1. Deployment overview
Strimzi simplifies the process of running Apache Kafka in a Kubernetes cluster.
This guide provides instructions on all the options available for deploying and upgrading Strimzi, describing what is deployed, and the order of deployment required to run Apache Kafka in a Kubernetes cluster.
As well as describing the deployment steps, the guide also provides pre- and post-deployment instructions to prepare for and verify a deployment. The guide also describes additional deployment options for introducing metrics.
Upgrade instructions are provided for Strimzi and Kafka upgrades.
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.
1.1. Configuring a deployment
The deployment procedures in this guide are designed to help you set up the initial structure of your deployment. After setting up the structure, you can use custom resources to configure the deployment to your precise needs. The deployment procedures use the example installation files provided with Strimzi. The procedures highlight any important configuration considerations, but they do not describe all the configuration options available.
You might want to review the configuration options available for Kafka components before you deploy Strimzi. For more information on the configuration options, see the Custom resource API reference.
1.1.1. Securing Kafka
On deployment, the Cluster Operator automatically sets up TLS certificates for data encryption and authentication within your cluster.
Strimzi provides additional configuration options for encryption, authentication and authorization:
-
Secure data exchange between the Kafka cluster and clients by managing secure access to Kafka.
-
Configure your deployment to use an authorization server to provide OAuth 2.0 authentication and OAuth 2.0 authorization.
1.1.2. Monitoring a deployment
Strimzi supports additional deployment options to monitor your deployment.
-
Extract metrics and monitor Kafka components by deploying Prometheus and Grafana with your Kafka cluster.
-
Extract additional metrics, particularly related to monitoring consumer lag, by deploying Kafka Exporter with your Kafka cluster.
-
Track messages end-to-end by setting up distributed tracing.
1.1.3. CPU and memory resource limits and requests
By default, the Strimzi Cluster Operator does not specify requests and limits for CPU and memory resources for any operands it deploys.
Having sufficient resources is important for applications like Kafka to be stable and deliver good performance.
The right amount of resources you should use depends on the specific requirements and use-cases.
You should consider configuring the CPU and memory resources. You can set resource requests and limits for each container in the Strimzi custom resources.
1.2. Strimzi custom resources
A deployment of Kafka components to a Kubernetes cluster using Strimzi is highly configurable through the application of custom resources. Custom resources are created as instances of APIs added by Custom resource definitions (CRDs) to extend Kubernetes resources.
CRDs act as configuration instructions to describe the custom resources in a Kubernetes cluster, and are provided with Strimzi for each Kafka component used in a deployment, as well as users and topics. CRDs and custom resources are defined as YAML files. Example YAML files are provided with the Strimzi distribution.
CRDs also allow Strimzi resources to benefit from native Kubernetes features like CLI accessibility and configuration validation.
1.2.1. Strimzi custom resource example
CRDs require a one-time installation in a cluster to define the schemas used to instantiate and manage Strimzi-specific resources.
After a new custom resource type is added to your cluster by installing a CRD, you can create instances of the resource based on its specification.
Depending on the cluster setup, installation typically requires cluster admin privileges.
Note
|
Access to manage custom resources is limited to Strimzi administrators. For more information, see Designating Strimzi administrators. |
A CRD defines a new kind
of resource, such as kind:Kafka
, within a Kubernetes cluster.
The Kubernetes API server allows custom resources to be created based on the kind
and understands from the CRD how to validate and store the custom resource when it is added to the Kubernetes cluster.
Warning
|
When a CustomResourceDefinition is deleted, custom resources of that type are also deleted. Additionally, Kubernetes resources created by the custom resource are also deleted, such as Deployment , Pod , Service and ConfigMap resources.
|
Each Strimzi-specific custom resource conforms to the schema defined by the CRD for the resource’s kind
.
The custom resources for Strimzi components have common configuration properties, which are defined under spec
.
To understand the relationship between a CRD and a custom resource, let’s look at a sample of the CRD for a Kafka topic.
apiVersion: kafka.strimzi.io/v1beta2
kind: CustomResourceDefinition
metadata: (1)
name: kafkatopics.kafka.strimzi.io
labels:
app: strimzi
spec: (2)
group: kafka.strimzi.io
versions:
v1beta2
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/v1beta2
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 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.
1.3. Using the Kafka Bridge to connect with a Kafka cluster
You can use the Strimzi Kafka Bridge API to create and manage consumers and send and receive records over HTTP rather than the native Kafka protocol.
When you set up the Kafka Bridge you configure HTTP access to the Kafka cluster. You can then use the Kafka Bridge to produce and consume messages from the cluster, as well as performing other operations through its REST interface.
-
For information on installing and using the Kafka Bridge, see Using the Strimzi Kafka Bridge.
1.4. Document Conventions
User-replaced values, also known as replaceables, are shown in italics with angle brackets (< >).
Underscores ( _ ) are used for multi-word values.
If the value refers to code or commands, monospace
is also used.
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. Strimzi installation methods
You can install Strimzi on Kubernetes 1.19 and later in three ways.
Installation method | Description |
---|---|
Download the release artifacts from the GitHub releases page. Download the Deploy the YAML installation artifacts to your Kubernetes cluster using You can also use the
|
|
Use the Strimzi Kafka operator in the OperatorHub.io to deploy the Cluster Operator. You then deploy Strimzi components using custom resources. |
|
Use a Helm chart to deploy the Cluster Operator. You then deploy Strimzi components using custom resources. |
For the greatest flexibility, choose the installation artifacts method. The OperatorHub.io method provides a standard configuration and allows you to take advantage of automatic updates. Helm charts provide a convenient way to manage the installation of applications.
3. What is deployed with Strimzi
Apache Kafka components are provided for deployment to Kubernetes with the Strimzi distribution. The Kafka components are generally run as clusters for availability.
A typical deployment incorporating Kafka components might include:
-
Kafka cluster of broker nodes
-
ZooKeeper cluster of replicated ZooKeeper instances
-
Kafka Connect cluster for external data connections
-
Kafka MirrorMaker cluster to mirror the Kafka cluster in a secondary cluster
-
Kafka Exporter to extract additional Kafka metrics data for monitoring
-
Kafka Bridge to make HTTP-based requests to the Kafka cluster
Not all of these components are mandatory, though you need Kafka and ZooKeeper as a minimum. Some components can be deployed without Kafka, such as MirrorMaker or Kafka Connect.
3.1. Order of deployment
The required order of deployment to a Kubernetes cluster is as follows:
-
Deploy the Cluster Operator to manage your Kafka cluster
-
Deploy the Kafka cluster with the ZooKeeper cluster, and include the Topic Operator and User Operator in the deployment
-
Optionally deploy:
-
The Topic Operator and User Operator standalone if you did not deploy them with the Kafka cluster
-
Kafka Connect
-
Kafka MirrorMaker
-
Kafka Bridge
-
Components for the monitoring of metrics
-
The Cluster Operator creates Kubernetes resources for the components,
such as Deployment
, Service
, and Pod
resources.
The names of the Kubernetes resources are appended with the name specified for a component when it’s deployed.
For example, a Kafka cluster named my-kafka-cluster
has a service named my-kafka-cluster-kafka
.
4. Preparing for your Strimzi deployment
This section shows how you prepare for a Strimzi deployment, describing:
Note
|
To run the commands in this guide, your cluster user must have the rights to manage role-based access control (RBAC) and CRDs. |
4.1. Deployment prerequisites
To deploy Strimzi, you will need the following:
-
A Kubernetes 1.19 and later cluster.
If you do not have access to a Kubernetes cluster, you can install Strimzi with Minikube.
-
The
kubectl
command-line tool is installed and configured to connect to the running cluster.
Note
|
Strimzi supports some features that are specific to OpenShift, where such integration benefits OpenShift users and there is no equivalent implementation using standard Kubernetes. |
oc
and kubectl
commands
The oc
command functions as an alternative to kubectl
.
In almost all cases the example kubectl
commands used in this guide can be done using oc
simply by replacing the command name (options and arguments remain the same).
In other words, instead of using:
kubectl apply -f your-file
when using OpenShift you can use:
oc apply -f your-file
Note
|
As an exception to this general rule, oc uses oc adm subcommands for cluster management functionality,
whereas kubectl does not make this distinction.
For example, the oc equivalent of kubectl taint is oc adm taint .
|
4.2. Downloading Strimzi release artifacts
To use deployment files to install Strimzi, download and extract the files from the GitHub releases page.
Strimzi release artifacts include sample YAML files to help you deploy the components of Strimzi to Kubernetes, perform common operations, and configure your Kafka cluster.
Use kubectl
to deploy the Cluster Operator from the install/cluster-operator
folder of the downloaded ZIP file.
For more information about deploying and configuring the Cluster Operator, see Deploying the Cluster Operator.
In addition, if you want to use standalone installations of the Topic and User Operators with a Kafka cluster that is not managed by the Strimzi Cluster Operator, you can deploy them from the install/topic-operator
and install/user-operator
folders.
Note
|
Additionally, Strimzi container images are available through the Container Registry. However, we recommend that you use the YAML files provided to deploy Strimzi. |
4.3. Example configuration and deployment files
Use the example configuration and deployment files provided with Strimzi to deploy Kafka components with different configurations and monitor your deployment. Example configuration files for custom resources contain important properties and values, which you can extend with additional supported configuration properties for your own deployment.
4.3.1. Example files location
The example files are provided with the downloadable release artifacts from the GitHub releases page.
You can also access the example files directly from the
examples
directory.
You can download and apply the examples using the kubectl
command-line tool.
The examples can serve as a starting point when building your own Kafka component configuration for deployment.
Note
|
If you installed Strimzi using the Operator, you can still download the example files and use them to upload configuration. |
4.3.2. Example files provided with Strimzi
The release artifacts include an examples
directory that contains the configuration examples.
examples
├── user (1)
├── topic (2)
├── security (3)
│ ├── tls-auth
│ ├── scram-sha-512-auth
│ └── keycloak-authorization
├── mirror-maker (4)
├── metrics (5)
├── kafka (6)
├── cruise-control (7)
├── connect (8)
└── bridge (9)
-
KafkaUser
custom resource configuration, which is managed by the User Operator. -
KafkaTopic
custom resource configuration, which is managed by Topic Operator. -
Authentication and authorization configuration for Kafka components. Includes example configuration for TLS and SCRAM-SHA-512 authentication. The Keycloak example includes
Kafka
custom resource configuration and a Keycloak realm specification. You can use the example to try Keycloak authorization services. There is also an example with enabledoauth
authentication andkeycloak
authorization metrics. -
Kafka
custom resource configuration for a deployment of Mirror Maker. Includes example configuration for replication policy and synchronization frequency. -
Metrics configuration, including Prometheus installation and Grafana dashboard files.
-
Kafka
custom resource configuration for a deployment of Kafka. Includes example configuration for an ephemeral or persistent single or multi-node deployment. -
Kafka
custom resource with a deployment configuration for Cruise Control. IncludesKafkaRebalance
custom resources to generate optimizations proposals from Cruise Control, with example configurations to use the default or user optimization goals. -
KafkaConnect
andKafkaConnector
custom resource configuration for a deployment of Kafka Connect. Includes example configuration for a single or multi-node deployment. -
KafkaBridge
custom resource configuration for a deployment of Kafka Bridge.
4.4. Pushing container images to your own registry
Container images for Strimzi are available in the Container Registry. The installation YAML files provided by Strimzi will pull the images directly from the Container Registry.
If you do not have access to the Container Registry 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 YAML files used in deployment
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 |
Strimzi Drain Cleaner |
|
Strimzi image for running the Strimzi Drain Cleaner |
4.5. Designating Strimzi administrators
Strimzi provides custom resources for configuration of your deployment. By default, permission to view, create, edit, and delete these resources is limited to Kubernetes cluster administrators. Strimzi provides two cluster roles that you can use to assign these rights to other users:
-
strimzi-view
allows users to view and list Strimzi resources. -
strimzi-admin
allows users to also create, edit or delete Strimzi resources.
When you install these roles, they will automatically aggregate (add) these rights to the default Kubernetes cluster roles.
strimzi-view
aggregates to the view
role, and strimzi-admin
aggregates to the edit
and admin
roles.
Because of the aggregation, you might not need to assign these roles to users who already have similar rights.
The following procedure shows how to assign a strimzi-admin
role that allows non-cluster administrators to manage Strimzi resources.
A system administrator can designate Strimzi administrators after the Cluster Operator is deployed.
-
The Strimzi Custom Resource Definitions (CRDs) and role-based access control (RBAC) resources to manage the CRDs have been deployed with the Cluster Operator.
-
Create the
strimzi-view
andstrimzi-admin
cluster roles in Kubernetes.kubectl create -f install/strimzi-admin
-
If needed, assign the roles that provide access rights to users that require them.
kubectl create clusterrolebinding strimzi-admin --clusterrole=strimzi-admin --user=user1 --user=user2
4.6. Installing a local Kubernetes cluster with Minikube
Minikube offers an easy way to get started with Kubernetes. If a Kubernetes cluster is unavailable, you can use Minikube to create a local cluster.
You can download and install Minikube from the Kubernetes website, which also provides documentation. Depending on the number of brokers you want to deploy inside the cluster, and whether you want to run Kafka Connect as well, try running Minikube with at least with 4 GB of RAM instead of the default 2 GB.
Once installed, start Minikube using:
minikube start --memory 4096
To interact with the cluster, install the kubectl
utility.
5. Deploying Strimzi using installation artifacts
Having prepared your environment for a deployment of Strimzi, you can deploy Strimzi to a Kubernetes cluster. Use the installation files provided with the release artifacts.
You can deploy Strimzi 0.35.1 on Kubernetes 1.19 and later.
