restart of the pods should not affect availability of the service (assuming correct broker and topic configurations), it could affect performance of the Kafka client applications. Maintenance time windows allow you to schedule such spontaneous rolling updates of your Kafka and ZooKeeper clusters to start at a convenient time. If maintenance time windows are not configured for a cluster then it is possible that such spontaneous rolling updates will happen at an inconvenient time, such as during a predictable period of high load.
Maintenance time window definition
You configure maintenance time windows by entering an array of strings in the Kafka.spec.maintenanceTimeWindows
property.
Each string is a cron expression interpreted as being in UTC (Coordinated Universal Time, which for practical purposes is the same as Greenwich Mean Time).
The following example configures a single maintenance time window that starts at midnight and ends at 01:59am (UTC), on Sundays, Mondays, Tuesdays, Wednesdays, and Thursdays:
# ...
maintenanceTimeWindows:
- "* * 0-1 ? * SUN,MON,TUE,WED,THU *"
# ...
In practice, maintenance windows should be set in conjunction with the Kafka.spec.clusterCa.renewalDays
and Kafka.spec.clientsCa.renewalDays
properties of the Kafka
resource, to ensure that the necessary CA certificate renewal can be completed in the configured maintenance time windows.
Note
|
Strimzi does not schedule maintenance operations exactly according to the given windows. Instead, for each reconciliation, it checks whether a maintenance window is currently "open". This means that the start of maintenance operations within a given time window can be delayed by up to the Cluster Operator reconciliation interval. Maintenance time windows must therefore be at least this long. |
-
For more information about the Cluster Operator configuration, see Cluster Operator Configuration.
Configuring a maintenance time window
You can configure a maintenance time window for rolling updates triggered by supported processes.
-
A Kubernetes cluster.
-
The Cluster Operator is running.
-
Add or edit the
maintenanceTimeWindows
property in theKafka
resource. For example to allow maintenance between 0800 and 1059 and between 1400 and 1559 you would set themaintenanceTimeWindows
as shown below:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... maintenanceTimeWindows: - "* * 8-10 * * ?" - "* * 14-15 * * ?"
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
-
Performing a rolling update of a Kafka cluster, see Performing a rolling update of a Kafka cluster
-
Performing a rolling update of a ZooKeeper cluster, see Performing a rolling update of a ZooKeeper cluster
3.1.28. Renewing CA certificates manually
Unless the Kafka.spec.clusterCa.generateCertificateAuthority
and Kafka.spec.clientsCa.generateCertificateAuthority
objects are set to false
, the cluster and clients CA certificates will auto-renew at the start of their respective certificate renewal periods.
You can manually renew one or both of these certificates before the certificate renewal period starts, if required for security reasons.
A renewed certificate uses the same private key as the old certificate.
-
The Cluster Operator is running.
-
A Kafka cluster in which CA certificates and private keys are installed.
-
Apply the
strimzi.io/force-renew
annotation to theSecret
that contains the CA certificate that you want to renew.Certificate Secret Annotate command Cluster CA
<cluster-name>-cluster-ca-cert
kubectl annotate secret <cluster-name>-cluster-ca-cert strimzi.io/force-renew=true
Clients CA
<cluster-name>-clients-ca-cert
kubectl annotate secret <cluster-name>-clients-ca-cert strimzi.io/force-renew=true
At the next reconciliation the Cluster Operator will generate a new CA certificate for the Secret
that you annotated.
If maintenance time windows are configured, the Cluster Operator will generate the new CA certificate at the first reconciliation within the next maintenance time window.
Client applications must reload the cluster and clients CA certificates that were renewed by the Cluster Operator.
3.1.29. Replacing private keys
You can replace the private keys used by the cluster CA and clients CA certificates. When a private key is replaced, the Cluster Operator generates a new CA certificate for the new private key.
-
The Cluster Operator is running.
-
A Kafka cluster in which CA certificates and private keys are installed.
-
Apply the
strimzi.io/force-replace
annotation to theSecret
that contains the private key that you want to renew.Private key for Secret Annotate command Cluster CA
<cluster-name>-cluster-ca
kubectl annotate secret <cluster-name>-cluster-ca strimzi.io/force-replace=true
Clients CA
<cluster-name>-clients-ca
kubectl annotate secret <cluster-name>-clients-ca strimzi.io/force-replace=true
At the next reconciliation the Cluster Operator will:
-
Generate a new private key for the
Secret
that you annotated -
Generate a new CA certificate
If maintenance time windows are configured, the Cluster Operator will generate the new private key and CA certificate at the first reconciliation within the next maintenance time window.
Client applications must reload the cluster and clients CA certificates that were renewed by the Cluster Operator.
3.1.30. List of resources created as part of Kafka cluster
The following resources will created by the Cluster Operator in the Kubernetes cluster:
cluster-name-kafka
-
StatefulSet which is in charge of managing the Kafka broker pods.
cluster-name-kafka-brokers
-
Service needed to have DNS resolve the Kafka broker pods IP addresses directly.
cluster-name-kafka-bootstrap
-
Service can be used as bootstrap servers for Kafka clients.
cluster-name-kafka-external-bootstrap
-
Bootstrap service for clients connecting from outside of the Kubernetes cluster. This resource will be created only when external listener is enabled.
cluster-name-kafka-pod-id
-
Service used to route traffic from outside of the Kubernetes cluster to individual pods. This resource will be created only when external listener is enabled.
cluster-name-kafka-external-bootstrap
-
Bootstrap route for clients connecting from outside of the Kubernetes cluster. This resource will be created only when external listener is enabled and set to type
route
. cluster-name-kafka-pod-id
-
Route for traffic from outside of the Kubernetes cluster to individual pods. This resource will be created only when external listener is enabled and set to type
route
. cluster-name-kafka-config
-
ConfigMap which contains the Kafka ancillary configuration and is mounted as a volume by the Kafka broker pods.
cluster-name-kafka-brokers
-
Secret with Kafka broker keys.
cluster-name-kafka
-
Service account used by the Kafka brokers.
cluster-name-kafka
-
Pod Disruption Budget configured for the Kafka brokers.
strimzi-namespace-name-cluster-name-kafka-init
-
Cluster role binding used by the Kafka brokers.
cluster-name-zookeeper
-
StatefulSet which is in charge of managing the ZooKeeper node pods.
cluster-name-zookeeper-nodes
-
Service needed to have DNS resolve the ZooKeeper pods IP addresses directly.
cluster-name-zookeeper-client
-
Service used by Kafka brokers to connect to ZooKeeper nodes as clients.
cluster-name-zookeeper-config
-
ConfigMap which contains the ZooKeeper ancillary configuration and is mounted as a volume by the ZooKeeper node pods.
cluster-name-zookeeper-nodes
-
Secret with ZooKeeper node keys.
cluster-name-zookeeper
-
Pod Disruption Budget configured for the ZooKeeper nodes.
cluster-name-entity-operator
-
Deployment with Topic and User Operators. This resource will be created only if Cluster Operator deployed Entity Operator.
cluster-name-entity-topic-operator-config
-
Configmap with ancillary configuration for Topic Operators. This resource will be created only if Cluster Operator deployed Entity Operator.
cluster-name-entity-user-operator-config
-
Configmap with ancillary configuration for User Operators. This resource will be created only if Cluster Operator deployed Entity Operator.
cluster-name-entity-operator-certs
-
Secret with Entitiy operators keys for communication with Kafka and ZooKeeper. This resource will be created only if Cluster Operator deployed Entity Operator.
cluster-name-entity-operator
-
Service account used by the Entity Operator.
strimzi-cluster-name-topic-operator
-
Role binding used by the Entity Operator.
strimzi-cluster-name-user-operator
-
Role binding used by the Entity Operator.
cluster-name-cluster-ca
-
Secret with the Cluster CA used to encrypt the cluster communication.
cluster-name-cluster-ca-cert
-
Secret with the Cluster CA public key. This key can be used to verify the identity of the Kafka brokers.
cluster-name-clients-ca
-
Secret with the Clients CA used to encrypt the communication between Kafka brokers and Kafka clients.
cluster-name-clients-ca-cert
-
Secret with the Clients CA public key. This key can be used to verify the identity of the Kafka brokers.
cluster-name-cluster-operator-certs
-
Secret with Cluster operators keys for communication with Kafka and ZooKeeper.
data-cluster-name-kafka-idx
-
Persistent Volume Claim for the volume used for storing data for the Kafka broker pod
idx
. This resource will be created only if persistent storage is selected for provisioning persistent volumes to store data. data-id-cluster-name-kafka-idx
-
Persistent Volume Claim for the volume
id
used for storing data for the Kafka broker podidx
. This resource is only created if persistent storage is selected for JBOD volumes when provisioning persistent volumes to store data. data-cluster-name-zookeeper-idx
-
Persistent Volume Claim for the volume used for storing data for the ZooKeeper node pod
idx
. This resource will be created only if persistent storage is selected for provisioning persistent volumes to store data.
3.2. Kafka Connect cluster configuration
The full schema of the KafkaConnect
resource is described in the KafkaConnect
schema reference.
All labels that are applied to the desired KafkaConnect
resource will also be applied to the Kubernetes resources making up the Kafka Connect cluster.
This provides a convenient mechanism for resources to be labeled as required.
3.2.1. Replicas
Kafka Connect clusters can run multiple of nodes.
The number of nodes is defined in the KafkaConnect
and KafkaConnectS2I
resources.
Running a Kafka Connect cluster with multiple nodes can provide better availability and scalability.
However, when running Kafka Connect on Kubernetes it is not absolutely necessary to run multiple nodes of Kafka Connect for high availability.
If a node where Kafka Connect is deployed to crashes, Kubernetes will automatically reschedule the Kafka Connect pod to a different node.
However, running Kafka Connect with multiple nodes can provide faster failover times, because the other nodes will be up and running already.
Configuring the number of nodes
The number of Kafka Connect nodes is configured using the replicas
property in KafkaConnect.spec
and KafkaConnectS2I.spec
.
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
replicas
property in theKafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnectS2I metadata: name: my-cluster spec: # ... replicas: 3 # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.2.2. Bootstrap servers
A Kafka Connect cluster always works in combination with a Kafka cluster.
A Kafka cluster is specified as a list of bootstrap servers.
On Kubernetes, the list must ideally contain the Kafka cluster bootstrap service named cluster-name-kafka-bootstrap
, and a port of 9092 for plain traffic or 9093 for encrypted traffic.
The list of bootstrap servers is configured in the bootstrapServers
property in KafkaConnect.spec
and KafkaConnectS2I.spec
. The servers must be defined as a comma-separated list specifying one or more Kafka brokers, or a service pointing to Kafka brokers specified as a hostname:_port_
pairs.
When using Kafka Connect with a Kafka cluster not managed by Strimzi, you can specify the bootstrap servers list according to the configuration of the cluster.
Configuring bootstrap servers
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
bootstrapServers
property in theKafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnect metadata: name: my-cluster spec: # ... bootstrapServers: my-cluster-kafka-bootstrap:9092 # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.2.3. Connecting to Kafka brokers using TLS
By default, Kafka Connect tries to connect to Kafka brokers using a plain text connection. If you prefer to use TLS, additional configuration is required.
TLS support in Kafka Connect
TLS support is configured in the tls
property in KafkaConnect.spec
and KafkaConnectS2I.spec
.
The tls
property contains a list of secrets with key names under which the certificates are stored.
The certificates must be stored in X509 format.
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
name: my-cluster
spec:
# ...
tls:
trustedCertificates:
- secretName: my-secret
certificate: ca.crt
- secretName: my-other-secret
certificate: certificate.crt
# ...
When multiple certificates are stored in the same secret, it can be listed multiple times.
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnectS2I
metadata:
name: my-cluster
spec:
# ...
tls:
trustedCertificates:
- secretName: my-secret
certificate: ca.crt
- secretName: my-secret
certificate: ca2.crt
# ...
Configuring TLS in Kafka Connect
-
A Kubernetes cluster
-
A running Cluster Operator
-
If they exist, the name of the
Secret
for the certificate used for TLS Server Authentication, and the key under which the certificate is stored in theSecret
-
(Optional) If they do not already exist, prepare the TLS certificate used in authentication in a file and create a
Secret
.NoteThe secrets created by the Cluster Operator for Kafka cluster may be used directly. This can be done using
kubectl create
:kubectl create secret generic my-secret --from-file=my-file.crt
-
Edit the
tls
property in theKafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnect metadata: name: my-connect spec: # ... tls: trustedCertificates: - secretName: my-cluster-cluster-cert certificate: ca.crt # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.2.4. Connecting to Kafka brokers with Authentication
By default, Kafka Connect will try to connect to Kafka brokers without authentication.
Authentication is enabled through the KafkaConnect
and KafkaConnectS2I
resources.
Authentication support in Kafka Connect
Authentication is configured through the authentication
property in KafkaConnect.spec
and KafkaConnectS2I.spec
.
The authentication
property specifies the type of the authentication mechanisms which should be used and additional configuration details depending on the mechanism.
The supported authentication types are:
-
TLS client authentication
-
SASL-based authentication using the SCRAM-SHA-512 mechanism
-
SASL-based authentication using the PLAIN mechanism
TLS Client Authentication
To use TLS client authentication, set the type
property to the value tls
.
TLS client authentication uses a TLS certificate to authenticate.
The certificate is specified in the certificateAndKey
property and is always loaded from a Kubernetes secret.
In the secret, the certificate must be stored in X509 format under two different keys: public and private.
Note
|
TLS client authentication can be used only with TLS connections. For more details about TLS configuration in Kafka Connect see Connecting to Kafka brokers using TLS. |
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
name: my-cluster
spec:
# ...
authentication:
type: tls
certificateAndKey:
secretName: my-secret
certificate: public.crt
key: private.key
# ...
SASL based SCRAM-SHA-512 authentication
To configure Kafka Connect to use SASL-based SCRAM-SHA-512 authentication, set the type
property to scram-sha-512
.
This authentication mechanism requires a username and password.
-
Specify the username in the
username
property. -
In the
passwordSecret
property, specify a link to aSecret
containing the password. ThesecretName
property contains the name of theSecret
and thepassword
property contains the name of the key under which the password is stored inside theSecret
.
Important
|
Do not specify the actual password in the password field.
|
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
name: my-cluster
spec:
# ...
authentication:
type: scram-sha-512
username: my-connect-user
passwordSecret:
secretName: my-connect-user
password: my-connect-password-key
# ...
SASL based PLAIN authentication
To configure Kafka Connect to use SASL-based PLAIN authentication, set the type
property to plain
.
This authentication mechanism requires a username and password.
Warning
|
The SASL PLAIN mechanism will transfer the username and password across the network in cleartext. Only use SASL PLAIN authentication if TLS encryption is enabled. |
-
Specify the username in the
username
property. -
In the
passwordSecret
property, specify a link to aSecret
containing the password. ThesecretName
property contains the name of such aSecret
and thepassword
property contains the name of the key under which the password is stored inside theSecret
.
Important
|
Do not specify the actual password in the password field.
|
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
name: my-cluster
spec:
# ...
authentication:
type: plain
username: my-connect-user
passwordSecret:
secretName: my-connect-user
password: my-connect-password-key
# ...
Configuring TLS client authentication in Kafka Connect
-
A Kubernetes cluster
-
A running Cluster Operator
-
If they exist, the name of the
Secret
with the public and private keys used for TLS Client Authentication, and the keys under which they are stored in theSecret
-
(Optional) If they do not already exist, prepare the keys used for authentication in a file and create the
Secret
.NoteSecrets created by the User Operator may be used. This can be done using
kubectl create
:kubectl create secret generic my-secret --from-file=my-public.crt --from-file=my-private.key
-
Edit the
authentication
property in theKafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnect metadata: name: my-connect spec: # ... authentication: type: tls certificateAndKey: secretName: my-secret certificate: my-public.crt key: my-private.key # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
Configuring SCRAM-SHA-512 authentication in Kafka Connect
-
A Kubernetes cluster
-
A running Cluster Operator
-
Username of the user which should be used for authentication
-
If they exist, the name of the
Secret
with the password used for authentication and the key under which the password is stored in theSecret
-
(Optional) If they do not already exist, prepare a file with the password used in authentication and create the
Secret
.NoteSecrets created by the User Operator may be used. This can be done using
kubectl create
:echo -n '<password>' > <my-password.txt> kubectl create secret generic <my-secret> --from-file=<my-password.txt>
-
Edit the
authentication
property in theKafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnect metadata: name: my-connect spec: # ... authentication: type: scram-sha-512 username: _<my-username>_ passwordSecret: secretName: _<my-secret>_ password: _<my-password.txt>_ # ...
-
Create or update the resource.
On Kubernetes this can be done using
kubectl apply
:kubectl apply -f your-file
3.2.5. Kafka Connect configuration
Strimzi allows you to customize the configuration of Apache Kafka Connect nodes by editing certain options listed in Apache Kafka documentation.
Configuration options that cannot be configured relate to:
-
Kafka cluster bootstrap address
-
Security (Encryption, Authentication, and Authorization)
-
Listener / REST interface configuration
-
Plugin path configuration
These options are automatically configured by Strimzi.
Kafka Connect configuration
Kafka Connect is configured using the config
property in KafkaConnect.spec
and KafkaConnectS2I.spec
.
This property contains the Kafka Connect configuration options as keys.
The values can be one of the following JSON types:
-
String
-
Number
-
Boolean
You can specify and configure the options listed in the Apache Kafka documentation with the exception of those options that are managed directly by Strimzi. Specifically, configuration options with keys equal to or starting with one of the following strings are forbidden:
-
ssl.
-
sasl.
-
security.
-
listeners
-
plugin.path
-
rest.
-
bootstrap.servers
When a forbidden option is present in the config
property, it is ignored and a warning message is printed to the Custer Operator log file.
All other options are passed to Kafka Connect.
Important
|
The Cluster Operator does not validate keys or values in the config object provided.
When an invalid configuration is provided, the Kafka Connect cluster might not start or might become unstable.
In this circumstance, fix the configuration in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config object, then the Cluster Operator can roll out the new configuration to all Kafka Connect nodes.
|
Certain options have default values:
-
group.id
with default 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
These options are automatically configured in case they are not present in the KafkaConnect.spec.config
or KafkaConnectS2I.spec.config
properties.
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
name: my-connect
spec:
# ...
config:
group.id: my-connect-cluster
offset.storage.topic: my-connect-cluster-offsets
config.storage.topic: my-connect-cluster-configs
status.storage.topic: my-connect-cluster-status
key.converter: org.apache.kafka.connect.json.JsonConverter
value.converter: org.apache.kafka.connect.json.JsonConverter
key.converter.schemas.enable: true
value.converter.schemas.enable: true
config.storage.replication.factor: 3
offset.storage.replication.factor: 3
status.storage.replication.factor: 3
# ...
Kafka Connect configuration for multiple instances
If you are running multiple instances of Kafka Connect, pay attention to the default configuration of the following properties:
# ...
group.id: connect-cluster (1)
offset.storage.topic: connect-cluster-offsets (2)
config.storage.topic: connect-cluster-configs (3)
status.storage.topic: connect-cluster-status (4)
# ...
-
Kafka Connect cluster group the instance belongs to.
-
Kafka topic that stores connector offsets.
-
Kafka topic that stores connector and task status configurations.
-
Kafka topic that stores connector and task status updates.
Note
|
Values for the three topics must be the same for all Kafka Connect instances with the same group.id .
|
Unless you change the default settings, each Kafka Connect instance connecting to the same Kafka cluster is deployed with the same values. What happens, in effect, is all instances are coupled to run in a cluster and use the same topics.
If multiple Kafka Connect clusters try to use the same topics, Kafka Connect will not work as expected and generate errors.
If you wish to run multiple Kafka Connect instances, change the values of these properties for each instance.
Configuring Kafka Connect
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
config
property in theKafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnect metadata: name: my-connect spec: # ... config: group.id: my-connect-cluster offset.storage.topic: my-connect-cluster-offsets config.storage.topic: my-connect-cluster-configs status.storage.topic: my-connect-cluster-status key.converter: org.apache.kafka.connect.json.JsonConverter value.converter: org.apache.kafka.connect.json.JsonConverter key.converter.schemas.enable: true value.converter.schemas.enable: true config.storage.replication.factor: 3 offset.storage.replication.factor: 3 status.storage.replication.factor: 3 # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.2.6. CPU and memory resources
For every deployed container, Strimzi allows you to request specific resources and define the maximum consumption of those resources.
Strimzi supports two types of resources:
-
CPU
-
Memory
Strimzi uses the Kubernetes syntax for specifying CPU and memory resources.
Resource limits and requests
Resource limits and requests are configured using the resources
property in the following resources:
-
Kafka.spec.kafka
-
Kafka.spec.kafka.tlsSidecar
-
Kafka.spec.zookeeper
-
Kafka.spec.zookeeper.tlsSidecar
-
Kafka.spec.entityOperator.topicOperator
-
Kafka.spec.entityOperator.userOperator
-
Kafka.spec.entityOperator.tlsSidecar
-
Kafka.spec.KafkaExporter
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
-
KafkaBridge.spec
-
For more information about managing computing resources on Kubernetes, see Managing Compute Resources for Containers.
Resource requests
Requests specify the resources to reserve for a given container. Reserving the resources ensures that they are always available.
Important
|
If the resource request is for more than the available free resources in the Kubernetes cluster, the pod is not scheduled. |
Resources requests are specified in the requests
property.
Resources requests currently supported by Strimzi:
-
cpu
-
memory
A request may be configured for one or more supported resources.
# ...
resources:
requests:
cpu: 12
memory: 64Gi
# ...
Resource limits
Limits specify the maximum resources that can be consumed by a given container. The limit is not reserved and might not always be available. A container can use the resources up to the limit only when they are available. Resource limits should be always higher than the resource requests.
Resource limits are specified in the limits
property.
Resource limits currently supported by Strimzi:
-
cpu
-
memory
A resource may be configured for one or more supported limits.
# ...
resources:
limits:
cpu: 12
memory: 64Gi
# ...
Supported CPU formats
CPU requests and limits are supported in the following formats:
-
Number of CPU cores as integer (
5
CPU core) or decimal (2.5
CPU core). -
Number or millicpus / millicores (
100m
) where 1000 millicores is the same1
CPU core.
# ...
resources:
requests:
cpu: 500m
limits:
cpu: 2.5
# ...
Note
|
The computing power of 1 CPU core may differ depending on the platform where Kubernetes is deployed. |
-
For more information on CPU specification, see the Meaning of CPU.
Supported memory formats
Memory requests and limits are specified in megabytes, gigabytes, mebibytes, and gibibytes.
-
To specify memory in megabytes, use the
M
suffix. For 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 memory specification and additional supported units, see Meaning of memory.
Configuring resource requests and limits
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
resources
property in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... resources: requests: cpu: "8" memory: 64Gi limits: cpu: "12" memory: 128Gi # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
-
For more information about the schema, see
Resources
schema reference.
3.2.7. Logging
This section provides information on loggers and how to configure log levels.
You can set the log levels by specifying the loggers and their levels directly (inline) or use a custom (external) config map.
Kafka Connect loggers
Kafka Connect has its own configurable loggers:
-
connect.root.logger.level
-
log4j.logger.org.apache.zookeeper
-
log4j.logger.org.I0Itec.zkclient
-
log4j.logger.org.reflections
Specifying inline logging
-
Edit the YAML file to specify the loggers and logging level for the required components.
For example, the logging level here is set to INFO:
apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnect spec: # ... logging: type: inline loggers: logger.name: "INFO" # ...
You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.
For more information about the log levels, see the log4j manual.
-
Create or update the Kafka resource in Kubernetes.
This can be done using
kubectl apply
:kubectl apply -f your-file
Specifying an external ConfigMap for logging
-
Edit the YAML file to specify the name of the
ConfigMap
to use for the required components. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnect spec: # ... logging: type: external name: customConfigMap # ...
Remember to place your custom ConfigMap under the
log4j.properties
orlog4j2.properties
key. -
Create or update the Kafka resource in Kubernetes.
This can be done using
kubectl apply
:kubectl apply -f your-file
Garbage collector (GC) logging can also be enabled (or disabled). For more information on GC, see JVM configuration
3.2.8. Healthchecks
Healthchecks are periodical tests which verify the health of an application. When a Healthcheck probe fails, Kubernetes assumes that the application is not healthy and attempts to fix it.
Kubernetes supports two types of Healthcheck probes:
-
Liveness probes
-
Readiness probes
For more details about the probes, see Configure Liveness and Readiness Probes. Both types of probes are used in Strimzi components.
Users can configure selected options for liveness and readiness probes.
Healthcheck configurations
Liveness and readiness probes can be configured using the livenessProbe
and readinessProbe
properties in following resources:
-
Kafka.spec.kafka
-
Kafka.spec.kafka.tlsSidecar
-
Kafka.spec.zookeeper
-
Kafka.spec.zookeeper.tlsSidecar
-
Kafka.spec.entityOperator.tlsSidecar
-
Kafka.spec.entityOperator.topicOperator
-
Kafka.spec.entityOperator.userOperator
-
Kafka.spec.KafkaExporter
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
-
KafkaMirrorMaker.spec
-
KafkaBridge.spec
Both livenessProbe
and readinessProbe
support the following options:
-
initialDelaySeconds
-
timeoutSeconds
-
periodSeconds
-
successThreshold
-
failureThreshold
For more information about the livenessProbe
and readinessProbe
options, see Probe
schema reference.
# ...
readinessProbe:
initialDelaySeconds: 15
timeoutSeconds: 5
livenessProbe:
initialDelaySeconds: 15
timeoutSeconds: 5
# ...
Configuring healthchecks
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
livenessProbe
orreadinessProbe
property in theKafka
,KafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... readinessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.2.9. Prometheus metrics
Strimzi supports Prometheus metrics using Prometheus JMX exporter to convert the JMX metrics supported by Apache Kafka and ZooKeeper to Prometheus metrics. When metrics are enabled, they are exposed on port 9404.
For more information about configuring Prometheus and Grafana, see Metrics.
Metrics configuration
Prometheus metrics are enabled by configuring the metrics
property in following resources:
-
Kafka.spec.kafka
-
Kafka.spec.zookeeper
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
When the metrics
property is not defined in the resource, the Prometheus metrics will be disabled.
To enable Prometheus metrics export without any further configuration, you can set it to an empty object ({}
).
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
metrics: {}
# ...
zookeeper:
# ...
The metrics
property might contain additional configuration for the Prometheus JMX exporter.
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
metrics:
lowercaseOutputName: true
rules:
- pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*><>Count"
name: "kafka_server_$1_$2_total"
- pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*, topic=(.+)><>Count"
name: "kafka_server_$1_$2_total"
labels:
topic: "$3"
# ...
zookeeper:
# ...
Configuring Prometheus metrics
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
metrics
property in theKafka
,KafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... metrics: lowercaseOutputName: true # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.2.10. JVM Options
The following components of Strimzi run inside a Virtual Machine (VM):
-
Apache Kafka
-
Apache ZooKeeper
-
Apache Kafka Connect
-
Apache Kafka Mirror Maker
-
Strimzi Kafka Bridge
JVM configuration options optimize the performance for different platforms and architectures. Strimzi allows you to configure some of these options.
JVM configuration
JVM options can be configured using the jvmOptions
property in following resources:
-
Kafka.spec.kafka
-
Kafka.spec.zookeeper
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
-
KafkaMirrorMaker.spec
-
KafkaBridge.spec
Only a selected subset of available JVM options can be configured. The following options are supported:
-Xms
configures the minimum initial allocation heap size when the JVM starts.
-Xmx
configures the maximum heap size.
Note
|
The units accepted by JVM settings such as -Xmx and -Xms are those accepted by the JDK java binary in the corresponding image.
Accordingly, 1g or 1G means 1,073,741,824 bytes, and Gi is not a valid unit suffix.
This is in contrast to the units used for memory requests and limits, which follow the Kubernetes convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes
|
The default values used for -Xms
and -Xmx
depends on whether there is a memory request limit configured for the container:
-
If there is a memory limit then the JVM’s minimum and maximum memory will be set to a value corresponding to the limit.
-
If there is no memory limit then the JVM’s minimum memory will be set to
128M
and the JVM’s maximum memory will not be defined. This allows for the JVM’s memory to grow as-needed, which is ideal for single node environments in test and development.
Important
|
Setting
|
When setting -Xmx
explicitly, it is recommended to:
-
set the memory request and the memory limit to the same value,
-
use a memory request that is at least 4.5 × the
-Xmx
, -
consider setting
-Xms
to the same value as-Xmx
.
Important
|
Containers doing lots of disk I/O (such as Kafka broker containers) will need to leave some memory available for use as operating system page cache. On such containers, the requested memory should be significantly higher than the memory used by the JVM. |
-Xmx
and -Xms
# ...
jvmOptions:
"-Xmx": "2g"
"-Xms": "2g"
# ...
In the above example, the JVM will use 2 GiB (=2,147,483,648 bytes) for its heap. Its total memory usage will be approximately 8GiB.
Setting the same value for initial (-Xms
) and maximum (-Xmx
) heap sizes avoids the JVM having to allocate memory after startup, at the cost of possibly allocating more heap than is really needed.
For Kafka and ZooKeeper pods such allocation could cause unwanted latency.
For Kafka Connect avoiding over allocation may be the most important concern, especially in distributed mode where the effects of over-allocation will be multiplied by the number of consumers.
-server
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
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.
|
Garbage collector logging
The jvmOptions
section also allows you to enable and disable garbage collector (GC) logging.
GC logging is disabled by default.
To enable it, set the gcLoggingEnabled
property as follows:
# ...
jvmOptions:
gcLoggingEnabled: true
# ...
Configuring JVM options
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
jvmOptions
property in theKafka
,KafkaConnect
,KafkaConnectS2I
,KafkaMirrorMaker
, orKafkaBridge
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... jvmOptions: "-Xmx": "8g" "-Xms": "8g" # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.2.11. Container images
Strimzi allows you to configure container images which will be used for its components. Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by Strimzi. In such a case, you should either copy the Strimzi images or build them from the source. If the configured image is not compatible with Strimzi images, it might not work properly.
Container image configurations
You can specify which container image to use for each component using the image
property in the following resources:
-
Kafka.spec.kafka
-
Kafka.spec.kafka.tlsSidecar
-
Kafka.spec.zookeeper
-
Kafka.spec.zookeeper.tlsSidecar
-
Kafka.spec.entityOperator.topicOperator
-
Kafka.spec.entityOperator.userOperator
-
Kafka.spec.entityOperator.tlsSidecar
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
-
KafkaBridge.spec
Configuring the image
property for Kafka, Kafka Connect, and Kafka Mirror Maker
Kafka, Kafka Connect (including Kafka Connect with S2I support), and Kafka Mirror Maker support multiple versions of Kafka. Each component requires its own image. The default images for the different Kafka versions are configured in the following environment variables:
-
STRIMZI_KAFKA_IMAGES
-
STRIMZI_KAFKA_CONNECT_IMAGES
-
STRIMZI_KAFKA_CONNECT_S2I_IMAGES
-
STRIMZI_KAFKA_MIRROR_MAKER_IMAGES
These environment variables contain mappings between the Kafka versions and their corresponding images.
The mappings are used together with the image
and version
properties:
-
If neither
image
norversion
are given in the custom resource then theversion
will default to the Cluster Operator’s default Kafka version, and the image will be the one corresponding to this version in the environment variable. -
If
image
is given butversion
is not, then the given image is used and theversion
is assumed to be the Cluster Operator’s default Kafka version. -
If
version
is given butimage
is not, then the image that corresponds to the given version in the environment variable is used. -
If both
version
andimage
are given, then the given image is used. The image is assumed to contain a Kafka image with the given version.
The image
and version
for the different components can be configured in the following properties:
-
For Kafka in
spec.kafka.image
andspec.kafka.version
. -
For Kafka Connect, Kafka Connect S2I, and Kafka Mirror Maker in
spec.image
andspec.version
.
Warning
|
It is recommended to provide only the version and leave the image property unspecified.
This reduces the chance of making a mistake when configuring the custom resource.
If you need to change the images used for different versions of Kafka, it is preferable to configure the Cluster Operator’s environment variables.
|
Configuring the image
property in other resources
For the image
property in the other custom resources, the given value will be used during deployment.
If the image
property is missing, the image
specified in the Cluster Operator configuration will be used.
If the image
name is not defined in the Cluster Operator configuration, then the default value will be used.
-
For Kafka broker TLS sidecar:
-
Container image specified in the
STRIMZI_DEFAULT_TLS_SIDECAR_KAFKA_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/kafka:0.15.0-kafka-2.3.1
container image.
-
-
For ZooKeeper nodes:
-
Container image specified in the
STRIMZI_DEFAULT_ZOOKEEPER_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/kafka:0.15.0-kafka-2.3.1
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/kafka:0.15.0-kafka-2.3.1
container image.