The steps to deploy Strimzi using the installation files are as follows:
-
Use the Cluster Operator to deploy the following:
-
Optionally, deploy the following Kafka components according to your requirements:
Note
|
To run the commands in this guide, a Kubernetes user must have the rights to manage role-based access control (RBAC) and CRDs. |
5.1. Basic deployment path
You can set up a deployment where Strimzi manages a single Kafka cluster in the same namespace. You might use this configuration for development or testing. Or you can use Strimzi in a production environment to manage a number of Kafka clusters in different namespaces.
The first step for any deployment of Strimzi is to install the Cluster Operator using the install/cluster-operator
files.
A single command applies all the installation files in the cluster-operator
folder: kubectl apply -f ./install/cluster-operator
.
The command sets up everything you need to be able to create and manage a Kafka deployment, including the following:
-
Cluster Operator (
Deployment
,ConfigMap
) -
Strimzi CRDs (
CustomResourceDefinition
) -
RBAC resources (
ClusterRole
,ClusterRoleBinding
,RoleBinding
) -
Service account (
ServiceAccount
)
The basic deployment path is as follows:
-
Create a Kubernetes namespace in which to deploy the Cluster Operator
-
-
Update the
install/cluster-operator
files to use the namespace created for the Cluster Operator -
Install the Cluster Operator to watch one, multiple, or all namespaces
-
After which, you can deploy other Kafka components and set up monitoring of your deployment.
5.2. Deploying the Cluster Operator
The Cluster Operator is responsible for deploying and managing Kafka clusters within a Kubernetes cluster.
When the Cluster Operator is running, it starts to watch for updates of Kafka resources.
By default, a single replica of the Cluster Operator is deployed. You can add replicas with leader election so that additional Cluster Operators are on standby in case of disruption. For more information, see Running multiple Cluster Operator replicas with leader election.
5.2.1. Specifying the namespaces the Cluster Operator watches
The Cluster Operator watches for updates in the namespaces where the Kafka resources are deployed. When you deploy the Cluster Operator, you specify which namespaces to watch. You can specify the following namespaces:
-
A single namespace (the same namespace containing the Cluster Operator)
Note
|
The Cluster Operator can watch one, multiple, or all namespaces in a Kubernetes cluster.
The Topic Operator and User Operator watch for KafkaTopic and KafkaUser resources in a single namespace.
For more information, see Watching namespaces with Strimzi operators.
|
The Cluster Operator watches for changes to the following resources:
-
Kafka
for the Kafka cluster. -
KafkaConnect
for the Kafka Connect cluster. -
KafkaConnector
for creating and managing connectors in a Kafka Connect cluster. -
KafkaMirrorMaker
for the Kafka MirrorMaker instance. -
KafkaMirrorMaker2
for the Kafka MirrorMaker 2 instance. -
KafkaBridge
for the Kafka Bridge instance. -
KafkaRebalance
for the Cruise Control optimization requests.
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 Deployments, Pods, 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.
5.2.2. Deploying the Cluster Operator to watch a single namespace
This procedure shows how to deploy the Cluster Operator to watch Strimzi resources in a single namespace in your Kubernetes cluster.
-
You need an account with permission to create and manage
CustomResourceDefinition
and RBAC (ClusterRole
, andRoleBinding
) resources.
-
Edit the Strimzi installation files to use the namespace the Cluster Operator is going to be installed into.
For example, in this procedure the Cluster Operator is installed into the namespace
my-cluster-operator-namespace
.On Linux, use:
sed -i 's/namespace: .*/namespace: my-cluster-operator-namespace/' install/cluster-operator/*RoleBinding*.yaml
On MacOS, use:
sed -i '' 's/namespace: .*/namespace: my-cluster-operator-namespace/' install/cluster-operator/*RoleBinding*.yaml
-
Deploy the Cluster Operator:
kubectl create -f install/cluster-operator -n my-cluster-operator-namespace
-
Check the status of the deployment:
kubectl get deployments -n my-cluster-operator-namespace
Output shows the deployment name and readinessNAME READY UP-TO-DATE AVAILABLE strimzi-cluster-operator 1/1 1 1
READY
shows the number of replicas that are ready/expected. The deployment is successful when theAVAILABLE
output shows1
.
5.2.3. Deploying the Cluster Operator to watch multiple namespaces
This procedure shows how to deploy the Cluster Operator to watch Strimzi resources across multiple namespaces in your Kubernetes cluster.
-
You need an account with permission to create and manage
CustomResourceDefinition
and RBAC (ClusterRole
, andRoleBinding
) resources.
-
Edit the Strimzi installation files to use the namespace the Cluster Operator is going to be installed into.
For example, in this procedure the Cluster Operator is installed into the namespace
my-cluster-operator-namespace
.On Linux, use:
sed -i 's/namespace: .*/namespace: my-cluster-operator-namespace/' install/cluster-operator/*RoleBinding*.yaml
On MacOS, use:
sed -i '' 's/namespace: .*/namespace: my-cluster-operator-namespace/' install/cluster-operator/*RoleBinding*.yaml
-
Edit the
install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml
file to add a list of all the namespaces the Cluster Operator will watch to theSTRIMZI_NAMESPACE
environment variable.For example, in this procedure the Cluster Operator will watch the namespaces
watched-namespace-1
,watched-namespace-2
,watched-namespace-3
.apiVersion: apps/v1 kind: Deployment spec: # ... template: spec: serviceAccountName: strimzi-cluster-operator containers: - name: strimzi-cluster-operator image: quay.io/strimzi/operator:0.35.1 imagePullPolicy: IfNotPresent env: - name: STRIMZI_NAMESPACE value: watched-namespace-1,watched-namespace-2,watched-namespace-3
-
For each namespace listed, install the
RoleBindings
.In this example, we replace
watched-namespace
in these commands with the namespaces listed in the previous step, repeating them forwatched-namespace-1
,watched-namespace-2
,watched-namespace-3
:kubectl create -f install/cluster-operator/020-RoleBinding-strimzi-cluster-operator.yaml -n <watched_namespace> kubectl create -f install/cluster-operator/023-RoleBinding-strimzi-cluster-operator.yaml -n <watched_namespace> kubectl create -f install/cluster-operator/031-RoleBinding-strimzi-cluster-operator-entity-operator-delegation.yaml -n <watched_namespace>
-
Deploy the Cluster Operator:
kubectl create -f install/cluster-operator -n my-cluster-operator-namespace
-
Check the status of the deployment:
kubectl get deployments -n my-cluster-operator-namespace
Output shows the deployment name and readinessNAME READY UP-TO-DATE AVAILABLE strimzi-cluster-operator 1/1 1 1
READY
shows the number of replicas that are ready/expected. The deployment is successful when theAVAILABLE
output shows1
.
5.2.4. Deploying the Cluster Operator to watch all namespaces
This procedure shows how to deploy the Cluster Operator to watch Strimzi resources across all namespaces in your Kubernetes cluster.
When running in this mode, the Cluster Operator automatically manages clusters in any new namespaces that are created.
-
You need an account with permission to create and manage
CustomResourceDefinition
and RBAC (ClusterRole
, andRoleBinding
) resources.
-
Edit the Strimzi installation files to use the namespace the Cluster Operator is going to be installed into.
For example, in this procedure the Cluster Operator is installed into the namespace
my-cluster-operator-namespace
.On Linux, use:
sed -i 's/namespace: .*/namespace: my-cluster-operator-namespace/' install/cluster-operator/*RoleBinding*.yaml
On MacOS, use:
sed -i '' 's/namespace: .*/namespace: my-cluster-operator-namespace/' install/cluster-operator/*RoleBinding*.yaml
-
Edit the
install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml
file to set the value of theSTRIMZI_NAMESPACE
environment variable to*
.apiVersion: apps/v1 kind: Deployment spec: # ... template: spec: # ... serviceAccountName: strimzi-cluster-operator containers: - name: strimzi-cluster-operator image: quay.io/strimzi/operator:0.35.1 imagePullPolicy: IfNotPresent env: - name: STRIMZI_NAMESPACE value: "*" # ...
-
Create
ClusterRoleBindings
that grant cluster-wide access for all namespaces to the Cluster Operator.kubectl create clusterrolebinding strimzi-cluster-operator-namespaced --clusterrole=strimzi-cluster-operator-namespaced --serviceaccount my-cluster-operator-namespace:strimzi-cluster-operator kubectl create clusterrolebinding strimzi-cluster-operator-watched --clusterrole=strimzi-cluster-operator-watched --serviceaccount my-cluster-operator-namespace:strimzi-cluster-operator kubectl create clusterrolebinding strimzi-cluster-operator-entity-operator-delegation --clusterrole=strimzi-entity-operator --serviceaccount my-cluster-operator-namespace:strimzi-cluster-operator
-
Deploy the Cluster Operator to your Kubernetes cluster.
kubectl create -f install/cluster-operator -n my-cluster-operator-namespace
-
Check the status of the deployment:
kubectl get deployments -n my-cluster-operator-namespace
Output shows the deployment name and readinessNAME READY UP-TO-DATE AVAILABLE strimzi-cluster-operator 1/1 1 1
READY
shows the number of replicas that are ready/expected. The deployment is successful when theAVAILABLE
output shows1
.
5.3. Deploying Kafka
To be able to manage a Kafka cluster with the Cluster Operator, you must deploy it as a Kafka
resource.
Strimzi provides example deployment files to do this.
You can use these files to deploy the Topic Operator and User Operator at the same time.
After you have deployed the Cluster Operator, use a Kafka
resource to deploy the following components:
When installing Kafka, Strimzi also installs a ZooKeeper cluster and adds the necessary configuration to connect Kafka with ZooKeeper.
If you haven’t deployed a Kafka cluster as a Kafka
resource, you can’t use the Cluster Operator to manage it.
This applies, for example, to a Kafka cluster running outside of Kubernetes.
However, you can use the Topic Operator and User Operator with a Kafka cluster that is not managed by Strimzi, by deploying them as standalone components.
You can also deploy and use other Kafka components with a Kafka cluster not managed by Strimzi.
5.3.1. Deploying the Kafka cluster
This procedure shows how to deploy a Kafka cluster to your Kubernetes cluster using the Cluster Operator.
The deployment uses a YAML file to provide the specification to create a Kafka
resource.
Strimzi provides the following example files you can use to create 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.
In this procedure, we use the examples for an ephemeral and persistent Kafka cluster deployment.
- Ephemeral cluster
-
In general, an ephemeral (or temporary) Kafka cluster is suitable for development and testing purposes, not for production. This deployment uses
emptyDir
volumes for storing broker information (for ZooKeeper) and topics or partitions (for Kafka). Using 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 persistent volumes to store ZooKeeper and Kafka data. A
PersistentVolume
is acquired using aPersistentVolumeClaim
to make it independent of the actual type of thePersistentVolume
. ThePersistentVolumeClaim
can use aStorageClass
to trigger automatic volume provisioning. When noStorageClass
is specified, Kubernetes will try to use the defaultStorageClass
.The following examples show some common types of persistent volumes:
-
If your Kubernetes cluster runs on Amazon AWS, Kubernetes can provision Amazon EBS volumes
-
If your Kubernetes cluster runs on Microsoft Azure, Kubernetes can provision Azure Disk Storage volumes
-
If your Kubernetes cluster runs on Google Cloud, Kubernetes can provision Persistent Disk volumes
-
If your Kubernetes cluster runs on bare metal, Kubernetes can provision local persistent volumes
-
The example YAML files specify the latest supported Kafka version, and configuration for its supported log message format version and inter-broker protocol version.
The inter.broker.protocol.version
property for the Kafka config
must be the version supported by the specified Kafka version (spec.kafka.version
).
The property represents the version of Kafka protocol used in a Kafka cluster.
From Kafka 3.0.0, when the inter.broker.protocol.version
is set to 3.0
or higher, the log.message.format.version
option is ignored and doesn’t need to be set.
An update to the inter.broker.protocol.version
is required when upgrading Kafka.
The example clusters are named my-cluster
by default.
The cluster name is defined by the name of the resource and cannot be changed after the cluster has been deployed.
To change the cluster name before you deploy the cluster, edit the Kafka.metadata.name
property of the Kafka
resource in the relevant YAML file.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
version: 3.4.0
#...
config:
#...
log.message.format.version: "3.4"
inter.broker.protocol.version: "3.4"
# ...
-
Create and deploy an ephemeral or persistent cluster.
-
To create and deploy an ephemeral cluster:
kubectl apply -f examples/kafka/kafka-ephemeral.yaml
-
To create and deploy a persistent cluster:
kubectl apply -f examples/kafka/kafka-persistent.yaml
-
-
Check the status of the deployment:
kubectl get pods -n <my_cluster_operator_namespace>
Output shows the pod names and readinessNAME READY STATUS RESTARTS my-cluster-entity-operator 3/3 Running 0 my-cluster-kafka-0 1/1 Running 0 my-cluster-kafka-1 1/1 Running 0 my-cluster-kafka-2 1/1 Running 0 my-cluster-zookeeper-0 1/1 Running 0 my-cluster-zookeeper-1 1/1 Running 0 my-cluster-zookeeper-2 1/1 Running 0
my-cluster
is the name of the Kafka cluster.A sequential index number starting with
0
identifies each Kafka and ZooKeeper pod created.With the default deployment, you create an Entity Operator cluster, 3 Kafka pods, and 3 ZooKeeper pods.
READY
shows the number of replicas that are ready/expected. The deployment is successful when theSTATUS
shows asRunning
.
5.3.2. Deploying the Topic Operator using the Cluster Operator
This procedure describes how to deploy the Topic Operator using the Cluster Operator.
You configure the entityOperator
property of the Kafka
resource to include the topicOperator
.
By default, the Topic Operator watches for KafkaTopic
resources in the namespace of the Kafka cluster deployed by the Cluster Operator.
You can also specify a namespace using watchedNamespace
in the Topic Operator spec
.
A single Topic Operator can watch a single namespace.
One namespace should be watched by only one Topic Operator.