-
-
For Topic Operator:
-
Container image specified in the
STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/operator:0.15.0
container image.
-
-
For User Operator:
-
Container image specified in the
STRIMZI_DEFAULT_USER_OPERATOR_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/operator:0.15.0
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/kafka:0.15.0-kafka-2.3.1
container image.
-
-
For Kafka Exporter:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_EXPORTER_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/kafka:0.15.0-kafka-2.3.1
container image.
-
-
For Kafka Bridge:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_BRIDGE_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/kafka-bridge:0.15.0
container image.
-
-
For Kafka broker initializer:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_INIT_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/operator:0.15.0
container image.
-
Warning
|
Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by Strimzi. In such case, you should either copy the Strimzi images or build them from source. In case the configured image is not compatible with Strimzi images, it might not work properly. |
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
image: my-org/my-image:latest
# ...
zookeeper:
# ...
Configuring container images
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
image
property in theKafka
,KafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... image: my-org/my-image:latest # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.2.12. Configuring pod scheduling
Important
|
When two applications are scheduled to the same Kubernetes node, both applications might use the same resources like disk I/O and impact performance. That can lead to performance degradation. Scheduling Kafka pods in a way that avoids sharing nodes with other critical workloads, using the right nodes or dedicated a set of nodes only for Kafka are the best ways how to avoid such problems. |
Scheduling pods based on other applications
Avoid critical applications to share the node
Pod anti-affinity can be used to ensure that critical applications are never scheduled on the same disk. When running Kafka cluster, it is recommended to use pod anti-affinity to ensure that the Kafka brokers do not share the nodes with other workloads like databases.
Affinity
Affinity can be configured using the affinity
property in following resources:
-
Kafka.spec.kafka.template.pod
-
Kafka.spec.zookeeper.template.pod
-
Kafka.spec.entityOperator.template.pod
-
KafkaConnect.spec.template.pod
-
KafkaConnectS2I.spec.template.pod
-
KafkaBridge.spec.template.pod
The affinity configuration can include different types of affinity:
-
Pod affinity and anti-affinity
-
Node affinity
The format of the affinity
property follows the Kubernetes specification.
For more details, see the Kubernetes node and pod affinity documentation.
Configuring pod anti-affinity in Kafka components
-
A 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/v1beta1 kind: Kafka spec: kafka: # ... template: pod: affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: application operator: In values: - postgresql - mongodb topologyKey: "kubernetes.io/hostname" # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
Scheduling pods to specific nodes
Node scheduling
The Kubernetes cluster usually consists of many different types of worker nodes. Some are optimized for CPU heavy workloads, some for memory, while other might be optimized for storage (fast local SSDs) or network. Using different nodes helps to optimize both costs and performance. To achieve the best possible performance, it is important to allow scheduling of Strimzi components to use the right nodes.
Kubernetes uses node affinity to schedule workloads onto specific nodes.
Node affinity allows you to create a scheduling constraint for the node on which the pod will be scheduled.
The constraint is specified as a label selector.
You can specify the label using either the built-in node label like beta.kubernetes.io/instance-type
or custom labels to select the right node.
Affinity
Affinity can be configured using the affinity
property in following resources:
-
Kafka.spec.kafka.template.pod
-
Kafka.spec.zookeeper.template.pod
-
Kafka.spec.entityOperator.template.pod
-
KafkaConnect.spec.template.pod
-
KafkaConnectS2I.spec.template.pod
-
KafkaBridge.spec.template.pod
The affinity configuration can include different types of affinity:
-
Pod affinity and anti-affinity
-
Node affinity
The format of the affinity
property follows the Kubernetes specification.
For more details, see the Kubernetes node and pod affinity documentation.
Configuring node affinity in Kafka components
-
A Kubernetes cluster
-
A running Cluster Operator
-
Label the nodes where Strimzi components should be scheduled.
This can be done using
kubectl label
:kubectl label node your-node node-type=fast-network
Alternatively, some of the existing labels might be reused.
-
Edit the
affinity
property in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... template: pod: affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: node-type operator: In values: - fast-network # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
Using dedicated nodes
Dedicated nodes
Cluster administrators can mark selected Kubernetes nodes as tainted. Nodes with taints are excluded from regular scheduling and normal pods will not be scheduled to run on them. Only services which can tolerate the taint set on the node can be scheduled on it. The only other services running on such nodes will be system services such as log collectors or software defined networks.
Taints can be used to create dedicated nodes. Running Kafka and its components on dedicated nodes can have many advantages. There will be no other applications running on the same nodes which could cause disturbance or consume the resources needed for Kafka. That can lead to improved performance and stability.
To schedule Kafka pods on the dedicated nodes, configure node affinity and tolerations.
Affinity
Affinity can be configured using the affinity
property in following resources:
-
Kafka.spec.kafka.template.pod
-
Kafka.spec.zookeeper.template.pod
-
Kafka.spec.entityOperator.template.pod
-
KafkaConnect.spec.template.pod
-
KafkaConnectS2I.spec.template.pod
-
KafkaBridge.spec.template.pod
The affinity configuration can include different types of affinity:
-
Pod affinity and anti-affinity
-
Node affinity
The format of the affinity
property follows the Kubernetes specification.
For more details, see the Kubernetes node and pod affinity documentation.
Tolerations
Tolerations can be configured using the tolerations
property in following resources:
-
Kafka.spec.kafka.template.pod
-
Kafka.spec.zookeeper.template.pod
-
Kafka.spec.entityOperator.template.pod
-
KafkaConnect.spec.template.pod
-
KafkaConnectS2I.spec.template.pod
-
KafkaBridge.spec.template.pod
The format of the tolerations
property follows the Kubernetes specification.
For more details, see the Kubernetes taints and tolerations.
Setting up dedicated nodes and scheduling pods on them
-
A 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:
This can be done using
kubectl taint
:kubectl taint node your-node dedicated=Kafka:NoSchedule
-
Additionally, add a label to the selected nodes as well.
This can be done using
kubectl label
:kubectl label node your-node dedicated=Kafka
-
Edit the
affinity
andtolerations
properties in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... template: pod: tolerations: - key: "dedicated" operator: "Equal" value: "Kafka" effect: "NoSchedule" affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: dedicated operator: In values: - Kafka # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.2.13. Using external configuration and secrets
Kafka Connect connectors are configured using an HTTP REST interface. The connector configuration is passed to Kafka Connect as part of an HTTP request and stored within Kafka itself.
Some parts of the configuration of a Kafka Connect connector can be externalized using ConfigMaps or Secrets. You can then reference the configuration values in HTTP REST commands (this keeps the configuration separate and more secure, if needed). This method applies especially to confidential data, such as usernames, passwords, or certificates.
ConfigMaps and Secrets are standard Kubernetes resources used for storing of configurations and confidential data.
Storing connector configurations externally
You can mount ConfigMaps or Secrets into a Kafka Connect pod as volumes or environment variables.
Volumes and environment variables are configured in the externalConfiguration
property in KafkaConnect.spec
and KafkaConnectS2I.spec
.
External configuration as environment variables
The env
property is used to specify one or more environment variables.
These variables can contain a value from either a ConfigMap or a Secret.
Note
|
The names of user-defined environment variables cannot start with KAFKA_ or STRIMZI_ .
|
To mount a value from a Secret to an environment variable, use the valueFrom
property and the secretKeyRef
as shown in the following example.
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
name: my-connect
spec:
# ...
externalConfiguration:
env:
- name: MY_ENVIRONMENT_VARIABLE
valueFrom:
secretKeyRef:
name: my-secret
key: my-key
A common use case for mounting Secrets to environment variables is when your connector needs to communicate with Amazon AWS and needs to read the AWS_ACCESS_KEY_ID
and AWS_SECRET_ACCESS_KEY
environment variables with credentials.
To mount a value from a ConfigMap to an environment variable, use configMapKeyRef
in the valueFrom
property as shown in the following example.
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
name: my-connect
spec:
# ...
externalConfiguration:
env:
- name: MY_ENVIRONMENT_VARIABLE
valueFrom:
configMapKeyRef:
name: my-config-map
key: my-key
External configuration as volumes
You can also mount ConfigMaps or Secrets to a Kafka Connect pod as volumes. Using volumes instead of environment variables is useful in the following scenarios:
-
Mounting truststores or keystores with TLS certificates
-
Mounting a properties file that is used to configure Kafka Connect connectors
In the volumes
property of the externalConfiguration
resource, list the ConfigMaps or Secrets that will be mounted as volumes.
Each volume must specify a name in the name
property and a reference to ConfigMap or Secret.
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
name: my-connect
spec:
# ...
externalConfiguration:
volumes:
- name: connector1
configMap:
name: connector1-configuration
- name: connector1-certificates
secret:
secretName: connector1-certificates
The volumes will be mounted inside the Kafka Connect containers in the path /opt/kafka/external-configuration/<volume-name>
.
For example, the files from a volume named connector1
would appear in the directory /opt/kafka/external-configuration/connector1
.
The FileConfigProvider
has to be used to read the values from the mounted properties files in connector configurations.
Mounting Secrets as environment variables
You can create a Kubernetes Secret and mount it to Kafka Connect as an environment variable.
-
A running Cluster Operator.
-
Create a secret containing the information that will be mounted as an environment variable. For example:
apiVersion: v1 kind: Secret metadata: name: aws-creds type: Opaque data: awsAccessKey: QUtJQVhYWFhYWFhYWFhYWFg= awsSecretAccessKey: Ylhsd1lYTnpkMjl5WkE=
-
Create or edit the Kafka Connect resource. Configure the
externalConfiguration
section of theKafkaConnect
orKafkaConnectS2I
custom resource to reference the secret. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnect metadata: name: my-connect spec: # ... externalConfiguration: env: - name: AWS_ACCESS_KEY_ID valueFrom: secretKeyRef: name: aws-creds key: awsAccessKey - name: AWS_SECRET_ACCESS_KEY valueFrom: secretKeyRef: name: aws-creds key: awsSecretAccessKey
-
Apply the changes to your Kafka Connect deployment.
Use
kubectl apply
:kubectl apply -f your-file
The environment variables are now available for use when developing your connectors.
-
For more information about external configuration in Kafka Connect, see
ExternalConfiguration
schema reference.
Mounting Secrets as volumes
You can create a Kubernetes Secret, mount it as a volume to Kafka Connect, and then use it to configure a Kafka Connect connector.
-
A running Cluster Operator.
-
Create a secret containing a properties file that defines the configuration options for your connector configuration. For example:
apiVersion: v1 kind: Secret metadata: name: mysecret type: Opaque stringData: connector.properties: |- dbUsername: my-user dbPassword: my-password
-
Create or edit the Kafka Connect resource. Configure the
FileConfigProvider
in theconfig
section and theexternalConfiguration
section of theKafkaConnect
orKafkaConnectS2I
custom resource to reference the secret. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnect metadata: name: my-connect spec: # ... config: config.providers: file config.providers.file.class: org.apache.kafka.common.config.provider.FileConfigProvider #... externalConfiguration: volumes: - name: connector-config secret: secretName: mysecret
-
Apply the changes to your Kafka Connect deployment.
Use
kubectl apply
:kubectl apply -f your-file
-
Use the values from the mounted properties file in your JSON payload with connector configuration. For example:
{ "name":"my-connector", "config":{ "connector.class":"MyDbConnector", "tasks.max":"3", "database": "my-postgresql:5432" "username":"${file:/opt/kafka/external-configuration/connector-config/connector.properties:dbUsername}", "password":"${file:/opt/kafka/external-configuration/connector-config/connector.properties:dbPassword}", # ... } }
-
For more information about external configuration in Kafka Connect, see
ExternalConfiguration
schema reference.
3.2.14. List of resources created as part of Kafka Connect cluster
The following resources will created by the Cluster Operator in the Kubernetes cluster:
- connect-cluster-name-connect
-
Deployment which is in charge to create the Kafka Connect worker node pods.
- connect-cluster-name-connect-api
-
Service which exposes the REST interface for managing the Kafka Connect cluster.
- connect-cluster-name-config
-
ConfigMap which contains the Kafka Connect ancillary configuration and is mounted as a volume by the Kafka broker pods.
- connect-cluster-name-connect
-
Pod Disruption Budget configured for the Kafka Connect worker nodes.
3.3. Kafka Connect cluster with Source2Image support
The full schema of the KafkaConnectS2I
resource is described in the KafkaConnectS2I
schema reference.
All labels that are applied to the desired KafkaConnectS2I
resource will also be applied to the Kubernetes resources making up the Kafka Connect cluster with Source2Image support.
This provides a convenient mechanism for resources to be labeled as required.
3.3.1. Replicas
Kafka Connect clusters can run multiple of nodes.
The number of nodes is defined in the KafkaConnect
and KafkaConnectS2I
resources.
Running a Kafka Connect cluster with multiple nodes can provide better availability and scalability.
However, when running Kafka Connect on Kubernetes it is not absolutely necessary to run multiple nodes of Kafka Connect for high availability.
If a node where Kafka Connect is deployed to crashes, Kubernetes will automatically reschedule the Kafka Connect pod to a different node.
However, running Kafka Connect with multiple nodes can provide faster failover times, because the other nodes will be up and running already.
Configuring the number of nodes
The number of Kafka Connect nodes is configured using the replicas
property in KafkaConnect.spec
and KafkaConnectS2I.spec
.
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
replicas
property in theKafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnectS2I metadata: name: my-cluster spec: # ... replicas: 3 # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.3.2. Bootstrap servers
A Kafka Connect cluster always works in combination with a Kafka cluster.
A Kafka cluster is specified as a list of bootstrap servers.
On Kubernetes, the list must ideally contain the Kafka cluster bootstrap service named cluster-name-kafka-bootstrap
, and a port of 9092 for plain traffic or 9093 for encrypted traffic.
The list of bootstrap servers is configured in the bootstrapServers
property in KafkaConnect.spec
and KafkaConnectS2I.spec
. The servers must be defined as a comma-separated list specifying one or more Kafka brokers, or a service pointing to Kafka brokers specified as a hostname:_port_
pairs.
When using Kafka Connect with a Kafka cluster not managed by Strimzi, you can specify the bootstrap servers list according to the configuration of the cluster.
Configuring bootstrap servers
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
bootstrapServers
property in theKafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnect metadata: name: my-cluster spec: # ... bootstrapServers: my-cluster-kafka-bootstrap:9092 # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.3.3. Connecting to Kafka brokers using TLS
By default, Kafka Connect tries to connect to Kafka brokers using a plain text connection. If you prefer to use TLS, additional configuration is required.
TLS support in Kafka Connect
TLS support is configured in the tls
property in KafkaConnect.spec
and KafkaConnectS2I.spec
.
The tls
property contains a list of secrets with key names under which the certificates are stored.
The certificates must be stored in X509 format.
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
name: my-cluster
spec:
# ...
tls:
trustedCertificates:
- secretName: my-secret
certificate: ca.crt
- secretName: my-other-secret
certificate: certificate.crt
# ...
When multiple certificates are stored in the same secret, it can be listed multiple times.
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnectS2I
metadata:
name: my-cluster
spec:
# ...
tls:
trustedCertificates:
- secretName: my-secret
certificate: ca.crt
- secretName: my-secret
certificate: ca2.crt
# ...
Configuring TLS in Kafka Connect
-
A Kubernetes cluster
-
A running Cluster Operator
-
If they exist, the name of the
Secret
for the certificate used for TLS Server Authentication, and the key under which the certificate is stored in theSecret
-
(Optional) If they do not already exist, prepare the TLS certificate used in authentication in a file and create a
Secret
.NoteThe secrets created by the Cluster Operator for Kafka cluster may be used directly. This can be done using
kubectl create
:kubectl create secret generic my-secret --from-file=my-file.crt
-
Edit the
tls
property in theKafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnect metadata: name: my-connect spec: # ... tls: trustedCertificates: - secretName: my-cluster-cluster-cert certificate: ca.crt # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.3.4. Connecting to Kafka brokers with Authentication
By default, Kafka Connect will try to connect to Kafka brokers without authentication.
Authentication is enabled through the KafkaConnect
and KafkaConnectS2I
resources.
Authentication support in Kafka Connect
Authentication is configured through the authentication
property in KafkaConnect.spec
and KafkaConnectS2I.spec
.
The authentication
property specifies the type of the authentication mechanisms which should be used and additional configuration details depending on the mechanism.
The supported authentication types are:
-
TLS client authentication
-
SASL-based authentication using the SCRAM-SHA-512 mechanism
-
SASL-based authentication using the PLAIN mechanism
TLS Client Authentication
To use TLS client authentication, set the type
property to the value tls
.
TLS client authentication uses a TLS certificate to authenticate.
The certificate is specified in the certificateAndKey
property and is always loaded from a Kubernetes secret.
In the secret, the certificate must be stored in X509 format under two different keys: public and private.
Note
|
TLS client authentication can be used only with TLS connections. For more details about TLS configuration in Kafka Connect see Connecting to Kafka brokers using TLS. |
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
name: my-cluster
spec:
# ...
authentication:
type: tls
certificateAndKey:
secretName: my-secret
certificate: public.crt
key: private.key
# ...
SASL based SCRAM-SHA-512 authentication
To configure Kafka Connect to use SASL-based SCRAM-SHA-512 authentication, set the type
property to scram-sha-512
.
This authentication mechanism requires a username and password.
-
Specify the username in the
username
property. -
In the
passwordSecret
property, specify a link to aSecret
containing the password. ThesecretName
property contains the name of theSecret
and thepassword
property contains the name of the key under which the password is stored inside theSecret
.
Important
|
Do not specify the actual password in the password field.
|
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
name: my-cluster
spec:
# ...
authentication:
type: scram-sha-512
username: my-connect-user
passwordSecret:
secretName: my-connect-user
password: my-connect-password-key
# ...
SASL based PLAIN authentication
To configure Kafka Connect to use SASL-based PLAIN authentication, set the type
property to plain
.
This authentication mechanism requires a username and password.
Warning
|
The SASL PLAIN mechanism will transfer the username and password across the network in cleartext. Only use SASL PLAIN authentication if TLS encryption is enabled. |
-
Specify the username in the
username
property. -
In the
passwordSecret
property, specify a link to aSecret
containing the password. ThesecretName
property contains the name of such aSecret
and thepassword
property contains the name of the key under which the password is stored inside theSecret
.
Important
|
Do not specify the actual password in the password field.
|
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
name: my-cluster
spec:
# ...
authentication:
type: plain
username: my-connect-user
passwordSecret:
secretName: my-connect-user
password: my-connect-password-key
# ...
Configuring TLS client authentication in Kafka Connect
-
A Kubernetes cluster
-
A running Cluster Operator
-
If they exist, the name of the
Secret
with the public and private keys used for TLS Client Authentication, and the keys under which they are stored in theSecret
-
(Optional) If they do not already exist, prepare the keys used for authentication in a file and create the
Secret
.NoteSecrets created by the User Operator may be used. This can be done using
kubectl create
:kubectl create secret generic my-secret --from-file=my-public.crt --from-file=my-private.key
-
Edit the
authentication
property in theKafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnect metadata: name: my-connect spec: # ... authentication: type: tls certificateAndKey: secretName: my-secret certificate: my-public.crt key: my-private.key # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
Configuring SCRAM-SHA-512 authentication in Kafka Connect
-
A Kubernetes cluster
-
A running Cluster Operator
-
Username of the user which should be used for authentication
-
If they exist, the name of the
Secret
with the password used for authentication and the key under which the password is stored in theSecret
-
(Optional) If they do not already exist, prepare a file with the password used in authentication and create the
Secret
.NoteSecrets created by the User Operator may be used. This can be done using
kubectl create
:echo -n '<password>' > <my-password.txt> kubectl create secret generic <my-secret> --from-file=<my-password.txt>
-
Edit the
authentication
property in theKafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnect metadata: name: my-connect spec: # ... authentication: type: scram-sha-512 username: _<my-username>_ passwordSecret: secretName: _<my-secret>_ password: _<my-password.txt>_ # ...
-
Create or update the resource.
On Kubernetes this can be done using
kubectl apply
:kubectl apply -f your-file
3.3.5. Kafka Connect configuration
Strimzi allows you to customize the configuration of Apache Kafka Connect nodes by editing certain options listed in Apache Kafka documentation.
Configuration options that cannot be configured relate to:
-
Kafka cluster bootstrap address
-
Security (Encryption, Authentication, and Authorization)
-
Listener / REST interface configuration
-
Plugin path configuration
These options are automatically configured by Strimzi.
Kafka Connect configuration
Kafka Connect is configured using the config
property in KafkaConnect.spec
and KafkaConnectS2I.spec
.
This property contains the Kafka Connect configuration options as keys.
The values can be one of the following JSON types:
-
String
-
Number
-
Boolean
You can specify and configure the options listed in the Apache Kafka documentation with the exception of those options that are managed directly by Strimzi. Specifically, configuration options with keys equal to or starting with one of the following strings are forbidden:
-
ssl.
-
sasl.
-
security.
-
listeners
-
plugin.path
-
rest.
-
bootstrap.servers
When a forbidden option is present in the config
property, it is ignored and a warning message is printed to the Custer Operator log file.
All other options are passed to Kafka Connect.
Important
|
The Cluster Operator does not validate keys or values in the config object provided.
When an invalid configuration is provided, the Kafka Connect cluster might not start or might become unstable.
In this circumstance, fix the configuration in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config object, then the Cluster Operator can roll out the new configuration to all Kafka Connect nodes.
|
Certain options have default values:
-
group.id
with default 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
These options are automatically configured in case they are not present in the KafkaConnect.spec.config
or KafkaConnectS2I.spec.config
properties.
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
name: my-connect
spec:
# ...
config:
group.id: my-connect-cluster
offset.storage.topic: my-connect-cluster-offsets
config.storage.topic: my-connect-cluster-configs
status.storage.topic: my-connect-cluster-status
key.converter: org.apache.kafka.connect.json.JsonConverter
value.converter: org.apache.kafka.connect.json.JsonConverter
key.converter.schemas.enable: true
value.converter.schemas.enable: true
config.storage.replication.factor: 3
offset.storage.replication.factor: 3
status.storage.replication.factor: 3
# ...
Kafka Connect configuration for multiple instances
If you are running multiple instances of Kafka Connect, pay attention to the default configuration of the following properties:
# ...
group.id: connect-cluster (1)
offset.storage.topic: connect-cluster-offsets (2)
config.storage.topic: connect-cluster-configs (3)
status.storage.topic: connect-cluster-status (4)
# ...
-
Kafka Connect cluster group the instance belongs to.
-
Kafka topic that stores connector offsets.
-
Kafka topic that stores connector and task status configurations.
-
Kafka topic that stores connector and task status updates.
Note
|
Values for the three topics must be the same for all Kafka Connect instances with the same group.id .
|
Unless you change the default settings, each Kafka Connect instance connecting to the same Kafka cluster is deployed with the same values. What happens, in effect, is all instances are coupled to run in a cluster and use the same topics.
If multiple Kafka Connect clusters try to use the same topics, Kafka Connect will not work as expected and generate errors.
If you wish to run multiple Kafka Connect instances, change the values of these properties for each instance.
Configuring Kafka Connect
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
config
property in theKafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnect metadata: name: my-connect spec: # ... config: group.id: my-connect-cluster offset.storage.topic: my-connect-cluster-offsets config.storage.topic: my-connect-cluster-configs status.storage.topic: my-connect-cluster-status key.converter: org.apache.kafka.connect.json.JsonConverter value.converter: org.apache.kafka.connect.json.JsonConverter key.converter.schemas.enable: true value.converter.schemas.enable: true config.storage.replication.factor: 3 offset.storage.replication.factor: 3 status.storage.replication.factor: 3 # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.3.6. CPU and memory resources
For every deployed container, Strimzi allows you to request specific resources and define the maximum consumption of those resources.
Strimzi supports two types of resources:
-
CPU
-
Memory
Strimzi uses the Kubernetes syntax for specifying CPU and memory resources.
Resource limits and requests
Resource limits and requests are configured using the resources
property in the following resources:
-
Kafka.spec.kafka
-
Kafka.spec.kafka.tlsSidecar
-
Kafka.spec.zookeeper
-
Kafka.spec.zookeeper.tlsSidecar
-
Kafka.spec.entityOperator.topicOperator
-
Kafka.spec.entityOperator.userOperator
-
Kafka.spec.entityOperator.tlsSidecar
-
Kafka.spec.KafkaExporter
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
-
KafkaBridge.spec
-
For more information about managing computing resources on Kubernetes, see Managing Compute Resources for Containers.
Resource requests
Requests specify the resources to reserve for a given container. Reserving the resources ensures that they are always available.
Important
|
If the resource request is for more than the available free resources in the Kubernetes cluster, the pod is not scheduled. |
Resources requests are specified in the requests
property.
Resources requests currently supported by Strimzi:
-
cpu
-
memory
A request may be configured for one or more supported resources.
# ...
resources:
requests:
cpu: 12
memory: 64Gi
# ...
Resource limits
Limits specify the maximum resources that can be consumed by a given container. The limit is not reserved and might not always be available. A container can use the resources up to the limit only when they are available. Resource limits should be always higher than the resource requests.
Resource limits are specified in the limits
property.
Resource limits currently supported by Strimzi:
-
cpu
-
memory
A resource may be configured for one or more supported limits.
# ...
resources:
limits:
cpu: 12
memory: 64Gi
# ...
Supported CPU formats
CPU requests and limits are supported in the following formats:
-
Number of CPU cores as integer (
5
CPU core) or decimal (2.5
CPU core). -
Number or millicpus / millicores (
100m
) where 1000 millicores is the same1
CPU core.
# ...
resources:
requests:
cpu: 500m
limits:
cpu: 2.5
# ...
Note
|
The computing power of 1 CPU core may differ depending on the platform where Kubernetes is deployed. |
-
For more information on CPU specification, see the Meaning of CPU.
Supported memory formats
Memory requests and limits are specified in megabytes, gigabytes, mebibytes, and gibibytes.
-
To specify memory in megabytes, use the
M
suffix. For 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 memory specification and additional supported units, see Meaning of memory.
Configuring resource requests and limits
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
resources
property in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... resources: requests: cpu: "8" memory: 64Gi limits: cpu: "12" memory: 128Gi # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
-
For more information about the schema, see
Resources
schema reference.
3.3.7. Logging
This section provides information on loggers and how to configure log levels.
You can set the log levels by specifying the loggers and their levels directly (inline) or use a custom (external) config map.
Kafka Connect with Source2Image loggers
Kafka Connect with Source2Image support has its own configurable loggers:
-
connect.root.logger.level
-
log4j.logger.org.apache.zookeeper
-
log4j.logger.org.I0Itec.zkclient
-
log4j.logger.org.reflections
Specifying inline logging
-
Edit the YAML file to specify the loggers and logging level for the required components.
For example, the logging level here is set to INFO:
apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnectS2I spec: # ... logging: type: inline loggers: logger.name: "INFO" # ...
You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.
For more information about the log levels, see the log4j manual.
-
Create or update the Kafka resource in Kubernetes.
This can be done using
kubectl apply
:kubectl apply -f your-file
Specifying an external ConfigMap for logging
-
Edit the YAML file to specify the name of the
ConfigMap
to use for the required components. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnectS2I spec: # ... logging: type: external name: customConfigMap # ...
Remember to place your custom ConfigMap under the
log4j.properties
orlog4j2.properties
key. -
Create or update the Kafka resource in Kubernetes.
This can be done using
kubectl apply
:kubectl apply -f your-file
Garbage collector (GC) logging can also be enabled (or disabled). For more information on GC, see JVM configuration
3.3.8. Healthchecks
Healthchecks are periodical tests which verify the health of an application. When a Healthcheck probe fails, Kubernetes assumes that the application is not healthy and attempts to fix it.
Kubernetes supports two types of Healthcheck probes:
-
Liveness probes
-
Readiness probes
For more details about the probes, see Configure Liveness and Readiness Probes. Both types of probes are used in Strimzi components.
Users can configure selected options for liveness and readiness probes.
Healthcheck configurations
Liveness and readiness probes can be configured using the livenessProbe
and readinessProbe
properties in following resources:
-
Kafka.spec.kafka
-
Kafka.spec.kafka.tlsSidecar
-
Kafka.spec.zookeeper
-
Kafka.spec.zookeeper.tlsSidecar
-
Kafka.spec.entityOperator.tlsSidecar
-
Kafka.spec.entityOperator.topicOperator
-
Kafka.spec.entityOperator.userOperator
-
Kafka.spec.KafkaExporter
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
-
KafkaMirrorMaker.spec
-
KafkaBridge.spec
Both livenessProbe
and readinessProbe
support the following options:
-
initialDelaySeconds
-
timeoutSeconds
-
periodSeconds
-
successThreshold
-
failureThreshold
For more information about the livenessProbe
and readinessProbe
options, see Probe
schema reference.
# ...
readinessProbe:
initialDelaySeconds: 15
timeoutSeconds: 5
livenessProbe:
initialDelaySeconds: 15
timeoutSeconds: 5
# ...
Configuring healthchecks
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
livenessProbe
orreadinessProbe
property in theKafka
,KafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... readinessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.3.9. Prometheus metrics
Strimzi supports Prometheus metrics using Prometheus JMX exporter to convert the JMX metrics supported by Apache Kafka and ZooKeeper to Prometheus metrics. When metrics are enabled, they are exposed on port 9404.
For more information about configuring Prometheus and Grafana, see Metrics.
Metrics configuration
Prometheus metrics are enabled by configuring the metrics
property in following resources:
-
Kafka.spec.kafka
-
Kafka.spec.zookeeper
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
When the metrics
property is not defined in the resource, the Prometheus metrics will be disabled.
To enable Prometheus metrics export without any further configuration, you can set it to an empty object ({}
).
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
metrics: {}
# ...
zookeeper:
# ...
The metrics
property might contain additional configuration for the Prometheus JMX exporter.
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
metrics:
lowercaseOutputName: true
rules:
- pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*><>Count"
name: "kafka_server_$1_$2_total"
- pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*, topic=(.+)><>Count"
name: "kafka_server_$1_$2_total"
labels:
topic: "$3"
# ...
zookeeper:
# ...
Configuring Prometheus metrics
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
metrics
property in theKafka
,KafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... metrics: lowercaseOutputName: true # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.3.10. JVM Options
The following components of Strimzi run inside a Virtual Machine (VM):
-
Apache Kafka
-
Apache ZooKeeper
-
Apache Kafka Connect
-
Apache Kafka Mirror Maker
-
Strimzi Kafka Bridge
JVM configuration options optimize the performance for different platforms and architectures. Strimzi allows you to configure some of these options.
JVM configuration
JVM options can be configured using the jvmOptions
property in following resources:
-
Kafka.spec.kafka
-
Kafka.spec.zookeeper
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
-
KafkaMirrorMaker.spec
-
KafkaBridge.spec
Only a selected subset of available JVM options can be configured. The following options are supported:
-Xms
configures the minimum initial allocation heap size when the JVM starts.
-Xmx
configures the maximum heap size.
Note
|
The units accepted by JVM settings such as -Xmx and -Xms are those accepted by the JDK java binary in the corresponding image.
Accordingly, 1g or 1G means 1,073,741,824 bytes, and Gi is not a valid unit suffix.
This is in contrast to the units used for memory requests and limits, which follow the Kubernetes convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes
|
The default values used for -Xms
and -Xmx
depends on whether there is a memory request limit configured for the container:
-
If there is a memory limit then the JVM’s minimum and maximum memory will be set to a value corresponding to the limit.
-
If there is no memory limit then the JVM’s minimum memory will be set to
128M
and the JVM’s maximum memory will not be defined. This allows for the JVM’s memory to grow as-needed, which is ideal for single node environments in test and development.
Important
|
Setting
|
When setting -Xmx
explicitly, it is recommended to:
-
set the memory request and the memory limit to the same value,
-
use a memory request that is at least 4.5 × the
-Xmx
, -
consider setting
-Xms
to the same value as-Xmx
.
Important
|
Containers doing lots of disk I/O (such as Kafka broker containers) will need to leave some memory available for use as operating system page cache. On such containers, the requested memory should be significantly higher than the memory used by the JVM. |
-Xmx
and -Xms
# ...
jvmOptions:
"-Xmx": "2g"
"-Xms": "2g"
# ...
In the above example, the JVM will use 2 GiB (=2,147,483,648 bytes) for its heap. Its total memory usage will be approximately 8GiB.
Setting the same value for initial (-Xms
) and maximum (-Xmx
) heap sizes avoids the JVM having to allocate memory after startup, at the cost of possibly allocating more heap than is really needed.
For Kafka and ZooKeeper pods such allocation could cause unwanted latency.
For Kafka Connect avoiding over allocation may be the most important concern, especially in distributed mode where the effects of over-allocation will be multiplied by the number of consumers.
-server
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
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.
|
Garbage collector logging
The jvmOptions
section also allows you to enable and disable garbage collector (GC) logging.
GC logging is disabled by default.
To enable it, set the gcLoggingEnabled
property as follows:
# ...
jvmOptions:
gcLoggingEnabled: true
# ...
Configuring JVM options
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
jvmOptions
property in theKafka
,KafkaConnect
,KafkaConnectS2I
,KafkaMirrorMaker
, orKafkaBridge
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... jvmOptions: "-Xmx": "8g" "-Xms": "8g" # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.3.11. Container images
Strimzi allows you to configure container images which will be used for its components. Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by Strimzi. In such a case, you should either copy the Strimzi images or build them from the source. If the configured image is not compatible with Strimzi images, it might not work properly.
Container image configurations
You can specify which container image to use for each component using the image
property in the following resources:
-
Kafka.spec.kafka
-
Kafka.spec.kafka.tlsSidecar
-
Kafka.spec.zookeeper
-
Kafka.spec.zookeeper.tlsSidecar
-
Kafka.spec.entityOperator.topicOperator
-
Kafka.spec.entityOperator.userOperator
-
Kafka.spec.entityOperator.tlsSidecar
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
-
KafkaBridge.spec
Configuring the image
property for Kafka, Kafka Connect, and Kafka Mirror Maker
Kafka, Kafka Connect (including Kafka Connect with S2I support), and Kafka Mirror Maker support multiple versions of Kafka. Each component requires its own image. The default images for the different Kafka versions are configured in the following environment variables:
-
STRIMZI_KAFKA_IMAGES
-
STRIMZI_KAFKA_CONNECT_IMAGES
-
STRIMZI_KAFKA_CONNECT_S2I_IMAGES
-
STRIMZI_KAFKA_MIRROR_MAKER_IMAGES
These environment variables contain mappings between the Kafka versions and their corresponding images.
The mappings are used together with the image
and version
properties:
-
If neither
image
norversion
are given in the custom resource then theversion
will default to the Cluster Operator’s default Kafka version, and the image will be the one corresponding to this version in the environment variable. -
If
image
is given butversion
is not, then the given image is used and theversion
is assumed to be the Cluster Operator’s default Kafka version. -
If
version
is given butimage
is not, then the image that corresponds to the given version in the environment variable is used. -
If both
version
andimage
are given, then the given image is used. The image is assumed to contain a Kafka image with the given version.
The image
and version
for the different components can be configured in the following properties:
-
For Kafka in
spec.kafka.image
andspec.kafka.version
. -
For Kafka Connect, Kafka Connect S2I, and Kafka Mirror Maker in
spec.image
andspec.version
.
Warning
|
It is recommended to provide only the version and leave the image property unspecified.
This reduces the chance of making a mistake when configuring the custom resource.
If you need to change the images used for different versions of Kafka, it is preferable to configure the Cluster Operator’s environment variables.
|
Configuring the image
property in other resources
For the image
property in the other custom resources, the given value will be used during deployment.
If the image
property is missing, the image
specified in the Cluster Operator configuration will be used.
If the image
name is not defined in the Cluster Operator configuration, then the default value will be used.
-
For Kafka broker TLS sidecar:
-
Container image specified in the
STRIMZI_DEFAULT_TLS_SIDECAR_KAFKA_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/kafka:0.15.0-kafka-2.3.1
container image.
-
-
For ZooKeeper nodes:
-
Container image specified in the
STRIMZI_DEFAULT_ZOOKEEPER_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/kafka:0.15.0-kafka-2.3.1
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/kafka:0.15.0-kafka-2.3.1
container image.
-
-
For Topic Operator:
-
Container image specified in the
STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/operator:0.15.0
container image.
-
-
For User Operator:
-
Container image specified in the
STRIMZI_DEFAULT_USER_OPERATOR_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/operator:0.15.0
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/kafka:0.15.0-kafka-2.3.1
container image.
-
-
For Kafka Exporter:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_EXPORTER_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/kafka:0.15.0-kafka-2.3.1
container image.
-
-
For Kafka Bridge:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_BRIDGE_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/kafka-bridge:0.15.0
container image.
-
-
For Kafka broker initializer:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_INIT_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/operator:0.15.0
container image.
-
Warning
|
Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by Strimzi. In such case, you should either copy the Strimzi images or build them from source. In case the configured image is not compatible with Strimzi images, it might not work properly. |
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
image: my-org/my-image:latest
# ...
zookeeper:
# ...
Configuring container images
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
image
property in theKafka
,KafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... image: my-org/my-image:latest # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.3.12. Configuring pod scheduling
Important
|
When two applications are scheduled to the same Kubernetes node, both applications might use the same resources like disk I/O and impact performance. That can lead to performance degradation. Scheduling Kafka pods in a way that avoids sharing nodes with other critical workloads, using the right nodes or dedicated a set of nodes only for Kafka are the best ways how to avoid such problems. |
Scheduling pods based on other applications
Avoid critical applications to share the node
Pod anti-affinity can be used to ensure that critical applications are never scheduled on the same disk. When running Kafka cluster, it is recommended to use pod anti-affinity to ensure that the Kafka brokers do not share the nodes with other workloads like databases.
Affinity
Affinity can be configured using the affinity
property in following resources:
-
Kafka.spec.kafka.template.pod
-
Kafka.spec.zookeeper.template.pod
-
Kafka.spec.entityOperator.template.pod
-
KafkaConnect.spec.template.pod
-
KafkaConnectS2I.spec.template.pod
-
KafkaBridge.spec.template.pod
The affinity configuration can include different types of affinity:
-
Pod affinity and anti-affinity
-
Node affinity
The format of the affinity
property follows the Kubernetes specification.
For more details, see the Kubernetes node and pod affinity documentation.
Configuring pod anti-affinity in Kafka components
-
A 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/v1beta1 kind: Kafka spec: kafka: # ... template: pod: affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: application operator: In values: - postgresql - mongodb topologyKey: "kubernetes.io/hostname" # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
Scheduling pods to specific nodes
Node scheduling
The Kubernetes cluster usually consists of many different types of worker nodes. Some are optimized for CPU heavy workloads, some for memory, while other might be optimized for storage (fast local SSDs) or network. Using different nodes helps to optimize both costs and performance. To achieve the best possible performance, it is important to allow scheduling of Strimzi components to use the right nodes.
Kubernetes uses node affinity to schedule workloads onto specific nodes.
Node affinity allows you to create a scheduling constraint for the node on which the pod will be scheduled.
The constraint is specified as a label selector.
You can specify the label using either the built-in node label like beta.kubernetes.io/instance-type
or custom labels to select the right node.
Affinity
Affinity can be configured using the affinity
property in following resources:
-
Kafka.spec.kafka.template.pod
-
Kafka.spec.zookeeper.template.pod
-
Kafka.spec.entityOperator.template.pod
-
KafkaConnect.spec.template.pod
-
KafkaConnectS2I.spec.template.pod
-
KafkaBridge.spec.template.pod
The affinity configuration can include different types of affinity:
-
Pod affinity and anti-affinity
-
Node affinity
The format of the affinity
property follows the Kubernetes specification.
For more details, see the Kubernetes node and pod affinity documentation.
Configuring node affinity in Kafka components
-
A Kubernetes cluster
-
A running Cluster Operator
-
Label the nodes where Strimzi components should be scheduled.
This can be done using
kubectl label
:kubectl label node your-node node-type=fast-network
Alternatively, some of the existing labels might be reused.
-
Edit the
affinity
property in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... template: pod: affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: node-type operator: In values: - fast-network # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
Using dedicated nodes
Dedicated nodes
Cluster administrators can mark selected Kubernetes nodes as tainted. Nodes with taints are excluded from regular scheduling and normal pods will not be scheduled to run on them. Only services which can tolerate the taint set on the node can be scheduled on it. The only other services running on such nodes will be system services such as log collectors or software defined networks.
Taints can be used to create dedicated nodes. Running Kafka and its components on dedicated nodes can have many advantages. There will be no other applications running on the same nodes which could cause disturbance or consume the resources needed for Kafka. That can lead to improved performance and stability.
To schedule Kafka pods on the dedicated nodes, configure node affinity and tolerations.
Affinity
Affinity can be configured using the affinity
property in following resources:
-
Kafka.spec.kafka.template.pod
-
Kafka.spec.zookeeper.template.pod
-
Kafka.spec.entityOperator.template.pod
-
KafkaConnect.spec.template.pod
-
KafkaConnectS2I.spec.template.pod
-
KafkaBridge.spec.template.pod
The affinity configuration can include different types of affinity:
-
Pod affinity and anti-affinity
-
Node affinity
The format of the affinity
property follows the Kubernetes specification.
For more details, see the Kubernetes node and pod affinity documentation.
Tolerations
Tolerations can be configured using the tolerations
property in following resources:
-
Kafka.spec.kafka.template.pod
-
Kafka.spec.zookeeper.template.pod
-
Kafka.spec.entityOperator.template.pod
-
KafkaConnect.spec.template.pod
-
KafkaConnectS2I.spec.template.pod
-
KafkaBridge.spec.template.pod
The format of the tolerations
property follows the Kubernetes specification.
For more details, see the Kubernetes taints and tolerations.
Setting up dedicated nodes and scheduling pods on them
-
A 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:
This can be done using
kubectl taint
:kubectl taint node your-node dedicated=Kafka:NoSchedule
-
Additionally, add a label to the selected nodes as well.
This can be done using
kubectl label
:kubectl label node your-node dedicated=Kafka
-
Edit the
affinity
andtolerations
properties in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... template: pod: tolerations: - key: "dedicated" operator: "Equal" value: "Kafka" effect: "NoSchedule" affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: dedicated operator: In values: - Kafka # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.3.13. Using external configuration and secrets
Kafka Connect connectors are configured using an HTTP REST interface. The connector configuration is passed to Kafka Connect as part of an HTTP request and stored within Kafka itself.
Some parts of the configuration of a Kafka Connect connector can be externalized using ConfigMaps or Secrets. You can then reference the configuration values in HTTP REST commands (this keeps the configuration separate and more secure, if needed). This method applies especially to confidential data, such as usernames, passwords, or certificates.
ConfigMaps and Secrets are standard Kubernetes resources used for storing of configurations and confidential data.
Storing connector configurations externally
You can mount ConfigMaps or Secrets into a Kafka Connect pod as volumes or environment variables.
Volumes and environment variables are configured in the externalConfiguration
property in KafkaConnect.spec
and KafkaConnectS2I.spec
.
External configuration as environment variables
The env
property is used to specify one or more environment variables.
These variables can contain a value from either a ConfigMap or a Secret.
Note
|
The names of user-defined environment variables cannot start with KAFKA_ or STRIMZI_ .
|
To mount a value from a Secret to an environment variable, use the valueFrom
property and the secretKeyRef
as shown in the following example.
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
name: my-connect
spec:
# ...
externalConfiguration:
env:
- name: MY_ENVIRONMENT_VARIABLE
valueFrom:
secretKeyRef:
name: my-secret
key: my-key
A common use case for mounting Secrets to environment variables is when your connector needs to communicate with Amazon AWS and needs to read the AWS_ACCESS_KEY_ID
and AWS_SECRET_ACCESS_KEY
environment variables with credentials.
To mount a value from a ConfigMap to an environment variable, use configMapKeyRef
in the valueFrom
property as shown in the following example.
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
name: my-connect
spec:
# ...
externalConfiguration:
env:
- name: MY_ENVIRONMENT_VARIABLE
valueFrom:
configMapKeyRef:
name: my-config-map
key: my-key
External configuration as volumes
You can also mount ConfigMaps or Secrets to a Kafka Connect pod as volumes. Using volumes instead of environment variables is useful in the following scenarios:
-
Mounting truststores or keystores with TLS certificates
-
Mounting a properties file that is used to configure Kafka Connect connectors
In the volumes
property of the externalConfiguration
resource, list the ConfigMaps or Secrets that will be mounted as volumes.
Each volume must specify a name in the name
property and a reference to ConfigMap or Secret.
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaConnect
metadata:
name: my-connect
spec:
# ...
externalConfiguration:
volumes:
- name: connector1
configMap:
name: connector1-configuration
- name: connector1-certificates
secret:
secretName: connector1-certificates
The volumes will be mounted inside the Kafka Connect containers in the path /opt/kafka/external-configuration/<volume-name>
.
For example, the files from a volume named connector1
would appear in the directory /opt/kafka/external-configuration/connector1
.
The FileConfigProvider
has to be used to read the values from the mounted properties files in connector configurations.
Mounting Secrets as environment variables
You can create a Kubernetes Secret and mount it to Kafka Connect as an environment variable.
-
A running Cluster Operator.
-
Create a secret containing the information that will be mounted as an environment variable. For example:
apiVersion: v1 kind: Secret metadata: name: aws-creds type: Opaque data: awsAccessKey: QUtJQVhYWFhYWFhYWFhYWFg= awsSecretAccessKey: Ylhsd1lYTnpkMjl5WkE=
-
Create or edit the Kafka Connect resource. Configure the
externalConfiguration
section of theKafkaConnect
orKafkaConnectS2I
custom resource to reference the secret. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnect metadata: name: my-connect spec: # ... externalConfiguration: env: - name: AWS_ACCESS_KEY_ID valueFrom: secretKeyRef: name: aws-creds key: awsAccessKey - name: AWS_SECRET_ACCESS_KEY valueFrom: secretKeyRef: name: aws-creds key: awsSecretAccessKey
-
Apply the changes to your Kafka Connect deployment.
Use
kubectl apply
:kubectl apply -f your-file
The environment variables are now available for use when developing your connectors.
-
For more information about external configuration in Kafka Connect, see
ExternalConfiguration
schema reference.
Mounting Secrets as volumes
You can create a Kubernetes Secret, mount it as a volume to Kafka Connect, and then use it to configure a Kafka Connect connector.
-
A running Cluster Operator.
-
Create a secret containing a properties file that defines the configuration options for your connector configuration. For example:
apiVersion: v1 kind: Secret metadata: name: mysecret type: Opaque stringData: connector.properties: |- dbUsername: my-user dbPassword: my-password
-
Create or edit the Kafka Connect resource. Configure the
FileConfigProvider
in theconfig
section and theexternalConfiguration
section of theKafkaConnect
orKafkaConnectS2I
custom resource to reference the secret. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnect metadata: name: my-connect spec: # ... config: config.providers: file config.providers.file.class: org.apache.kafka.common.config.provider.FileConfigProvider #... externalConfiguration: volumes: - name: connector-config secret: secretName: mysecret
-
Apply the changes to your Kafka Connect deployment.
Use
kubectl apply
:kubectl apply -f your-file
-
Use the values from the mounted properties file in your JSON payload with connector configuration. For example:
{ "name":"my-connector", "config":{ "connector.class":"MyDbConnector", "tasks.max":"3", "database": "my-postgresql:5432" "username":"${file:/opt/kafka/external-configuration/connector-config/connector.properties:dbUsername}", "password":"${file:/opt/kafka/external-configuration/connector-config/connector.properties:dbPassword}", # ... } }
-
For more information about external configuration in Kafka Connect, see
ExternalConfiguration
schema reference.
3.3.14. List of resources created as part of Kafka Connect cluster with Source2Image support
The following resources will created by the Cluster Operator in the Kubernetes cluster:
- connect-cluster-name-connect-source
-
ImageStream which is used as the base image for the newly-built Docker images.
- connect-cluster-name-connect
-
BuildConfig which is responsible for building the new Kafka Connect Docker images.
- connect-cluster-name-connect
-
ImageStream where the newly built Docker images will be pushed.
- connect-cluster-name-connect
-
DeploymentConfig which is in charge of creating the Kafka Connect worker node pods.
- connect-cluster-name-connect-api
-
Service which exposes the REST interface for managing the Kafka Connect cluster.
- connect-cluster-name-config
-
ConfigMap which contains the Kafka Connect ancillary configuration and is mounted as a volume by the Kafka broker pods.
- connect-cluster-name-connect
-
Pod Disruption Budget configured for the Kafka Connect worker nodes.
3.3.15. Creating a container image using OpenShift builds and Source-to-Image
You can use OpenShift builds and the Source-to-Image (S2I) framework to create new container images. An OpenShift build takes a builder image with S2I support, together with source code and binaries provided by the user, and uses them to build a new container image. Once built, container images are stored in OpenShift’s local container image repository and are available for use in deployments.
A Kafka Connect builder image with S2I support is provided on the Docker Hub as part of the strimzi/kafka:0.15.0-kafka-2.3.1
image. This S2I image takes your binaries (with plug-ins and connectors) and stores them in the /tmp/kafka-plugins/s2i
directory. It creates a new Kafka Connect image from this directory, which can then be used with the Kafka Connect deployment. When started using the enhanced image, Kafka Connect loads any third-party plug-ins from the /tmp/kafka-plugins/s2i
directory.
-
On the command line, use the
oc apply
command to create and deploy a Kafka Connect S2I cluster:oc apply -f examples/kafka-connect/kafka-connect-s2i.yaml
-
Create a directory with Kafka Connect plug-ins:
$ tree ./my-plugins/ ./my-plugins/ ├── debezium-connector-mongodb │ ├── bson-3.4.2.jar │ ├── CHANGELOG.md │ ├── CONTRIBUTE.md │ ├── COPYRIGHT.txt │ ├── debezium-connector-mongodb-0.7.1.jar │ ├── debezium-core-0.7.1.jar │ ├── LICENSE.txt │ ├── mongodb-driver-3.4.2.jar │ ├── mongodb-driver-core-3.4.2.jar │ └── README.md ├── debezium-connector-mysql │ ├── CHANGELOG.md │ ├── CONTRIBUTE.md │ ├── COPYRIGHT.txt │ ├── debezium-connector-mysql-0.7.1.jar │ ├── debezium-core-0.7.1.jar │ ├── LICENSE.txt │ ├── mysql-binlog-connector-java-0.13.0.jar │ ├── mysql-connector-java-5.1.40.jar │ ├── README.md │ └── wkb-1.0.2.jar └── debezium-connector-postgres ├── CHANGELOG.md ├── CONTRIBUTE.md ├── COPYRIGHT.txt ├── debezium-connector-postgres-0.7.1.jar ├── debezium-core-0.7.1.jar ├── LICENSE.txt ├── postgresql-42.0.0.jar ├── protobuf-java-2.6.1.jar └── README.md
-
Use the
oc start-build
command to start a new build of the image using the prepared directory:oc start-build my-connect-cluster-connect --from-dir ./my-plugins/
NoteThe name of the build is the same as the name of the deployed Kafka Connect cluster. -
Once the build has finished, the new image is used automatically by the Kafka Connect deployment.
3.4. Kafka Mirror Maker configuration
This chapter describes how to configure the KafkaMirrorMaker
resource to support a Kafka Mirror Maker deployment in your cluster.
The following procedures show how the resource is configured:
Supported properties are also described in more detail for your reference:
The full schema of the KafkaMirrorMaker
resource is described in the KafkaMirrorMaker schema reference.
Note
|
Labels applied to a KafkaMirrorMaker resource are also applied to the Kubernetes resources comprising Kafka Mirror Maker.
This provides a convenient mechanism for resources to be labeled as required.
|
3.4.1. Configuring Kafka Mirror Maker
Use the properties of the KafkaMirrorMaker
resource to configure your Kafka Mirror Maker deployment.
You can configure access control for producers and consumers using TLS or SASL authentication. This procedure shows a configuration that uses TLS encryption and authentication on the consumer and producer side.
-
Source and target Kafka clusters are available
-
Edit the
spec
properties for theKafkaMirrorMaker
resource.The properties you can configure are shown in this example configuration:
apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaMirrorMaker metadata: name: my-mirror-maker spec: replicas: 3 (1) consumer: bootstrapServers: my-source-cluster-kafka-bootstrap:9092 (2) groupId: "my-group" (3) numStreams: 2 (4) offsetCommitInterval: 120000 (5) tls: (6) trustedCertificates: - secretName: my-source-cluster-ca-cert certificate: ca.crt authentication: (7) type: tls certificateAndKey: secretName: my-source-secret certificate: public.crt key: private.key config: (8) max.poll.records: 100 receive.buffer.bytes: 32768 producer: bootstrapServers: my-target-cluster-kafka-bootstrap:9092 abortOnSendFailure: false (9) tls: trustedCertificates: - secretName: my-target-cluster-ca-cert certificate: ca.crt authentication: type: tls certificateAndKey: secretName: my-target-secret certificate: public.crt key: private.key config: compression.type: gzip batch.size: 8192 whitelist: "my-topic|other-topic" (10) resources: (11) requests: cpu: "1" memory: 2Gi limits: cpu: "2" memory: 2Gi logging: (12) type: inline loggers: mirrormaker.root.logger: "INFO" readinessProbe: (13) initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 metrics: (14) lowercaseOutputName: true rules: - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*><>Count" name: "kafka_server_$1_$2_total" - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*, topic=(.+)><>Count" name: "kafka_server_$1_$2_total" labels: topic: "$3" jvmOptions: (15) "-Xmx": "1g" "-Xms": "1g" image: my-org/my-image:latest (16) template: (17) pod: affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: application operator: In values: - postgresql - mongodb topologyKey: "kubernetes.io/hostname"
-
The number of replica nodes.
-
Bootstrap servers for consumer and producer.
-
Group ID for the consumer.
-
The number of consumer streams.
-
The offset auto-commit interval in milliseconds.
-
TLS encryption with key names under which TLS certificates are stored in X.509 format for consumer or producer. For more details see
KafkaMirrorMakerTls
schema reference. -
Authentication for consumer or producer, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-512 or PLAIN mechanism.
-
Kafka configuration options for consumer and producer.
-
If set to
true
, Kafka Mirror Maker will exit and the container will restart following a send failure for a message. -
Topics mirrored from source to target Kafka cluster.
-
Requests for reservation of supported resources, currently
cpu
andmemory
, and limits to specify the maximum resources that can be consumed. -
Specified loggers and log levels added directly (
inline
) or indirectly (external
) through a ConfigMap. A custom ConfigMap must be placed under thelog4j.properties
orlog4j2.properties
key. Mirror Maker has a single logger calledmirrormaker.root.logger
. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. -
Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).
-
Prometheus metrics, which are enabled with configuration for the Prometheus JMX exporter in this example. You can enable metrics without further configuration using
metrics: {}
. -
JVM configuration options to optimize performance for the Virtual Machine (VM) running Kafka Mirror Maker.
-
ADVANCED OPTION: Container image configuration, which is recommended only in special situations.
-
Template customization. Here a pod is scheduled based with anti-affinity, so the pod is not scheduled on nodes with the same hostname.
WarningWith the abortOnSendFailure
property set tofalse
, the producer attempts to send the next message in a topic. The original message might be lost, as there is no attempt to resend a failed message. -
-
Create or update the resource:
kubectl apply -f <your-file>
3.4.2. Kafka Mirror Maker configuration properties
Use the spec
configuration properties of the KafkaMirrorMaker
resource to set up your Mirror Maker deployment.
Supported properties are described here for your reference.
Replicas
Use the replicas
property to configure replicas.
You can run multiple Mirror Maker replicas to provide better availability and scalability. When running Kafka Mirror Maker on Kubernetes it is not absolutely necessary to run multiple replicas of the Kafka Mirror Maker for high availability. When the node where the Kafka Mirror Maker has deployed crashes, Kubernetes will automatically reschedule the Kafka Mirror Maker pod to a different node. However, running Kafka Mirror Maker with multiple replicas can provide faster failover times as the other nodes will be up and running.
Bootstrap servers
Use the consumer.bootstrapServers
and producer.bootstrapServers
properties to configure lists of bootstrap servers for the consumer and producer.
Kafka Mirror Maker always works together with two Kafka clusters (source and target).
The source and the target Kafka clusters are specified in the form of two lists of comma-separated list of <hostname>:<port>
pairs.
Each comma-separated list contains one or more Kafka brokers or a Service
pointing to Kafka brokers specified as a <hostname>:<port>
pairs.
The bootstrap server lists can refer to Kafka clusters that do not need to be deployed in the same Kubernetes cluster. They can even refer to a Kafka cluster not deployed by Strimzi, or deployed by Strimzi but on a different Kubernetes cluster accessible outside.
If on the same Kubernetes cluster, each list must ideally contain the Kafka cluster bootstrap service which is named <cluster-name>-kafka-bootstrap
and a port of 9092 for plain traffic or 9093 for encrypted traffic.
If deployed by Strimzi but on different Kubernetes clusters, the list content depends on the approach used for exposing the clusters (routes, nodeports or loadbalancers).
When using Kafka Mirror Maker with a Kafka cluster not managed by Strimzi, you can specify the bootstrap servers list according to the configuration of the given cluster.
Whitelist
Use the whitelist
property to configure a list of topics that Kafka Mirror Maker mirrors from the source to the target Kafka cluster.
The property allows any regular expression from the simplest case with a single topic name to complex patterns. For example, you can mirror topics A and B using "A|B" or all topics using "*". You can also pass multiple regular expressions separated by commas to the Kafka Mirror Maker.
Consumer group identifier
Use the consumer.groupId
property to configure a consumer group identifier for the consumer.
Kafka Mirror Maker uses a Kafka consumer to consume messages, behaving like any other Kafka consumer client. Messages consumed from the source Kafka cluster are mirrored to a target Kafka cluster. A group identifier is required, as the consumer needs to be part of a consumer group for the assignment of partitions.
Consumer streams
Use the consumer.numStreams
property to configure the number of streams for the consumer.
You can increase the throughput in mirroring topics by increasing the number of consumer threads. Consumer threads belong to the consumer group specified for Kafka Mirror Maker. Topic partitions are assigned across the consumer threads, which consume messages in parallel.
Offset auto-commit interval
Use the consumer.offsetCommitInterval
property to configure an offset auto-commit interval for the consumer.
You can specify the regular time interval at which an offset is committed after Kafka Mirror Maker has consumed data from the source Kafka cluster. The time interval is set in milliseconds, with a default value of 60,000.
Abort on message send failure
Use the producer.abortOnSendFailure
property to configure how to handle message send failure from the producer.
By default, if an error occurs when sending a message from Kafka Mirror Maker to a Kafka cluster:
-
The Kafka Mirror Maker container is terminated in Kubernetes.
-
The container is then recreated.
If the abortOnSendFailure
option is set to false
, message sending errors are ignored.
Kafka producer and consumer
Use the consumer.config
and producer.config
properties to configure Kafka options for the consumer and producer.
The config
property contains the Kafka Mirror Maker consumer and producer configuration options as keys, with values set in one of the following JSON types:
-
String
-
Number
-
Boolean
You can specify and configure standard Kafka consumer and producer options:
However, there are exceptions for options automatically configured and managed directly by Strimzi related to:
-
Kafka cluster bootstrap address
-
Security (encryption, authentication, and authorization)
-
Consumer group identifier
Specifically, all configuration options with keys equal to or starting with one of the following strings are forbidden:
-
ssl.
-
sasl.
-
security.
-
bootstrap.servers
-
group.id
When a forbidden option is present in the config
property, it is ignored and a warning message is printed to the Custer Operator log file.
All other options are passed to Kafka Mirror Maker.
Important
|
The Cluster Operator does not validate keys or values in the provided config object.
When an invalid configuration is provided, the Kafka Mirror Maker might not start or might become unstable.
In such cases, the configuration in the KafkaMirrorMaker.spec.consumer.config or KafkaMirrorMaker.spec.producer.config object should be fixed and the Cluster Operator will roll out the new configuration for Kafka Mirror Maker.
|
CPU and memory resources
Use the reources.requests
and resources.limits
properties to configure resource requests and limits.
For every deployed container, Strimzi allows you to request specific resources and define the maximum consumption of those resources.
Strimzi supports requests and limits for the following types of resources:
-
cpu
-
memory
Strimzi uses the Kubernetes syntax for specifying these resources.
For more information about managing computing resources on Kubernetes, see Managing Compute Resources for Containers.
Requests specify the resources to reserve for a given container. Reserving the resources ensures that they are always available.
Important
|
If the resource request is for more than the available free resources in the Kubernetes cluster, the pod is not scheduled. |
A request may be configured for one or more supported resources.
Limits specify the maximum resources that can be consumed by a given container. The limit is not reserved and might not always be available. A container can use the resources up to the limit only when they are available. Resource limits should be always higher than the resource requests.
A resource may be configured for one or more supported limits.
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.
Note
|
The computing power of 1 CPU core may differ depending on the platform where Kubernetes is deployed. |
For more information on CPU specification, see the Meaning of CPU.
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
.
For more details about memory specification and additional supported units, see Meaning of memory.
Kafka Mirror Maker loggers
Kafka Mirror Maker has its own configurable logger:
-
mirrormaker.root.logger
Use the logging
property to configure loggers and logger levels.
You can set the log levels by specifying the logger and level directly (inline) or use a custom (external) ConfigMap.
Here we see examples of inline
and external
logging:
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
spec:
# ...
logging:
type: inline
loggers:
mirrormaker.root.logger: "INFO"
# ...
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaMirrorMaker
spec:
# ...
logging:
type: external
name: customConfigMap
# ...
-
Garbage collector (GC) logging can also be enabled (or disabled). For more information on GC, see JVM configuration
-
For more information about log levels, see the log4j manual.
Healthchecks
Use the livenessProbe
and readinessProbe
properties to configure healthcheck probes supported in Strimzi.
Healthchecks are periodical tests which verify the health of an application. When a Healthcheck probe fails, Kubernetes assumes that the application is not healthy and attempts to fix it.
For more details about the probes, see Configure Liveness and Readiness Probes.
Both livenessProbe
and readinessProbe
support the following options:
-
initialDelaySeconds
-
timeoutSeconds
-
periodSeconds
-
successThreshold
-
failureThreshold
# ...
readinessProbe:
initialDelaySeconds: 15
timeoutSeconds: 5
livenessProbe:
initialDelaySeconds: 15
timeoutSeconds: 5
# ...
For more information about the livenessProbe
and readinessProbe
options, see Probe schema reference.
Prometheus metrics
Use the metrics
property to enable and configure Prometheus metrics.
The metrics
property can also contain additional configuration for the Prometheus JMX exporter.
Strimzi supports Prometheus metrics using Prometheus JMX exporter to convert the JMX metrics supported by Apache Kafka and ZooKeeper to Prometheus metrics.
To enable Prometheus metrics export without any further configuration, you can set it to an empty object ({}
).
When metrics are enabled, they are exposed on port 9404.
When the metrics
property is not defined in the resource, the Prometheus metrics are disabled.
For more information about configuring Prometheus and Grafana, see Metrics.
JVM Options
Use the jvmOptions
property to configure supported options for the JVM on which the component is running.
Supported JVM options help to optimize performance for different platforms and architectures.
For more information on the supported options, see JVM configuration.
Container images
Use the image
property to configure the container image used by the component.
Overriding container images is recommended only in special situations where you need to use a different container registry or a customized image.
For example, if your network does not allow access to the container repository used by Strimzi, you can copy the Strimzi images or build them from the source. However, if the configured image is not compatible with Strimzi images, it might not work properly.
A copy of the container image might also be customized and used for debugging.
For more information see Container image configurations.
3.4.3. List of resources created as part of Kafka Mirror Maker
The following resources are created by the Cluster Operator in the Kubernetes cluster:
- <mirror-maker-name>-mirror-maker
-
Deployment which is responsible for creating the Kafka Mirror Maker pods.
- <mirror-maker-name>-config
-
ConfigMap which contains ancillary configuration for the the Kafka Mirror Maker, and is mounted as a volume by the Kafka broker pods.
- <mirror-maker-name>-mirror-maker
-
Pod Disruption Budget configured for the Kafka Mirror Maker worker nodes.
3.5. Kafka Bridge configuration
The full schema of the KafkaBridge
resource is described in the KafkaBridge
schema reference.
All labels that are applied to the desired KafkaBridge
resource will also be applied to the Kubernetes resources making up the Kafka Bridge cluster.
This provides a convenient mechanism for resources to be labeled as required.
3.5.1. Replicas
Kafka Bridge can run multiple nodes.
The number of nodes is defined in the KafkaBridge
resource.
Running a Kafka Bridge with multiple nodes can provide better availability and scalability.
However, when running Kafka Bridge on Kubernetes it is not absolutely necessary to run multiple nodes of Kafka Bridge for high availability.
Important
|
If a node where Kafka Bridge is deployed to crashes, Kubernetes will automatically reschedule the Kafka Bridge pod to a different node. In order to prevent issues arising when client consumer requests are processed by different Kafka Bridge instances, addressed-based routing must be employed to ensure that requests are routed to the right Kafka Bridge instance. Additionally, each independent Kafka Bridge instance must have a replica. A Kafka Bridge instance has its own state which is not shared with another instances. |
Configuring the number of nodes
The number of Kafka Bridge nodes is configured using the replicas
property in KafkaBridge.spec
.