If you use Strimzi to deploy multiple Kafka clusters into the same namespace, enable the Topic Operator for only one Kafka cluster or use the watchedNamespace
property to configure the Topic Operators to watch other namespaces.
If you want to use the Topic Operator with a Kafka cluster that is not managed by Strimzi, you must deploy the Topic Operator as a standalone component.
For more information about configuring the entityOperator
and topicOperator
properties,
see Configuring the Entity Operator.
-
Edit the
entityOperator
properties of theKafka
resource to includetopicOperator
:apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: #... entityOperator: topicOperator: {} userOperator: {}
-
Configure the Topic Operator
spec
using the properties described in theEntityTopicOperatorSpec
schema reference.Use an empty object (
{}
) if you want all properties to use their default values. -
Create or update the resource:
kubectl apply -f <kafka_configuration_file>
-
Check the status of the deployment:
kubectl get pods -n <my_cluster_operator_namespace>
Output shows the pod name and readinessNAME READY STATUS RESTARTS my-cluster-entity-operator 3/3 Running 0 # ...
my-cluster
is the name of the Kafka cluster.READY
shows the number of replicas that are ready/expected. The deployment is successful when theSTATUS
shows asRunning
.
5.3.3. Deploying the User Operator using the Cluster Operator
This procedure describes how to deploy the User Operator using the Cluster Operator.
You configure the entityOperator
property of the Kafka
resource to include the userOperator
.
By default, the User Operator watches for KafkaUser
resources in the namespace of the Kafka cluster deployment.
You can also specify a namespace using watchedNamespace
in the User Operator spec
.
A single User Operator can watch a single namespace.
One namespace should be watched by only one User Operator.
If you want to use the User Operator with a Kafka cluster that is not managed by Strimzi, you must deploy the User Operator as a standalone component.
For more information about configuring the entityOperator
and userOperator
properties, see Configuring the Entity Operator.
-
Edit the
entityOperator
properties of theKafka
resource to includeuserOperator
:apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: #... entityOperator: topicOperator: {} userOperator: {}
-
Configure the User Operator
spec
using the properties described inEntityUserOperatorSpec
schema reference.Use an empty object (
{}
) if you want all properties to use their default values. -
Create or update the resource:
kubectl apply -f <kafka_configuration_file>
-
Check the status of the deployment:
kubectl get pods -n <my_cluster_operator_namespace>
Output shows the pod name and readinessNAME READY STATUS RESTARTS my-cluster-entity-operator 3/3 Running 0 # ...
my-cluster
is the name of the Kafka cluster.READY
shows the number of replicas that are ready/expected. The deployment is successful when theSTATUS
shows asRunning
.
5.4. Deploying Kafka Connect
Kafka Connect is a tool for streaming data between Apache Kafka and other systems. For example, Kafka Connect might integrate Kafka with external databases or storage and messaging systems.
In Strimzi, Kafka Connect is deployed in distributed mode. Kafka Connect can also work in standalone mode, but this is not supported by Strimzi.
Using the concept of connectors, Kafka Connect provides a framework for moving large amounts of data into and out of your Kafka cluster while maintaining scalability and reliability.
The Cluster Operator manages Kafka Connect clusters deployed using the KafkaConnect
resource and connectors created using the KafkaConnector
resource.
In order to use Kafka Connect, you need to do the following.
Note
|
The term connector is used interchangeably to mean a connector instance running within a Kafka Connect cluster, or a connector class. In this guide, the term connector is used when the meaning is clear from the context. |
5.4.1. Deploying Kafka Connect to your Kubernetes cluster
This procedure shows how to deploy a Kafka Connect cluster to your Kubernetes cluster using the Cluster Operator.
A Kafka Connect cluster deployment is implemented with a configurable number of nodes (also called workers) that distribute the workload of connectors as tasks so that the message flow is highly scalable and reliable.
The deployment uses a YAML file to provide the specification to create a KafkaConnect
resource.
Strimzi provides example configuration files. In this procedure, we use the following example file:
-
examples/connect/kafka-connect.yaml
-
Deploy Kafka Connect to your Kubernetes cluster. Use the
examples/connect/kafka-connect.yaml
file to deploy Kafka Connect.kubectl apply -f examples/connect/kafka-connect.yaml
-
Check the status of the deployment:
kubectl get pods -n <my_cluster_operator_namespace>
Output shows the deployment name and readinessNAME READY STATUS RESTARTS my-connect-cluster-connect-<pod_id> 1/1 Running 0
my-connect-cluster
is the name of the Kafka Connect cluster.A pod ID identifies each pod created.
With the default deployment, you create a single Kafka Connect pod.
READY
shows the number of replicas that are ready/expected. The deployment is successful when theSTATUS
shows asRunning
.
5.4.2. Configuring Kafka Connect for multiple instances
If you are running multiple instances of Kafka Connect, you have to change the default configuration of the following config
properties:
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
name: my-connect
spec:
# ...
config:
group.id: connect-cluster (1)
offset.storage.topic: connect-cluster-offsets (2)
config.storage.topic: connect-cluster-configs (3)
status.storage.topic: connect-cluster-status (4)
# ...
# ...
-
The Kafka Connect cluster ID within Kafka.
-
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.
5.4.3. Adding connectors
Kafka Connect uses connectors to integrate with other systems to stream data.
A connector is an instance of a Kafka Connector
class, which can be one of the following type:
- Source connector
-
A source connector is a runtime entity that fetches data from an external system and feeds it to Kafka as messages.
- Sink connector
-
A sink connector is a runtime entity that fetches messages from Kafka topics and feeds them to an external system.
Kafka Connect uses a plugin architecture to provide the implementation artifacts for connectors. Plugins allow connections to other systems and provide additional configuration to manipulate data. Plugins include connectors and other components, such as data converters and transforms. A connector operates with a specific type of external system. Each connector defines a schema for its configuration. You supply the configuration to Kafka Connect to create a connector instance within Kafka Connect. Connector instances then define a set of tasks for moving data between systems.
Add connector plugins to Kafka Connect in one of the following ways:
-
Configure Kafka Connect to build a new container image with plugins automatically
-
Create a Docker image from the base Kafka Connect image (manually or using continuous integration)
After plugins have been added to the container image, you can start, stop, and manage connector instances in the following ways:
You can also create new connector instances using these options.
Building a new container image with connector plugins automatically
Configure Kafka Connect so that Strimzi automatically builds a new container image with additional connectors.
You define the connector plugins using the .spec.build.plugins
property of the KafkaConnect
custom resource.
Strimzi will automatically download and add the connector plugins into a new container image.
The container is pushed into the container repository specified in .spec.build.output
and automatically used in the Kafka Connect deployment.
-
A container registry.
You need to provide your own container registry where images can be pushed to, stored, and pulled from. Strimzi supports private container registries as well as public registries such as Quay or Docker Hub.
-
Configure the
KafkaConnect
custom resource by specifying the container registry in.spec.build.output
, and additional connectors in.spec.build.plugins
:apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnect metadata: name: my-connect-cluster spec: # (1) #... build: output: # (2) type: docker image: my-registry.io/my-org/my-connect-cluster:latest pushSecret: my-registry-credentials plugins: # (3) - name: debezium-postgres-connector artifacts: - type: tgz url: https://repo1.maven.org/maven2/io/debezium/debezium-connector-postgres/2.1.3.Final/debezium-connector-postgres-2.1.3.Final-plugin.tar.gz sha512sum: c4ddc97846de561755dc0b021a62aba656098829c70eb3ade3b817ce06d852ca12ae50c0281cc791a5a131cb7fc21fb15f4b8ee76c6cae5dd07f9c11cb7c6e79 - name: camel-telegram artifacts: - type: tgz url: https://repo.maven.apache.org/maven2/org/apache/camel/kafkaconnector/camel-telegram-kafka-connector/0.11.5/camel-telegram-kafka-connector-0.11.5-package.tar.gz sha512sum: d6d9f45e0d1dbfcc9f6d1c7ca2046168c764389c78bc4b867dab32d24f710bb74ccf2a007d7d7a8af2dfca09d9a52ccbc2831fc715c195a3634cca055185bd91 #...
-
(Required) Configuration of the container registry where new images are pushed.
-
(Required) List of connector plugins and their artifacts to add to the new container image. Each plugin must be configured with at least one
artifact
.
-
Create or update the resource:
$ kubectl apply -f <kafka_connect_configuration_file>
-
Wait for the new container image to build, and for the Kafka Connect cluster to be deployed.
-
Use the Kafka Connect REST API or
KafkaConnector
custom resources to use the connector plugins you added.
Building a new container image with connector plugins from the Kafka Connect base image
Create a custom Docker image with connector plugins from the Kafka Connect base image
Add the custom image to the /opt/kafka/plugins
directory.
You can use the Kafka container image on Container Registry as a base image for creating your own custom image with additional connector plugins.
At startup, the Strimzi version of Kafka Connect loads any third-party connector plugins contained in the /opt/kafka/plugins
directory.
-
Create a new
Dockerfile
usingquay.io/strimzi/kafka:0.35.1-kafka-3.4.0
as the base image:FROM quay.io/strimzi/kafka:0.35.1-kafka-3.4.0 USER root:root COPY ./my-plugins/ /opt/kafka/plugins/ USER 1001
Example plugins file$ tree ./my-plugins/ ./my-plugins/ ├── debezium-connector-mongodb │ ├── bson-<version>.jar │ ├── CHANGELOG.md │ ├── CONTRIBUTE.md │ ├── COPYRIGHT.txt │ ├── debezium-connector-mongodb-<version>.jar │ ├── debezium-core-<version>.jar │ ├── LICENSE.txt │ ├── mongodb-driver-core-<version>.jar │ ├── README.md │ └── # ... ├── debezium-connector-mysql │ ├── CHANGELOG.md │ ├── CONTRIBUTE.md │ ├── COPYRIGHT.txt │ ├── debezium-connector-mysql-<version>.jar │ ├── debezium-core-<version>.jar │ ├── LICENSE.txt │ ├── mysql-binlog-connector-java-<version>.jar │ ├── mysql-connector-java-<version>.jar │ ├── README.md │ └── # ... └── debezium-connector-postgres ├── CHANGELOG.md ├── CONTRIBUTE.md ├── COPYRIGHT.txt ├── debezium-connector-postgres-<version>.jar ├── debezium-core-<version>.jar ├── LICENSE.txt ├── postgresql-<version>.jar ├── protobuf-java-<version>.jar ├── README.md └── # ...
The COPY command points to the plugin files to copy to the container image.
This example adds plugins for Debezium connectors (MongoDB, MySQL, and PostgreSQL), though not all files are listed for brevity. Debezium running in Kafka Connect looks the same as any other Kafka Connect task.
-
Build the container image.
-
Push your custom image to your container registry.
-
Point to the new container image.
You can point to the image in one of the following ways:
-
Edit the
KafkaConnect.spec.image
property of theKafkaConnect
custom resource.If set, this property overrides the
STRIMZI_KAFKA_CONNECT_IMAGES
environment variable in the Cluster Operator.apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnect metadata: name: my-connect-cluster spec: (1) #... image: my-new-container-image (2) config: (3) #...
-
The docker image for the pods.
-
Configuration of the Kafka Connect workers (not connectors).
-
Edit the
STRIMZI_KAFKA_CONNECT_IMAGES
environment variable in theinstall/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml
file to point to the new container image, and then reinstall the Cluster Operator.
-
Deploying KafkaConnector resources
Deploy KafkaConnector
resources to manage connectors.
The KafkaConnector
custom resource offers a Kubernetes-native approach to management of connectors by the Cluster Operator.
You don’t need to send HTTP requests to manage connectors, as with the Kafka Connect REST API.
You manage a running connector instance by updating its corresponding KafkaConnector
resource, and then applying the updates.
The Cluster Operator updates the configurations of the running connector instances.
You remove a connector by deleting its corresponding KafkaConnector
.
KafkaConnector
resources must be deployed to the same namespace as the Kafka Connect cluster they link to.
In the configuration shown in this procedure, the autoRestart
property is set to true
.
This enables automatic restarts of failed connectors and tasks.
Up to seven restart attempts are made, after which restarts must be made manually.
You annotate the KafkaConnector
resource to restart a connector or restart a connector task manually.
You can use your own connectors or try the examples provided by Strimzi. Up until Apache Kafka 3.1.0, example file connector plugins were included with Apache Kafka. Starting from the 3.1.1 and 3.2.0 releases of Apache Kafka, the examples need to be added to the plugin path as any other connector.
Strimzi provides an example KafkaConnector
configuration file (examples/connect/source-connector.yaml
) for the example file connector plugins, which creates the following connector instances as KafkaConnector
resources:
-
A
FileStreamSourceConnector
instance that reads each line from the Kafka license file (the source) and writes the data as messages to a single Kafka topic. -
A
FileStreamSinkConnector
instance that reads messages from the Kafka topic and writes the messages to a temporary file (the sink).
We use the example file to create connectors in this procedure.
Note
|
The example connectors are not intended for use in a production environment. |
-
A Kafka Connect deployment
-
The Cluster Operator is running
-
Add the
FileStreamSourceConnector
andFileStreamSinkConnector
plugins to Kafka Connect in one of the following ways:-
Configure Kafka Connect to build a new container image with plugins automatically
-
Create a Docker image from the base Kafka Connect image (manually or using continuous integration)
-
-
Set the
strimzi.io/use-connector-resources annotation
totrue
in the Kafka Connect configuration.apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnect metadata: name: my-connect-cluster annotations: strimzi.io/use-connector-resources: "true" spec: # ...
With the
KafkaConnector
resources enabled, the Cluster Operator watches for them. -
Edit the
examples/connect/source-connector.yaml
file:apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnector metadata: name: my-source-connector # (1) labels: strimzi.io/cluster: my-connect-cluster # (2) spec: class: org.apache.kafka.connect.file.FileStreamSourceConnector # (3) tasksMax: 2 # (4) autoRestart: # (5) enabled: true config: # (6) file: "/opt/kafka/LICENSE" # (7) topic: my-topic (8) # ...
-
Name of the
KafkaConnector
resource, which is used as the name of the connector. Use any name that is valid for a Kubernetes resource. -
Name of the Kafka Connect cluster to create the connector instance in. Connectors must be deployed to the same namespace as the Kafka Connect cluster they link to.
-
Full name or alias of the connector class. This should be present in the image being used by the Kafka Connect cluster.
-
Maximum number of Kafka Connect tasks that the connector can create.
-
Enables automatic restarts of failed connectors and tasks.
-
Connector configuration as key-value pairs.
-
This example source connector configuration reads data from the
/opt/kafka/LICENSE
file. -
Kafka topic to publish the source data to.
-
-
Create the source
KafkaConnector
in your Kubernetes cluster:kubectl apply -f examples/connect/source-connector.yaml
-
Create an
examples/connect/sink-connector.yaml
file:touch examples/connect/sink-connector.yaml
-
Paste the following YAML into the
sink-connector.yaml
file:apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnector metadata: name: my-sink-connector labels: strimzi.io/cluster: my-connect spec: class: org.apache.kafka.connect.file.FileStreamSinkConnector (1) tasksMax: 2 config: (2) file: "/tmp/my-file" (3) topics: my-topic (4)
-
Full name or alias of the connector class. This should be present in the image being used by the Kafka Connect cluster.
-
Connector configuration as key-value pairs.
-
Temporary file to publish the source data to.
-
Kafka topic to read the source data from.
-
-
Create the sink
KafkaConnector
in your Kubernetes cluster:kubectl apply -f examples/connect/sink-connector.yaml
-
Check that the connector resources were created:
kubectl get kctr --selector strimzi.io/cluster=<my_connect_cluster> -o name my-source-connector my-sink-connector
Replace <my_connect_cluster> with the name of your Kafka Connect cluster.
-
In the container, execute
kafka-console-consumer.sh
to read the messages that were written to the topic by the source connector:kubectl exec <my_kafka_cluster>-kafka-0 -i -t -- bin/kafka-console-consumer.sh --bootstrap-server <my_kafka_cluster>-kafka-bootstrap.NAMESPACE.svc:9092 --topic my-topic --from-beginning
Replace <my_kafka_cluster> with the name of your Kafka cluster.
Source and sink connector configuration options
The connector configuration is defined in the spec.config
property of the KafkaConnector
resource.
The FileStreamSourceConnector
and FileStreamSinkConnector
classes support the same configuration options as the Kafka Connect REST API.
Other connectors support different configuration options.
Name | Type | Default value | Description |
---|---|---|---|
|
String |
Null |
Source file to write messages to. If not specified, the standard input is used. |
|
List |
Null |
The Kafka topic to publish data to. |
Name | Type | Default value | Description |
---|---|---|---|
|
String |
Null |
Destination file to write messages to. If not specified, the standard output is used. |
|
List |
Null |
One or more Kafka topics to read data from. |
|
String |
Null |
A regular expression matching one or more Kafka topics to read data from. |
Manually restarting connectors
If you are using KafkaConnector
resources to manage connectors, use the restart
annotation to manually trigger a restart of a connector.
-
The Cluster Operator is running.
-
Find the name of the
KafkaConnector
custom resource that controls the Kafka connector you want to restart:kubectl get KafkaConnector
-
Restart the connector by annotating the
KafkaConnector
resource in Kubernetes.kubectl annotate KafkaConnector <kafka_connector_name> strimzi.io/restart=true
The
restart
annotation is set totrue
. -
Wait for the next reconciliation to occur (every two minutes by default).
The Kafka connector is restarted, as long as the annotation was detected by the reconciliation process. When Kafka Connect accepts the restart request, the annotation is removed from the
KafkaConnector
custom resource.
Manually restarting Kafka connector tasks
If you are using KafkaConnector
resources to manage connectors, use the restart-task
annotation to manually trigger a restart of a connector task.
-
The Cluster Operator is running.
-
Find the name of the
KafkaConnector
custom resource that controls the Kafka connector task you want to restart:kubectl get KafkaConnector
-
Find the ID of the task to be restarted from the
KafkaConnector
custom resource. Task IDs are non-negative integers, starting from 0:kubectl describe KafkaConnector <kafka_connector_name>
-
Use the ID to restart the connector task by annotating the
KafkaConnector
resource in Kubernetes:kubectl annotate KafkaConnector <kafka_connector_name> strimzi.io/restart-task=0
In this example, task
0
is restarted. -
Wait for the next reconciliation to occur (every two minutes by default).
The Kafka connector task is restarted, as long as the annotation was detected by the reconciliation process. When Kafka Connect accepts the restart request, the annotation is removed from the
KafkaConnector
custom resource.
Exposing the Kafka Connect API
Use the Kafka Connect REST API as an alternative to using KafkaConnector
resources to manage connectors.
The Kafka Connect REST API is available as a service running on <connect_cluster_name>-connect-api:8083
, where <connect_cluster_name> is the name of your Kafka Connect cluster.
The service is created when you create a Kafka Connect instance.
The operations supported by the Kafka Connect REST API are described in the Apache Kafka Connect API documentation.
Note
|
The strimzi.io/use-connector-resources annotation enables KafkaConnectors.
If you applied the annotation to your KafkaConnect resource configuration, you need to remove it to use the Kafka Connect API.
Otherwise, manual changes made directly using the Kafka Connect REST API are reverted by the Cluster Operator.
|
You can add the connector configuration as a JSON object.
curl -X POST \
http://my-connect-cluster-connect-api:8083/connectors \
-H 'Content-Type: application/json' \
-d '{ "name": "my-source-connector",
"config":
{
"connector.class":"org.apache.kafka.connect.file.FileStreamSourceConnector",
"file": "/opt/kafka/LICENSE",
"topic":"my-topic",
"tasksMax": "4",
"type": "source"
}
}'
The API is only accessible within the Kubernetes cluster. If you want to make the Kafka Connect API accessible to applications running outside of the Kubernetes cluster, you can expose it manually by creating one of the following features:
-
LoadBalancer
orNodePort
type services -
Ingress
resources (Kubernetes only) -
OpenShift routes (OpenShift only)
Note
|
The connection is insecure, so allow external access advisedly. |
If you decide to create services, use the labels from the selector
of the <connect_cluster_name>-connect-api
service to configure the pods to which the service will route the traffic:
# ...
selector:
strimzi.io/cluster: my-connect-cluster (1)
strimzi.io/kind: KafkaConnect
strimzi.io/name: my-connect-cluster-connect (2)
#...
-
Name of the Kafka Connect custom resource in your Kubernetes cluster.
-
Name of the Kafka Connect deployment created by the Cluster Operator.
You must also create a NetworkPolicy
that allows HTTP requests from external clients.
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: my-custom-connect-network-policy
spec:
ingress:
- from:
- podSelector: (1)
matchLabels:
app: my-connector-manager
ports:
- port: 8083
protocol: TCP
podSelector:
matchLabels:
strimzi.io/cluster: my-connect-cluster
strimzi.io/kind: KafkaConnect
strimzi.io/name: my-connect-cluster-connect
policyTypes:
- Ingress
-
The label of the pod that is allowed to connect to the API.
To add the connector configuration outside the cluster, use the URL of the resource that exposes the API in the curl command.
Limiting access to the Kafka Connect API
It is crucial to restrict access to the Kafka Connect API only to trusted users to prevent unauthorized actions and potential security issues. The Kafka Connect API provides extensive capabilities for altering connector configurations, which makes it all the more important to take security precautions. Someone with access to the Kafka Connect API could potentially obtain sensitive information that an administrator may assume is secure.
The Kafka Connect REST API can be accessed by anyone who has authenticated access to the Kubernetes cluster and knows the endpoint URL, which includes the hostname/IP address and port number.
For example, suppose an organization uses a Kafka Connect cluster and connectors to stream sensitive data from a customer database to a central database. The administrator uses a configuration provider plugin to store sensitive information related to connecting to the customer database and the central database, such as database connection details and authentication credentials. The configuration provider protects this sensitive information from being exposed to unauthorized users. However, someone who has access to the Kafka Connect API can still obtain access to the customer database without the consent of the administrator. They can do this by setting up a fake database and configuring a connector to connect to it. They then modify the connector configuration to point to the customer database, but instead of sending the data to the central database, they send it to the fake database. By configuring the connector to connect to the fake database, the login details and credentials for connecting to the customer database are intercepted, even though they are stored securely in the configuration provider.
If you are using the KafkaConnector
custom resources, then by default the Kubernetes RBAC rules permit only Kubernetes cluster administrators to make changes to connectors.
You can also designate non-cluster administrators to manage Strimzi resources.
With KafkaConnector
resources enabled in your Kafka Connect configuration, changes made directly using the Kafka Connect REST API are reverted by the Cluster Operator.
If you are not using the KafkaConnector
resource, the default RBAC rules do not limit access to the Kafka Connect API.
If you want to limit direct access to the Kafka Connect REST API using Kubernetes RBAC, you need to enable and use the KafkaConnector
resources.
For improved security, we recommend configuring the following properties for the Kafka Connect API:
org.apache.kafka.disallowed.login.modules
-
(Kafka 3.4 or later) Set the
org.apache.kafka.disallowed.login.modules
Java system property to prevent the use of insecure login modules. For example, specifyingcom.sun.security.auth.module.JndiLoginModule
prevents the use of the KafkaJndiLoginModule
.Example configuration for disallowing login modulesapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnect metadata: name: my-connect-cluster annotations: strimzi.io/use-connector-resources: "true" spec: # ... jvmOptions: javaSystemProperties: - name: org.apache.kafka.disallowed.login.modules value: com.sun.security.auth.module.JndiLoginModule, org.apache.kafka.common.security.kerberos.KerberosLoginModule # ...
Only allow trusted login modules and follow the latest advice from Kafka for the version you are using. As a best practice, you should explicitly disallow insecure login modules in your Kafka Connect configuration by using the
org.apache.kafka.disallowed.login.modules
system property. connector.client.config.override.policy
-
Set the
connector.client.config.override.policy
property toNone
to prevent connector configurations from overriding the Kafka Connect configuration and the consumers and producers it uses.Example configuration to specify connector override policyapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnect metadata: name: my-connect-cluster annotations: strimzi.io/use-connector-resources: "true" spec: # ... config: connector.client.config.override.policy: None # ...
Switching from using the Kafka Connect API to using KafkaConnector custom resources
You can switch from using the Kafka Connect API to using KafkaConnector
custom resources to manage your connectors.
To make the switch, do the following in the order shown:
-
Deploy
KafkaConnector
resources with the configuration to create your connector instances. -
Enable
KafkaConnector
resources in your Kafka Connect configuration by setting thestrimzi.io/use-connector-resources
annotation totrue
.
Warning
|
If you enable KafkaConnector resources before creating them, you delete all connectors.
|
To switch from using KafkaConnector
resources to using the Kafka Connect API, first remove the annotation that enables the KafkaConnector
resources from your Kafka Connect configuration.
Otherwise, manual changes made directly using the Kafka Connect REST API are reverted by the Cluster Operator.
When making the switch, check the status of the KafkaConnect
resource.
The value of metadata.generation
(the current version of the deployment) must match status.observedGeneration
(the latest reconciliation of the resource).
When the Kafka Connect cluster is Ready
, you can delete the KafkaConnector
resources.
5.5. Deploying Kafka MirrorMaker
The Cluster Operator deploys one or more Kafka MirrorMaker replicas to replicate data between Kafka clusters. This process is called mirroring to avoid confusion with the Kafka partitions replication concept. MirrorMaker consumes messages from the source cluster and republishes those messages to the target cluster.
5.5.1. Deploying Kafka MirrorMaker to your Kubernetes cluster
This procedure shows how to deploy a Kafka MirrorMaker cluster to your Kubernetes cluster using the Cluster Operator.
The deployment uses a YAML file to provide the specification to create a KafkaMirrorMaker
or KafkaMirrorMaker2
resource depending on the version of MirrorMaker deployed.
Important
|
Kafka MirrorMaker 1 (referred to as just MirrorMaker in the documentation) has been deprecated in Apache Kafka 3.0.0 and will be removed in Apache Kafka 4.0.0.
As a result, the KafkaMirrorMaker custom resource which is used to deploy Kafka MirrorMaker 1 has been deprecated in Strimzi as well.
The KafkaMirrorMaker resource will be removed from Strimzi when we adopt Apache Kafka 4.0.0.
As a replacement, use the KafkaMirrorMaker2 custom resource with the IdentityReplicationPolicy .
|
Strimzi provides example configuration files. In this procedure, we use the following example files:
-
examples/mirror-maker/kafka-mirror-maker.yaml
-
examples/mirror-maker/kafka-mirror-maker-2.yaml
-
Deploy Kafka MirrorMaker to your Kubernetes cluster:
For MirrorMaker:
kubectl apply -f examples/mirror-maker/kafka-mirror-maker.yaml
For MirrorMaker 2:
kubectl apply -f examples/mirror-maker/kafka-mirror-maker-2.yaml
-
Check the status of the deployment:
kubectl get pods -n <my_cluster_operator_namespace>
Output shows the deployment name and readinessNAME READY STATUS RESTARTS my-mirror-maker-mirror-maker-<pod_id> 1/1 Running 1 my-mm2-cluster-mirrormaker2-<pod_id> 1/1 Running 1
my-mirror-maker
is the name of the Kafka MirrorMaker cluster.my-mm2-cluster
is the name of the Kafka MirrorMaker 2 cluster.A pod ID identifies each pod created.
With the default deployment, you install a single MirrorMaker or MirrorMaker 2 pod.
READY
shows the number of replicas that are ready/expected. The deployment is successful when theSTATUS
shows asRunning
.