-
An Kubernetes cluster
-
A running Cluster Operator
-
Edit the
replicas
property in theKafkaBridge
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaBridge metadata: name: my-bridge spec: # ... replicas: 3 # ...
-
Create or update the resource.
kubectl apply -f your-file
3.5.2. Bootstrap servers
A Kafka Bridge always works in combination with a Kafka cluster.
A Kafka cluster is specified as a list of bootstrap servers.
On Kubernetes, the list must ideally contain the Kafka cluster bootstrap service named cluster-name-kafka-bootstrap
, and a port of 9092 for plain traffic or 9093 for encrypted traffic.
The list of bootstrap servers is configured in the bootstrapServers
property in KafkaBridge.kafka.spec
. The servers must be defined as a comma-separated list specifying one or more Kafka brokers, or a service pointing to Kafka brokers specified as a hostname:_port_
pairs.
When using Kafka Bridge with a Kafka cluster not managed by Strimzi, you can specify the bootstrap servers list according to the configuration of the cluster.
Configuring bootstrap servers
-
An Kubernetes cluster
-
A running Cluster Operator
-
Edit the
bootstrapServers
property in theKafkaBridge
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaBridge metadata: name: my-bridge spec: # ... bootstrapServers: my-cluster-kafka-bootstrap:9092 # ...
-
Create or update the resource.
kubectl apply -f your-file
3.5.3. Connecting to Kafka brokers using TLS
By default, Kafka Bridge tries to connect to Kafka brokers using a plain text connection. If you prefer to use TLS, additional configuration is required.
TLS support for Kafka connection to the Kafka Bridge
TLS support for Kafka connection is configured in the tls
property in KafkaBridge.spec
.
The tls
property contains a list of secrets with key names under which the certificates are stored.
The certificates must be stored in X509 format.
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
name: my-bridge
spec:
# ...
tls:
trustedCertificates:
- secretName: my-secret
certificate: ca.crt
- secretName: my-other-secret
certificate: certificate.crt
# ...
When multiple certificates are stored in the same secret, it can be listed multiple times.
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
name: my-bridge
spec:
# ...
tls:
trustedCertificates:
- secretName: my-secret
certificate: ca.crt
- secretName: my-secret
certificate: ca2.crt
# ...
Configuring TLS in Kafka Bridge
-
An Kubernetes cluster
-
A running Cluster Operator
-
If they exist, the name of the
Secret
for the certificate used for TLS Server Authentication, and the key under which the certificate is stored in theSecret
-
(Optional) If they do not already exist, prepare the TLS certificate used in authentication in a file and create a
Secret
.NoteThe secrets created by the Cluster Operator for Kafka cluster may be used directly. kubectl create secret generic my-secret --from-file=my-file.crt
-
Edit the
tls
property in theKafkaBridge
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaBridge metadata: name: my-bridge spec: # ... tls: trustedCertificates: - secretName: my-cluster-cluster-cert certificate: ca.crt # ...
-
Create or update the resource.
kubectl apply -f your-file
3.5.4. Connecting to Kafka brokers with Authentication
By default, Kafka Bridge will try to connect to Kafka brokers without authentication.
Authentication is enabled through the KafkaBridge
resources.
Authentication support in Kafka Bridge
Authentication is configured through the authentication
property in KafkaBridge.spec
.
The authentication
property specifies the type of the authentication mechanisms which should be used and additional configuration details depending on the mechanism.
The currently supported authentication types are:
-
TLS client authentication
-
SASL-based authentication using the SCRAM-SHA-512 mechanism
-
SASL-based authentication using the PLAIN mechanism
TLS Client Authentication
To use TLS client authentication, set the type
property to the value tls
.
TLS client authentication uses a TLS certificate to authenticate.
The certificate is specified in the certificateAndKey
property and is always loaded from an Kubernetes secret.
In the secret, the certificate must be stored in X509 format under two different keys: public and private.
Note
|
TLS client authentication can be used only with TLS connections. For more details about TLS configuration in Kafka Bridge see Connecting to Kafka brokers using TLS. |
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
name: my-bridge
spec:
# ...
authentication:
type: tls
certificateAndKey:
secretName: my-secret
certificate: public.crt
key: private.key
# ...
SCRAM-SHA-512 authentication
To configure Kafka Bridge to use SASL-based SCRAM-SHA-512 authentication, set the type
property to scram-sha-512
.
This authentication mechanism requires a username and password.
-
Specify the username in the
username
property. -
In the
passwordSecret
property, specify a link to aSecret
containing the password. ThesecretName
property contains the name of theSecret
and thepassword
property contains the name of the key under which the password is stored inside theSecret
.
Important
|
Do not specify the actual password in the password field.
|
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
name: my-bridge
spec:
# ...
authentication:
type: scram-sha-512
username: my-bridge-user
passwordSecret:
secretName: my-bridge-user
password: my-bridge-password-key
# ...
SASL-based PLAIN authentication
To configure Kafka Bridge to use SASL-based PLAIN authentication, set the type
property to plain
.
This authentication mechanism requires a username and password.
Warning
|
The SASL PLAIN mechanism will transfer the username and password across the network in cleartext. Only use SASL PLAIN authentication if TLS encryption is enabled. |
-
Specify the username in the
username
property. -
In the
passwordSecret
property, specify a link to aSecret
containing the password. ThesecretName
property contains the name theSecret
and thepassword
property contains the name of the key under which the password is stored inside theSecret
.
Important
|
Do not specify the actual password in the password field.
|
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
name: my-bridge
spec:
# ...
authentication:
type: plain
username: my-bridge-user
passwordSecret:
secretName: my-bridge-user
password: my-bridge-password-key
# ...
Configuring TLS client authentication in Kafka Bridge
-
An Kubernetes cluster
-
A running Cluster Operator
-
If they exist, the name of the
Secret
with the public and private keys used for TLS Client Authentication, and the keys under which they are stored in theSecret
-
(Optional) If they do not already exist, prepare the keys used for authentication in a file and create the
Secret
.NoteSecrets created by the User Operator may be used. kubectl create secret generic my-secret --from-file=my-public.crt --from-file=my-private.key
-
Edit the
authentication
property in theKafkaBridge
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaBridge metadata: name: my-bridge spec: # ... authentication: type: tls certificateAndKey: secretName: my-secret certificate: my-public.crt key: my-private.key # ...
-
Create or update the resource.
kubectl apply -f your-file
Configuring SCRAM-SHA-512 authentication in Kafka Bridge
-
An Kubernetes cluster
-
A running Cluster Operator
-
Username of the user which should be used for authentication
-
If they exist, the name of the
Secret
with the password used for authentication and the key under which the password is stored in theSecret
-
(Optional) If they do not already exist, prepare a file with the password used in authentication and create the
Secret
.NoteSecrets created by the User Operator may be used. echo -n '<password>' > <my-password.txt> kubectl create secret generic <my-secret> --from-file=<my-password.txt>
-
Edit the
authentication
property in theKafkaBridge
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaBridge metadata: name: my-bridge spec: # ... authentication: type: scram-sha-512 username: _<my-username>_ passwordSecret: secretName: _<my-secret>_ password: _<my-password.txt>_ # ...
-
Create or update the resource.
kubectl apply -f your-file
3.5.5. Kafka Bridge configuration
Strimzi allows you to customize the configuration of Apache Kafka Bridge nodes by editing certain options listed in Apache Kafka configuration documentation for consumers and Apache Kafka configuration documentation for producers.
Configuration options that can be configured relate to:
-
Kafka cluster bootstrap address
-
Security (Encryption, Authentication, and Authorization)
-
Consumer configuration
-
Producer configuration
-
HTTP configuration
Kafka Bridge Consumer configuration
Kafka Bridge consumer is configured using the properties in KafkaBridge.spec.consumer
.
This property contains the Kafka Bridge consumer configuration options as keys.
The values can be one of the following JSON types:
-
String
-
Number
-
Boolean
Users can specify and configure the options listed in the Apache Kafka configuration documentation for consumers with the exception of those options which are managed directly by Strimzi. Specifically, all configuration options with keys equal to or starting with one of the following strings are forbidden:
-
ssl.
-
sasl.
-
security.
-
bootstrap.servers
-
group.id
When one of the forbidden options is present in the config
property, it will be ignored and a warning message will be printed to the Custer Operator log file.
All other options will be passed to Kafka
Important
|
The Cluster Operator does not validate keys or values in the config object provided.
When an invalid configuration is provided, the Kafka Bridge cluster might not start or might become unstable.
In this circumstance, fix the configuration in the KafkaBridge.spec.consumer.config object, then the Cluster Operator can roll out the new configuration to all Kafka Bridge nodes.
|
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
name: my-bridge
spec:
# ...
consumer:
config:
auto.offset.reset: earliest
enable.auto.commit: true
# ...
Kafka Bridge Producer configuration
Kafka Bridge producer is configured using the properties in KafkaBridge.spec.producer
.
This property contains the Kafka Bridge producer configuration options as keys.
The values can be one of the following JSON types:
-
String
-
Number
-
Boolean
Users can specify and configure the options listed in the Apache Kafka configuration documentation for producers with the exception of those options which are managed directly by Strimzi. Specifically, all configuration options with keys equal to or starting with one of the following strings are forbidden:
-
ssl.
-
sasl.
-
security.
-
bootstrap.servers
Important
|
The Cluster Operator does not validate keys or values in the config object provided.
When an invalid configuration is provided, the Kafka Bridge cluster might not start or might become unstable.
In this circumstance, fix the configuration in the KafkaBridge.spec.producer.config object, then the Cluster Operator can roll out the new configuration to all Kafka Bridge nodes.
|
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
name: my-bridge
spec:
# ...
producer:
config:
acks: 1
delivery.timeout.ms: 300000
# ...
Kafka Bridge HTTP configuration
Kafka Bridge HTTP configuration is set using the properties in KafkaBridge.spec.http
.
This property contains the Kafka Bridge HTTP configuration options.
-
port
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaBridge
metadata:
name: my-bridge
spec:
# ...
http:
port: 8080
# ...
Configuring Kafka Bridge
-
An Kubernetes cluster
-
A running Cluster Operator
-
Edit the
kafka
,http
,consumer
orproducer
property in theKafkaBridge
resource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaBridge metadata: name: my-bridge spec: # ... bootstrapServers: my-cluster-kafka:9092 http: port: 8080 consumer: config: auto.offset.reset: earliest producer: config: delivery.timeout.ms: 300000 # ...
-
Create or update the resource.
kubectl apply -f your-file
3.5.6. CPU and memory resources
For every deployed container, Strimzi allows you to request specific resources and define the maximum consumption of those resources.
Strimzi supports two types of resources:
-
CPU
-
Memory
Strimzi uses the Kubernetes syntax for specifying CPU and memory resources.
Resource limits and requests
Resource limits and requests are configured using the resources
property in the following resources:
-
Kafka.spec.kafka
-
Kafka.spec.kafka.tlsSidecar
-
Kafka.spec.zookeeper
-
Kafka.spec.zookeeper.tlsSidecar
-
Kafka.spec.entityOperator.topicOperator
-
Kafka.spec.entityOperator.userOperator
-
Kafka.spec.entityOperator.tlsSidecar
-
Kafka.spec.KafkaExporter
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
-
KafkaBridge.spec
-
For more information about managing computing resources on Kubernetes, see Managing Compute Resources for Containers.
Resource requests
Requests specify the resources to reserve for a given container. Reserving the resources ensures that they are always available.
Important
|
If the resource request is for more than the available free resources in the Kubernetes cluster, the pod is not scheduled. |
Resources requests are specified in the requests
property.
Resources requests currently supported by Strimzi:
-
cpu
-
memory
A request may be configured for one or more supported resources.
# ...
resources:
requests:
cpu: 12
memory: 64Gi
# ...
Resource limits
Limits specify the maximum resources that can be consumed by a given container. The limit is not reserved and might not always be available. A container can use the resources up to the limit only when they are available. Resource limits should be always higher than the resource requests.
Resource limits are specified in the limits
property.
Resource limits currently supported by Strimzi:
-
cpu
-
memory
A resource may be configured for one or more supported limits.
# ...
resources:
limits:
cpu: 12
memory: 64Gi
# ...
Supported CPU formats
CPU requests and limits are supported in the following formats:
-
Number of CPU cores as integer (
5
CPU core) or decimal (2.5
CPU core). -
Number or millicpus / millicores (
100m
) where 1000 millicores is the same1
CPU core.
# ...
resources:
requests:
cpu: 500m
limits:
cpu: 2.5
# ...
Note
|
The computing power of 1 CPU core may differ depending on the platform where Kubernetes is deployed. |
-
For more information on CPU specification, see the Meaning of CPU.
Supported memory formats
Memory requests and limits are specified in megabytes, gigabytes, mebibytes, and gibibytes.
-
To specify memory in megabytes, use the
M
suffix. For 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 memory specification and additional supported units, see Meaning of memory.
Configuring resource requests and limits
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
resources
property in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... resources: requests: cpu: "8" memory: 64Gi limits: cpu: "12" memory: 128Gi # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
-
For more information about the schema, see
Resources
schema reference.
3.5.7. Logging
This section provides information on loggers and how to configure log levels.
You can set the log levels by specifying the loggers and their levels directly (inline) or use a custom (external) config map.
Kafka Bridge loggers
Kafka Bridge has configurable loggers for each OpenAPI operation: Loggers are formatted as follows:
log4j.logger.http.openapi.operation.<operation-id>
Where <operation-id>
is the identifier of the specific operation.
Following is the list of operations defined by the OpenAPI specification:
-
createConsumer
-
deleteConsumer
-
subscribe
-
unsubscribe
-
poll
-
assign
-
commit
-
send
-
sendToPartition
-
seekToBeginning
-
seekToEnd
-
seek
-
healthy
-
ready
-
openapi
Specifying inline logging
-
Edit the YAML file to specify the loggers and logging level for the required components.
For example, the logging level here is set to INFO:
apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaBridge spec: # ... logging: type: inline loggers: logger.name: "INFO" # ...
You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF.
For more information about the log levels, see the log4j manual.
-
Create or update the Kafka resource in Kubernetes.
This can be done using
kubectl apply
:kubectl apply -f your-file
Specifying an external ConfigMap for logging
-
Edit the YAML file to specify the name of the
ConfigMap
to use for the required components. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaBridge spec: # ... logging: type: external name: customConfigMap # ...
Remember to place your custom ConfigMap under the
log4j.properties
orlog4j2.properties
key. -
Create or update the Kafka resource in Kubernetes.
This can be done using
kubectl apply
:kubectl apply -f your-file
Garbage collector (GC) logging can also be enabled (or disabled). For more information on GC, see JVM configuration
3.5.8. JVM Options
The following components of Strimzi run inside a Virtual Machine (VM):
-
Apache Kafka
-
Apache ZooKeeper
-
Apache Kafka Connect
-
Apache Kafka Mirror Maker
-
Strimzi Kafka Bridge
JVM configuration options optimize the performance for different platforms and architectures. Strimzi allows you to configure some of these options.
JVM configuration
JVM options can be configured using the jvmOptions
property in following resources:
-
Kafka.spec.kafka
-
Kafka.spec.zookeeper
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
-
KafkaMirrorMaker.spec
-
KafkaBridge.spec
Only a selected subset of available JVM options can be configured. The following options are supported:
-Xms
configures the minimum initial allocation heap size when the JVM starts.
-Xmx
configures the maximum heap size.
Note
|
The units accepted by JVM settings such as -Xmx and -Xms are those accepted by the JDK java binary in the corresponding image.
Accordingly, 1g or 1G means 1,073,741,824 bytes, and Gi is not a valid unit suffix.
This is in contrast to the units used for memory requests and limits, which follow the Kubernetes convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes
|
The default values used for -Xms
and -Xmx
depends on whether there is a memory request limit configured for the container:
-
If there is a memory limit then the JVM’s minimum and maximum memory will be set to a value corresponding to the limit.
-
If there is no memory limit then the JVM’s minimum memory will be set to
128M
and the JVM’s maximum memory will not be defined. This allows for the JVM’s memory to grow as-needed, which is ideal for single node environments in test and development.
Important
|
Setting
|
When setting -Xmx
explicitly, it is recommended to:
-
set the memory request and the memory limit to the same value,
-
use a memory request that is at least 4.5 × the
-Xmx
, -
consider setting
-Xms
to the same value as-Xmx
.
Important
|
Containers doing lots of disk I/O (such as Kafka broker containers) will need to leave some memory available for use as operating system page cache. On such containers, the requested memory should be significantly higher than the memory used by the JVM. |
-Xmx
and -Xms
# ...
jvmOptions:
"-Xmx": "2g"
"-Xms": "2g"
# ...
In the above example, the JVM will use 2 GiB (=2,147,483,648 bytes) for its heap. Its total memory usage will be approximately 8GiB.
Setting the same value for initial (-Xms
) and maximum (-Xmx
) heap sizes avoids the JVM having to allocate memory after startup, at the cost of possibly allocating more heap than is really needed.
For Kafka and ZooKeeper pods such allocation could cause unwanted latency.
For Kafka Connect avoiding over allocation may be the most important concern, especially in distributed mode where the effects of over-allocation will be multiplied by the number of consumers.
-server
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
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.
|
Garbage collector logging
The jvmOptions
section also allows you to enable and disable garbage collector (GC) logging.
GC logging is disabled by default.
To enable it, set the gcLoggingEnabled
property as follows:
# ...
jvmOptions:
gcLoggingEnabled: true
# ...
Configuring JVM options
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
jvmOptions
property in theKafka
,KafkaConnect
,KafkaConnectS2I
,KafkaMirrorMaker
, orKafkaBridge
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... jvmOptions: "-Xmx": "8g" "-Xms": "8g" # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.5.9. Healthchecks
Healthchecks are periodical tests which verify the health of an application. When a Healthcheck probe fails, Kubernetes assumes that the application is not healthy and attempts to fix it.
Kubernetes supports two types of Healthcheck probes:
-
Liveness probes
-
Readiness probes
For more details about the probes, see Configure Liveness and Readiness Probes. Both types of probes are used in Strimzi components.
Users can configure selected options for liveness and readiness probes.
Healthcheck configurations
Liveness and readiness probes can be configured using the livenessProbe
and readinessProbe
properties in following resources:
-
Kafka.spec.kafka
-
Kafka.spec.kafka.tlsSidecar
-
Kafka.spec.zookeeper
-
Kafka.spec.zookeeper.tlsSidecar
-
Kafka.spec.entityOperator.tlsSidecar
-
Kafka.spec.entityOperator.topicOperator
-
Kafka.spec.entityOperator.userOperator
-
Kafka.spec.KafkaExporter
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
-
KafkaMirrorMaker.spec
-
KafkaBridge.spec
Both livenessProbe
and readinessProbe
support the following options:
-
initialDelaySeconds
-
timeoutSeconds
-
periodSeconds
-
successThreshold
-
failureThreshold
For more information about the livenessProbe
and readinessProbe
options, see Probe
schema reference.
# ...
readinessProbe:
initialDelaySeconds: 15
timeoutSeconds: 5
livenessProbe:
initialDelaySeconds: 15
timeoutSeconds: 5
# ...
Configuring healthchecks
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
livenessProbe
orreadinessProbe
property in theKafka
,KafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... readinessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.5.10. Container images
Strimzi allows you to configure container images which will be used for its components. Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by Strimzi. In such a case, you should either copy the Strimzi images or build them from the source. If the configured image is not compatible with Strimzi images, it might not work properly.
Container image configurations
You can specify which container image to use for each component using the image
property in the following resources:
-
Kafka.spec.kafka
-
Kafka.spec.kafka.tlsSidecar
-
Kafka.spec.zookeeper
-
Kafka.spec.zookeeper.tlsSidecar
-
Kafka.spec.entityOperator.topicOperator
-
Kafka.spec.entityOperator.userOperator
-
Kafka.spec.entityOperator.tlsSidecar
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
-
KafkaBridge.spec
Configuring the image
property for Kafka, Kafka Connect, and Kafka Mirror Maker
Kafka, Kafka Connect (including Kafka Connect with S2I support), and Kafka Mirror Maker support multiple versions of Kafka. Each component requires its own image. The default images for the different Kafka versions are configured in the following environment variables:
-
STRIMZI_KAFKA_IMAGES
-
STRIMZI_KAFKA_CONNECT_IMAGES
-
STRIMZI_KAFKA_CONNECT_S2I_IMAGES
-
STRIMZI_KAFKA_MIRROR_MAKER_IMAGES
These environment variables contain mappings between the Kafka versions and their corresponding images.
The mappings are used together with the image
and version
properties:
-
If neither
image
norversion
are given in the custom resource then theversion
will default to the Cluster Operator’s default Kafka version, and the image will be the one corresponding to this version in the environment variable. -
If
image
is given butversion
is not, then the given image is used and theversion
is assumed to be the Cluster Operator’s default Kafka version. -
If
version
is given butimage
is not, then the image that corresponds to the given version in the environment variable is used. -
If both
version
andimage
are given, then the given image is used. The image is assumed to contain a Kafka image with the given version.
The image
and version
for the different components can be configured in the following properties:
-
For Kafka in
spec.kafka.image
andspec.kafka.version
. -
For Kafka Connect, Kafka Connect S2I, and Kafka Mirror Maker in
spec.image
andspec.version
.
Warning
|
It is recommended to provide only the version and leave the image property unspecified.
This reduces the chance of making a mistake when configuring the custom resource.
If you need to change the images used for different versions of Kafka, it is preferable to configure the Cluster Operator’s environment variables.
|
Configuring the image
property in other resources
For the image
property in the other custom resources, the given value will be used during deployment.
If the image
property is missing, the image
specified in the Cluster Operator configuration will be used.
If the image
name is not defined in the Cluster Operator configuration, then the default value will be used.
-
For Kafka broker TLS sidecar:
-
Container image specified in the
STRIMZI_DEFAULT_TLS_SIDECAR_KAFKA_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/kafka:0.15.0-kafka-2.3.1
container image.
-
-
For ZooKeeper nodes:
-
Container image specified in the
STRIMZI_DEFAULT_ZOOKEEPER_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/kafka:0.15.0-kafka-2.3.1
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/kafka:0.15.0-kafka-2.3.1
container image.
-
-
For Topic Operator:
-
Container image specified in the
STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/operator:0.15.0
container image.
-
-
For User Operator:
-
Container image specified in the
STRIMZI_DEFAULT_USER_OPERATOR_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/operator:0.15.0
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/kafka:0.15.0-kafka-2.3.1
container image.
-
-
For Kafka Exporter:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_EXPORTER_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/kafka:0.15.0-kafka-2.3.1
container image.
-
-
For Kafka Bridge:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_BRIDGE_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/kafka-bridge:0.15.0
container image.
-
-
For Kafka broker initializer:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_INIT_IMAGE
environment variable from the Cluster Operator configuration. -
strimzi/operator:0.15.0
container image.
-
Warning
|
Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by Strimzi. In such case, you should either copy the Strimzi images or build them from source. In case the configured image is not compatible with Strimzi images, it might not work properly. |
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
image: my-org/my-image:latest
# ...
zookeeper:
# ...
Configuring container images
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
image
property in theKafka
,KafkaConnect
orKafkaConnectS2I
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... image: my-org/my-image:latest # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.5.11. Configuring pod scheduling
Important
|
When two applications are scheduled to the same Kubernetes node, both applications might use the same resources like disk I/O and impact performance. That can lead to performance degradation. Scheduling Kafka pods in a way that avoids sharing nodes with other critical workloads, using the right nodes or dedicated a set of nodes only for Kafka are the best ways how to avoid such problems. |
Scheduling pods based on other applications
Avoid critical applications to share the node
Pod anti-affinity can be used to ensure that critical applications are never scheduled on the same disk. When running Kafka cluster, it is recommended to use pod anti-affinity to ensure that the Kafka brokers do not share the nodes with other workloads like databases.
Affinity
Affinity can be configured using the affinity
property in following resources:
-
Kafka.spec.kafka.template.pod
-
Kafka.spec.zookeeper.template.pod
-
Kafka.spec.entityOperator.template.pod
-
KafkaConnect.spec.template.pod
-
KafkaConnectS2I.spec.template.pod
-
KafkaBridge.spec.template.pod
The affinity configuration can include different types of affinity:
-
Pod affinity and anti-affinity
-
Node affinity
The format of the affinity
property follows the Kubernetes specification.
For more details, see the Kubernetes node and pod affinity documentation.
Configuring pod anti-affinity in Kafka components
-
A 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/v1beta1 kind: Kafka spec: kafka: # ... template: pod: affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: application operator: In values: - postgresql - mongodb topologyKey: "kubernetes.io/hostname" # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
Scheduling pods to specific nodes
Node scheduling
The Kubernetes cluster usually consists of many different types of worker nodes. Some are optimized for CPU heavy workloads, some for memory, while other might be optimized for storage (fast local SSDs) or network. Using different nodes helps to optimize both costs and performance. To achieve the best possible performance, it is important to allow scheduling of Strimzi components to use the right nodes.
Kubernetes uses node affinity to schedule workloads onto specific nodes.
Node affinity allows you to create a scheduling constraint for the node on which the pod will be scheduled.
The constraint is specified as a label selector.
You can specify the label using either the built-in node label like beta.kubernetes.io/instance-type
or custom labels to select the right node.
Affinity
Affinity can be configured using the affinity
property in following resources:
-
Kafka.spec.kafka.template.pod
-
Kafka.spec.zookeeper.template.pod
-
Kafka.spec.entityOperator.template.pod
-
KafkaConnect.spec.template.pod
-
KafkaConnectS2I.spec.template.pod
-
KafkaBridge.spec.template.pod
The affinity configuration can include different types of affinity:
-
Pod affinity and anti-affinity
-
Node affinity
The format of the affinity
property follows the Kubernetes specification.
For more details, see the Kubernetes node and pod affinity documentation.
Configuring node affinity in Kafka components
-
A Kubernetes cluster
-
A running Cluster Operator
-
Label the nodes where Strimzi components should be scheduled.
This can be done using
kubectl label
:kubectl label node your-node node-type=fast-network
Alternatively, some of the existing labels might be reused.
-
Edit the
affinity
property in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... template: pod: affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: node-type operator: In values: - fast-network # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
Using dedicated nodes
Dedicated nodes
Cluster administrators can mark selected Kubernetes nodes as tainted. Nodes with taints are excluded from regular scheduling and normal pods will not be scheduled to run on them. Only services which can tolerate the taint set on the node can be scheduled on it. The only other services running on such nodes will be system services such as log collectors or software defined networks.
Taints can be used to create dedicated nodes. Running Kafka and its components on dedicated nodes can have many advantages. There will be no other applications running on the same nodes which could cause disturbance or consume the resources needed for Kafka. That can lead to improved performance and stability.
To schedule Kafka pods on the dedicated nodes, configure node affinity and tolerations.
Affinity
Affinity can be configured using the affinity
property in following resources:
-
Kafka.spec.kafka.template.pod
-
Kafka.spec.zookeeper.template.pod
-
Kafka.spec.entityOperator.template.pod
-
KafkaConnect.spec.template.pod
-
KafkaConnectS2I.spec.template.pod
-
KafkaBridge.spec.template.pod
The affinity configuration can include different types of affinity:
-
Pod affinity and anti-affinity
-
Node affinity
The format of the affinity
property follows the Kubernetes specification.
For more details, see the Kubernetes node and pod affinity documentation.
Tolerations
Tolerations can be configured using the tolerations
property in following resources:
-
Kafka.spec.kafka.template.pod
-
Kafka.spec.zookeeper.template.pod
-
Kafka.spec.entityOperator.template.pod
-
KafkaConnect.spec.template.pod
-
KafkaConnectS2I.spec.template.pod
-
KafkaBridge.spec.template.pod
The format of the tolerations
property follows the Kubernetes specification.
For more details, see the Kubernetes taints and tolerations.
Setting up dedicated nodes and scheduling pods on them
-
A 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:
This can be done using
kubectl taint
:kubectl taint node your-node dedicated=Kafka:NoSchedule
-
Additionally, add a label to the selected nodes as well.
This can be done using
kubectl label
:kubectl label node your-node dedicated=Kafka
-
Edit the
affinity
andtolerations
properties in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... template: pod: tolerations: - key: "dedicated" operator: "Equal" value: "Kafka" effect: "NoSchedule" affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: dedicated operator: In values: - Kafka # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f your-file
3.5.12. List of resources created as part of Kafka Bridge cluster
The following resources are created by the Cluster Operator in the Kubernetes cluster:
- bridge-cluster-name-bridge
-
Deployment which is in charge to create the Kafka Bridge worker node pods.
- bridge-cluster-name-bridge-service
-
Service which exposes the REST interface of the Kafka Bridge cluster.
- bridge-cluster-name-bridge-config
-
ConfigMap which contains the Kafka Bridge ancillary configuration and is mounted as a volume by the Kafka broker pods.
- bridge-cluster-name-bridge
-
Pod Disruption Budget configured for the Kafka Bridge worker nodes.
3.6. Using OAuth 2.0 token based authentication
Strimzi supports the use of OAuth 2.0 authentication using the SASL OAUTHBEARER mechanism.
OAuth 2.0 enables standardized token based authentication and authorization between applications, using a central authorization server to issue tokens that grant limited access to resources.
OAuth 2.0 is currently only supported for authentication, with no authorization support. However, OAuth 2.0 authentication can be used in conjunction with ACL-based Kafka authorization.
Using OAuth 2.0 token based authentication, application clients can access resources on application servers (called ‘resource servers’) without exposing account credentials. The client passes an access token as a means of authenticating, which application servers can also use to find more information about the level of access granted. The authorization server handles the granting of access and inquiries about access.
In the context of Strimzi:
-
Kafka brokers act as resource servers
-
Kafka clients act as resource clients
The brokers and clients communicate with the OAuth 2.0 authorization server, as necessary, to obtain or validate access tokens.
For a deployment of Strimzi, OAuth 2.0 integration provides:
-
Server-side OAuth 2.0 support for Kafka brokers
-
Client-side OAuth 2.0 support for Kafka Mirror Maker, Kafka Connect and the Kafka Bridge
3.6.1. OAuth 2.0 authentication mechanism
Support for OAuth 2.0 is based on the Kafka SASL OAUTHBEARER mechanism, which is used to establish authenticated sessions with a Kafka broker.
A Kafka client initiates a session with the Kafka broker using the SASL OAUTHBEARER mechanism for credentials exchange, where credentials take the form of an access token.
Kafka brokers and clients need to be configured to use OAuth 2.0.
The Kafka broker must be configured to validate the token received during session initiation. The recommended approach is to create a client definition in an authorization server with:
-
Client ID of
kafka-broker
-
Client ID and secret as the authentication mechanism
A Kafka client is configured with either:
-
Credentials required to obtain a valid access token from an authorization server
-
A valid long-lived access token, obtained using tools provided by an authorization server
Credentials are never sent to the Kafka broker. The only information ever sent to the Kafka broker is an access token. When a client obtains an access token, no further communication with the authorization server is needed.
The simplest mechanism, which requires no additional usage of authorization server tools, is authentication with a client ID and secret. Using a long-lived access token, or a long-lived refresh token, is more complex.
Note
|
If you are using long-lived access tokens, you can set policy in the authorization server to increase the maximum lifetime of the token. |
If the Kafka client is not configured with an access token directly, the client exchanges credentials for an access token during Kafka session initiation by contacting the authorization server. The Kafka client uses one of two mechanisms:
-
Client id and secret
-
Client id, refresh token, and (optionally) a secret
3.6.2. OAuth 2.0 client authentication flow
In this section, we explain and visualize the communication flow between Kafka client, Kafka broker, and authorization server during Kafka session initiation. The flow depends on the client and server configuration.
When a Kafka client sends an access token as credentials to a Kafka broker, the token needs to be validated.
Depending on the authorization server used, and the configuration options available, you may prefer to use:
-
Fast local token validation based on JWT signature checking and local token introspection, without contacting the authorization server
-
An OAuth 2.0 introspection endpoint provided by the authorization server
Using fast local token validation requires the authorization server to provide a JWKS endpoint with public certificates that are used to validate signatures on the tokens.
Note
|
An authorization server might only allow the use of opaque access tokens, which means that local token validation is not possible. |
Another option is to use an OAuth 2.0 introspection endpoint on the authorization server. Each time a Kafka broker connection is established, the broker sends the access token it receives to the authorization server, and a response confirming whether or not the token is valid is returned.
Kafka client credentials can also be configured for:
-
Direct local access using a previously generated long-lived access token
-
Contact with the authorization server for a new access token to be issued and sent to the Kafka broker
Example client authentication flows
Here you can see the communication flows, for different configurations of Kafka clients and brokers, during Kafka session authentication.