5.6. Deploying Kafka Bridge
The Cluster Operator deploys one or more Kafka bridge replicas to send data between Kafka clusters and clients via HTTP API.
5.6.1. Deploying Kafka Bridge to your Kubernetes cluster
This procedure shows how to deploy a Kafka Bridge cluster to your Kubernetes cluster using the Cluster Operator.
The deployment uses a YAML file to provide the specification to create a KafkaBridge
resource.
Strimzi provides example configuration files. In this procedure, we use the following example file:
-
examples/bridge/kafka-bridge.yaml
-
Deploy Kafka Bridge to your Kubernetes cluster:
kubectl apply -f examples/bridge/kafka-bridge.yaml
-
Check the status of the deployment:
kubectl get pods -n <my_cluster_operator_namespace>
Output shows the deployment name and readinessNAME READY STATUS RESTARTS my-bridge-bridge-<pod_id> 1/1 Running 0
my-bridge
is the name of the Kafka Bridge cluster.A pod ID identifies each pod created.
With the default deployment, you install a single Kafka Bridge pod.
READY
shows the number of replicas that are ready/expected. The deployment is successful when theSTATUS
shows asRunning
.
5.6.2. Exposing the Kafka Bridge service to your local machine
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/my-bridge-bridge-<pod_id>
-
Connect to the Kafka Bridge pod on port
8080
:kubectl port-forward pod/my-bridge-bridge-<pod_id> 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.
5.6.3. 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 creating one of the following features:
-
LoadBalancer
orNodePort
type services -
Ingress
resources (Kubernetes only) -
OpenShift routes (OpenShift only)
If you decide to create Services, use the labels from the selector
of the <kafka_bridge_name>-bridge-service
service 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.
5.7. Alternative standalone deployment options for Strimzi operators
You can perform a standalone deployment of the Topic Operator and User Operator. Consider a standalone deployment of these operators if you are using a Kafka cluster that is not managed by the Cluster Operator.
You deploy the operators to Kubernetes. Kafka can be running outside of Kubernetes. For example, you might be using a Kafka as a managed service. You adjust the deployment configuration for the standalone operator to match the address of your Kafka cluster.
5.7.1. Deploying the standalone Topic Operator
This procedure shows how to deploy the Topic Operator as a standalone component for topic management. You can use a standalone Topic Operator with a Kafka cluster that is not managed by the Cluster Operator.
A standalone deployment can operate with any Kafka cluster.
Standalone deployment files are provided with Strimzi.
Use the 05-Deployment-strimzi-topic-operator.yaml
deployment file to deploy the Topic Operator.
Add or set the environment variables needed to make a connection to a Kafka cluster.
The Topic Operator watches for KafkaTopic
resources in a single namespace.
You specify the namespace to watch, and the connection to the Kafka cluster, in the Topic Operator configuration.
A single Topic Operator can watch a single namespace.
One namespace should be watched by only one Topic Operator.
If you want to use more than one Topic Operator, configure each of them to watch different namespaces.
In this way, you can use Topic Operators with multiple Kafka clusters.
-
You are running a Kafka cluster for the Topic Operator to connect to.
As long as the standalone Topic Operator is correctly configured for connection, the Kafka cluster can be running on a bare-metal environment, a virtual machine, or as a managed cloud application service.
-
Edit the
env
properties in theinstall/topic-operator/05-Deployment-strimzi-topic-operator.yaml
standalone deployment file.Example standalone Topic Operator deployment configurationapiVersion: apps/v1 kind: Deployment metadata: name: strimzi-topic-operator labels: app: strimzi spec: # ... template: # ... spec: # ... containers: - name: strimzi-topic-operator # ... env: - name: STRIMZI_NAMESPACE (1) valueFrom: fieldRef: fieldPath: metadata.namespace - name: STRIMZI_KAFKA_BOOTSTRAP_SERVERS (2) value: my-kafka-bootstrap-address:9092 - name: STRIMZI_RESOURCE_LABELS (3) value: "strimzi.io/cluster=my-cluster" - name: STRIMZI_ZOOKEEPER_CONNECT (4) value: my-cluster-zookeeper-client:2181 - name: STRIMZI_ZOOKEEPER_SESSION_TIMEOUT_MS (5) value: "18000" - name: STRIMZI_FULL_RECONCILIATION_INTERVAL_MS (6) value: "120000" - name: STRIMZI_TOPIC_METADATA_MAX_ATTEMPTS (7) value: "6" - name: STRIMZI_LOG_LEVEL (8) value: INFO - name: STRIMZI_TLS_ENABLED (9) value: "false" - name: STRIMZI_JAVA_OPTS (10) value: "-Xmx=512M -Xms=256M" - name: STRIMZI_JAVA_SYSTEM_PROPERTIES (11) value: "-Djavax.net.debug=verbose -DpropertyName=value" - name: STRIMZI_PUBLIC_CA (12) value: "false" - name: STRIMZI_TLS_AUTH_ENABLED (13) value: "false" - name: STRIMZI_SASL_ENABLED (14) value: "false" - name: STRIMZI_SASL_USERNAME (15) value: "admin" - name: STRIMZI_SASL_PASSWORD (16) value: "password" - name: STRIMZI_SASL_MECHANISM (17) value: "scram-sha-512" - name: STRIMZI_SECURITY_PROTOCOL (18) value: "SSL"
-
The Kubernetes namespace for the Topic Operator to watch for
KafkaTopic
resources. Specify the namespace of the Kafka cluster. -
The host and port pair of the bootstrap broker address to discover and connect to all brokers in the Kafka cluster. Use a comma-separated list to specify two or three broker addresses in case a server is down.
-
The label to identify the
KafkaTopic
resources managed by the Topic Operator. This does not have to be the name of the Kafka cluster. It can be the label assigned to theKafkaTopic
resource. If you deploy more than one Topic Operator, the labels must be unique for each. That is, the operators cannot manage the same resources. -
The host and port pair of the address to connect to the ZooKeeper cluster. This must be the same ZooKeeper cluster that your Kafka cluster is using.
-
The ZooKeeper session timeout, in milliseconds. The default is
18000
(18 seconds). -
The interval between periodic reconciliations, in milliseconds. The default is
120000
(2 minutes). -
The number of attempts at getting topic metadata from Kafka. The time between each attempt is defined as an exponential backoff. Consider increasing this value when topic creation takes more time due to the number of partitions or replicas. The default is
6
attempts. -
The level for printing logging messages. You can set the level to
ERROR
,WARNING
,INFO
,DEBUG
, orTRACE
. -
Enables TLS support for encrypted communication with the Kafka brokers.
-
(Optional) The Java options used by the JVM running the Topic Operator.
-
(Optional) The debugging (
-D
) options set for the Topic Operator. -
(Optional) Skips the generation of trust store certificates if TLS is enabled through
STRIMZI_TLS_ENABLED
. If this environment variable is enabled, the brokers must use a public trusted certificate authority for their TLS certificates. The default isfalse
. -
(Optional) Generates key store certificates for mTLS authentication. Setting this to
false
disables client authentication with mTLS to the Kafka brokers. The default istrue
. -
(Optional) Enables SASL support for client authentication when connecting to Kafka brokers. The default is
false
. -
(Optional) The SASL username for client authentication. Mandatory only if SASL is enabled through
STRIMZI_SASL_ENABLED
. -
(Optional) The SASL password for client authentication. Mandatory only if SASL is enabled through
STRIMZI_SASL_ENABLED
. -
(Optional) The SASL mechanism for client authentication. Mandatory only if SASL is enabled through
STRIMZI_SASL_ENABLED
. You can set the value toplain
,scram-sha-256
, orscram-sha-512
. -
(Optional) The security protocol used for communication with Kafka brokers. The default value is "PLAINTEXT". You can set the value to
PLAINTEXT
,SSL
,SASL_PLAINTEXT
, orSASL_SSL
.
-
-
If you want to connect to Kafka brokers that are using certificates from a public certificate authority, set
STRIMZI_PUBLIC_CA
totrue
. Set this property totrue
, for example, if you are using Amazon AWS MSK service. -
If you enabled mTLS with the
STRIMZI_TLS_ENABLED
environment variable, specify the keystore and truststore used to authenticate connection to the Kafka cluster.Example mTLS configuration# .... env: - name: STRIMZI_TRUSTSTORE_LOCATION (1) value: "/path/to/truststore.p12" - name: STRIMZI_TRUSTSTORE_PASSWORD (2) value: "TRUSTSTORE-PASSWORD" - name: STRIMZI_KEYSTORE_LOCATION (3) value: "/path/to/keystore.p12" - name: STRIMZI_KEYSTORE_PASSWORD (4) value: "KEYSTORE-PASSWORD" # ...
-
The truststore contains the public keys of the Certificate Authorities used to sign the Kafka and ZooKeeper server certificates.
-
The password for accessing the truststore.
-
The keystore contains the private key for mTLS authentication.
-
The password for accessing the keystore.
-
-
Deploy the Topic Operator.
kubectl create -f install/topic-operator
-
Check the status of the deployment:
kubectl get deployments
Output shows the deployment name and readinessNAME READY UP-TO-DATE AVAILABLE strimzi-topic-operator 1/1 1 1
READY
shows the number of replicas that are ready/expected. The deployment is successful when theAVAILABLE
output shows1
.
5.7.2. Deploying the standalone User Operator
This procedure shows how to deploy the User Operator as a standalone component for user management. You can use a standalone User Operator with a Kafka cluster that is not managed by the Cluster Operator.
A standalone deployment can operate with any Kafka cluster.
Standalone deployment files are provided with Strimzi.
Use the 05-Deployment-strimzi-user-operator.yaml
deployment file to deploy the User Operator.
Add or set the environment variables needed to make a connection to a Kafka cluster.
The User Operator watches for KafkaUser
resources in a single namespace.
You specify the namespace to watch, and the connection to the Kafka cluster, in the User Operator configuration.
A single User Operator can watch a single namespace.
One namespace should be watched by only one User Operator.
If you want to use more than one User Operator, configure each of them to watch different namespaces.
In this way, you can use the User Operator with multiple Kafka clusters.
-
You are running a Kafka cluster for the User Operator to connect to.
As long as the standalone User Operator is correctly configured for connection, the Kafka cluster can be running on a bare-metal environment, a virtual machine, or as a managed cloud application service.
-
Edit the following
env
properties in theinstall/user-operator/05-Deployment-strimzi-user-operator.yaml
standalone deployment file.Example standalone User Operator deployment configurationapiVersion: apps/v1 kind: Deployment metadata: name: strimzi-user-operator labels: app: strimzi spec: # ... template: # ... spec: # ... containers: - name: strimzi-user-operator # ... env: - name: STRIMZI_NAMESPACE (1) valueFrom: fieldRef: fieldPath: metadata.namespace - name: STRIMZI_KAFKA_BOOTSTRAP_SERVERS (2) value: my-kafka-bootstrap-address:9092 - name: STRIMZI_CA_CERT_NAME (3) value: my-cluster-clients-ca-cert - name: STRIMZI_CA_KEY_NAME (4) value: my-cluster-clients-ca - name: STRIMZI_LABELS (5) value: "strimzi.io/cluster=my-cluster" - name: STRIMZI_FULL_RECONCILIATION_INTERVAL_MS (6) value: "120000" - name: STRIMZI_WORK_QUEUE_SIZE (7) value: 10000 - name: STRIMZI_CONTROLLER_THREAD_POOL_SIZE (8) value: 10 - name: STRIMZI_USER_OPERATIONS_THREAD_POOL_SIZE (9) value: 4 - name: STRIMZI_LOG_LEVEL (10) value: INFO - name: STRIMZI_GC_LOG_ENABLED (11) value: "true" - name: STRIMZI_CA_VALIDITY (12) value: "365" - name: STRIMZI_CA_RENEWAL (13) value: "30" - name: STRIMZI_JAVA_OPTS (14) value: "-Xmx=512M -Xms=256M" - name: STRIMZI_JAVA_SYSTEM_PROPERTIES (15) value: "-Djavax.net.debug=verbose -DpropertyName=value" - name: STRIMZI_SECRET_PREFIX (16) value: "kafka-" - name: STRIMZI_ACLS_ADMIN_API_SUPPORTED (17) value: "true" - name: STRIMZI_MAINTENANCE_TIME_WINDOWS (18) value: '* * 8-10 * * ?;* * 14-15 * * ?' - name: STRIMZI_KAFKA_ADMIN_CLIENT_CONFIGURATION (19) value: | default.api.timeout.ms=120000 request.timeout.ms=60000 - name: STRIMZI_KRAFT_ENABLED (20) value: "false"
-
The Kubernetes namespace for the User Operator to watch for
KafkaUser
resources. Only one namespace can be specified. -
The host and port pair of the bootstrap broker address to discover and connect to all brokers in the Kafka cluster. Use a comma-separated list to specify two or three broker addresses in case a server is down.
-
The Kubernetes
Secret
that contains the public key (ca.crt
) value of the CA (certificate authority) that signs new user certificates for mTLS authentication. -
The Kubernetes
Secret
that contains the private key (ca.key
) value of the CA that signs new user certificates for mTLS authentication. -
The label to identify the
KafkaUser
resources managed by the User Operator. This does not have to be the name of the Kafka cluster. It can be the label assigned to theKafkaUser
resource. If you deploy more than one User Operator, the labels must be unique for each. That is, the operators cannot manage the same resources. -
The interval between periodic reconciliations, in milliseconds. The default is
120000
(2 minutes). -
The size of the controller event queue. The size of the queue should be at least as big as the maximal amount of users you expect the User Operator to operate. The default is
1024
. -
The size of the worker pool for reconciling the users. Bigger pool might require more resources, but it will also handle more
KafkaUser
resources The default is50
. -
The size of the worker pool for Kafka Admin API and Kubernetes operations. Bigger pool might require more resources, but it will also handle more
KafkaUser
resources The default is4
. -
The level for printing logging messages. You can set the level to
ERROR
,WARNING
,INFO
,DEBUG
, orTRACE
. -
Enables garbage collection (GC) logging. The default is
true
. -
The validity period for the CA. The default is
365
days. -
The renewal period for the CA. The renewal period is measured backwards from the expiry date of the current certificate. The default is
30
days to initiate certificate renewal before the old certificates expire. -
(Optional) The Java options used by the JVM running the User Operator
-
(Optional) The debugging (
-D
) options set for the User Operator -
(Optional) Prefix for the names of Kubernetes secrets created by the User Operator.