-
Client using client ID and secret, with broker delegating validation to authorization server
-
Client using client ID and secret, with broker performing fast local token validation
-
Client using long-lived access token, with broker delegating validation to authorization server
-
Client using long-lived access token, with broker performing fast local validation
-
Kafka client requests access token from authorization server, using client ID and secret, and optionally a refresh token.
-
Authorization server generates a new access token.
-
Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the access token.
-
Kafka broker validates the access token by calling a token introspection endpoint on authorization server, using its own client ID and secret.
-
Kafka client session is established if the token is valid.
-
Kafka client authenticates with authorization server from the token endpoint, using a client ID and secret, and optionally a refresh token.
-
Authorization server generates a new access token.
-
Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the access token.
-
Kafka broker validates the access token locally using a JWT token signature check, and local token introspection.
-
Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the long-lived access token.
-
Kafka broker validates the access token by calling a token introspection endpoint on authorization server, using its own client ID and secret.
-
Kafka client session is established if the token is valid.
-
Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the long-lived access token.
-
Kafka broker validates the access token locally using JWT token signature check, and local token introspection.
Warning
|
Fast local JWT token signature validation is suitable only for short-lived tokens as there is no check with the authorization server if a token has been revoked. Token expiration is written into the token, but revocation can happen at any time, so cannot be accounted for without contacting the authorization server. Any issued token would be considered valid until it expires. |
3.6.3. Configuring OAuth 2.0 authentication
OAuth 2.0 is used for interaction between Kafka clients and Strimzi components.
In order to use OAuth 2.0 for Strimzi, you must:
Configuring an OAuth 2.0 authorization server
This procedure describes in general what you need to do to configure an authorization server for integration with Strimzi.
These instructions are not product specific.
The steps are dependent on the chosen authorization server. Consult the product documentation for the authorization server for information on how to set up OAuth 2.0 access.
Note
|
If you already have an authorization server deployed, you can skip the deployment step and use your current deployment. |
-
Deploy the authorization server to your cluster.
-
Access the CLI or admin console for the authorization server to configure OAuth 2.0 for Strimzi.
Now prepare the authorization server to work with Strimzi.
-
Configure a
kafka-broker
client. -
Configure clients for each Kafka client component of your application.
After deploying and configuring the authorization server, configure the Kafka brokers to use OAuth 2.0.
Configuring OAuth 2.0 support for Kafka brokers
This procedure describes how to configure Kafka brokers so that the broker listeners are enabled to use OAuth 2.0 authentication using an authorization server.
If the authorization server is using certificates signed by the trusted CA and matching the OAuth 2.0 server hostname, TLS connection works using the default settings. Otherwise, you have two connection options for your external listener configuration when delegating token validation to the authorization server:
-
TLS connection to the authorization server with trusted certificates
-
Direct connection using an introspection endpoint configuration
Both options are described in this procedure.
For more information on the configuration and authentication of Kafka broker listeners, see:
-
Strimzi and Kafka are running
-
An OAuth 2.0 authorization server is deployed
-
Update the Kafka broker configuration (
Kafka.spec.kafka
) of yourKafka
resource in an editor.kubectl edit kafka my-cluster
-
Configure the Kafka broker
listeners
configuration.The configuration for each type of listener does not have to be the same, as they are independent.
For example:
#... authentication: type: oauth (1) validIssuerUri: <https://<authorization-server-address>/auth/realms/master> (2) jwksEndpointUri: <https://<authorization-server-address>/auth/realms/master/protocol/openid-connect/certs> (3) userNameClaim: preferred_username (4) #external configuration external: type: loadbalancer //Option 1 authentication: (5) type: oauth validIssuerUri: <https://<authorization-server-address>/auth/realms/external> jwksEndpointUri: <https://<authorization-server-address>/auth/realms/external/protocol/openid-connect/certs> userNameClaim: preferred_username tlsTrustedCertificates: (6) - secretName: oauth-server-cert certificate: ca.crt disableTlsHostnameVerification: true (7) //Option 2 authentication: (8) type: oauth validIssuerUri: <https://<authorization-server-address>/auth/realms/external> introspectionEndpointUri: <https://<authorization-server-address>/auth/realms/external/protocol/openid-connect/token/introspect> clientId: kafka-broker clientSecret: secretName: my-cluster-oauth key: clientSecret
-
Listener type set to
oauth
. -
URI of the token issuer used for authentication.
-
URI of the JWKS certificate endpoint used for local JWT validation.
-
The user name profile claim (or key) that contains the actual user name in the token. The user name is the principal used to identify the user. The
userNameClaim
value will depend on the authentication flow and the authorization server used. -
OPTION 1: TLS connection to the authorization server.
-
(Optional) Trusted certificates for TLS connection to the authorization server.
-
(Optional) Disable TLS hostname verification. Default is
false
. -
OPTION 2: Introspection endpoint to connect directly to the authorization server.
-
-
Save and exit the editor, then wait for rolling updates to complete.
-
Check the update in the logs or by watching the pod state transitions:
kubectl logs -f ${POD_NAME} -c ${CONTAINER_NAME} kubectl get po -w
The rolling update configures the brokers to use OAuth 2.0 authentication.
Configuring Kafka Java clients to use OAuth 2.0
This procedure describes how to configure Kafka producer and consumer APIs to use OAuth 2.0 for interaction with Kafka brokers.
Add a client callback plugin to your pom.xml file, and configure the system properties.
-
Strimzi and Kafka are running
-
An OAuth 2.0 authorization server is deployed and configured for OAuth access to Kafka brokers
-
Kafka brokers are configured for OAuth 2.0
-
Add the client library with OAuth 2.0 support to the
pom.xml
file for the Kafka client:<dependency> <groupId>io.strimzi</groupId> <artifactId>kafka-oauth-client</artifactId> <version>0.1.0.redhat-00002</version> </dependency>
-
Configure the system properties for the callback:
For example:
System.setProperty(ClientConfig.OAUTH_TOKEN_ENDPOINT_URI, “https://<authorization-server-address>/auth/realms/master/protocol/openid-connect/token”); (1) System.setProperty(ClientConfig.OAUTH_CLIENT_ID, "<client-name>"); (2) System.setProperty(ClientConfig.OAUTH_CLIENT_SECRET, "<client-secret>"); (3)
-
URI of the authorization server token endpoint.
-
Client ID, which is the name used when creating the client in the authorization server.
-
Client secret created when creating the client in the authorization server.
-
-
Enable the SASL OAUTHBEARER mechanism on a TLS encrypted connection in the Kafka client configuration:
For example:
props.put("sasl.jaas.config", "org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required;"); props.put("security.protocol", "SASL_SSL"); (1) props.put("sasl.mechanism", "OAUTHBEARER"); props.put("sasl.login.callback.handler.class", "io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler");
-
Here we use
SASL_SSL
for use over TLS connections. UseSASL_PLAINTEXT
over unencrypted connections.
-
-
Verify that the Kafka client can access the Kafka brokers.
Configuring OAuth for Kafka components
This procedure describes how to configure Kafka components to use OAuth 2.0 authentication using an authorization server.
You can configure authentication for:
-
Kafka Connect
-
Kafka Mirror Maker
-
Kafka Bridge
In this scenario, the Kafka component and the authorization server are running in the same cluster.
-
Strimzi and Kafka are running
-
An OAuth 2.0 authorization server is deployed and configured for OAuth access to Kafka brokers
-
Kafka brokers are configured for OAuth 2.0
-
Create a client secret and mount it to the component as an environment variable.
For example, here we are creating a client
Secret
for the Kafka Bridge:apiVersion: kafka.strimzi.io/v1beta1 kind: Secret metadata: name: my-bridge-oauth type: Opaque data: clientSecret: MGQ1OTRmMzYtZTllZS00MDY2LWI5OGEtMTM5MzM2NjdlZjQw (1)
-
The
clientSecret
key must be in base64 format.
-
-
Create or edit the resource for the Kafka component so that OAuth 2.0 authentication is configured for the authentication property.
For example, here OAuth 2.0 is assigned to the Kafka Bridge client:
apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaBridge metadata: name: my-bridge spec: # ... authentication: type: oauth (1) tokenEndpointUri: https://<authorization-server-address>/auth/realms/master/protocol/openid-connect/token (2) clientId: kafka-bridge clientSecret: secretName: my-bridge-oauth key: clientSecret tlsTrustedCertificates: (3) - secretName: oauth-server-cert certificate: tls.crt
-
Authentication type set to
oauth
. -
URI of the token endpoint for authentication.
-
Trusted certificates for TLS connection to the authorization server.
-
-
Apply the changes to the deployment of your Kafka resource.
oc apply -f your-file
-
Check the update in the logs or by watching the pod state transitions:
kubectl logs -f ${POD_NAME} -c ${CONTAINER_NAME} kubectl get pod -w
The rolling updates configure the component for interaction with Kafka brokers using OAuth 2.0 authentication.
3.7. Customizing deployments
Strimzi creates several Kubernetes resources, such as Deployments
, StatefulSets
, Pods
, and Services
, which are managed by Kubernetes operators.
Only the operator that is responsible for managing a particular Kubernetes resource can change that resource.
If you try to manually change an operator-managed Kubernetes resource, the operator will revert your changes back.
However, changing an operator-managed Kubernetes resource can be useful if you want to perform certain tasks, such as:
-
Adding custom labels or annotations that control how
Pods
are treated by Istio or other services; -
Managing how
Loadbalancer
-type Services are created by the cluster.
You can make these types of changes using the template
property in the Strimzi custom resources.
3.7.1. Template properties
You can use the template
property to configure aspects of the resource creation process.
You can include it in the following resources and properties:
-
Kafka.spec.kafka
-
Kafka.spec.zookeeper
-
Kafka.spec.entityOperator
-
Kafka.spec.kafkaExporter
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
-
KafkaMirrorMakerSpec
-
KafkaBridge.spec
In the following example, the template
property is used to modify the labels in a Kafka broker’s StatefulSet
:
apiVersion: kafka.strimzi.io/v1beta1
kind: Kafka
metadata:
name: my-cluster
labels:
app: my-cluster
spec:
kafka:
# ...
template:
statefulset:
metadata:
labels:
mylabel: myvalue
# ...
Supported template properties for a Kafka cluster
statefulset
-
Configures the
StatefulSet
used by the Kafka broker. pod
-
Configures the Kafka broker
Pods
created by theStatefulSet
. bootstrapService
-
Configures the bootstrap service used by clients running within Kubernetes to connect to the Kafka broker.
brokersService
-
Configures the headless service.
externalBootstrapService
-
Configures the bootstrap service used by clients connecting to Kafka brokers from outside of Kubernetes.
perPodService
-
Configures the per-Pod services used by clients connecting to the Kafka broker from outside Kubernetes to access individual brokers.
externalBootstrapRoute
-
Configures the bootstrap route used by clients connecting to the Kafka brokers from outside of OpenShift using OpenShift
Routes
. perPodRoute
-
Configures the per-Pod routes used by clients connecting to the Kafka broker from outside OpenShift to access individual brokers using OpenShift
Routes
. podDisruptionBudget
-
Configures the Pod Disruption Budget for Kafka broker
StatefulSet
. kafkaContainer
-
Configures the container used to run the Kafka broker, including custom environment variables.
tlsSidecarContainer
-
Configures the TLS sidecar container, including custom environment variables.
initContainer
-
Configures the container used to initialize the brokers.
persistentVolumeClaim
-
Configures the metadata of the Kafka
PersistentVolumeClaims
.
Supported template properties for a ZooKeeper cluster
statefulset
-
Configures the ZooKeeper
StatefulSet
. pod
-
Configures the ZooKeeper
Pods
created by theStatefulSet
. clientsService
-
Configures the service used by clients to access ZooKeeper.
nodesService
-
Configures the headless service.
podDisruptionBudget
-
Configures the Pod Disruption Budget for ZooKeeper
StatefulSet
. zookeeperContainer
-
Configures the container used to run the ZooKeeper Node, including custom environment variables.
tlsSidecarContainer
-
Configures the TLS sidecar container, including custom environment variables.
persistentVolumeClaim
-
Configures the metadata of the ZooKeeper
PersistentVolumeClaims
.
Supported template properties for Entity Operator
deployment
-
Configures the Deployment used by the Entity Operator.
pod
-
Configures the Entity Operator
Pod
created by theDeployment
. topicOperatorContainer
-
Configures the container used to run the Topic Operator, including custom environment variables.
userOperatorContainer
-
Configures the container used to run the User Operator, including custom environment variables.
tlsSidecarContainer
-
Configures the TLS sidecar container, including custom environment variables.
Supported template properties for Kafka Exporter
deployment
-
Configures the Deployment used by Kafka Exporter.
pod
-
Configures the Kafka Exporter
Pod
created by theDeployment
. services
-
Configures the Kafka Exporter services.
container
-
Configures the container used to run Kafka Exporter, including custom environment variables.
Supported template properties for Kafka Connect and Kafka Connect with Source2Image support
deployment
-
Configures the Kafka Connect
Deployment
. pod
-
Configures the Kafka Connect
Pods
created by theDeployment
. apiService
-
Configures the service used by the Kafka Connect REST API.
podDisruptionBudget
-
Configures the Pod Disruption Budget for Kafka Connect
Deployment
. connectContainer
-
Configures the container used to run Kafka Connect, including custom environment variables.
Supported template properties for Kafka Mirror Maker
deployment
-
Configures the Kafka Mirror Maker
Deployment
. pod
-
Configures the Kafka Mirror Maker
Pods
created by theDeployment
. podDisruptionBudget
-
Configures the Pod Disruption Budget for Kafka Mirror Maker
Deployment
. mirrorMakerContainer
-
Configures the container used to run Kafka Mirror Maker, including custom environment variables.
3.7.2. Labels and Annotations
For every resource, you can configure additional Labels
and Annotations
.
Labels
and Annotations
are used to identify and organize resources, and are configured in the metadata
property.
For example:
# ...
template:
statefulset:
metadata:
labels:
label1: value1
label2: value2
annotations:
annotation1: value1
annotation2: value2
# ...
The labels
and annotations
fields can contain any labels or annotations that do not contain the reserved string strimzi.io
.
Labels and annotations containing strimzi.io
are used internally by Strimzi and cannot be configured.
Note
|
The metadata property is not applicable to container templates, such as the kafkaContainer .
|
3.7.3. Customizing Pods
In addition to Labels and Annotations, you can customize some other fields on Pods. These fields are described in the following table and affect how the Pod is created.
Field | Description |
---|---|
|
Defines the period of time, in seconds, by which the Pod must have terminated gracefully.
After the grace period, the Pod and its containers are forcefully terminated (killed).
The default value is NOTE: You might need to increase the grace period for very large Kafka clusters, so that the Kafka brokers have enough time to transfer their work to another broker before they are terminated. |
|
Defines a list of references to Kubernetes Secrets that can be used for pulling container images from private repositories. For more information about how to create a Secret with the credentials, see Pull an Image from a Private Registry. NOTE: When the |
|
Configures pod-level security attributes for containers running as part of a given Pod. For more information about configuring SecurityContext, see Configure a Security Context for a Pod or Container. |
|
Configures the name of the Priority Class which will be used for given a Pod. For more information about Priority Classes, see Pod Priority and Preemption. |
|
The name of the scheduler used to dispatch this |
These fields are effective on each type of cluster (Kafka and ZooKeeper; Kafka Connect and Kafka Connect with S2I support; and Kafka Mirror Maker).
The following example shows these customized fields on a template
property:
# ...
template:
pod:
metadata:
labels:
label1: value1
imagePullSecrets:
- name: my-docker-credentials
securityContext:
runAsUser: 1000001
fsGroup: 0
terminationGracePeriodSeconds: 120
# ...
-
For more information, see
PodTemplate
schema reference.
3.7.4. Customizing containers with environment variables
You can set custom environment variables for a container by using the relevant template
container property.
The following table lists the Strimzi containers and the relevant template configuration property (defined under spec
) for each custom resource.
Strimzi Element | Container | Configuration property |
---|---|---|
Kafka |
Kafka Broker |
|
Kafka |
Kafka Broker TLS Sidecar |
|
Kafka |
Kafka Initialization |
|
Kafka |
ZooKeeper Node |
|
Kafka |
ZooKeeper TLS Sidecar |
|
Kafka |
Topic Operator |
|
Kafka |
User Operator |
|
Kafka |
Entity Operator TLS Sidecar |
|
KafkaConnect |
Connect and ConnectS2I |
|
KafkaMirrorMaker |
Mirror Maker |
|
KafkaBridge |
Bridge |
|
The environment variables are defined under the env
property as a list of objects with name
and value
fields.
The following example shows two custom environment variables set for the Kafka broker containers:
# ...
kind: Kafka
spec:
kafka:
template:
kafkaContainer:
env:
- name: TEST_ENV_1
value: test.env.one
- name: TEST_ENV_2
value: test.env.two
# ...
Environment variables prefixed with KAFKA_
are internal to Strimzi and should be avoided.
If you set a custom environment variable that is already in use by Strimzi, it is ignored and a warning is recorded in the log.
-
For more information, see
ContainerTemplate
schema reference.
3.7.5. Customizing the image pull policy
Strimzi allows you to customize the image pull policy for containers in all pods deployed by the Cluster Operator.
The image pull policy is configured using the environment variable STRIMZI_IMAGE_PULL_POLICY
in the Cluster Operator deployment.
The STRIMZI_IMAGE_PULL_POLICY
environment variable can be set to three different values:
Always
-
Container images are pulled from the registry every time the pod is started or restarted.
IfNotPresent
-
Container images are pulled from the registry only when they were not pulled before.
Never
-
Container images are never pulled from the registry.
The image pull policy can be currently customized only for all Kafka, Kafka Connect, and Kafka Mirror Maker clusters at once. Changing the policy will result in a rolling update of all your Kafka, Kafka Connect, and Kafka Mirror Maker clusters.
-
For more information about Cluster Operator configuration, see Cluster Operator.
-
For more information about Image Pull Policies, see Disruptions.
3.7.6. Customizing Pod Disruption Budgets
Strimzi creates a pod disruption budget for every new StatefulSet
or Deployment
.
By default, these pod disruption budgets only allow a single pod to be unavailable at a given time by setting the maxUnavailable
value in the`PodDisruptionBudget.spec` resource to 1.
You can change the amount of unavailable pods allowed by changing the default value of maxUnavailable
in the pod disruption budget template.
This template applies to each type of cluster (Kafka and ZooKeeper; Kafka Connect and Kafka Connect with S2I support; and Kafka Mirror Maker).
The following example shows customized podDisruptionBudget
fields on a template
property:
# ...
template:
podDisruptionBudget:
metadata:
labels:
key1: label1
key2: label2
annotations:
key1: label1
key2: label2
maxUnavailable: 1
# ...
-
For more information, see
PodDisruptionBudgetTemplate
schema reference. -
The Disruptions chapter of the Kubernetes documentation.
3.7.7. Customizing deployments
This procedure describes how to customize Labels
of a Kafka cluster.
-
A Kubernetes cluster.
-
A running Cluster Operator.
-
Edit the
template
property in theKafka
,KafkaConnect
,KafkaConnectS2I
, orKafkaMirrorMaker
resource. For example, to modify the labels for the Kafka brokerStatefulSet
, use:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster labels: app: my-cluster spec: kafka: # ... template: statefulset: metadata: labels: mylabel: myvalue # ...
-
Create or update the resource.
Use
kubectl apply
:kubectl apply -f your-file
Alternatively, use
kubectl edit
:kubectl edit Resource ClusterName
4. Operators
4.1. Cluster Operator
4.1.1. Overview of the Cluster Operator component
Strimzi uses the Cluster Operator to deploy and manage clusters for:
-
Kafka (including ZooKeeper, Entity Operator and Kafka Exporter)
-
Kafka Connect
-
Kafka Mirror Maker
-
Kafka Bridge
Custom resources are used to deploy the clusters.
For example, to deploy a Kafka cluster:
-
A
Kafka
resource with the cluster configuration is created within the Kubernetes cluster. -
The Cluster Operator deploys a corresponding Kafka cluster, based on what is declared in the
Kafka
resource.
The Cluster Operator can also deploy (through Entity Operator configuration of the Kafka
resource):
-
A Topic Operator to provide operator-style topic management through
KafkaTopic
custom resources -
A User Operator to provide operator-style user management through
KafkaUser
custom resources
For more information on the configuration options supported by the Kafka
resource, see Kafka cluster configuration.
Note
|
On OpenShift, a Kafka Connect deployment can incorporate a Source2Image feature to provides a convenient way to include connectors. |
4.1.2. Watch options for a Cluster Operator deployment
When the Cluster Operator is running, it starts to watch for updates of Kafka resources.
Depending on the deployment, the Cluster Operator can watch Kafka resources from:
Note
|
Strimzi provides example YAML files to make the deployment process easier. |
The Cluster Operator watches the following resources:
-
Kafka
for the Kafka cluster. -
KafkaConnect
for the Kafka Connect cluster. -
KafkaConnectS2I
for the Kafka Connect cluster with Source2Image support. -
KafkaMirrorMaker
for the Kafka Mirror Maker instance. -
KafkaBridge
for the Kafka Bridge instance
When one of these resources is created in the Kubernetes cluster, the operator gets the cluster description from the resource and starts creating a new cluster for the resource by creating the necessary Kubernetes resources, such as StatefulSets, Services and ConfigMaps.
Each time a Kafka resource is updated, the operator performs corresponding updates on the Kubernetes resources that make up the cluster for the resource.
Resources are either patched or deleted, and then recreated in order to make the cluster for the resource reflect the desired state of the cluster. This operation might cause a rolling update that might lead to service disruption.
When a resource is deleted, the operator undeploys the cluster and deletes all related Kubernetes resources.
4.1.3. Deploying the Cluster Operator to watch a single namespace
-
This procedure requires use of a Kubernetes user account which is able to create
CustomResourceDefinitions
,ClusterRoles
andClusterRoleBindings
. Use of Role Base Access Control (RBAC) in the Kubernetes cluster usually means that permission to create, edit, and delete these resources is limited to Kubernetes cluster administrators, such assystem:admin
. -
Modify the installation files according to the namespace the Cluster Operator is going to be installed in.
On Linux, use:
sed -i 's/namespace: .*/namespace: my-namespace/' install/cluster-operator/*RoleBinding*.yaml
On MacOS, use:
sed -i '' 's/namespace: .*/namespace: my-namespace/' install/cluster-operator/*RoleBinding*.yaml
-
Deploy the Cluster Operator:
kubectl apply -f install/cluster-operator -n my-namespace
4.1.4. Deploying the Cluster Operator to watch multiple namespaces
-
This procedure requires use of a Kubernetes user account which is able to create
CustomResourceDefinitions
,ClusterRoles
andClusterRoleBindings
. Use of Role Base Access Control (RBAC) in the Kubernetes cluster usually means that permission to create, edit, and delete these resources is limited to Kubernetes cluster administrators, such assystem:admin
. -
Edit the installation files according to the namespace the Cluster Operator is going to be installed in.
On Linux, use:
sed -i 's/namespace: .*/namespace: my-namespace/' install/cluster-operator/*RoleBinding*.yaml
On MacOS, use:
sed -i '' 's/namespace: .*/namespace: my-namespace/' install/cluster-operator/*RoleBinding*.yaml
-
Edit the file
install/cluster-operator/050-Deployment-strimzi-cluster-operator.yaml
and in the environment variableSTRIMZI_NAMESPACE
list all the namespaces where Cluster Operator should watch for resources. For example:apiVersion: apps/v1 kind: Deployment spec: # ... template: spec: serviceAccountName: strimzi-cluster-operator containers: - name: strimzi-cluster-operator image: strimzi/operator:0.15.0 imagePullPolicy: IfNotPresent env: - name: STRIMZI_NAMESPACE value: watched-namespace-1,watched-namespace-2,watched-namespace-3
-
For all namespaces which should be watched by the Cluster Operator (
watched-namespace-1
,watched-namespace-2
,watched-namespace-3
in the above example), install theRoleBindings
. Replace thewatched-namespace
with the namespace used in the previous step.This can be done using
kubectl apply
:kubectl apply -f install/cluster-operator/020-RoleBinding-strimzi-cluster-operator.yaml -n watched-namespace kubectl apply -f install/cluster-operator/031-RoleBinding-strimzi-cluster-operator-entity-operator-delegation.yaml -n watched-namespace kubectl apply -f install/cluster-operator/032-RoleBinding-strimzi-cluster-operator-topic-operator-delegation.yaml -n watched-namespace
-
Deploy the Cluster Operator
This can be done using
kubectl apply
:kubectl apply -f install/cluster-operator -n my-namespace
4.1.5. Deploying the Cluster Operator to watch all namespaces
You can configure the Cluster Operator to watch Strimzi resources across all namespaces in your Kubernetes cluster. When running in this mode, the Cluster Operator automatically manages clusters in any new namespaces that are created.
-
This procedure requires use of a Kubernetes user account which is able to create
CustomResourceDefinitions
,ClusterRoles
andClusterRoleBindings
. Use of Role Base Access Control (RBAC) in the Kubernetes cluster usually means that permission to create, edit, and delete these resources is limited to Kubernetes cluster administrators, such assystem:admin
. -
Your Kubernetes cluster is running.
-
Configure the Cluster Operator to watch all namespaces:
-
Edit the
050-Deployment-strimzi-cluster-operator.yaml
file. -
Set the value of the
STRIMZI_NAMESPACE
environment variable to*
.apiVersion: apps/v1 kind: Deployment spec: # ... template: spec: # ... serviceAccountName: strimzi-cluster-operator containers: - name: strimzi-cluster-operator image: strimzi/operator:0.15.0 imagePullPolicy: IfNotPresent env: - name: STRIMZI_NAMESPACE value: "*" # ...
-
-
Create
ClusterRoleBindings
that grant cluster-wide access to all namespaces to the Cluster Operator.Use the
kubectl create clusterrolebinding
command:kubectl create clusterrolebinding strimzi-cluster-operator-namespaced --clusterrole=strimzi-cluster-operator-namespaced --serviceaccount my-namespace:strimzi-cluster-operator kubectl create clusterrolebinding strimzi-cluster-operator-entity-operator-delegation --clusterrole=strimzi-entity-operator --serviceaccount my-namespace:strimzi-cluster-operator kubectl create clusterrolebinding strimzi-cluster-operator-topic-operator-delegation --clusterrole=strimzi-topic-operator --serviceaccount my-namespace:strimzi-cluster-operator
Replace
my-namespace
with the namespace in which you want to install the Cluster Operator. -
Deploy the Cluster Operator to your Kubernetes cluster.
Use the
kubectl apply
command:kubectl apply -f install/cluster-operator -n my-namespace
4.1.6. Deploying the Cluster Operator using Helm Chart
-
Helm client has to be installed on the local machine.
-
Helm has to be installed in the Kubernetes cluster.
-
Add the Strimzi Helm Chart repository:
helm repo add strimzi https://strimzi.io/charts/
-
Deploy the Cluster Operator using the Helm command line tool:
helm install strimzi/strimzi-kafka-operator
-
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.
4.1.7. Deploying the Cluster Operator from OperatorHub.io
OperatorHub.io is a catalog of Kubernetes Operators sourced from multiple providers. It offers you an alternative way to install stable versions of Strimzi using the Strimzi Kafka Operator.
The Operator Lifecycle Manager is used for the installation and management of all Operators published on OperatorHub.io.
To install Strimzi from OperatorHub.io, locate the Strimzi Kafka Operator and follow the instructions provided.
4.1.8. Reconciliation
Although the operator reacts to all notifications about the desired cluster resources received from the Kubernetes cluster, if the operator is not running, or if a notification is not received for any reason, the desired resources will get out of sync with the state of the running Kubernetes cluster.
In order to handle failovers properly, a periodic reconciliation process is executed by the Cluster Operator so that it can compare the state of the desired resources with the current cluster deployments in order to have a consistent state across all of them.
You can set the time interval for the periodic reconciliations using the STRIMZI_FULL_RECONCILIATION_INTERVAL_MS
variable.
4.1.9. Cluster Operator Configuration
The Cluster Operator can be configured through the following supported environment variables:
STRIMZI_NAMESPACE
-
A comma-separated list of namespaces that the operator should operate in. When not set, set to empty string, or to
*
the Cluster Operator will operate in all namespaces. The Cluster Operator deployment might use the Kubernetes Downward API to set this automatically to the namespace the Cluster Operator is deployed in. See the example below:env: - name: STRIMZI_NAMESPACE valueFrom: fieldRef: fieldPath: metadata.namespace
-
STRIMZI_FULL_RECONCILIATION_INTERVAL_MS
-
Optional, default is 120000 ms. The interval between periodic reconciliations, in milliseconds.
STRIMZI_LOG_LEVEL
-
Optional, default
INFO
. The level for printing logging messages. The value can be set to:ERROR
,WARNING
,INFO
,DEBUG
, 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 Kubernetes operations take longer than usual (because of slow downloading of Docker images, for example).
STRIMZI_KAFKA_IMAGES
-
Required. This provides a mapping from Kafka version to the corresponding Docker image containing a Kafka broker of that version. The required syntax is whitespace or comma separated
<version>=<image>
pairs. For example2.2.1=strimzi/kafka:0.15.0-kafka-2.2.1, 2.3.1=strimzi/kafka:0.15.0-kafka-2.3.1
. This is used when aKafka.spec.kafka.version
property is specified but not theKafka.spec.kafka.image
, as described in Container images. STRIMZI_DEFAULT_KAFKA_INIT_IMAGE
-
Optional, default
strimzi/operator:0.15.0
. The image name to use as default for the init container started before the broker for initial configuration work (that is, rack support), if no image is specified as thekafka-init-image
in the Container images. STRIMZI_DEFAULT_TLS_SIDECAR_KAFKA_IMAGE
-
Optional, default
strimzi/kafka:0.15.0-kafka-2.3.1
. 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/kafka:0.15.0-kafka-2.3.1
. 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/kafka:0.15.0-kafka-2.3.1
. 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_KAFKA_CONNECT_IMAGES
-
Required. This provides a mapping from the Kafka version to the corresponding Docker image containing a Kafka connect of that version. The required syntax is whitespace or comma separated
<version>=<image>
pairs. For example2.2.1=strimzi/kafka:0.15.0-kafka-2.2.1, 2.3.1=strimzi/kafka:0.15.0-kafka-2.3.1
. This is used when aKafkaConnect.spec.version
property is specified but not theKafkaConnect.spec.image
, as described in Container images. STRIMZI_KAFKA_CONNECT_S2I_IMAGES
-
Required. This provides a mapping from the Kafka version to the corresponding Docker image containing a Kafka connect of that version. The required syntax is whitespace or comma separated
<version>=<image>
pairs. For example2.2.1=strimzi/kafka:0.15.0-kafka-2.2.1, 2.3.1=strimzi/kafka:0.15.0-kafka-2.3.1
. This is used when aKafkaConnectS2I.spec.version
property is specified but not theKafkaConnectS2I.spec.image
, as described in Container images. STRIMZI_KAFKA_MIRROR_MAKER_IMAGES
-
Required. This provides a mapping from the Kafka version to the corresponding Docker image containing a Kafka mirror maker of that version. The required syntax is whitespace or comma separated
<version>=<image>
pairs. For example2.2.1=strimzi/kafka:0.15.0-kafka-2.2.1, 2.3.1=strimzi/kafka:0.15.0-kafka-2.3.1
. This is used when aKafkaMirrorMaker.spec.version
property is specified but not theKafkaMirrorMaker.spec.image
, as described in Container images. STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE
-
Optional, default
strimzi/operator:0.15.0
. 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/operator:0.15.0
. 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/kafka:0.15.0-kafka-2.3.1
. 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. STRIMZI_IMAGE_PULL_POLICY
-
Optional. The
ImagePullPolicy
which will be applied to containers in all pods managed by Strimzi Cluster Operator. The valid values areAlways
,IfNotPresent
, andNever
. If not specified, the Kubernetes defaults will be used. Changing the policy will result in a rolling update of all your Kafka, Kafka Connect, and Kafka Mirror Maker clusters. STRIMZI_IMAGE_PULL_SECRETS
-
Optional. A comma-separated list of
Secret
names. The secrets referenced here contain the credentials to the container registries where the container images are pulled from. The secrets are used in theimagePullSecrets
field for allPods
created by the Cluster Operator. Changing this list results in a rolling update of all your Kafka, Kafka Connect, and Kafka Mirror Maker clusters. STRIMZI_KUBERNETES_VERSION
-
Optional. Overrides the Kubernetes version information detected from the API server. See the example below:
env: - name: STRIMZI_KUBERNETES_VERSION value: | major=1 minor=16 gitVersion=v1.16.2 gitCommit=c97fe5036ef3df2967d086711e6c0c405941e14b gitTreeState=clean buildDate=2019-10-15T19:09:08Z goVersion=go1.12.10 compiler=gc platform=linux/amd64
4.1.10. Role-Based Access Control (RBAC)
Provisioning Role-Based Access Control (RBAC) for the Cluster Operator
For the Cluster Operator to function it needs permission within the Kubernetes cluster to interact with resources such as Kafka
, KafkaConnect
, and so on, as well as the managed resources, such as ConfigMaps
, Pods
, Deployments
, StatefulSets
, Services
, and so on.
Such permission is described in terms of Kubernetes role-based access control (RBAC) resources:
-
ServiceAccount
, -
Role
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 Kubernetes resources.
Kubernetes also includes privilege escalation protections that prevent components operating under one ServiceAccount
from granting other ServiceAccounts
privileges that the granting ServiceAccount
does not have.
Because the Cluster Operator must be able to create the ClusterRoleBindings
, and RoleBindings
needed by resources it manages, the Cluster Operator must also have those same privileges.
Delegated privileges
When the Cluster Operator deploys resources for a desired Kafka
resource it also creates ServiceAccounts
, RoleBindings
, and ClusterRoleBindings
, as follows:
-
The Kafka broker pods use a
ServiceAccount
calledcluster-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 a
ServiceAccount
calledcluster-name-zookeeper
-
The Entity Operator pod uses a
ServiceAccount
calledcluster-name-entity-operator
-
The Topic Operator produces Kubernetes events with status information, so the
ServiceAccount
is bound to aClusterRole
calledstrimzi-entity-operator
which grants this access via thestrimzi-entity-operator
RoleBinding
-
-
The pods for
KafkaConnect
andKafkaConnectS2I
resources use aServiceAccount
calledcluster-name-cluster-connect
-
The pods for
KafkaMirrorMaker
use aServiceAccount
calledcluster-name-mirror-maker
-
The pods for
KafkaBridge
use aServiceAccount
calledcluster-name-bridge
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: apps/v1
kind: Deployment
metadata:
name: strimzi-cluster-operator
labels:
app: strimzi
spec:
replicas: 1
selector:
matchLabels:
name: strimzi-cluster-operator
strimzi.io/kind: cluster-operator
template:
# ...
Note line 12, where the the strimzi-cluster-operator
ServiceAccount
is specified as the serviceAccountName
.
ClusterRoles
The Cluster Operator needs to operate using ClusterRoles
that gives access to the necessary resources.
Depending on the Kubernetes cluster setup, a cluster administrator might be needed to create the ClusterRoles
.
Note
|
Cluster administrator rights are only needed for the creation of the ClusterRoles .
The Cluster Operator will not run under the cluster admin account.
|
The ClusterRoles
follow the principle of least privilege and contain only those privileges needed by the Cluster Operator to operate Kafka, Kafka Connect, and ZooKeeper clusters. The first set of assigned privileges allow the Cluster Operator to manage Kubernetes resources such as StatefulSets
, Deployments
, Pods
, and ConfigMaps
.
Cluster Operator uses ClusterRoles to grant permission at the namespace-scoped resources level and cluster-scoped resources level:
ClusterRole
with namespaced resources for the Cluster OperatorapiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: strimzi-cluster-operator-namespaced
labels:
app: strimzi
rules:
- apiGroups:
- ""
resources:
- serviceaccounts
verbs:
- get
- create
- delete
- patch
- update
- apiGroups:
- rbac.authorization.k8s.io
resources:
- rolebindings
verbs:
- get
- create
- delete
- patch
- update
- apiGroups:
- ""
resources:
- configmaps
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
- apiGroups:
- kafka.strimzi.io
resources:
- kafkas
- kafkas/status
- kafkaconnects
- kafkaconnects/status
- kafkaconnects2is
- kafkaconnects2is/status
- kafkamirrormakers
- kafkamirrormakers/status
- kafkabridges
- kafkabridges/status
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
- apiGroups:
- ""
resources:
- pods
verbs:
- get
- list
- watch
- delete
- apiGroups:
- ""
resources:
- services
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
- apiGroups:
- ""
resources:
- endpoints
verbs:
- get
- list
- watch
- apiGroups:
- extensions
resources:
- deployments
- deployments/scale
- replicasets
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
- apiGroups:
- apps
resources:
- deployments
- deployments/scale
- deployments/status
- statefulsets
- replicasets
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
- apiGroups:
- ""
resources:
- events
verbs:
- create
- apiGroups:
- extensions
resources:
- replicationcontrollers
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
- apiGroups:
- apps.openshift.io
resources:
- deploymentconfigs
- deploymentconfigs/scale
- deploymentconfigs/status
- deploymentconfigs/finalizers
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
- apiGroups:
- build.openshift.io
resources:
- buildconfigs
- builds
verbs:
- create
- delete
- get
- list
- patch
- watch
- update
- apiGroups:
- image.openshift.io
resources:
- imagestreams
- imagestreams/status
verbs:
- create
- delete
- get
- list
- watch
- patch
- update
- apiGroups:
- ""
resources:
- replicationcontrollers
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
- apiGroups:
- ""
resources:
- secrets
verbs:
- get
- list
- create
- delete
- patch
- update
- apiGroups:
- extensions
resources:
- networkpolicies
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
- apiGroups:
- networking.k8s.io
resources:
- networkpolicies
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
- apiGroups:
- route.openshift.io
resources:
- routes
- routes/custom-host
verbs:
- get
- list
- create
- delete
- patch
- update
- apiGroups:
- ""
resources:
- persistentvolumeclaims
verbs:
- get
- list
- create
- delete
- patch
- update
- apiGroups:
- policy
resources:
- poddisruptionbudgets
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
- apiGroups:
- extensions
resources:
- ingresses
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
The second includes the permissions needed for cluster-scoped resources.
ClusterRole
with cluster-scoped resources for the Cluster OperatorapiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: strimzi-cluster-operator-global
labels:
app: strimzi
rules:
- apiGroups:
- rbac.authorization.k8s.io
resources:
- clusterrolebindings
verbs:
- get
- create
- delete
- patch
- update
- watch
- apiGroups:
- storage.k8s.io
resources:
- storageclasses
verbs:
- get
The strimzi-kafka-broker
ClusterRole
represents the access needed by the init container in Kafka pods that is used for the rack feature. As described in the Delegated privileges section, this role is also needed by the Cluster Operator in order to be able to delegate this access.
ClusterRole
for the Cluster Operator allowing it to delegate access to Kubernetes nodes to the Kafka broker podsapiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: strimzi-kafka-broker
labels:
app: strimzi
rules:
- apiGroups:
- ""
resources:
- nodes
verbs:
- get
The strimzi-topic-operator
ClusterRole
represents the access needed by the Topic Operator. As described in the Delegated privileges section, this role is also needed by the Cluster Operator in order to be able to delegate this access.
ClusterRole
for the Cluster Operator allowing it to delegate access to events to the Topic OperatorapiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: strimzi-entity-operator
labels:
app: strimzi
rules:
- apiGroups:
- kafka.strimzi.io
resources:
- kafkatopics
- kafkatopics/status
verbs:
- get
- list
- watch
- create
- patch
- update
- delete
- apiGroups:
- ""
resources:
- events
verbs:
- create
- apiGroups:
- kafka.strimzi.io
resources:
- kafkausers
- kafkausers/status
verbs:
- get
- list
- watch
- create
- patch
- update
- delete
- apiGroups:
- ""
resources:
- secrets
verbs:
- get
- list
- create
- patch
- update
- delete
ClusterRoleBindings
The operator needs ClusterRoleBindings
and RoleBindings
which associates its ClusterRole
with its ServiceAccount
:
ClusterRoleBindings
are needed for ClusterRoles
containing cluster-scoped resources.
ClusterRoleBinding
for the Cluster OperatorapiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
name: strimzi-cluster-operator
labels:
app: strimzi
subjects:
- kind: ServiceAccount
name: strimzi-cluster-operator
namespace: myproject
roleRef:
kind: ClusterRole
name: strimzi-cluster-operator-global
apiGroup: rbac.authorization.k8s.io
ClusterRoleBindings
are also needed for the ClusterRoles
needed for delegation:
RoleBinding
for the Cluster OperatorapiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
name: strimzi-cluster-operator-kafka-broker-delegation
labels:
app: strimzi
subjects:
- kind: ServiceAccount
name: strimzi-cluster-operator
namespace: myproject
roleRef:
kind: ClusterRole
name: strimzi-kafka-broker
apiGroup: rbac.authorization.k8s.io
ClusterRoles
containing only namespaced resources are bound using RoleBindings
only.
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
name: strimzi-cluster-operator
labels:
app: strimzi
subjects:
- kind: ServiceAccount
name: strimzi-cluster-operator
namespace: myproject
roleRef:
kind: ClusterRole
name: strimzi-cluster-operator-namespaced
apiGroup: rbac.authorization.k8s.io
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
name: strimzi-cluster-operator-entity-operator-delegation
labels:
app: strimzi
subjects:
- kind: ServiceAccount
name: strimzi-cluster-operator
namespace: myproject
roleRef:
kind: ClusterRole
name: strimzi-entity-operator
apiGroup: rbac.authorization.k8s.io
4.2. Topic Operator
4.2.1. Overview of the Topic Operator component
The Topic Operator provides a way of managing topics in a Kafka cluster via Kubernetes resources.
The role of the Topic Operator is to keep a set of KafkaTopic
Kubernetes resources describing Kafka topics in-sync with corresponding Kafka topics.
Specifically, if a KafkaTopic
is:
-
Created, the operator will create the topic it describes
-
Deleted, the operator will delete the topic it describes
-
Changed, the operator will update the topic it describes
And also, in the other direction, if a topic is:
-
Created within the Kafka cluster, the operator will create a
KafkaTopic
describing it -
Deleted from the Kafka cluster, the operator will delete the
KafkaTopic
describing it -
Changed in the Kafka cluster, the operator will update the
KafkaTopic
describing it
This allows you to declare a KafkaTopic
as part of your application’s deployment and the Topic Operator will take care of creating the topic for you.
Your application just needs to deal with producing or consuming from the necessary topics.
If the topic is reconfigured or reassigned to different Kafka nodes, the KafkaTopic
will always be up to date.
For more details about creating, modifying and deleting topics, see Using the Topic Operator.
4.2.2. Identifying a Kafka cluster for topic handling
A KafkaTopic
resource includes a label that defines the appropriate name of the Kafka cluster (derived from the name of the Kafka
resource) to which it belongs.
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaTopic
metadata:
name: my-topic
labels:
strimzi.io/cluster: my-cluster
The label is used by the Topic Operator to identify the KafkaTopic
resource and create a new topic, and also in subsequent handling of the topic.
If the label does not match the Kafka cluster, the Topic Operator cannot identify the KafkaTopic
and the topic is not created.
4.2.3. Understanding the Topic Operator
A fundamental problem that the operator has to solve is that there is no single source of truth:
Both the KafkaTopic
resource and the topic within Kafka can be modified independently of the operator.
Complicating this, the Topic Operator might not always be able to observe changes at each end in real time (for example, the operator might be down).
To resolve this, the operator maintains its own private copy of the information about each topic. When a change happens either in the Kafka cluster, or in Kubernetes, it looks at both the state of the other system and at its private copy in order to determine what needs to change to keep everything in sync. The same thing happens whenever the operator starts, and periodically while it is running.
For example, suppose the Topic Operator is not running, and a KafkaTopic
my-topic
gets created.
When the operator starts it will lack a private copy of "my-topic", so it can infer that the KafkaTopic
has been created since it was last running.
The operator will create the topic corresponding to "my-topic" and also store a private copy of the metadata for "my-topic".
The private copy allows the operator to cope with scenarios where the topic configuration gets changed both in Kafka and in Kubernetes, so long as the changes are not incompatible (for example, both changing the same topic config key, but to different values).
In the case of incompatible changes, the Kafka configuration wins, and the KafkaTopic
will be updated to reflect that.
The private copy is held in the same ZooKeeper ensemble used by Kafka itself. This mitigates availability concerns, because if ZooKeeper is not running then Kafka itself cannot run, so the operator will be no less available than it would even if it was stateless.
4.2.4. Deploying the Topic Operator using the Cluster Operator
This procedure describes how to deploy the Topic Operator using the Cluster Operator. If you want to use the Topic Operator with a Kafka cluster that is not managed by Strimzi, you must deploy the Topic Operator as a standalone component. For more information, see Deploying the standalone Topic Operator.
-
A running Cluster Operator
-
A
Kafka
resource to be created or updated
-
Ensure that the
Kafka.spec.entityOperator
object exists in theKafka
resource. This configures the Entity Operator.apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: #... entityOperator: topicOperator: {} userOperator: {}
-
Configure the Topic Operator using the fields described in
EntityTopicOperatorSpec
schema reference. -
Create or update the Kafka resource in Kubernetes.
Use
kubectl apply
:kubectl 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.
4.2.5. Configuring the Topic Operator with resource requests and limits
You can allocate resources, such as CPU and memory, to the Topic Operator and set a limit on the amount of resources it can consume.
-
The Cluster Operator is running.
-
Update the Kafka cluster configuration in an editor, as required:
Use
kubectl edit
:kubectl edit kafka my-cluster
-
In the
spec.entityOperator.topicOperator.resources
property in theKafka
resource, set the resource requests and limits for the Topic Operator.apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: # kafka and zookeeper sections... entityOperator: topicOperator: resources: request: cpu: "1" memory: 500Mi limit: cpu: "1" memory: 500Mi
-
Apply the new configuration to create or update the resource.
Use
kubectl apply
:kubectl apply -f kafka.yaml
-
For more information about the schema of the
resources
object, seeResourceRequirements
schema reference.
4.2.6. Deploying the standalone Topic Operator
Deploying the Topic Operator as a standalone component is more complicated than installing it using the Cluster Operator, but it is more flexible. For instance, it can operate with any Kafka cluster, not necessarily one deployed by the Cluster Operator.
-
An existing Kafka cluster for the Topic Operator to connect to.
-
Edit the
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 ofhostname: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 ofhostname: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 Kubernetes namespace in which you want the operator to watch forKafkaTopic
resources.
-
-
Deploy the Topic Operator.
This can be done using
kubectl apply
:kubectl apply -f install/topic-operator
-
Verify that the Topic Operator has been deployed successfully. This can be done using
kubectl describe
:kubectl describe deployment strimzi-topic-operator
The Topic Operator is deployed once the
Replicas:
entry shows1 available
.NoteThis could take some time if you have a slow connection to the 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.
4.2.7. Topic Operator environment
When deployed standalone the Topic Operator can be configured using environment variables.
Note
|
The Topic Operator should be configured using the Kafka.spec.entityOperator.topicOperator property when deployed by the Cluster Operator.
|
STRIMZI_RESOURCE_LABELS
-
The label selector used to identify
KafkaTopics
to be managed by the operator. STRIMZI_ZOOKEEPER_SESSION_TIMEOUT_MS
-
The ZooKeeper session timeout, in milliseconds. For example,
10000
. Default20000
(20 seconds). STRIMZI_KAFKA_BOOTSTRAP_SERVERS
-
The list of Kafka bootstrap servers. This variable is mandatory.
STRIMZI_ZOOKEEPER_CONNECT
-
The ZooKeeper connection information. This variable is mandatory.
STRIMZI_FULL_RECONCILIATION_INTERVAL_MS
-
The interval between periodic reconciliations, in milliseconds.
STRIMZI_TOPIC_METADATA_MAX_ATTEMPTS
-
The number of attempts at getting topic metadata from Kafka. The time between each attempt is defined as an exponential back-off. Consider increasing this value when topic creation could take more time due to the number of partitions or replicas. Default
6
. STRIMZI_LOG_LEVEL
-
The level for printing logging messages. The value can be set to:
ERROR
,WARNING
,INFO
,DEBUG
, 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 manages Kafka users through custom resources.
4.3.1. Overview of the User Operator component
The User Operator manages Kafka users for a Kafka cluster by watching for KafkaUser
resources that describe Kafka users and ensuring that they are configured properly in the Kafka cluster.
For example:
-
if a
KafkaUser
is created, the User Operator will create the user it describes -
if a
KafkaUser
is deleted, the User Operator will delete the user it describes -
if a
KafkaUser
is changed, the User Operator will update the user it describes
Unlike the Topic Operator, the User Operator does not sync any changes from the Kafka cluster with the Kubernetes resources. Unlike the Kafka topics which might be created by applications directly in Kafka, it is not expected that the users will be managed directly in the Kafka cluster in parallel with the User Operator, so this should not be needed.
The User Operator allows you to declare a KafkaUser
as part of your application’s deployment.
When the user is created, the user credentials will be created in a Secret
.
Your application needs to use the user and its credentials for authentication and to produce or consume messages.
In addition to managing credentials for authentication, the User Operator also manages authorization rules by including a description of the user’s rights in the KafkaUser
declaration.
4.3.2. Identifying a Kafka cluster for user handling
A KafkaUser
resource includes a label that defines the appropriate name of the Kafka cluster (derived from the name of the Kafka
resource) to which it belongs.
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaUser
metadata:
name: my-user
labels:
strimzi.io/cluster: my-cluster
The label is used by the User Operator to identify the KafkaUser
resource and create a new user, and also in subsequent handling of the user.
If the label does not match the Kafka cluster, the User Operator cannot identify the kafkaUser
and the user is not created.
4.3.3. Deploying the User Operator using the Cluster Operator
-
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 Kubernetes.
This can be done using
kubectl apply
:kubectl 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.
4.3.4. Configuring the User Operator with resource requests and limits
You can allocate resources, such as CPU and memory, to the User Operator and set a limit on the amount of resources it can consume.
-
The Cluster Operator is running.
-
Update the Kafka cluster configuration in an editor, as required:
kubectl edit kafka my-cluster
-
In the
spec.entityOperator.userOperator.resources
property in theKafka
resource, set the resource requests and limits for the User Operator.apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: # kafka and zookeeper sections... entityOperator: userOperator: resources: request: cpu: "1" memory: 500Mi limit: cpu: "1" memory: 500Mi
Save the file and exit the editor. The Cluster Operator will apply the changes automatically.
-
For more information about the schema of the
resources
object, seeResourceRequirements
schema reference.
4.3.5. Deploying the standalone User Operator
Deploying the User Operator as a standalone component is more complicated than installing it using the Cluster Operator, but it is more flexible. For instance, it can operate with any Kafka cluster, not only the one deployed by the Cluster Operator.
-
An existing Kafka cluster for the User Operator to connect to.
-
Edit the
install/user-operator/05-Deployment-strimzi-user-operator.yaml
resource. You will need to change the following-
The
STRIMZI_CA_CERT_NAME
environment variable inDeployment.spec.template.spec.containers[0].env
should be set to point to a KubernetesSecret
which should contain the public key of the Certificate Authority for signing new user certificates for TLS Client Authentication. TheSecret
should contain the public key of the Certificate Authority under the keyca.crt
. -
The
STRIMZI_CA_KEY_NAME
environment variable inDeployment.spec.template.spec.containers[0].env
should be set to point to a KubernetesSecret
which should contain the private key of the Certificate Authority for signing new user certificates for TLS Client Authentication. TheSecret
should contain the private key of the Certificate Authority under the keyca.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 ofhostname: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 Kubernetes namespace in which you want the operator to watch forKafkaUser
resources.
-
-
Deploy the User Operator.
This can be done using
kubectl apply
:kubectl apply -f install/user-operator
-
Verify that the User Operator has been deployed successfully. This can be done using
kubectl describe
:kubectl describe deployment strimzi-user-operator
The User Operator is deployed once the
Replicas:
entry shows1 available
.NoteThis could take some time if you have a slow connection to the 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
When working with topics, be consistent and always operate on either KafkaTopic
resources or topics directly. Avoid routinely switching between both methods for a given topic.
Use topic names that reflect the nature of the topic, and remember that names cannot be changed later.
If creating a topic in Kafka, use a name that is a valid Kubernetes resource name, otherwise the Topic Operator will need to create the corresponding KafkaTopic
with a name that conforms to the Kubernetes rules.
Note
|
Recommendations for identifiers and names in Kubernetes are outlined in Identifiers and Names in Kubernetes community article. |
Kafka and Kubernetes impose their own validation rules for the naming of topics in Kafka and KafkaTopic.metadata.name
respectively.
There are valid names for each which are invalid in the other.
Using the spec.topicName property
, it is possible to create a valid topic in Kafka with a name that would be invalid for the KafkaTopic in Kubernetes.
The spec.topicName
property inherits Kafka naming validation rules:
-
The name must not be longer than 249 characters.
-
Valid characters for Kafka topics are ASCII alphanumerics,
.
,_
, and-
. -
The name cannot be
.
or..
, though.
can be used in a name, such asexampleTopic.
or.exampleTopic
.
spec.topicName
must not be changed.
For example:
kind: KafkaTopic
metadata:
name: topic-name-1
spec:
topicName: topicName-1 # Upper case is invalid in Kubernetes
# ...
cannot be changed to
kind: KafkaTopic
metadata:
name: topic-name-1
spec:
topicName: name-2
# ...
Note
|
Some Kafka client applications, such as Kafka Streams, can create topics in Kafka programmatically. If those topics have names that are invalid Kubernetes resource names, the Topic Operator gives them valid names based on the Kafka names. Invalid characters are replaced and a hash is appended to the name. |
5.2. Creating a topic
This procedure describes how to create a Kafka topic using a KafkaTopic
Kubernetes resource.
-
A running Kafka cluster.
-
A running Topic Operator (typically deployed with the Entity Operator).
-
Prepare a file containing the
KafkaTopic
to be createdAn exampleKafkaTopic
apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaTopic metadata: name: orders labels: strimzi.io/cluster: my-cluster spec: partitions: 10 replicas: 2
NoteIt is recommended that the topic name given is a valid Kubernetes resource name, as it is then not necessary to set the KafkaTopic.spec.topicName
property. TheKafkaTopic.spec.topicName
cannot be changed after creation.NoteThe KafkaTopic.spec.partitions
cannot be decreased. -
Create the
KafkaTopic
resource in Kubernetes.This can be done using
kubectl apply
:kubectl 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 standalone Topic Operator, see Deploying the standalone Topic Operator.
5.3. Changing a topic
This procedure describes how to change the configuration of an existing Kafka topic by using a KafkaTopic
Kubernetes resource.
-
A running Kafka cluster.
-
A running Topic Operator (typically deployed with the Entity Operator).
-
An existing
KafkaTopic
to be changed.
-
Prepare a file containing the desired
KafkaTopic
An exampleKafkaTopic
apiVersion: kafka.strimzi.io/v1beta1 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 kubectl 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 Kubernetes.This can be done using
kubectl apply
:kubectl 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
Kubernetes resource.
-
A running Kafka cluster.
-
A running Topic Operator (typically deployed with the Entity Operator).
-
An existing
KafkaTopic
to be deleted. -
delete.topic.enable=true
(default)
Note
|
The delete.topic.enable property must be set to true in Kafka.spec.kafka.config . Otherwise, the steps outlined here will delete the KafkaTopic resource, but the Kafka topic and its data will remain. After reconciliation by the Topic Operator, the custom resource is then recreated.
|
-
Delete the
KafkaTopic
resource in Kubernetes.This can be done using
kubectl delete
:kubectl delete kafkatopic your-topic-name
-
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 Kubernetes resources.
6.1. Overview of the User Operator component
The User Operator manages Kafka users for a Kafka cluster by watching for KafkaUser
resources that describe Kafka users and ensuring that they are configured properly in the Kafka cluster.
For example:
-
if a
KafkaUser
is created, the User Operator will create the user it describes -
if a
KafkaUser
is deleted, the User Operator will delete the user it describes -
if a
KafkaUser
is changed, the User Operator will update the user it describes
Unlike the Topic Operator, the User Operator does not sync any changes from the Kafka cluster with the Kubernetes resources. Unlike the Kafka topics which might be created by applications directly in Kafka, it is not expected that the users will be managed directly in the Kafka cluster in parallel with the User Operator, so this should not be needed.
The User Operator allows you to declare a KafkaUser
as part of your application’s deployment.
When the user is created, the user credentials will be created in a Secret
.
Your application needs to use the user and its credentials for authentication and to produce or consume messages.
In addition to managing credentials for authentication, the User Operator also manages authorization rules by including a description of the user’s rights in the KafkaUser
declaration.
6.2. Mutual TLS authentication
Mutual TLS authentication is always used for the communication between Kafka brokers and ZooKeeper pods.
Mutual authentication or two-way authentication is when both the server and the client present certificates. Strimzi can configure Kafka to use TLS (Transport Layer Security) to provide encrypted communication between Kafka brokers and clients either with or without mutual authentication. When you configure mutual authentication, the broker authenticates the client and the client authenticates the broker.
Note
|
TLS authentication is more commonly one-way, with one party authenticating the identity of another. For example, when HTTPS is used between a web browser and a web server, the server obtains proof of the identity of the browser. |
6.2.1. When to use mutual TLS authentication for clients
Mutual TLS authentication is recommended for authenticating Kafka clients when:
-
The client supports authentication using mutual TLS authentication
-
It is necessary to use the TLS certificates rather than passwords
-
You can reconfigure and restart client applications periodically so that they do not use expired certificates.
6.3. Creating a Kafka user with mutual TLS authentication
-
A running Kafka cluster configured with a listener using TLS authentication.
-
A running User Operator (typically deployed with the Entity Operator).
-
Prepare a YAML file containing the
KafkaUser
to be created.An exampleKafkaUser
apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaUser metadata: name: my-user labels: strimzi.io/cluster: my-cluster spec: authentication: type: tls authorization: type: simple acls: - resource: type: topic name: my-topic patternType: literal operation: Read - resource: type: topic name: my-topic patternType: literal operation: Describe - resource: type: group name: my-group patternType: literal operation: Read
-
Create the
KafkaUser
resource in Kubernetes.This can be done using
kubectl apply
:kubectl 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 configuring a listener that authenticates using TLS see Kafka broker listeners.
-
For more information about deploying the Entity Operator, see Entity Operator.
-
For more information about the
KafkaUser
object, seeKafkaUser
schema reference.
6.4. SCRAM-SHA authentication
SCRAM (Salted Challenge Response Authentication Mechanism) is an authentication protocol that can establish mutual authentication using passwords. Strimzi can configure Kafka to use SASL (Simple Authentication and Security Layer) SCRAM-SHA-512 to provide authentication on both unencrypted and TLS-encrypted client connections. TLS authentication is always used internally between Kafka brokers and ZooKeeper nodes. When used with a TLS client connection, the TLS protocol provides encryption, but is not used for authentication.
The following properties of SCRAM make it safe to use SCRAM-SHA even on unencrypted connections:
-
The passwords are not sent in the clear over the communication channel. Instead the client and the server are each challenged by the other to offer proof that they know the password of the authenticating user.
-
The server and client each generate a new challenge for each authentication exchange. This means that the exchange is resilient against replay attacks.
6.4.1. Supported SCRAM credentials
Strimzi supports SCRAM-SHA-512 only.
When a KafkaUser.spec.authentication.type
is configured with scram-sha-512
the User Operator will generate a random 12 character password consisting of upper and lowercase ASCII letters and numbers.
6.4.2. When to use SCRAM-SHA authentication for clients
SCRAM-SHA is recommended for authenticating Kafka clients when:
-
The client supports authentication using SCRAM-SHA-512
-
It is necessary to use passwords rather than the TLS certificates
-
Authentication for unencrypted communication is required
6.5. Creating a Kafka user with SCRAM SHA authentication
-
A running Kafka cluster configured with a listener using SCRAM SHA authentication.
-
A running User Operator (typically deployed with the Entity Operator).
-
Prepare a YAML file containing the
KafkaUser
to be created.An exampleKafkaUser
apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaUser metadata: name: my-user labels: strimzi.io/cluster: my-cluster spec: authentication: type: scram-sha-512 authorization: type: simple acls: - resource: type: topic name: my-topic patternType: literal operation: Read - resource: type: topic name: my-topic patternType: literal operation: Describe - resource: type: group name: my-group patternType: literal operation: Read
-
Create the
KafkaUser
resource in Kubernetes.This can be done using
kubectl apply
:kubectl 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 configuring a listener that authenticates using SCRAM SHA see Kafka broker listeners.
-
For more information about deploying the Entity Operator, see Entity Operator.
-
For more information about the
KafkaUser
object, seeKafkaUser
schema reference.
6.6. Editing a Kafka user
This procedure describes how to change the configuration of an existing Kafka user by using a KafkaUser
Kubernetes resource.
-
A running Kafka cluster.
-
A running User Operator (typically deployed with the Entity Operator).
-
An existing
KafkaUser
to be changed.
-
Prepare a YAML file containing the desired
KafkaUser
.An exampleKafkaUser
apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaUser metadata: name: my-user labels: strimzi.io/cluster: my-cluster spec: authentication: type: tls authorization: type: simple acls: - resource: type: topic name: my-topic patternType: literal operation: Read - resource: type: topic name: my-topic patternType: literal operation: Describe - resource: type: group name: my-group patternType: literal operation: Read
-
Update the
KafkaUser
resource in Kubernetes.This can be done using
kubectl apply
:kubectl apply -f your-file
-
Use the updated credentials from the
my-user
secret in your application.
-
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
KafkaUser
object, seeKafkaUser
schema reference.
6.7. Deleting a Kafka user
This procedure describes how to delete a Kafka user created with KafkaUser
Kubernetes resource.
-
A running Kafka cluster.
-
A running User Operator (typically deployed with the Entity Operator).
-
An existing
KafkaUser
to be deleted.
-
Delete the
KafkaUser
resource in Kubernetes.This can be done using
kubectl delete
:kubectl delete kafkauser your-user-name
-
For more information about deploying the Cluster Operator, see Cluster Operator.
-
For more information about the
KafkaUser
object, seeKafkaUser
schema reference.
6.8. Kafka User resource
The KafkaUser
resource is used to declare a user with its authentication mechanism, authorization mechanism, and access rights.
6.8.1. Authentication
Authentication is configured using the authentication
property in KafkaUser.spec
.
The authentication mechanism enabled for this user will be specified using the type
field.
Currently, the only supported authentication mechanisms are the TLS Client Authentication mechanism and the SCRAM-SHA-512 mechanism.
When no authentication mechanism is specified, User Operator will not create the user or its credentials.
TLS Client Authentication
To use TLS client authentication, set the type
field to tls
.
KafkaUser
with enabled TLS Client AuthenticationapiVersion: kafka.strimzi.io/v1beta1
kind: KafkaUser
metadata:
name: my-user
labels:
strimzi.io/cluster: my-cluster
spec:
authentication:
type: tls
# ...
When the user is created by the User Operator, it will create a new secret with the same name as the KafkaUser
resource.
The secret will contain a public and private key which should be used for the TLS Client Authentication.
Bundled with them will be the public key of the client certification authority which was used to sign the user certificate.
All keys will be in X509 format.
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
SCRAM-SHA-512 Authentication
To use SCRAM-SHA-512 authentication mechanism, set the type
field to scram-sha-512
.
KafkaUser
with enabled SCRAM-SHA-512 authenticationapiVersion: kafka.strimzi.io/v1beta1
kind: KafkaUser
metadata:
name: my-user
labels:
strimzi.io/cluster: my-cluster
spec:
authentication:
type: scram-sha-512
# ...
When the user is created by the User Operator, the User Operator will create a new secret with the same name as the KafkaUser
resource.
The secret contains the generated password in the password
key, which is encoded with base64. In order to use the password it must be decoded.
Secret
with user credentialsapiVersion: v1
kind: Secret
metadata:
name: my-user
labels:
strimzi.io/kind: KafkaUser
strimzi.io/cluster: my-cluster
type: Opaque
data:
password: Z2VuZXJhdGVkcGFzc3dvcmQ= # Generated password
For decode the generated password:
echo "Z2VuZXJhdGVkcGFzc3dvcmQ=" | base64 --decode
6.8.2. Authorization
Authorization is configured using the authorization
property in KafkaUser.spec
.
The authorization type enabled for a user is specified using the type
field.
Currently, the only supported authorization type is simple authorization.
If no authorization is specified, the User Operator does not provision any access rights for the user.
Simple authorization
Simple authorization uses the default Kafka authorization plugin, SimpleAclAuthorizer
.
To use simple authorization, set the type
property to simple
in KafkaUser.spec
.
SimpleAclAuthorizer
uses ACL rules to manage access to Kafka brokers.
ACL rules grant access rights to the user, which you specify in the acls
property.