-
(Optional) Indicates whether the Kafka cluster supports management of authorization ACL rules using the Kafka Admin API. When set to
false
, the User Operator will reject all resources withsimple
authorization ACL rules. This helps to avoid unnecessary exceptions in the Kafka cluster logs. The default istrue
. -
(Optional) Semi-colon separated list of Cron Expressions defining the maintenance time windows during which the expiring user certificates will be renewed.
-
(Optional) Configuration options for configuring the Kafka Admin client used by the User Operator in the properties format.
-
(Optional) Indicates whether the Kafka cluster the User Operator is connecting to is using KRaft instead of ZooKeeper. Set this variable to
true
if the Kafka cluster uses KRaft. The default isfalse
. Note that some features are not available when running against KRaft clusters. For example, management of SCRAM-SHA-512 users is disabled because Apache Kafka currently does not support it.
-
-
If you are using mTLS to connect to the Kafka cluster, specify the secrets used to authenticate connection. Otherwise, go to the next step.
Example mTLS configuration# .... env: - name: STRIMZI_CLUSTER_CA_CERT_SECRET_NAME (1) value: my-cluster-cluster-ca-cert - name: STRIMZI_EO_KEY_SECRET_NAME (2) value: my-cluster-entity-operator-certs # ..."
-
The Kubernetes
Secret
that contains the public key (ca.crt
) value of the CA that signs Kafka broker certificates. -
The Kubernetes
Secret
that contains the certificate public key (entity-operator.crt
) and private key (entity-operator.key
) that is used for mTLS authentication against the Kafka cluster.
-
-
Deploy the User Operator.
kubectl create -f install/user-operator
-
Check the status of the deployment:
kubectl get deployments
Output shows the deployment name and readinessNAME READY UP-TO-DATE AVAILABLE strimzi-user-operator 1/1 1 1
READY
shows the number of replicas that are ready/expected. The deployment is successful when theAVAILABLE
output shows1
.
6. Deploying Strimzi from OperatorHub.io
OperatorHub.io is a catalog of Kubernetes operators sourced from multiple providers. It offers you an alternative way to install a stable version of Strimzi.
The Operator Lifecycle Manager is used for the installation and management of all operators published on OperatorHub.io. Operator Lifecycle Manager is a prerequisite for installing the Strimzi Kafka operator
To install Strimzi, locate Strimzi from OperatorHub.io, and follow the instructions provided to deploy the Cluster Operator.
After you have deployed the Cluster Operator, you can deploy Strimzi components using custom resources.
For example, you can deploy the Kafka
custom resource, and the installed Cluster Operator will create a Kafka cluster.
Upgrades between versions might include manual steps. Always read the release notes before upgrading.
For information on upgrades, see Cluster Operator upgrade options.
Warning
|
Make sure you use the appropriate update channel.
Installing Strimzi from the default stable channel is generally safe.
However, we do not recommend enabling automatic OLM updates on the stable channel.
An automatic upgrade will skip any necessary steps prior to upgrade.
For example, to upgrade from 0.22 or earlier
you must first update custom resources to support the v1beta2 API version.
Use automatic upgrades only on version-specific channels.
|
7. Deploying Strimzi using Helm
Helm charts are used to package, configure, and deploy Kubernetes resources. Strimzi provides a Helm chart to deploy the Cluster Operator.
After you have deployed the Cluster Operator this way, you can deploy Strimzi components using custom resources.
For example, you can deploy the Kafka
custom resource, and the installed Cluster Operator will create a Kafka cluster.
For information on upgrades, see Cluster Operator upgrade options.
-
The Helm client must be installed on a local machine.
-
Helm must be installed to 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-operator strimzi/strimzi-kafka-operator
-
Verify that the Cluster Operator has been deployed successfully using the Helm command line tool:
helm ls
-
Deploy Kafka and other Kafka components using custom resources.
8. Loading configuration values from external sources
Use configuration providers to load configuration data from external sources. The providers operate independently of Strimzi. You can use them to load configuration data for all Kafka components, including producers and consumers. You reference the external source in the configuration of the component and provide access rights. The provider loads data without needing to restart the Kafka component or extracting files, even when referencing a new external source. For example, use providers to supply the credentials for the Kafka Connect connector configuration. The configuration must include any access rights to the external source.
8.1. Enabling configuration providers
You can enable one or more configuration providers using the config.providers
properties in the spec
configuration of a component.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
name: my-connect
annotations:
strimzi.io/use-connector-resources: "true"
spec:
# ...
config:
# ...
config.providers: env
config.providers.env.class: io.strimzi.kafka.EnvVarConfigProvider
# ...
- KubernetesSecretConfigProvider
-
Loads configuration data from Kubernetes secrets. You specify the name of the secret and the key within the secret where the configuration data is stored. This provider is useful for storing sensitive configuration data like passwords or other user credentials.
- KubernetesConfigMapConfigProvider
-
Loads configuration data from Kubernetes config maps. You specify the name of the config map and the key within the config map where the configuration data is stored. This provider is useful for storing non-sensitive configuration data.
- EnvVarConfigProvider
-
Loads configuration data from environment variables. You specify the name of the environment variable where the configuration data is stored. This provider is useful for configuring applications running in containers, for example, to load certificates or JAAS configuration from environment variables mapped from secrets.
- FileConfigProvider
-
Loads configuration data from a file. You specify the path to the file where the configuration data is stored. This provider is useful for loading configuration data from files that are mounted into containers.
- DirectoryConfigProvider
-
Loads configuration data from files within a directory. You specify the path to the directory where the configuration files are stored. This provider is useful for loading multiple configuration files and for organizing configuration data into separate files.
To use KubernetesSecretConfigProvider
and KubernetesConfigMapConfigProvider
, which are part of the Kubernetes Configuration Provider plugin, you must set up access rights to the namespace that contains the configuration file.
You can use the other providers without setting up access rights. You can supply connector configuration for Kafka Connect or MirrorMaker 2 in this way by doing the following:
-
Mount config maps or secrets into the Kafka Connect pod as environment variables or volumes
-
Enable
EnvVarConfigProvider
,FileConfigProvider
, orDirectoryConfigProvider
in the Kafka Connect or MirrorMaker 2 configuration -
Pass connector configuration using the
externalConfiguration
property in thespec
of theKafkaConnect
orKafkaMirrorMaker2
resource
Using providers help prevent the passing of restricted information through the Kafka Connect REST interface. You can use this approach in the following scenarios:
-
Mounting environment variables with the values a connector uses to connect and communicate with a data source
-
Mounting a properties file with values that are used to configure Kafka Connect connectors
-
Mounting files in a directory that contains values for the TLS truststore and keystore used by a connector
Note
|
A restart is required when using a new Secret or ConfigMap for a connector, which can disrupt other connectors.
|
8.2. Loading configuration values from secrets or config maps
Use the KubernetesSecretConfigProvider
to provide configuration properties from a secret or the KubernetesConfigMapConfigProvider
to provide configuration properties from a config map.
In this procedure, a config map provides configuration properties for a connector. The properties are specified as key values of the config map. The config map is mounted into the Kafka Connect pod as a volume.
-
A Kafka cluster is running.
-
The Cluster Operator is running.
-
You have a config map containing the connector configuration.
apiVersion: v1
kind: ConfigMap
metadata:
name: my-connector-configuration
data:
option1: value1
option2: value2
-
Configure the
KafkaConnect
resource.-
Enable the
KubernetesConfigMapConfigProvider
The specification shown here can support loading values from config maps and secrets.
Example Kafka Connect configuration to use config maps and secretsapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnect metadata: name: my-connect annotations: strimzi.io/use-connector-resources: "true" spec: # ... config: # ... config.providers: secrets,configmaps # (1) config.providers.configmaps.class: io.strimzi.kafka.KubernetesConfigMapConfigProvider # (2) config.providers.secrets.class: io.strimzi.kafka.KubernetesSecretConfigProvider # (3) # ...
-
The alias for the configuration provider is used to define other configuration parameters. The provider parameters use the alias from
config.providers
, taking the formconfig.providers.${alias}.class
. -
KubernetesConfigMapConfigProvider
provides values from config maps. -
KubernetesSecretConfigProvider
provides values from secrets.
-
-
Create or update the resource to enable the provider.
kubectl apply -f <kafka_connect_configuration_file>
-
Create a role that permits access to the values in the external config map.
Example role to access values from a config mapapiVersion: rbac.authorization.k8s.io/v1 kind: Role metadata: name: connector-configuration-role rules: - apiGroups: [""] resources: ["configmaps"] resourceNames: ["my-connector-configuration"] verbs: ["get"] # ...
The rule gives the role permission to access the
my-connector-configuration
config map. -
Create a role binding to permit access to the namespace that contains the config map.
Example role binding to access the namespace that contains the config mapapiVersion: rbac.authorization.k8s.io/v1 kind: RoleBinding metadata: name: connector-configuration-role-binding subjects: - kind: ServiceAccount name: my-connect-connect namespace: my-project roleRef: kind: Role name: connector-configuration-role apiGroup: rbac.authorization.k8s.io # ...
The role binding gives the role permission to access the
my-project
namespace.The service account must be the same one used by the Kafka Connect deployment. The service account name format is
<cluster_name>-connect
, where<cluster_name>
is the name of theKafkaConnect
custom resource. -
Reference the config map in the connector configuration.
Example connector configuration referencing the config mapapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnector metadata: name: my-connector labels: strimzi.io/cluster: my-connect spec: # ... config: option: ${configmaps:my-project/my-connector-configuration:option1} # ... # ...
The placeholder structure is
configmaps:<path_and_file_name>:<property>
.KubernetesConfigMapConfigProvider
reads and extracts theoption1
property value from the external config map.
8.3. Loading configuration values from environment variables
Use the EnvVarConfigProvider
to provide configuration properties as environment variables.
Environment variables can contain values from config maps or secrets.
In this procedure, environment variables provide configuration properties for a connector to communicate with Amazon AWS.
The connector must be able to read the AWS_ACCESS_KEY_ID
and AWS_SECRET_ACCESS_KEY
.
The values of the environment variables are derived from a secret mounted into the Kafka Connect pod.
Note
|
The names of user-defined environment variables cannot start with KAFKA_ or STRIMZI_ .
|
-
A Kafka cluster is running.
-
The Cluster Operator is running.
-
You have a secret containing the connector configuration.
apiVersion: v1
kind: Secret
metadata:
name: aws-creds
type: Opaque
data:
awsAccessKey: QUtJQVhYWFhYWFhYWFhYWFg=
awsSecretAccessKey: Ylhsd1lYTnpkMjl5WkE=
-
Configure the
KafkaConnect
resource.-
Enable the
EnvVarConfigProvider
-
Specify the environment variables using the
externalConfiguration
property.
Example Kafka Connect configuration to use external environment variablesapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnect metadata: name: my-connect annotations: strimzi.io/use-connector-resources: "true" spec: # ... config: # ... config.providers: env # (1) config.providers.env.class: io.strimzi.kafka.EnvVarConfigProvider # (2) # ... externalConfiguration: env: - name: AWS_ACCESS_KEY_ID # (3) valueFrom: secretKeyRef: name: aws-creds # (4) key: awsAccessKey # (5) - name: AWS_SECRET_ACCESS_KEY valueFrom: secretKeyRef: name: aws-creds key: awsSecretAccessKey # ...
-
The alias for the configuration provider is used to define other configuration parameters. The provider parameters use the alias from
config.providers
, taking the formconfig.providers.${alias}.class
. -
EnvVarConfigProvider
provides values from environment variables. -
The environment variable takes a value from the secret.
-
The name of the secret containing the environment variable.
-
The name of the key stored in the secret.
NoteThe secretKeyRef
property references keys in a secret. If you are using a config map instead of a secret, use theconfigMapKeyRef
property. -
-
Create or update the resource to enable the provider.
kubectl apply -f <kafka_connect_configuration_file>
-
Reference the environment variable in the connector configuration.
Example connector configuration referencing the environment variableapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnector metadata: name: my-connector labels: strimzi.io/cluster: my-connect spec: # ... config: option: ${env:AWS_ACCESS_KEY_ID} option: ${env:AWS_SECRET_ACCESS_KEY} # ... # ...
The placeholder structure is
env:<environment_variable_name>
.EnvVarConfigProvider
reads and extracts the environment variable values from the mounted secret.
8.4. Loading configuration values from a file within a directory
Use the FileConfigProvider
to provide configuration properties from a file within a directory.
Files can be config maps or secrets.
In this procedure, a file provides configuration properties for a connector.
A database name and password are specified as properties of a secret.
The secret is mounted to the Kafka Connect pod as a volume.
Volumes are mounted on the path /opt/kafka/external-configuration/<volume-name>
.
-
A Kafka cluster is running.
-
The Cluster Operator is running.
-
You have a secret containing the connector configuration.
apiVersion: v1
kind: Secret
metadata:
name: mysecret
type: Opaque
stringData:
connector.properties: |- # (1)
dbUsername: my-username # (2)
dbPassword: my-password
-
The connector configuration in properties file format.
-
Database username and password properties used in the configuration.
-
Configure the
KafkaConnect
resource.-
Enable the
FileConfigProvider
-
Specify the file using the
externalConfiguration
property.