An AclRule
is specified as a set of properties:
resource
-
The
resource
property specifies the resource that the rule applies to.Simple authorization supports four resource types, which are specified in the
type
property:-
Topics (
topic
) -
Consumer Groups (
group
) -
Clusters (
cluster
) -
Transactional IDs (
transactionalId
)
For Topic, Group, and Transactional ID resources you can specify the name of the resource the rule applies to in the
name
property.Cluster type resources have no name.
A name is specified as a
literal
or aprefix
using thepatternType
property.-
Literal names are taken exactly as they are specified in the
name
field. -
Prefix names use the value from the
name
as a prefix, and will apply the rule to all resources with names starting with the value.
-
type
-
The
type
property specifies the type of ACL rule,allow
ordeny
.The
type
field is optional. Iftype
is unspecified, the ACL rule is treated as anallow
rule. operation
-
The
operation
specifies the operation to allow or deny.The following operations are supported:
-
Read
-
Write
-
Delete
-
Alter
-
Describe
-
All
-
IdempotentWrite
-
ClusterAction
-
Create
-
AlterConfigs
-
DescribeConfigs
Only certain operations work with each resource.
For more details about
SimpleAclAuthorizer
, ACLs and supported combinations of resources and operations, see Authorization and ACLs. -
host
-
The
host
property specifies a remote host from which the rule is allowed or denied.Use an asterisk (
*
) to allow or deny the operation from all hosts. Thehost
field is optional. Ifhost
is unspecified, the*
value is used by default.
For more information about the AclRule
object, see AclRule
schema reference.
KafkaUser
apiVersion: kafka.strimzi.io/v1beta1
kind: KafkaUser
metadata:
name: my-user
labels:
strimzi.io/cluster: my-cluster
spec:
# ...
authorization:
type: simple
acls:
- resource:
type: topic
name: my-topic
patternType: literal
operation: Read
- resource:
type: topic
name: my-topic
patternType: literal
operation: Describe
- resource:
type: group
name: my-group
patternType: prefix
operation: Read
Super user access to Kafka brokers
If a user is added to a list of super users in a Kafka broker configuration, the user is allowed unlimited access to the cluster regardless of any authorization constraints defined in ACLs.
For more information on configuring super users, see authentication and authorization of Kafka brokers.
6.8.3. Additional resources
-
For more information about the
KafkaUser
object, seeKafkaUser
schema reference. -
For more information about the TLS Client Authentication, see Mutual TLS authentication.
-
For more information about the SASL SCRAM-SHA-512 authentication, see SCRAM-SHA authentication.
7. Kafka Bridge
This chapter provides an overview of the Strimzi Kafka Bridge and helps you get started using its REST API to interact with Strimzi. To try out the Kafka Bridge in your local environment, see the Kafka Bridge quickstart later in this chapter.
-
For information on how to deploy the Kafka Bridge, see Kafka Bridge in the Getting started with Strimzi chapter.
-
For detailed information on configuring the Kafka Bridge, see Kafka Bridge configuration in the Deployment configuration chapter
-
To view the API documentation, including example requests and responses, see the Kafka Bridge API reference on the Strimzi website.
7.1. Kafka Bridge overview
The Strimzi Kafka Bridge provides an API for integrating HTTP-based clients with a Kafka cluster running on Kubernetes. The API enables such clients to produce and consume messages without the requirement to use the native Kafka protocol.
The API has two main resources — consumers
and topics
— that are exposed and made accessible through endpoints to interact with consumers and producers in your Kafka cluster. The resources relate only to the Kafka Bridge, not the consumers and producers connected directly to Kafka.
You can:
-
Send messages to a topic.
-
Create and delete consumers.
-
Subscribe consumers to topics, so that they start receiving messages from those topics.
-
Unsubscribe consumers from topics.
-
Assign partitions to consumers.
-
Retrieve messages from topics.
-
Commit a list of consumer offsets.
-
Seek on a partition, so that a consumer starts receiving messages from the first or last offset position, or a given offset position.
You deploy the Kafka Bridge into your Kubernetes cluster by using the Cluster Operator. For deployment instructions, see Deploying Kafka Bridge to your Kubernetes cluster.
After the Kafka Bridge is deployed, the Cluster Operator creates a Deployment, Service, and Pod in your Kubernetes cluster, each named strimzi-kafka-bridge
by default.
7.1.1. Supported clients for the Kafka Bridge
You can use the Kafka Bridge to integrate both internal and external HTTP client applications with your Kafka cluster.
-
Internal clients are container-based HTTP clients running in the same Kubernetes cluster as the Kafka Bridge itself.
-
External clients are HTTP clients running outside the Kubernetes cluster in which the Kafka Bridge is deployed and running.
Internal clients can access the Kafka Bridge on the host and port defined in the KafkaBridge
custom resource.
External clients can access the Kafka Bridge through an OpenShift Route, a loadbalancer service, or using an Ingress.
-
For more information on configuring the host and port for the
KafkaBridge
resource, see Kafka Bridge HTTP configuration. -
For more information on integrating external clients, see Accessing the Kafka Bridge outside of Kubernetes.
7.1.2. Securing the Kafka Bridge
Strimzi does not currently provide any encryption, authentication, or authorization for the Kafka Bridge. This means that requests sent from external clients to the Kafka Bridge are:
-
Not encrypted, and must use HTTP rather than HTTPS
-
Sent without authentication
However, you can secure the Kafka Bridge using other methods, such as:
-
Kubernetes Network Policies that define which pods can access the Kafka Bridge.
-
Reverse proxies with authentication or authorization, for example, OAuth2 proxies.
-
API Gateways.
-
Ingress or OpenShift Routes with TLS termination.
The Kafka Bridge supports TLS encryption and TLS and SASL authentication when connecting to the Kafka Brokers. Within your Kubernetes cluster, you can configure:
-
TLS or SASL-based authentication between the Kafka Bridge and your Kafka cluster
-
A TLS-encrypted connection between the Kafka Bridge and your Kafka cluster.
For more information, see Authentication support in Kafka Bridge.
You can use ACLs in Kafka brokers to restrict the topics that can be consumed and produced using the Kafka Bridge.
7.1.3. Accessing the Kafka Bridge outside of Kubernetes
After deployment, the Strimzi Kafka Bridge can only be accessed by applications running in the same Kubernetes cluster. These applications use the kafka-bridge-name-bridge-service
Service to access the API.
If you want to make the Kafka Bridge accessible to applications running outside of the Kubernetes cluster, you can expose it manually by using one of the following features:
-
Services of types LoadBalancer or NodePort
-
Ingress resources
-
OpenShift Routes
If you decide to create Services, use the following labels in the selector
to configure the pods to which the service will route the traffic:
# ...
selector:
strimzi.io/cluster: kafka-bridge-name (1)
strimzi.io/kind: KafkaBridge
#...
-
Name of the Kafka Bridge custom resource in your Kubernetes cluster.
7.1.4. Requests to the Kafka Bridge
Specify data formats and HTTP headers to ensure valid requests are submitted to the Kafka Bridge.
Content Type headers
API request and response bodies are always encoded as JSON.
-
When performing consumer operations,
POST
requests must provide the followingContent-Type
header if there is a non-empty body:Content-Type: application/vnd.kafka.v2+json
-
When performing producer operations,
POST
requests must provideContent-Type
headers specifying the desired embedded data format, eitherjson
orbinary
, as shown in the following table.Embedded data format Content-Type header JSON
Content-Type: application/vnd.kafka.json.v2+json
Binary
Content-Type: application/vnd.kafka.binary.v2+json
You set the embedded data format when creating a consumer using the consumers/groupid
endpoint—for more information, see the next section.
The Content-Type
must not be set if the POST
request has an empty body.
An empty body can be used to create a consumer with the default values.
Embedded data format
The embedded data format is the format of the Kafka messages that are transmitted, over HTTP, from a producer to a consumer using the Kafka Bridge. Two embedded data formats are supported: JSON and binary.
When creating a consumer using the /consumers/groupid
endpoint, the POST
request body must specify an embedded data format of either JSON or binary. This is specified in the format
field, for example:
{
"name": "my-consumer",
"format": "binary", (1)
...
}
-
A binary embedded data format.
The embedded data format specified when creating a consumer must match the data format of the Kafka messages it will consume.
If you choose to specify a binary embedded data format, subsequent producer requests must provide the binary data in the request body as Base64-encoded strings. For example, when sending messages using the /topics/topicname
endpoint, records.value
must be encoded in Base64:
{
"records": [
{
"key": "my-key",
"value": "ZWR3YXJkdGhldGhyZWVsZWdnZWRjYXQ="
},
]
}
Producer requests must also provide a Content-Type
header that corresponds to the embedded data format, for example, Content-Type: application/vnd.kafka.binary.v2+json
.
Accept headers
After creating a consumer, all subsequent GET requests must provide an Accept
header in the following format:
Accept: application/vnd.kafka.embedded-data-format.v2+json
The embedded-data-format
is either json
or binary
.
For example, when retrieving records for a subscribed consumer using an embedded data format of JSON, include this Accept header:
Accept: application/vnd.kafka.json.v2+json
7.1.5. Configuring loggers for the Kafka Bridge
The Strimzi Kafka bridge allows you to set a different log level for each operation that is defined by the related OpenAPI specification.
Each operation has a corresponding API endpoint through which the bridge receives requests from HTTP clients. You can change the log level on each endpoint to produce more or less fine-grained logging information about the incoming and outgoing HTTP requests.
Loggers are defined in the log4j.properties
file, which has the following default configuration for healthy
and ready
endpoints:
log4j.logger.http.openapi.operation.healthy=WARN, out
log4j.additivity.http.openapi.operation.healthy=false
log4j.logger.http.openapi.operation.ready=WARN, out
log4j.additivity.http.openapi.operation.ready=false
The log level of all other operations is set to INFO
by default.
Loggers are formatted as follows:
log4j.logger.http.openapi.operation.<operation-id>
Where <operation-id>
is the identifier of the specific operation.
Following is the list of operations defined by the OpenAPI specification:
-
createConsumer
-
deleteConsumer
-
subscribe
-
unsubscribe
-
poll
-
assign
-
commit
-
send
-
sendToPartition
-
seekToBeginning
-
seekToEnd
-
seek
-
healthy
-
ready
-
openapi
7.1.6. Kafka Bridge API resources
For the full list of REST API endpoints and descriptions, including example requests and responses, see the Kafka Bridge API reference on the Strimzi website.
7.2. Kafka Bridge quickstart
Use this quickstart to try out the Strimzi Kafka Bridge in your local development environment. You will learn how to:
-
Deploy the Kafka Bridge to your Kubernetes cluster
-
Expose the Kafka Bridge service to your local machine by using port-forwarding
-
Produce messages to topics and partitions in your Kafka cluster
-
Create a Kafka Bridge consumer
-
Perform basic consumer operations, such as subscribing the consumer to topics and retrieving the messages that you produced
In this quickstart, HTTP requests are formatted as curl commands that you can copy and paste to your terminal. Access to a Kubernetes cluster is required; to run and manage a local Kubernetes cluster, use a tool such as Minikube, CodeReady Containers, or MiniShift.
Ensure you have the prerequisites and then follow the tasks in the order provided in this chapter.
In this quickstart, you will produce and consume messages in JSON format, not binary. For more information on the data formats and HTTP headers used in the example requests, see Requests to the Kafka Bridge.
-
Cluster administrator access to a local or remote Kubernetes cluster.
-
Strimzi is installed.
-
A running Kafka cluster, deployed by the Cluster Operator, in a Kubernetes namespace.
-
The Entity Operator is deployed and running as part of the Kafka cluster.
7.2.1. Deploying the Kafka Bridge to your Kubernetes cluster
Strimzi includes a YAML example that specifies the configuration of the Strimzi Kafka Bridge. Make some minimal changes to this file and then deploy an instance of the Kafka Bridge to your Kubernetes cluster.
-
Edit the
examples/kafka-bridge/kafka-bridge.yaml
file.apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaBridge metadata: name: quickstart (1) spec: replicas: 1 bootstrapServers: <cluster-name>-kafka-bootstrap:9092 (2) http: port: 8080
-
When the Kafka Bridge is deployed,
-bridge
is appended to the name of the deployment and other related resources. In this example, the Kafka Bridge deployment is namedquickstart-bridge
and the accompanying Kafka Bridge service is namedquickstart-bridge-service
. -
In the
bootstrapServers
property, enter the name of the Kafka cluster as the<cluster-name>
.
-
-
Deploy the Kafka Bridge to your Kubernetes cluster:
kubectl apply -f examples/kafka-bridge/kafka-bridge.yaml
A
quickstart-bridge
deployment, service, and other related resources are created in your Kubernetes cluster. -
Verify that the Kafka Bridge was successfully deployed:
kubectl get deployments
NAME READY UP-TO-DATE AVAILABLE AGE quickstart-bridge 1/1 1 1 34m my-cluster-connect 1/1 1 1 24h my-cluster-entity-operator 1/1 1 1 24h #...
After deploying the Kafka Bridge to your Kubernetes cluster, expose the Kafka Bridge service to your local machine.
-
For more detailed information about configuring the Kafka Bridge, see Kafka Bridge configuration.
7.2.2. Exposing the Kafka Bridge service to your local machine
Next, use port forwarding to expose the Strimzi Kafka Bridge service to your local machine on http://localhost:8080.
Note
|
Port forwarding is only suitable for development and testing purposes. |
-
List the names of the pods in your Kubernetes cluster:
kubectl get pods -o name pod/kafka-consumer # ... pod/quickstart-bridge-589d78784d-9jcnr pod/strimzi-cluster-operator-76bcf9bc76-8dnfm
-
Connect to the
quickstart-bridge
pod on port8080
:kubectl port-forward pod/quickstart-bridge-589d78784d-9jcnr 8080:8080 &
NoteIf port 8080 on your local machine is already in use, use an alternative HTTP port, such as 8008
.
API requests are now forwarded from port 8080 on your local machine to port 8080 in the Kafka Bridge pod.
7.2.3. Producing messages to topics and partitions
Next, produce messages to topics in JSON format by using the topics endpoint. You can specify destination partitions for messages in the request body, as shown here. The partitions endpoint provides an alternative method for specifying a single destination partition for all messages as a path parameter.
-
In a text editor, create a YAML definition for a Kafka topic with three partitions.
apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaTopic metadata: name: bridge-quickstart-topic labels: strimzi.io/cluster: <kafka-cluster-name> (1) spec: partitions: 3 (2) replicas: 1 config: retention.ms: 7200000 segment.bytes: 1073741824
-
The name of the Kafka cluster in which the Kafka Bridge is deployed.
-
The number of partitions for the topic.
-
-
Save the file to the
examples/topic
directory asbridge-quickstart-topic.yaml
. -
Create the topic in your Kubernetes cluster:
kubectl apply -f examples/topic/bridge-quickstart-topic.yaml
-
Using the Kafka Bridge, produce three messages to the topic you created:
curl -X POST \ http://localhost:8080/topics/bridge-quickstart-topic \ -H 'content-type: application/vnd.kafka.json.v2+json' \ -d '{ "records": [ { "key": "my-key", "value": "sales-lead-0001" }, { "value": "sales-lead-0002", "partition": 2 }, { "value": "sales-lead-0003" } ] }'
-
sales-lead-0001
is sent to a partition based on the hash of the key. -
sales-lead-0002
is sent directly to partition 2. -
sales-lead-0003
is sent to a partition in thebridge-quickstart-topic
topic using a round-robin method.
-
-
If the request is successful, the Kafka Bridge returns an
offsets
array, along with a200
code and acontent-type
header ofapplication/vnd.kafka.v2+json
. For each message, theoffsets
array describes:-
The partition that the message was sent to
-
The current message offset of the partition
Example response#... { "offsets":[ { "partition":0, "offset":0 }, { "partition":2, "offset":0 }, { "partition":0, "offset":1 } ] }
-
After producing messages to topics and partitions, create a Kafka Bridge consumer.
-
POST /topics/{topicname} in the API reference documentation.
-
POST /topics/{topicname}/partitions/{partitionid} in the API reference documentation.
7.2.4. Creating a Kafka Bridge consumer
Before you can perform any consumer operations in the Kafka cluster, you must first create a consumer by using the consumers endpoint. The consumer is referred to as a Kafka Bridge consumer.
-
Create a Kafka Bridge consumer in a new consumer group named
bridge-quickstart-consumer-group
:curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group \ -H 'content-type: application/vnd.kafka.v2+json' \ -d '{ "name": "bridge-quickstart-consumer", "auto.offset.reset": "earliest", "format": "json", "enable.auto.commit": false, "fetch.min.bytes": 512, "consumer.request.timeout.ms": 30000 }'
-
The consumer is named
bridge-quickstart-consumer
and the embedded data format is set asjson
. -
Some basic configuration settings are defined.
-
The consumer will not commit offsets to the log automatically because the
enable.auto.commit
setting isfalse
. You will commit the offsets manually later in this quickstart.If the request is successful, the Kafka Bridge returns the consumer ID (
instance_id
) and base URL (base_uri
) in the response body, along with a200
code.Example response#... { "instance_id": "bridge-quickstart-consumer", "base_uri":"http://<bridge-name>-bridge-service:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer" }
-
-
Copy the base URL (
base_uri
) to use in the other consumer operations in this quickstart.
Now that you have created a Kafka Bridge consumer, you can subscribe it to topics.
-
POST /consumers/{groupid} in the API reference documentation.
7.2.5. Subscribing a Kafka Bridge consumer to topics
After you have created a Kafka Bridge consumer, subscribe it to one or more topics by using the subscription endpoint. Once subscribed, the consumer starts receiving all messages that are produced to the topic.
-
Subscribe the consumer to the
bridge-quickstart-topic
topic that you created earlier, in Producing messages to topics and partitions:curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/subscription \ -H 'content-type: application/vnd.kafka.v2+json' \ -d '{ "topics": [ "bridge-quickstart-topic" ] }'
The
topics
array can contain a single topic (as shown here) or multiple topics. If you want to subscribe the consumer to multiple topics that match a regular expression, you can use thetopic_pattern
string instead of thetopics
array.If the request is successful, the Kafka Bridge returns a
204
(No Content) code only.
After subscribing a Kafka Bridge consumer to topics, you can retrieve messages from the consumer.
-
POST /consumers/{groupid}/instances/{name}/subscription in the API reference documentation.
7.2.6. Retrieving the latest messages from a Kafka Bridge consumer
Next, retrieve the latest messages from the Kafka Bridge consumer by requesting data from the records endpoint. In production, HTTP clients can call this endpoint repeatedly (in a loop).
-
Produce additional messages to the Kafka Bridge consumer, as described in Producing messages to topics and partitions.
-
Submit a
GET
request to therecords
endpoint:curl -X GET http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/records \ -H 'accept: application/vnd.kafka.json.v2+json'
After creating and subscribing to a Kafka Bridge consumer, a first GET request will return an empty response because the poll operation starts a rebalancing process to assign partitions.
-
Repeat step two to retrieve messages from the Kafka Bridge consumer.
The Kafka Bridge returns an array of messages — describing the topic name, key, value, partition, and offset — in the response body, along with a
200
code. Messages are retrieved from the latest offset by default.HTTP/1.1 200 OK content-type: application/vnd.kafka.json.v2+json #... [ { "topic":"bridge-quickstart-topic", "key":"my-key", "value":"sales-lead-0001", "partition":0, "offset":0 }, { "topic":"bridge-quickstart-topic", "key":null, "value":"sales-lead-0003", "partition":0, "offset":1 }, #...
NoteIf an empty response is returned, produce more records to the consumer as described in Producing messages to topics and partitions, and then try retrieving messages again.
After retrieving messages from a Kafka Bridge consumer, try committing offsets to the log.
-
GET /consumers/{groupid}/instances/{name}/records in the API reference documentation.
7.2.7. Commiting offsets to the log
Next, use the offsets endpoint to manually commit offsets to the log for all messages received by the Kafka Bridge consumer. This is required because the Kafka Bridge consumer that you created earlier, in Creating a Kafka Bridge consumer, was configured with the enable.auto.commit
setting as false
.
-
Commit offsets to the log for the
bridge-quickstart-consumer
:curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/offsets
Because no request body is submitted, offsets are committed for all the records that have been received by the consumer. Alternatively, the request body can contain an array (OffsetCommitSeekList) that specifies the topics and partitions that you want to commit offsets for.
If the request is successful, the Kafka Bridge returns a
204
code only.
After committing offsets to the log, try out the endpoints for seeking to offsets.
-
POST /consumers/{groupid}/instances/{name}/offsets in the API reference documentation.
7.2.8. Seeking to offsets for a partition
Next, use the positions endpoints to configure the Kafka Bridge consumer to retrieve messages for a partition from a specific offset, and then from the latest offset. This is referred to in Apache Kafka as a seek operation.
-
Seek to a specific offset for partition 0 of the
quickstart-bridge-topic
topic:curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/positions \ -H 'content-type: application/vnd.kafka.v2+json' \ -d '{ "offsets": [ { "topic": "bridge-quickstart-topic", "partition": 0, "offset": 2 } ] }'
If the request is successful, the Kafka Bridge returns a
204
code only. -
Submit a
GET
request to therecords
endpoint:curl -X GET http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/records \ -H 'accept: application/vnd.kafka.json.v2+json'
The Kafka Bridge returns messages from the offset that you seeked to.
-
Restore the default message retrieval behavior by seeking to the last offset for the same partition. This time, use the positions/end endpoint.
curl -X POST http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer/positions/end \ -H 'content-type: application/vnd.kafka.v2+json' \ -d '{ "partitions": [ { "topic": "bridge-quickstart-topic", "partition": 0 } ] }'
If the request is successful, the Kafka Bridge returns another
204
code.
Note
|
You can also use the positions/beginning endpoint to seek to the first offset for one or more partitions. |
In this quickstart, you have used the Strimzi Kafka Bridge to perform several common operations on a Kafka cluster. You can now delete the Kafka Bridge consumer that you created earlier.
-
POST /consumers/{groupid}/instances/{name}/positions in the API reference documentation.
-
POST /consumers/{groupid}/instances/{name}/positions/beginning in the API reference documentation.
-
POST /consumers/{groupid}/instances/{name}/positions/end in the API reference documentation.
7.2.9. Deleting a Kafka Bridge consumer
Finally, delete the Kafa Bridge consumer that you used throughout this quickstart.
-
Delete the Kafka Bridge consumer by sending a
DELETE
request to the instances endpoint.curl -X DELETE http://localhost:8080/consumers/bridge-quickstart-consumer-group/instances/bridge-quickstart-consumer
If the request is successful, the Kafka Bridge returns a
204
code only.
-
DELETE /consumers/{groupid}/instances/{name} in the API reference documentation.
8. Introducing Metrics
This section describes how to monitor Strimzi Kafka, ZooKeeper and Kafka Connect clusters using Prometheus to provide monitoring data for example Grafana dashboards.
In order to run the example Grafana dashboards, you must:
Note
|
The resources referenced in this section are intended as a starting point for setting up monitoring, but they are provided as examples only. If you require further support on configuring and running Prometheus or Grafana in production, try reaching out to their respective communities. |
When you have Prometheus and Grafana enabled, you can also use Kafka Exporter to provide additional monitoring related to consumer lag. For more information, see Kafka Exporter.
-
For more information about Prometheus, see the Prometheus documentation.
-
For more information about Grafana, see the Grafana documentation.
-
Apache Kafka Monitoring describes JMX metrics exposed by Apache Kafka.
-
ZooKeeper JMX describes JMX metrics exposed by Apache ZooKeeper.
8.1. Example Metrics files
You can find the example metrics configuration files in the examples/metrics
directory.
metrics
├── grafana-install
│ ├── grafana.yaml (1)
├── grafana-dashboards (2)
│ ├── strimzi-kafka-connect.json
│ ├── strimzi-kafka.json
│ └── strimzi-zookeeper.json
│ └── strimzi-kafka-exporter.json (3)
├── kafka-connect-metrics.yaml (4)
├── kafka-metrics.yaml (5)
├── prometheus-additional-properties
│ └── prometheus-additional.yaml (6)
├── prometheus-alertmanager-config
│ └── alert-manager-config.yaml (7)
└── prometheus-install
├── alert-manager.yaml (8)
├── prometheus-rules.yaml (9)
├── prometheus.yaml (10)
└── strimzi-service-monitor.yaml (11)
-
Installation file for the Grafana image
-
Grafana dashboard configuration
-
Grafana dashboard configuration specific to Kafka Exporter
-
Metrics configuration that defines Prometheus JMX Exporter relabeling rules for Kafka Connect
-
Metrics configuration that defines Prometheus JMX Exporter relabeling rules for Kafka and ZooKeeper
-
Configuration to add roles for service monitoring
-
Hook definitions for sending notifications through Alertmanager
-
Resources for deploying and configuring Alertmanager
-
Alerting rules examples for use with Prometheus Alertmanager (deployed with Prometheus)
-
Installation file for the Prometheus image
-
Prometheus job definitions to scrape metrics data
8.2. Prometheus metrics
Strimzi uses the Prometheus JMX Exporter to expose JMX metrics from Kafka and ZooKeeper using an HTTP endpoint, which is then scraped by the Prometheus server.
8.2.1. Prometheus metrics configuration
Strimzi provides example configuration files for Grafana.
Grafana dashboards are dependent on Prometheus JMX Exporter relabeling rules, which are defined for:
-
Kafka and ZooKeeper as a
Kafka
resource configuration in the examplekafka-metrics.yaml
file -
Kafka Connect as
KafkaConnect
andKafkaConnectS2I
resources in the examplekafka-connect-metrics.yaml
file
A label is a name-value pair. Relabeling is the process of writing a label dynamically. For example, the value of a label may be derived from the name of a Kafka server and client ID.
Note
|
We show metrics configuration using kafka-metrics.yaml in this section, but the process is the same when configuring Kafka Connect using the kafka-connect-metrics.yaml file.
|
For more information on the use of relabeling, see Configuration in the Prometheus documentation.
8.2.2. Prometheus metrics deployment options
To apply the example metrics configuration of relabeling rules to your Kafka cluster, do one of the following:
8.2.3. Copying Prometheus metrics configuration to a Kafka resource
To use Grafana dashboards for monitoring, you can copy the example metrics configuration to a Kafka
resource.
Execute the following steps for each Kafka
resource in your deployment.
-
Update the
Kafka
resource in an editor.kubectl edit kafka my-cluster
-
Copy the example configuration in
kafka-metrics.yaml
to your ownKafka
resource definition. -
Save the file, exit the editor and wait for the updated resource to be reconciled.
8.2.4. Deploying a Kafka cluster with Prometheus metrics configuration
To use Grafana dashboards for monitoring, you can deploy an example Kafka cluster with metrics configuration.
-
Deploy the Kafka cluster with the metrics configuration:
kubectl apply -f kafka-metrics.yaml
8.3. Prometheus
Prometheus provides an open source set of components for systems monitoring and alert notification.
We describe here how you can use the CoreOS Prometheus Operator to run and manage a Prometheus server that is suitable for use in production environments, but with the correct configuration you can run any Prometheus server.
Note
|
The Prometheus server configuration uses service discovery to discover the pods in the cluster from which it gets metrics. For this feature to work correctly, the service account used for running the Prometheus service pod must have access to the API server so it can retrieve the pod list. For more information, see Discovering services. |
8.3.1. Prometheus configuration
Strimzi provides example configuration files for the Prometheus server.
A Prometheus image is provided for deployment:
-
prometheus.yaml
Additional Prometheus-related configuration is also provided in the following files:
-
prometheus-additional.yaml
-
prometheus-rules.yaml
-
strimzi-service-monitor.yaml
For Prometheus to obtain monitoring data:
Then use the configuration files to:
The prometheus-rules.yaml
file provides example alerting rule examples for use with Alertmanager.
8.3.2. Prometheus resources
When you apply the Prometheus configuration, the following resources are created in your Kubernetes cluster and managed by the Prometheus Operator:
-
A
ClusterRole
that grants permissions to Prometheus to read the health endpoints exposed by the Kafka and ZooKeeper pods, cAdvisor and the kubelet for container metrics. -
A
ServiceAccount
for the Prometheus pods to run under. -
A
ClusterRoleBinding
which binds theClusterRole
to theServiceAccount
. -
A
Deployment
to manage the Prometheus Operator pod. -
A
ServiceMonitor
to manage the configuration of the Prometheus pod. -
A
Prometheus
to manage the configuration of the Prometheus pod. -
A
PrometheusRule
to manage alerting rules for the Prometheus pod. -
A
Secret
to manage additional Prometheus settings. -
A
Service
to allow applications running in the cluster to connect to Prometheus (for example, Grafana using Prometheus as datasource).
8.3.3. Deploying the Prometheus Operator
To deploy the Prometheus Operator to your Kafka cluster, apply the YAML resource files from the Prometheus CoreOS repository.
-
Download the resource files from the repository and replace the example
namespace
with your own:On Linux, use:
curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-deployment.yaml | sed -e 's/namespace: .\*/namespace: my-namespace/' > prometheus-operator-deployment.yaml
curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-cluster-role.yaml > prometheus-operator-cluster-role.yaml
curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-cluster-role-binding.yaml | sed -e 's/namespace: .*/namespace: my-namespace/' > prometheus-operator-cluster-role-binding.yaml
curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-service-account.yaml | sed -e 's/namespace: .*/namespace: my-namespace/' > prometheus-operator-service-account.yaml
On MacOS, use:
curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-deployment.yaml | sed -e '' 's/namespace: .\*/namespace: my-namespace/' > prometheus-operator-deployment.yaml
curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-cluster-role.yaml > prometheus-operator-cluster-role.yaml
curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-cluster-role-binding.yaml | sed -e '' 's/namespace: .*/namespace: my-namespace/' > prometheus-operator-cluster-role-binding.yaml
curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/example/rbac/prometheus-operator/prometheus-operator-service-account.yaml | sed -e '' 's/namespace: .*/namespace: my-namespace/' > prometheus-operator-service-account.yaml
NoteIf it is not required, you can manually remove the spec.template.spec.securityContext
property from theprometheus-operator-deployment.yaml
file. -
Deploy the Prometheus Operator:
kubectl apply -f prometheus-operator-deployment.yaml kubectl apply -f prometheus-operator-cluster-role.yaml kubectl apply -f prometheus-operator-cluster-role-binding.yaml kubectl apply -f prometheus-operator-service-account.yaml
8.3.4. Deploying Prometheus
To deploy Prometheus to your Kafka cluster to obtain monitoring data, apply the example resource file for the Prometheus docker image and the YAML files for Prometheus-related resources.
The deployment process creates a ClusterRoleBinding
and discovers an Alertmanager instance in the namespace specified for the deployment.
Note
|
By default, the Prometheus Operator only supports jobs that include an endpoints role for service discovery. Targets are discovered and scraped for each endpoint port address. For endpoint discovery, the port address may be derived from service (role: service ) or pod (role: pod ) discovery.
|
-
Check the example alerting rules provided
-
Modify the Prometheus installation file (
prometheus.yaml
) according to the namespace Prometheus is going to be installed in:On Linux, use:
sed -i 's/namespace: .*/namespace: my-namespace/' prometheus.yaml
On MacOS, use:
sed -i '' 's/namespace: .*/namespace: my-namespace/' prometheus.yaml
-
Edit the
ServiceMonitor
resource instrimzi-service-monitor.yaml
to define Prometheus jobs that will scrape the metrics data. -
To use another role:
-
Create a
Secret
resource:oc create secret generic additional-scrape-configs --from-file=prometheus-additional.yaml
-
Edit the
additionalScrapeConfigs
property in theprometheus.yaml
file to include the name of theSecret
and the YAML file (prometheus-additional.yaml
) that contains the additional configuration.
-
-
Edit the
prometheus-rules.yaml
file that creates sample alert notification rules:On Linux, use:
sed -i 's/namespace: .*/namespace: my-namespace/' prometheus-rules.yaml
On MacOS, use:
sed -i '' 's/namespace: .*/namespace: my-namespace/' prometheus-rules.yaml
-
Deploy the Prometheus resources:
kubectl apply -f strimzi-service-monitor.yaml kubectl apply -f prometheus-rules.yaml kubectl apply -f prometheus.yaml
8.4. Prometheus Alertmanager
Prometheus Alertmanager is a plugin for handling alerts and routing them to a notification service. Alertmanager supports an essential aspect of monitoring, which is to be notified of conditions that indicate potential issues based on alerting rules.
8.4.1. Alertmanager configuration
Strimzi provides example configuration files for Prometheus Alertmanager.
A configuration file defines the resources for deploying Alertmanager:
-
alert-manager.yaml
An additional configuration file provides the hook definitions for sending notifications from your Kafka cluster.
-
alert-manager-config.yaml
For Alertmanger to handle Prometheus alerts, use the configuration files to:
8.4.2. Alerting rules
Alerting rules provide notifications about specific conditions observed in the metrics. Rules are declared on the Prometheus server, but Prometheus Alertmanager is responsible for alert notifications.
Prometheus alerting rules describe conditions using PromQL expressions that are continuously evaluated.
When an alert expression becomes true, the condition is met and the Prometheus server sends alert data to the Alertmanager. Alertmanager then sends out a notification using the communication method configured for its deployment.
Alertmanager can be configured to use email, chat messages or other notification methods.
For more information about setting up alerting rules, see Configuration in the Prometheus documentation.
8.4.3. Alerting rule examples
Example alerting rules for Kafka and ZooKeeper metrics are provided with Strimzi for use in a Prometheus deployment.
General points about the alerting rule definitions:
-
A
for
property is used with the rules to determine the period of time a condition must persist before an alert is triggered. -
A tick is a basic ZooKeeper time unit, which is measured in milliseconds and configured using the
tickTime
parameter ofKafka.spec.zookeeper.config
. For example, if ZooKeepertickTime=3000
, 3 ticks (3 x 3000) equals 9000 milliseconds. -
The availability of the
ZookeeperRunningOutOfSpace
metric and alert is dependent on the Kubernetes configuration and storage implementation used. Storage implementations for certain platforms may not be able to supply the information on available space required for the metric to provide an alert.
UnderReplicatedPartitions
-
Gives the number of partitions for which the current broker is the lead replica but which have fewer replicas than the
min.insync.replicas
configured for their topic. This metric provides insights about brokers that host the follower replicas. Those followers are not keeping up with the leader. Reasons for this could include being (or having been) offline, and over-throttled interbroker replication. An alert is raised when this value is greater than zero, providing information on the under-replicated partitions for each broker. AbnormalControllerState
-
Indicates whether the current broker is the controller for the cluster. The metric can be 0 or 1. During the life of a cluster, only one broker should be the controller and the cluster always needs to have an active controller. Having two or more brokers saying that they are controllers indicates a problem. If the condition persists, an alert is raised when the sum of all the values for this metric on all brokers is not equal to 1, meaning that there is no active controller (the sum is 0) or more than one controller (the sum is greater than 1).
UnderMinIsrPartitionCount
-
Indicates that the minimum number of in-sync replicas (ISRs) for a lead Kafka broker, specified using
min.insync.replicas
, that must acknowledge a write operation has not been reached. The metric defines the number of partitions that the broker leads for which the in-sync replicas count is less than the minimum in-sync. An alert is raised when this value is greater than zero, providing information on the partition count for each broker that did not achieve the minimum number of acknowledgments. OfflineLogDirectoryCount
-
Indicates the number of log directories which are offline (for example, due to a hardware failure) so that the broker cannot store incoming messages anymore. An alert is raised when this value is greater than zero, providing information on the number of offline log directories for each broker.
KafkaRunningOutOfSpace
-
Indicates the remaining amount of disk space that can be used for writing data. An alert is raised when this value is lower than 5GiB, providing information on the disk that is running out of space for each persistent volume claim. The threshold value may be changed in
prometheus-rules.yaml
.
AvgRequestLatency
-
Indicates the amount of time it takes for the server to respond to a client request. An alert is raised when this value is greater than 10 (ticks), providing the actual value of the average request latency for each server.
OutstandingRequests
-
Indicates the number of queued requests in the server. This value goes up when the server receives more requests than it can process. An alert is raised when this value is greater than 10, providing the actual number of outstanding requests for each server.
ZookeeperRunningOutOfSpace
-
Indicates the remaining amount of disk space that can be used for writing data to ZooKeeper. An alert is raised when this value is lower than 5GiB., providing information on the disk that is running out of space for each persistent volume claim.
8.4.4. Deploying Alertmanager
To deploy Alertmanager, apply the example configuration files.
The sample configuration provided with Strimzi configures the Alertmanager to send notifications to a Slack channel.
The following resources are defined on deployment:
-
An
Alertmanager
to manage the Alertmanager pod. -
A
Secret
to manage the configuration of the Alertmanager. -
A
Service
to provide an easy to reference hostname for other services to connect to Alertmanager (such as Prometheus).
-
Create a
Secret
resource from the Alertmanager configuration file (alert-manager-config.yaml
):kubectl create secret generic alertmanager-alertmanager --from-file=alert-manager-config.yaml
-
Update the
alert-manager-config.yaml
file to replace the:-
slack_api_url
property with the actual value of the Slack API URL related to the application for the Slack workspace -
channel
property with the actual Slack channel on which to send notifications
-
-
Deploy Alertmanager:
kubectl apply -f alert-manager.yaml
8.5. Grafana
Grafana provides visualizations of Prometheus metrics.
You can deploy and enable the example Grafana dashboards provided with Strimzi.
8.5.1. Grafana configuration
Strimzi provides example dashboard configuration files for Grafana.
A Grafana docker image is provided for deployment:
-
grafana.yaml
Example dashboards are also provided as JSON files:
-
strimzi-kafka.json
-
strimzi-kafka-connect.json
-
strimzi-zookeeper.json
The example dashboards are a good starting point for monitoring key metrics, but they do not represent all available metrics. You may need to modify the example dashboards or add other metrics, depending on your infrastructure.
For Grafana to present the dashboards, use the configuration files to:
8.5.2. Deploying Grafana
To deploy Grafana to provide visualizations of Prometheus metrics, apply the example configuration file.
-
Deploy Grafana:
kubectl apply -f grafana.yaml
8.5.3. Enabling the example Grafana dashboards
Set up a Prometheus data source and example dashboards to enable Grafana for monitoring.
Note
|
No alert notification rules are defined. |
When accessing a dashboard, you can use the port-forward
command to forward traffic from the Grafana pod to the host.
For example, you can access the Grafana user interface by:
-
Running
kubectl port-forward grafana-1-fbl7s 3000:3000
-
Pointing a browser to
http://localhost:3000
Note
|
The name of the Grafana pod is different for each user. |
-
Access the Grafana user interface using
admin/admin
credentials.On the initial view choose to reset the password.
-
Click the Add data source button.
-
Add Prometheus as a data source.
-
Specify a name
-
Add Prometheus as the type
-
Specify the connection string to the Prometheus server (http://prometheus-operated:9090) in the URL field
-
-
Click Add to test the connection to the data source.
-
Click Dashboards, then Import to open the Import Dashboard window and import the example dashboards (or paste the JSON).
After importing the dashboards, the Grafana dashboard homepage presents Kafka and ZooKeeper dashboards.
When the Prometheus server has been collecting metrics for a Strimzi cluster for some time, the dashboards are populated.
8.6. Using metrics with minikube
or minishift
When adding Prometheus and Grafana servers to an Apache Kafka deployment using minikube
or minishift
, the memory available to the virtual machine should be increased (to 4 GB of RAM, for example, instead of the default 2 GB).
For information on how to increase the default amount of memory, see:
-
Prometheus - Monitoring Docker Container Metrics using cAdvisor describes how to use cAdvisor (short for container Advisor) metrics with Prometheus to analyze and expose resource usage (CPU, Memory, and Disk) and performance data from running containers within pods on Kubernetes.
9. Distributed tracing
This chapter outlines the support for distributed tracing in Strimzi, using Jaeger.
How you configure distributed tracing varies by Strimzi client and component.
-
You instrument Kafka Producer, Consumer, and Streams API applications for distributed tracing using an OpenTracing client library. This involves adding instrumentation code to these clients, which monitors the execution of individual transactions in order to generate trace data.
-
Distributed tracing support is built in to the Kafka Connect, Mirror Maker, and Kafka Bridge components of Strimzi. To configure these components for distributed tracing, you configure and update the relevant custom resources.
Before configuring distributed tracing in Strimzi clients and components, you must first initialize and configure a Jaeger tracer in the Kafka cluster, as described in Initializing a Jaeger tracer for Kafka clients.
Note
|
Distributed tracing is not supported for Kafka brokers. |
9.1. Overview of distributed tracing in Strimzi
Distributed tracing allows developers and system administrators to track the progress of transactions between applications (and services in a microservice architecture) in a distributed system. This information is useful for monitoring application performance and investigating issues with target systems and end-user applications.
In Strimzi and data streaming platforms in general, distributed tracing facilitates the end-to-end tracking of messages: from source systems to the Kafka cluster and then to target systems and applications.
As an aspect of system observability, distributed tracing complements the metrics that are available to view in Grafana dashboards and the available loggers for each component.
Distributed tracing in Strimzi is implemented using the open source OpenTracing and Jaeger projects.
The OpenTracing specification defines APIs that developers can use to instrument applications for distributed tracing. It is independent from the tracing system.
When instrumented, applications generate traces for individual transactions. Traces are composed of spans, which define specific units of work.
To simplify the instrumentation of the Kafka Bridge and Kafka Producer, Consumer, and Streams API applications, Strimzi includes the OpenTracing Apache Kafka Client Instrumentation library.
Note
|
The OpenTracing project is merging with the OpenCensus project. The new, combined project is named OpenTelemetry. OpenTelemetry will provide compatibility for applications that are instrumented using the OpenTracing APIs. |
Jaeger, a tracing system, is an implementation of the OpenTracing APIs used for monitoring and troubleshooting microservices-based distributed systems. It consists of four main components and provides client libraries for instrumenting applications. You can use the Jaeger user interface to visualize, query, filter, and analyze trace data.
9.1.1. Distributed tracing support in Strimzi
In Strimzi, distributed tracing is supported in:
-
Kafka Connect (including Kafka Connect with Source2Image support)
-
Mirror Maker
-
The Strimzi Kafka Bridge
You enable and configure distributed tracing for these components by setting template configuration properties in the relevant custom resource (for example, KafkaConnect
and KafkaBridge
).
To enable distributed tracing in Kafka Producer, Consumer, and Streams API applications, you can instrument application code using the OpenTracing Apache Kafka Client Instrumentation library. When instrumented, these clients generate traces for messages (for example, when producing messages or writing offsets to the log).
Traces are sampled according to a sampling strategy and then visualized in the Jaeger user interface. This trace data is useful for monitoring the performance of your Kafka cluster and debugging issues with target systems and applications.
To set up distributed tracing for Strimzi, follow these procedures:
This chapter covers setting up distributed tracing for Strimzi clients and components only. Setting up distributed tracing for applications and systems beyond Strimzi is outside the scope of this chapter. To learn more about this subject, see the OpenTracing documentation and search for "inject and extract".
Before you set up distributed tracing for Strimzi, it is helpful to understand:
-
The basics of OpenTracing, including key concepts such as traces, spans, and tracers. Refer to the OpenTracing documentation.
-
The components of the Jaeger architecure.
-
The Jaeger backend components are deployed to your Kubernetes cluster. For deployment instructions, see the Jaeger deployment documentation.
9.2. Setting up tracing for Kafka clients
This section describes how to initialize a Jaeger tracer to allow you to instrument your client applications for distributed tracing.
9.2.1. Initializing a Jaeger tracer for Kafka clients
Configure and initialize a Jaeger tracer using a set of tracing environment variables.
Perform the following steps for each client application.
-
Add Maven dependencies for Jaeger to the
pom.xml
file for the client application:<dependency> <groupId>io.jaegertracing</groupId> <artifactId>jaeger-client</artifactId> <version>1.0.0</version> </dependency>
-
Define the configuration of the Jaeger tracer using the tracing environment variables.
-
Create the Jaeger tracer from the environment variables that you defined in step two:
Tracer tracer = Configuration.fromEnv().getTracer();
NoteFor alternative ways to initialize a Jaeger tracer, see the Java OpenTracing library documentation. -
Register the Jaeger tracer as a global tracer:
GlobalTracer.register(tracer);
A Jaeger tracer is now initialized for the client application to use.
9.2.2. Tracing environment variables
Use these environment variables when configuring a Jaeger tracer for Kafka clients.
Note
|
The tracing environment variables are part of the Jaeger project and are subject to change. For the latest environment variables, see the Jaeger documentation. |
Property | Required | Description |
---|---|---|
|
Yes |
The name of the Jaeger tracer service. |
|
No |
The hostname for communicating with the |
|
No |
The port used for communicating with the |
|
No |
The traces endpoint. Only define this variable if the client application will bypass the |
|
No |
The authentication token to send to the endpoint as a bearer token. |
|
No |
The username to send to the endpoint if using basic authentication. |
|
No |
The password to send to the endpoint if using basic authentication. |
|
No |
A comma-separated list of formats to use for propagating the trace context. Defaults to the standard Jaeger format. Valid values are |
|
No |
Indicates whether the reporter should also log the spans. |
|
No |
The reporter’s maximum queue size. |
|
No |
The reporter’s flush interval, in ms. Defines how frequently the Jaeger reporter flushes span batches. |
|
No |
The sampling strategy to use for client traces: Constant, Probabilistic, Rate Limiting, or Remote (the default type). To sample all traces, use the Constant sampling strategy with a parameter of 1. For more information, see the Jaeger documentation. |
|
No |
The sampler parameter (number). |
|
No |
The hostname and port to use if a Remote sampling strategy is selected. |
|
No |
A comma-separated list of tracer-level tags that are added to all reported spans. The value can also refer to an environment variable using the format |
9.3. Instrumenting Kafka clients with tracers
This section describes how to instrument Kafka Producer, Consumer, and Streams API applications for distributed tracing.
9.3.1. Instrumenting Kafka Producers and Consumers for tracing
Use a Decorator pattern or Interceptors to instrument your Java Producer and Consumer application code for distributed tracing.
Perform these steps in the application code of each Kafka Producer and Consumer application.
-
Add the Maven dependency for OpenTracing to the Producer or Consumer’s
pom.xml
file.<dependency> <groupId>io.opentracing.contrib</groupId> <artifactId>opentracing-kafka-client</artifactId> <version>0.1.4</version> </dependency>
-
Instrument your client application code using either a Decorator pattern or Interceptors.
-
If you prefer to use a Decorator pattern, use following example:
// Create an instance of the KafkaProducer: KafkaProducer<Integer, String> producer = new KafkaProducer<>(senderProps); // Create an instance of the TracingKafkaProducer: TracingKafkaProducer<Integer, String> tracingProducer = new TracingKafkaProducer<>(producer, tracer); // Send: tracingProducer.send(...); // Create an instance of the KafkaConsumer: KafkaConsumer<Integer, String> consumer = new KafkaConsumer<>(consumerProps); // Create an instance of the TracingKafkaConsumer: TracingKafkaConsumer<Integer, String> tracingConsumer = new TracingKafkaConsumer<>(consumer, tracer); // Subscribe: tracingConsumer.subscribe(Collections.singletonList("messages")); // Get messages: ConsumerRecords<Integer, String> records = tracingConsumer.poll(1000); // Retrieve SpanContext from polled record (consumer side): ConsumerRecord<Integer, String> record = ... SpanContext spanContext = TracingKafkaUtils.extractSpanContext(record.headers(), tracer);
-
If you prefer to use Interceptors, use the following example:
// Register the tracer with GlobalTracer: GlobalTracer.register(tracer); // Add the TracingProducerInterceptor to the sender properties: senderProps.put(ProducerConfig.INTERCEPTOR_CLASSES_CONFIG, TracingProducerInterceptor.class.getName()); // Create an instance of the KafkaProducer: KafkaProducer<Integer, String> producer = new KafkaProducer<>(senderProps); // Send: producer.send(...); // Add the TracingConsumerInterceptor to the consumer properties: consumerProps.put(ConsumerConfig.INTERCEPTOR_CLASSES_CONFIG, TracingConsumerInterceptor.class.getName()); // Create an instance of the KafkaConsumer: KafkaConsumer<Integer, String> consumer = new KafkaConsumer<>(consumerProps); // Subscribe: consumer.subscribe(Collections.singletonList("messages")); // Get messages: ConsumerRecords<Integer, String> records = consumer.poll(1000); // Retrieve the SpanContext from a polled message (consumer side): ConsumerRecord<Integer, String> record = ... SpanContext spanContext = TracingKafkaUtils.extractSpanContext(record.headers(), tracer);
-
Custom span names in a Decorator pattern
A span is a logical unit of work in Jaeger, with an operation name, start time, and duration.
If you use a Decorator pattern to instrument your Kafka Producer and Consumer applications, you can define custom span names by passing a BiFunction
object as an additional argument when creating the TracingKafkaProducer
and TracingKafkaConsumer
objects. The OpenTracing Apache Kafka Client Instrumentation library includes several built-in span names, which are described below.
// Create a BiFunction for the KafkaProducer that operates on (String operationName, ProducerRecord consumerRecord) and returns a String to be used as the name:
BiFunction<String, ProducerRecord, String> producerSpanNameProvider =
(operationName, producerRecord) -> "CUSTOM_PRODUCER_NAME";
// Create an instance of the KafkaProducer:
KafkaProducer<Integer, String> producer = new KafkaProducer<>(senderProps);
// Create an instance of the TracingKafkaProducer
TracingKafkaProducer<Integer, String> tracingProducer = new TracingKafkaProducer<>(producer,
tracer,
producerSpanNameProvider);
// Spans created by the tracingProducer will now have "CUSTOM_PRODUCER_NAME" as the span name.
// Create a BiFunction for the KafkaConsumer that operates on (String operationName, ConsumerRecord consumerRecord) and returns a String to be used as the name:
BiFunction<String, ConsumerRecord, String> consumerSpanNameProvider =
(operationName, consumerRecord) -> operationName.toUpperCase();
// Create an instance of the KafkaConsumer:
KafkaConsumer<Integer, String> consumer = new KafkaConsumer<>(consumerProps);
// Create an instance of the TracingKafkaConsumer, passing in the consumerSpanNameProvider BiFunction:
TracingKafkaConsumer<Integer, String> tracingConsumer = new TracingKafkaConsumer<>(consumer,
tracer,
consumerSpanNameProvider);
// Spans created by the tracingConsumer will have the operation name as the span name, in upper-case.
// "receive" -> "RECEIVE"
Built-in span names
When defining custom span names, you can use the following BiFunctions
in the ClientSpanNameProvider
class. If no spanNameProvider
is specified, CONSUMER_OPERATION_NAME
and PRODUCER_OPERATION_NAME
are used.
BiFunction | Description |
---|---|
|
Returns the |
|
Returns a String concatenation of |
|
Returns the name of the topic that the message was sent to or retrieved from in the format |
|
Returns a String concatenation of |
|
Returns the operation name and the topic name: |
|
Returns a String concatenation of |
9.3.2. Instrumenting Kafka Streams applications for tracing
This section describes how to instrument Kafka Streams API applications for distributed tracing.
Perform the following steps for each Kafka Streams API application.
-
Add the
opentracing-kafka-streams
dependency to the pom.xml file for your Kafka Streams API application:<dependency> <groupId>io.opentracing.contrib</groupId> <artifactId>opentracing-kafka-streams</artifactId> <version>0.1.4</version> </dependency>
-
Create an instance of the
TracingKafkaClientSupplier
supplier interface:KafkaClientSupplier supplier = new TracingKafkaClientSupplier(tracer);
-
Provide the supplier interface to
KafkaStreams
:KafkaStreams streams = new KafkaStreams(builder.build(), new StreamsConfig(config), supplier); streams.start();
9.4. Setting up tracing for Mirror Maker, Kafka Connect, and the Kafka Bridge
Distributed tracing is supported for Mirror Maker, Kafka Connect (including Kafka Connect with Source2Image support), and the Strimzi Kafka Bridge.
For Mirror Maker, messages are traced from the source cluster to the target cluster; the trace data records messages entering and leaving the Mirror Maker component.
Only messages produced and consumed by Kafka Connect itself are traced. To trace messages sent between Kafka Connect and external systems, you must configure tracing in the connectors for those systems. For more information, see Kafka Connect cluster configuration.
Messages produced and consumed by the Kafka Bridge are traced. Incoming HTTP requests from client applications to send and receive messages through the Kafka Bridge are also traced. In order to have end-to-end tracing, you must configure tracing in your HTTP clients.
9.4.1. Enabling tracing in Mirror Maker, Kafka Connect, and Kafka Bridge resources
Update the configuration of KafkaMirrorMaker
, KafkaConnect
, KafkaConnectS2I
, and KafkaBridge
custom resources to specify and configure a Jaeger tracer service for each resource. Updating a tracing-enabled resource in your Kubernetes cluster triggers two events:
-
Interceptor classes are updated in the integrated consumers and producers in Mirror Maker, Kafka Connect, or the Strimzi Kafka Bridge.
-
For Mirror Maker and Kafka Connect, the tracing agent initializes a Jaeger tracer based on the tracing configuration defined in the resource.
-
For the Kafka Bridge, a Jaeger tracer based on the tracing configuration defined in the resource is initialized by the Kafka Bridge itself.
Perform these steps for each KafkaMirrorMaker
, KafkaConnect
, KafkaConnectS2I
, and KafkaBridge
resource.
-
In the
spec.template
property, configure the Jaeger tracer service. For example:Jaeger tracer configuration for Kafka ConnectapiVersion: kafka.strimzi.io/v1beta1 kind: KafkaConnect metadata: name: my-connect-cluster spec: #... template: connectContainer: (1) env: - name: JAEGER_SERVICE_NAME value: my-jaeger-service - name: JAEGER_AGENT_HOST value: jaeger-agent-name - name: JAEGER_AGENT_PORT value: "6831" tracing: (2) type: jaeger #...
Jaeger tracer configuration for Mirror MakerapiVersion: kafka.strimzi.io/v1beta1 kind: KafkaMirrorMaker metadata: name: my-mirror-maker spec: #... template: mirrorMakerContainer: env: - name: JAEGER_SERVICE_NAME value: my-jaeger-service - name: JAEGER_AGENT_HOST value: jaeger-agent-name - name: JAEGER_AGENT_PORT value: "6831" tracing: type: jaeger #...
Jaeger tracer configuration for the Kafka BridgeapiVersion: kafka.strimzi.io/v1beta1 kind: KafkaBridge metadata: name: my-bridge spec: #... template: bridgeContainer: env: - name: JAEGER_SERVICE_NAME value: my-jaeger-service - name: JAEGER_AGENT_HOST value: jaeger-agent-name - name: JAEGER_AGENT_PORT value: "6831" tracing: type: jaeger #...
-
Use the tracing environment variables as template configuration properties.
-
Set the
spec.tracing.type
property tojaeger
.
-
-
Create or update the resource:
kubectl apply -f your-file
10. Kafka Exporter
Kafka Exporter is an open source project to enhance monitoring of Apache Kafka brokers and clients. Kafka Exporter is provided with Strimzi for deployment with a Kafka cluster to extract additional metrics data from Kafka brokers related to offsets, consumer groups, consumer lag, and topics.
The metrics data is used, for example, to help identify slow consumers.
Lag data is exposed as Prometheus metrics, which can then be presented in Grafana for analysis.
If you are already using Prometheus and Grafana for monitoring of built-in Kafka metrics, you can configure Prometheus to also scrape the Kafka Exporter Prometheus endpoint.
10.1. Consumer lag
Consumer lag indicates the difference in the rate of production and consumption of messages. Specifically, consumer lag for a given consumer group indicates the delay between the last message in the partition and the message being currently picked up by that consumer. The lag reflects the position of the consumer offset in relation to the end of the partition log.
This difference is sometimes referred to as the delta between the producer offset and consumer offset, the read and write positions in the Kafka broker topic partitions.
Suppose a topic streams 100 messages a second. A lag of 1000 messages between the producer offset (the topic partition head) and the last offset the consumer has read means a 10-second delay.
For applications that rely on the processing of (near) real-time data, it is critical to monitor consumer lag to check that it does not become too big. The greater the lag becomes, the further the process moves from the real-time processing objective.
Consumer lag, for example, might be a result of consuming too much old data that has not been purged, or through unplanned shutdowns.
Typical actions to reduce lag include:
-
Scaling-up consumer groups by adding new consumers
-
Increasing the retention time for a message to remain in a topic
-
Adding more disk capacity to increase the message buffer
Actions to reduce consumer lag depend on the underlying infrastructure and the use cases Strimzi is supporting. For instance, a lagging consumer is less likely to benefit from the broker being able to service a fetch request from its disk cache. And in certain cases, it might be acceptable to automatically drop messages until a consumer has caught up.
10.2. Kafka Exporter alerting rule examples
If you performed the steps to introduce metrics to your deployment, you will already have your Kafka cluster configured to use the alert notification rules that support Kafka Exporter.
The rules for Kafka Exporter are defined in prometheus-rules.yaml
, and are deployed with Prometheus.
For more information, see Prometheus.
The sample alert notification rules specific to Kafka Exporter are as follows:
UnderReplicatedPartition
-
An alert to warn that a topic is under-replicated and the broker is not replicating to enough partitions. The default configuration is for an alert if there are one or more under-replicated partitions for a topic. The alert might signify that a Kafka instance is down or the Kafka cluster is overloaded. A planned restart of the Kafka broker may be required to restart the replication process.
TooLargeConsumerGroupLag
-
An alert to warn that the lag on a consumer group is too large for a specific topic partition. The default configuration is 1000 records. A large lag might indicate that consumers are too slow and are falling behind the producers.
NoMessageForTooLong
-
An alert to warn that a topic has not received messages for a period of time. The default configuration for the time period is 10 minutes. The delay might be a result of a configuration issue preventing a producer from publishing messages to the topic.
Adapt the default configuration of these rules according to your specific needs.
10.3. Kafka Exporter metrics
Lag information is exposed by Kafka Exporter as Prometheus metrics for presentation in Grafana.
Kafka Exporter exposes metrics data for brokers, topics and consumer groups.
The data extracted is described here.
Name | Information |
---|---|
|
Number of brokers in the Kafka cluster |
Name | Information |
---|---|
|
Number of partitions for a topic |
|
Current topic partition offset for a broker |
|
Oldest topic partition offset for a broker |
|
Number of in-sync replicas for a topic partition |
|
Leader broker ID of a topic partition |
|
Shows |
|
Number of replicas for this topic partition |
|
Shows |
Name | Information |
---|---|
|
Current topic partition offset for a consumer group |
|
Current approximate lag for a consumer group at a topic partition |
10.4. Enabling the Kafka Exporter Grafana dashboard
If you deployed Kafka Exporter with your Kafka cluster, you can enable Grafana to present the metrics data it exposes.
A Kafka Exporter dashboard is provided in the examples/metrics
directory as a JSON file:
-
strimzi-kafka-exporter.json
This procedure assumes you already have access to the Grafana user interface and Prometheus has been added as a data source. If you are accessing the user interface for the first time, see Grafana.
-
Access the Grafana user interface.
-
Click Dashboards, then Import to open the Import Dashboard window and import the example Kafka Exporter dashboard (or paste the JSON).
When metrics data has been collected for some time, the Kafka Exporter charts are populated.
From the metrics, you can create charts to display:
-
Message in per second (from topics)
-
Message in per minute (from topics)
-
Lag by consumer group
-
Messages consumed per minute (by consumer groups)
Use the Grafana charts to analyze lag and to check if actions to reduce lag are having an impact on an affected consumer group. If, for example, Kafka brokers are adjusted to reduce lag, the dashboard will show the Lag by consumer group chart going down and the Messages consumed per minute chart going up.
11. Security
Strimzi supports encrypted communication between the Kafka and Strimzi components using the TLS protocol. Communication between Kafka brokers (interbroker communication), between ZooKeeper nodes (internodal communication), and between these and the Strimzi operators is always encrypted. Communication between Kafka clients and Kafka brokers is encrypted according to how the cluster is configured. For the Kafka and Strimzi components, TLS certificates are also used for authentication.
The Cluster Operator automatically sets up TLS certificates to enable encryption and authentication within your cluster. It also sets up other TLS certificates if you want to enable encryption or TLS authentication between Kafka brokers and clients.
11.1. Certificate Authorities
To support encryption, each Strimzi component needs its own private keys and public key certificates. All component certificates are signed by a Certificate Authority (CA) called the cluster CA.
Similarly, each Kafka client application connecting using TLS client authentication needs private keys and certificates. The clients CA is used to sign the certificates for the Kafka clients.
11.1.1. CA certificates
Each CA has a self-signed public key certificate.
Kafka brokers are configured to trust certificates signed by either the clients CA or the cluster CA. Components to which clients do not need to connect, such as ZooKeeper, only trust certificates signed by the cluster CA. Client applications that perform mutual TLS authentication have to trust the certificates signed by the cluster CA.
By default, Strimzi generates and renews CA certificates automatically. You can configure the management of CA certificates in the Kafka.spec.clusterCa
and Kafka.spec.clientsCa
objects.
11.2. Certificates and Secrets
Strimzi stores Certificate Authority (CA), component, and Kafka client private keys and certificates in Secrets
.
All keys are 2048 bits in size.
CA certificate validity periods are expressed as a number of days after certificate generation. You can configure the validity period of cluster CA certificates in Kafka.spec.clusterCa.validityDays
and client CA certificates in Kafka.spec.clientsCa.validityDays
.
11.2.1. Cluster CA Secrets
Secret name |
Field within Secret |
Description |
---|---|---|
|
|
The current private key for the cluster CA. |
|
|
The current certificate for the cluster CA. |
|
|
Certificate for Kafka broker pod <num>. Signed by a current or former cluster CA private key in |
|
Private key for Kafka broker pod <num>. |
|
|
|
Certificate for ZooKeeper node <num>. Signed by a current or former cluster CA private key in |
|
Private key for ZooKeeper pod <num>. |
|
|
|
Certificate for TLS communication between the Entity Operator and Kafka or ZooKeeper.
Signed by a current or former cluster CA private key in |
|
Private key for TLS communication between the Entity Operator and Kafka or ZooKeeper |
The CA certificates in <cluster>-cluster-ca-cert
must be trusted by Kafka client applications so that they validate the Kafka broker certificates when connecting to Kafka brokers over TLS.
Note
|
Only <cluster>-cluster-ca-cert needs to be used by clients.
All other Secrets in the table above only need to be accessed by the
Strimzi components.
You can enforce this using Kubernetes role-based access controls if necessary.
|
11.2.2. Client CA Secrets
Secret name |
Field within Secret |
Description |
---|---|---|
|
|
The current private key for the clients CA. |
|
|
The current certificate for the clients CA. |
The certificates in <cluster>-clients-ca-cert
are those which the Kafka brokers trust.
Note
|
<cluster>-clients-ca is used to sign certificates of client applications.
It needs to be accessible to the Strimzi components and for administrative access if you are intending to issue application certificates without using the User Operator.
You can enforce this using Kubernetes role-based access controls if necessary.
|
11.2.3. User Secrets
Secret name |
Field within Secret |
Description |
---|---|---|
|
|
Certificate for the user, signed by the clients CA |
|
Private key for the user |
11.3. Installing your own CA certificates
This procedure describes how to install your own CA certificates and private keys instead of using CA certificates and private keys generated by the Cluster Operator.
-
The Cluster Operator is running.
-
A Kafka cluster is not yet deployed.
-
Your own X.509 certificates and keys in PEM format for the cluster CA or clients CA.
-
If you want to use a cluster or clients CA which is not a Root CA, you have to include the whole chain in the certificate file. The chain should be in the following order:
-
The cluster or clients CA
-
One or more intermediate CAs
-
The root CA
-
-
All CAs in the chain should be configured as a CA in the X509v3 Basic Constraints.
-
-
Put your CA certificate in the corresponding
Secret
(<cluster>-cluster-ca-cert
for the cluster CA or<cluster>-clients-ca-cert
for the clients CA):Run the following commands:
# Delete any existing secret (ignore "Not Exists" errors) kubectl delete secret <ca-cert-secret> # Create and label the new one kubectl create secret generic <ca-cert-secret> --from-file=ca.crt=<ca-cert-file>
-
Put your CA key in the corresponding
Secret
(<cluster>-cluster-ca
for the cluster CA or<cluster>-clients-ca
for the clients CA) Run the following commands:# Delete the existing secret kubectl delete secret <ca-key-secret> # Create the new one kubectl create secret generic <ca-key-secret> --from-file=ca.key=<ca-key-file>
-
Label both
Secrets
with labelsstrimzi.io/kind=Kafka
andstrimzi.io/cluster=<my-cluster>
: Run the following commands:kubectl label secret <ca-cert-secret> strimzi.io/kind=Kafka strimzi.io/cluster=<my-cluster> kubectl label secret <ca-key-secret> strimzi.io/kind=Kafka strimzi.io/cluster=<my-cluster>
-
Create the
Kafka
resource for your cluster, configuring either theKafka.spec.clusterCa
or theKafka.spec.clientsCa
object to not use generated CAs:Example fragmentKafka
resource configuring the cluster CA to use certificates you supply for yourselfkind: Kafka version: kafka.strimzi.io/v1beta1 spec: # ... clusterCa: generateCertificateAuthority: false
11.4. Certificate renewal
The cluster CA and clients CA certificates are only valid for a limited time period, known as the validity period. This is usually defined as a number of days since the certificate was generated. For auto-generated CA certificates, you can configure the validity