Example Kafka Connect configuration to use an external property fileapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnect metadata: name: my-connect spec: # ... config: config.providers: file # (1) config.providers.file.class: org.apache.kafka.common.config.provider.FileConfigProvider # (2) #... externalConfiguration: volumes: - name: connector-config # (3) secret: secretName: mysecret # (4)
-
The alias for the configuration provider is used to define other configuration parameters.
-
FileConfigProvider
provides values from properties files. The parameter uses the alias fromconfig.providers
, taking the formconfig.providers.${alias}.class
. -
The name of the volume containing the secret.
-
The name of the secret.
-
-
Create or update the resource to enable the provider.
kubectl apply -f <kafka_connect_configuration_file>
-
Reference the file properties in the connector configuration as placeholders.
Example connector configuration referencing the fileapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnector metadata: name: my-source-connector labels: strimzi.io/cluster: my-connect-cluster spec: class: io.debezium.connector.mysql.MySqlConnector tasksMax: 2 config: database.hostname: 192.168.99.1 database.port: "3306" database.user: "${file:/opt/kafka/external-configuration/connector-config/mysecret:dbUsername}" database.password: "${file:/opt/kafka/external-configuration/connector-config/mysecret:dbPassword}" database.server.id: "184054" #...
The placeholder structure is
file:<path_and_file_name>:<property>
.FileConfigProvider
reads and extracts the database username and password property values from the mounted secret.
8.5. Loading configuration values from multiple files within a directory
Use the DirectoryConfigProvider
to provide configuration properties from multiple files within a directory.
Files can be config maps or secrets.
In this procedure, a secret provides the TLS keystore and truststore user credentials for a connector.
The credentials are in separate files.
The secrets are mounted into the Kafka Connect pod as volumes.
Volumes are mounted on the path /opt/kafka/external-configuration/<volume-name>
.
-
A Kafka cluster is running.
-
The Cluster Operator is running.
-
You have a secret containing the user credentials.
apiVersion: v1
kind: Secret
metadata:
name: my-user
labels:
strimzi.io/kind: KafkaUser
strimzi.io/cluster: my-cluster
type: Opaque
data:
ca.crt: <public_key> # Public key of the clients CA
user.crt: <user_certificate> # Public key of the user
user.key: <user_private_key> # Private key of the user
user.p12: <store> # PKCS #12 store for user certificates and keys
user.password: <password_for_store> # Protects the PKCS #12 store
The my-user
secret provides the keystore credentials (user.crt
and user.key
) for the connector.
The <cluster_name>-cluster-ca-cert
secret generated when deploying the Kafka cluster provides the cluster CA certificate as truststore credentials (ca.crt
).
-
Configure the
KafkaConnect
resource.-
Enable the
DirectoryConfigProvider
-
Specify the files using the
externalConfiguration
property.
Example Kafka Connect configuration to use external property filesapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnect metadata: name: my-connect spec: # ... config: config.providers: directory # (1) config.providers.directory.class: org.apache.kafka.common.config.provider.DirectoryConfigProvider # (2) #... externalConfiguration: volumes: # (3) - name: cluster-ca # (4) secret: secretName: my-cluster-cluster-ca-cert # (5) - name: my-user secret: secretName: my-user # (6)
-
The alias for the configuration provider is used to define other configuration parameters.
-
DirectoryConfigProvider
provides values from files in a directory. The parameter uses the alias fromconfig.providers
, taking the formconfig.providers.${alias}.class
. -
The names of the volumes containing the secrets.
-
The name of the secret for the cluster CA certificate to supply truststore configuration.
-
The name of the secret for the user to supply keystore configuration.
-
-
Create or update the resource to enable the provider.
kubectl apply -f <kafka_connect_configuration_file>
-
Reference the file properties in the connector configuration as placeholders.
Example connector configuration referencing the filesapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnector metadata: name: my-source-connector labels: strimzi.io/cluster: my-connect-cluster spec: class: io.debezium.connector.mysql.MySqlConnector tasksMax: 2 config: # ... database.history.producer.security.protocol: SSL database.history.producer.ssl.truststore.type: PEM database.history.producer.ssl.truststore.certificates: "${directory:/opt/kafka/external-configuration/cluster-ca:ca.crt}" database.history.producer.ssl.keystore.type: PEM database.history.producer.ssl.keystore.certificate.chain: "${directory:/opt/kafka/external-configuration/my-user:user.crt}" database.history.producer.ssl.keystore.key: "${directory:/opt/kafka/external-configuration/my-user:user.key}" #...
The placeholder structure is
directory:<path>:<file_name>
.DirectoryConfigProvider
reads and extracts the credentials from the mounted secrets.
9. Setting up client access to a Kafka cluster
After you have deployed Strimzi, you can set up client access to your Kafka cluster. To verify the deployment, you can deploy example producer and consumer clients. Otherwise, create listeners that provide client access within or outside the Kubernetes cluster.
9.1. Deploying example clients
Deploy example producer and consumer clients to send and receive messages. You can use these clients to verify a deployment of Strimzi.
-
The Kafka cluster is available for the clients.
-
Deploy a Kafka producer.
kubectl run kafka-producer -ti --image=quay.io/strimzi/kafka:0.35.1-kafka-3.4.0 --rm=true --restart=Never -- bin/kafka-console-producer.sh --bootstrap-server cluster-name-kafka-bootstrap:9092 --topic my-topic
-
Type a message into the console where the producer is running.
-
Press Enter to send the message.
-
Deploy a Kafka consumer.
kubectl run kafka-consumer -ti --image=quay.io/strimzi/kafka:0.35.1-kafka-3.4.0 --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.
9.2. Configuring listeners to connect to Kafka brokers
Use listeners for client connection to Kafka brokers.
Strimzi provides a generic GenericKafkaListener
schema with properties to configure listeners through the Kafka
resource.
The GenericKafkaListener
provides a flexible approach to listener configuration.
You can specify properties to configure internal listeners for connecting within the Kubernetes cluster or external listeners for connecting outside the Kubernetes cluster.
Specify a connection type
to expose Kafka in the listener configuration.
The type chosen depends on your requirements, and your environment and infrastructure.
The following listener types are supported:
- Internal listeners
-
-
internal
to connect within the same Kubernetes cluster -
cluster-ip
to expose Kafka using per-brokerClusterIP
services
-
- External listeners
-
-
nodeport
to use ports on Kubernetes nodes -
loadbalancer
to use loadbalancer services -
ingress
to use KubernetesIngress
and the Ingress NGINX Controller for Kubernetes (Kubernetes only) -
route
to use OpenShiftRoute
and the default HAProxy router (OpenShift only)
-
Important
|
Do not use ingress on OpenShift, use the route type instead. The Ingress NGINX Controller is only intended for use on Kubernetes. The route type is only supported on OpenShift.
|
An internal
type listener configuration uses a headless service and the DNS names given to the broker pods.
You might want to join your Kubernetes network to an outside network.
In which case, you can configure an internal
type listener (using the useServiceDnsDomain
property) so that the Kubernetes service DNS domain (typically .cluster.local
) is not used.
You can also configure a cluster-ip
type of listener that exposes a Kafka cluster based on per-broker ClusterIP
services.
This is a useful option when you can’t route through the headless service or you wish to incorporate a custom access mechanism.
For example, you might use this listener when building your own type of external listener for a specific Ingress controller or the Kubernetes Gateway API.
External listeners handle access to a Kafka cluster from networks that require different authentication mechanisms. You can configure external listeners for client access outside a Kubernetes environment using a specified connection mechanism, such as a loadbalancer or route. For example, loadbalancers might not be suitable for certain infrastructure, such as bare metal, where node ports provide a better option.
Each listener is defined as an array in the Kafka
resource.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
listeners:
- name: plain
port: 9092
type: internal
tls: false
configuration:
useServiceDnsDomain: true
- name: tls
port: 9093
type: internal
tls: true
authentication:
type: tls
- name: external
port: 9094
type: route
tls: true
configuration:
brokerCertChainAndKey:
secretName: my-secret
certificate: my-certificate.crt
key: my-key.key
# ...
You can configure as many listeners as required, as long as their names and ports are unique. You can also configure listeners for secure connection using authentication.
If you want to know more about the pros and cons of each connection type, refer to Accessing Apache Kafka in Strimzi.
Note
|
If you scale your Kafka cluster while using external listeners, it might trigger a rolling update of all Kafka brokers. This depends on the configuration. |
9.3. Setting up client access to a Kafka cluster using listeners
Using the address of the Kafka cluster, you can provide access to a client in the same Kubernetes cluster; or provide external access to a client on a different Kubernetes namespace or outside Kubernetes entirely. This procedure shows how to configure client access to a Kafka cluster from outside Kubernetes or from another Kubernetes cluster.
A Kafka listener provides access to the Kafka cluster. Client access is secured using the following configuration:
-
An external listener is configured for the Kafka cluster, with TLS encryption and mTLS authentication, and Kafka
simple
authorization enabled. -
A
KafkaUser
is created for the client, with mTLS authentication, and Access Control Lists (ACLs) defined forsimple
authorization.
You can configure your listener to use mutual tls
, scram-sha-512
, or oauth
authentication.
mTLS always uses encryption, but encryption is also recommended when using SCRAM-SHA-512 and OAuth 2.0 authentication.
You can configure simple
, oauth
, opa
, or custom
authorization for Kafka brokers.
When enabled, authorization is applied to all enabled listeners.
When you configure the KafkaUser
authentication and authorization mechanisms, ensure they match the equivalent Kafka configuration:
-
KafkaUser.spec.authentication
matchesKafka.spec.kafka.listeners[*].authentication
-
KafkaUser.spec.authorization
matchesKafka.spec.kafka.authorization
You should have at least one listener supporting the authentication you want to use for the KafkaUser
.
Note
|
Authentication between Kafka users and Kafka brokers depends on the authentication settings for each. For example, it is not possible to authenticate a user with mTLS if it is not also enabled in the Kafka configuration. |
Strimzi operators automate the configuration process and create the certificates required for authentication:
-
The Cluster Operator creates the listeners and sets up the cluster and client certificate authority (CA) certificates to enable authentication with the Kafka cluster.
-
The User Operator creates the user representing the client and the security credentials used for client authentication, based on the chosen authentication type.
You add the certificates to your client configuration.
In this procedure, the CA certificates generated by the Cluster Operator are used, but you can replace them by installing your own certificates. You can also configure your listener to use a Kafka listener certificate managed by an external CA (certificate authority).
Certificates are available in PEM (.crt) and PKCS #12 (.p12) formats. This procedure uses PEM certificates. Use PEM certificates with clients that use certificates in X.509 format.
Note
|
For internal clients in the same Kubernetes cluster and namespace, you can mount the cluster CA certificate in the pod specification. For more information, see Configuring internal clients to trust the cluster CA. |
-
The Kafka cluster is available for connection by a client running outside the Kubernetes cluster
-
The Cluster Operator and User Operator are running in the cluster
-
Configure the Kafka cluster with a Kafka listener.
-
Define the authentication required to access the Kafka broker through the listener.
-
Enable authorization on the Kafka broker.
Example listener configurationapiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster namespace: myproject spec: kafka: # ... listeners: # (1) - name: external # (2) port: 9094 (3) type: <listener_type> # (4) tls: true # (5) authentication: type: tls # (6) configuration: # (7) #... authorization: # (8) type: simple superUsers: - super-user-name # (9) # ...
-
Configuration options for enabling external listeners are described in the Generic Kafka listener schema reference.
-
Name to identify the listener. Must be unique within the Kafka cluster.
-
Port number used by the listener inside Kafka. The port number has to be unique within a given Kafka cluster. Allowed port numbers are 9092 and higher with the exception of ports 9404 and 9999, which are already used for Prometheus and JMX. Depending on the listener type, the port number might not be the same as the port number that connects Kafka clients.
-
External listener type specified as
route
(OpenShift only),loadbalancer
,nodeport
oringress
(Kubernetes only). An internal listener is specified asinternal
orcluster-ip
. -
Required. TLS encryption on the listener. For
route
andingress
type listeners it must be set totrue
. For mTLS authentication, also use theauthentication
property. -
Client authentication mechanism on the listener. For server and client authentication using mTLS, you specify
tls: true
andauthentication.type: tls
. -
(Optional) Depending on the requirements of the listener type, you can specify additional listener configuration.
-
Authorization specified as
simple
, which uses theAclAuthorizer
Kafka plugin. -
(Optional) Super users can access all brokers regardless of any access restrictions defined in ACLs.
WarningAn OpenShift Route address comprises the name of the Kafka cluster, the name of the listener, and the name of the namespace it is created in. For example, my-cluster-kafka-listener1-bootstrap-myproject
(CLUSTER-NAME-kafka-LISTENER-NAME-bootstrap-NAMESPACE). If you are using aroute
listener type, be careful that the whole length of the address does not exceed a maximum limit of 63 characters. -
-
-
Create or update the
Kafka
resource.kubectl apply -f <kafka_configuration_file>
The Kafka cluster is configured with a Kafka broker listener using mTLS authentication.
A service is created for each Kafka broker pod.
A service is created to serve as the bootstrap address for connection to the Kafka cluster.
A service is also created as the external bootstrap address for external connection to the Kafka cluster using
nodeport
listeners.The cluster CA certificate to verify the identity of the kafka brokers is also created in the secret
<cluster_name>-cluster-ca-cert
.NoteIf you scale your Kafka cluster while using external listeners, it might trigger a rolling update of all Kafka brokers. This depends on the configuration. -
Retrieve the bootstrap address you can use to access the Kafka cluster from the status of the
Kafka
resource.kubectl get kafka <kafka_cluster_name> -o=jsonpath='{.status.listeners[?(@.name=="<listener_name>")].bootstrapServers}{"\n"}'
For example:
kubectl get kafka my-cluster -o=jsonpath='{.status.listeners[?(@.name=="external")].bootstrapServers}{"\n"}'
Use the bootstrap address in your Kafka client to connect to the Kafka cluster.
-
Create or modify a user representing the client that requires access to the Kafka cluster.
-
Specify the same authentication type as the
Kafka
listener. -
Specify the authorization ACLs for
simple
authorization.Example user configurationapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaUser metadata: name: my-user labels: strimzi.io/cluster: my-cluster # (1) spec: authentication: type: tls # (2) authorization: type: simple acls: # (3) - resource: type: topic name: my-topic patternType: literal operations: - Describe - Read - resource: type: group name: my-group patternType: literal operations: - Read
-
The label must match the label of the Kafka cluster.
-
Authentication specified as mutual
tls
. -
Simple authorization requires an accompanying list of ACL rules to apply to the user. The rules define the operations allowed on Kafka resources based on the username (
my-user
).
-
-
-
Create or modify the
KafkaUser
resource.kubectl apply -f USER-CONFIG-FILE
The user is created, as well as a secret with the same name as the
KafkaUser
resource. The secret contains a public and private key for mTLS authentication.Example secretapiVersion: v1 kind: Secret metadata: name: my-user labels: strimzi.io/kind: KafkaUser strimzi.io/cluster: my-cluster type: Opaque data: ca.crt: <public_key> # Public key of the clients CA user.crt: <user_certificate> # Public key of the user user.key: <user_private_key> # Private key of the user user.p12: <store> # PKCS #12 store for user certificates and keys user.password: <password_for_store> # Protects the PKCS #12 store
-
Extract the cluster CA certificate from the
<cluster_name>-cluster-ca-cert
secret of the Kafka cluster.kubectl get secret <cluster_name>-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
-
Extract the user CA certificate from the
<user_name>
secret.kubectl get secret <user_name> -o jsonpath='{.data.user\.crt}' | base64 -d > user.crt
-
Extract the private key of the user from the
<user_name>
secret.kubectl get secret <user_name> -o jsonpath='{.data.user\.key}' | base64 -d > user.key
-
Configure your client with the bootstrap address hostname and port for connecting to the Kafka cluster:
props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "<hostname>:<port>");
-
Configure your client with the truststore credentials to verify the identity of the Kafka cluster.
Specify the public cluster CA certificate.
Example truststore configurationprops.put(CommonClientConfigs.SECURITY_PROTOCOL_CONFIG, "SSL"); props.put(SslConfigs.SSL_TRUSTSTORE_TYPE_CONFIG, "PEM"); props.put(SslConfigs.SSL_TRUSTSTORE_CERTIFICATES_CONFIG, "<ca.crt_file_content>");
SSL is the specified security protocol for mTLS authentication. Specify
SASL_SSL
for SCRAM-SHA-512 authentication over TLS. PEM is the file format of the truststore. -
Configure your client with the keystore credentials to verify the user when connecting to the Kafka cluster.
Specify the public certificate and private key.
Example keystore configurationprops.put(CommonClientConfigs.SECURITY_PROTOCOL_CONFIG, "SSL"); props.put(SslConfigs.SSL_KEYSTORE_TYPE_CONFIG, "PEM"); props.put(SslConfigs.SSL_KEYSTORE_CERTIFICATE_CHAIN_CONFIG, "<user.crt_file_content>"); props.put(SslConfigs.SSL_KEYSTORE_KEY_CONFIG, "<user.key_file_content>");
Add the keystore certificate and the private key directly to the configuration. Add as a single-line format. Between the
BEGIN CERTIFICATE
andEND CERTIFICATE
delimiters, start with a newline character (\n
). End each line from the original certificate with\n
too.Example keystore configurationprops.put(SslConfigs.SSL_KEYSTORE_CERTIFICATE_CHAIN_CONFIG, "-----BEGIN CERTIFICATE----- \n<user_certificate_content_line_1>\n<user_certificate_content_line_n>\n-----END CERTIFICATE---"); props.put(SslConfigs.SSL_KEYSTORE_KEY_CONFIG, "----BEGIN PRIVATE KEY-----\n<user_key_content_line_1>\n<user_key_content_line_n>\n-----END PRIVATE KEY-----");
Additional resources-
If you are using an authorization server, you can use token-based authentication and authorization:
9.4. Accessing Kafka using node ports
Use node ports to access a Strimzi Kafka cluster from an external client outside the Kubernetes cluster.
To connect to a broker, you specify a hostname and port number for the Kafka bootstrap address, as well as the certificate used for TLS encryption.
The procedure shows basic nodeport
listener configuration.
You can use listener properties to enable TLS encryption (tls
) and specify a client authentication mechanism (authentication
).
Add additional configuration using configuration
properties.
For example, you can use the following configuration properties with nodeport
listeners:
preferredNodePortAddressType
-
Specifies the first address type that’s checked as the node address.
externalTrafficPolicy
-
Specifies whether the service routes external traffic to node-local or cluster-wide endpoints.
nodePort
-
Overrides the assigned node port numbers for the bootstrap and broker services.
For more information on listener configuration, see the GenericKafkaListener
schema reference.
-
A running Cluster Operator
In this procedure, the Kafka cluster name is my-cluster
.
The name of the listener is external
.
-
Configure a
Kafka
resource with an external listener set to thenodeport
type.For example:
apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: labels: app: my-cluster name: my-cluster namespace: myproject spec: kafka: # ... listeners: - name: external port: 9094 type: nodeport tls: true authentication: type: tls # ... # ... zookeeper: # ...
-
Create or update the resource.
kubectl apply -f <kafka_configuration_file>
A cluster CA certificate to verify the identity of the kafka brokers is created in the secret
my-cluster-cluster-ca-cert
.NodePort
type services are created for each Kafka broker, as well as an external bootstrap service.Node port services created for the bootstrap and brokersNAME TYPE CLUSTER-IP PORT(S) my-cluster-kafka-external-0 NodePort 172.30.55.13 9094:31789/TCP my-cluster-kafka-external-1 NodePort 172.30.250.248 9094:30028/TCP my-cluster-kafka-external-2 NodePort 172.30.115.81 9094:32650/TCP my-cluster-kafka-external-bootstrap NodePort 172.30.30.23 9094:32650/TCP
The bootstrap address used for client connection is propagated to the
status
of theKafka
resource.Example status for the bootstrap addressstatus: clusterId: Y_RJQDGKRXmNF7fEcWldJQ conditions: - lastTransitionTime: '2023-01-31T14:59:37.113630Z' status: 'True' type: Ready listeners: # ... - addresses: - host: ip-10-0-224-199.us-west-2.compute.internal port: 32650 bootstrapServers: 'ip-10-0-224-199.us-west-2.compute.internal:32650' certificates: - | -----BEGIN CERTIFICATE----- -----END CERTIFICATE----- name: external type: external observedGeneration: 2 # ...
-
Retrieve the bootstrap address you can use to access the Kafka cluster from the status of the
Kafka
resource.kubectl get kafka my-cluster -o=jsonpath='{.status.listeners[?(@.name=="external")].bootstrapServers}{"\n"}' ip-10-0-224-199.us-west-2.compute.internal:32650
-
Extract the cluster CA certificate.
kubectl get secret my-cluster-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
-
Configure your client to connect to the brokers.
-
Specify the bootstrap host and port in your Kafka client as the bootstrap address to connect to the Kafka cluster. For example,
ip-10-0-224-199.us-west-2.compute.internal:32650
. -
Add the extracted certificate to the truststore of your Kafka client to configure a TLS connection.
If you enabled a client authentication mechanism, you will also need to configure it in your client.
-
Note
|
If you are using your own listener certificates, check whether you need to add the CA certificate to the client’s truststore configuration. If it is a public (external) CA, you usually won’t need to add it. |
9.5. Accessing Kafka using loadbalancers
Use loadbalancers to access a Strimzi Kafka cluster from an external client outside the Kubernetes cluster.
To connect to a broker, you specify a hostname and port number for the Kafka bootstrap address, as well as the certificate used for TLS encryption.
The procedure shows basic loadbalancer
listener configuration.
You can use listener properties to enable TLS encryption (tls
) and specify a client authentication mechanism (authentication
).
Add additional configuration using configuration
properties.
For example, you can use the following configuration properties with loadbalancer
listeners:
loadBalancerSourceRanges
-
Restricts traffic to a specified list of CIDR (Classless Inter-Domain Routing) ranges.
externalTrafficPolicy
-
Specifies whether the service routes external traffic to node-local or cluster-wide endpoints.
loadBalancerIP
-
Requests a specific IP address when creating a loadbalancer.
For more information on listener configuration, see the GenericKafkaListener
schema reference.
-
A running Cluster Operator
In this procedure, the Kafka cluster name is my-cluster
.
The name of the listener is external
.
-
Configure a
Kafka
resource with an external listener set to theloadbalancer
type.For example:
apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: labels: app: my-cluster name: my-cluster namespace: myproject spec: kafka: # ... listeners: - name: external port: 9095 type: loadbalancer tls: true authentication: type: tls # ... # ... zookeeper: # ...
-
Create or update the resource.
kubectl apply -f <kafka_configuration_file>
A cluster CA certificate to verify the identity of the kafka brokers is also created in the secret
my-cluster-cluster-ca-cert
.loadbalancer
type services and loadbalancers are created for each Kafka broker, as well as an external bootstrap service.Loadbalancer services and loadbalancers created for the bootstraps and brokersNAME TYPE CLUSTER-IP PORT(S) my-cluster-kafka-external-0 LoadBalancer 172.30.204.234 9095:30011/TCP my-cluster-kafka-external-1 LoadBalancer 172.30.164.89 9095:32544/TCP my-cluster-kafka-external-2 LoadBalancer 172.30.73.151 9095:32504/TCP my-cluster-kafka-external-bootstrap LoadBalancer 172.30.30.228 9095:30371/TCP NAME EXTERNAL-IP (loadbalancer) my-cluster-kafka-external-0 a8a519e464b924000b6c0f0a05e19f0d-1132975133.us-west-2.elb.amazonaws.com my-cluster-kafka-external-1 ab6adc22b556343afb0db5ea05d07347-611832211.us-west-2.elb.amazonaws.com my-cluster-kafka-external-2 a9173e8ccb1914778aeb17eca98713c0-777597560.us-west-2.elb.amazonaws.com my-cluster-kafka-external-bootstrap a8d4a6fb363bf447fb6e475fc3040176-36312313.us-west-2.elb.amazonaws.com
The bootstrap address used for client connection is propagated to the
status
of theKafka
resource.Example status for the bootstrap addressstatus: clusterId: Y_RJQDGKRXmNF7fEcWldJQ conditions: - lastTransitionTime: '2023-01-31T14:59:37.113630Z' status: 'True' type: Ready listeners: # ... - addresses: - host: >- a8d4a6fb363bf447fb6e475fc3040176-36312313.us-west-2.elb.amazonaws.com port: 9095 bootstrapServers: >- a8d4a6fb363bf447fb6e475fc3040176-36312313.us-west-2.elb.amazonaws.com:9095 certificates: - | -----BEGIN CERTIFICATE----- -----END CERTIFICATE----- name: external type: external observedGeneration: 2 # ...
The DNS addresses used for client connection are propagated to the
status
of each loadbalancer service.Example status for the bootstrap loadbalancerstatus: loadBalancer: ingress: - hostname: >- a8d4a6fb363bf447fb6e475fc3040176-36312313.us-west-2.elb.amazonaws.com # ...
-
Retrieve the bootstrap address you can use to access the Kafka cluster from the status of the
Kafka
resource.kubectl get kafka my-cluster -o=jsonpath='{.status.listeners[?(@.name=="external")].bootstrapServers}{"\n"}' a8d4a6fb363bf447fb6e475fc3040176-36312313.us-west-2.elb.amazonaws.com:9095
-
Extract the cluster CA certificate.
kubectl get secret my-cluster-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
-
Configure your client to connect to the brokers.
-
Specify the bootstrap host and port in your Kafka client as the bootstrap address to connect to the Kafka cluster. For example,
a8d4a6fb363bf447fb6e475fc3040176-36312313.us-west-2.elb.amazonaws.com:9095
. -
Add the extracted certificate to the truststore of your Kafka client to configure a TLS connection.
If you enabled a client authentication mechanism, you will also need to configure it in your client.
-
Note
|
If you are using your own listener certificates, check whether you need to add the CA certificate to the client’s truststore configuration. If it is a public (external) CA, you usually won’t need to add it. |
9.6. Accessing Kafka using an Ingress NGINX Controller for Kubernetes
Use an Ingress NGINX Controller for Kubernetes to access a Strimzi Kafka cluster from clients outside the Kubernetes cluster.
To be able to use an Ingress NGINX Controller for Kubernetes, add configuration for an ingress
type listener in the Kafka
custom resource.
When applied, the configuration creates a dedicated ingress and service for an external bootstrap and each broker in the cluster.
Clients connect to the bootstrap ingress, which routes them through the bootstrap service to connect to a broker.
Per-broker connections are then established using DNS names, which route traffic from the client to the broker through the broker-specific ingresses and services.
To connect to a broker, you specify a hostname for the ingress bootstrap address, as well as the certificate used for TLS encryption. For access using an ingress, the port used in the Kafka client is typically 443.
The procedure shows basic ingress
listener configuration.
TLS encryption (tls
) must be enabled.
You can also specify a client authentication mechanism (authentication
).
Add additional configuration using configuration
properties.
For example, you can use the class
configuration property with ingress
listeners to specify the ingress controller used.
For more information on listener configuration, see the GenericKafkaListener
schema reference.
Make sure that you enable TLS passthrough in your Ingress NGINX Controller for Kubernetes deployment. Kafka uses a binary protocol over TCP, but the Ingress NGINX Controller for Kubernetes is designed to work with a HTTP protocol. To be able to route TCP traffic through ingresses, Strimzi uses TLS passthrough with Server Name Indication (SNI).
SNI helps with identifying and passing connection to Kafka brokers.
In passthrough mode, TLS encryption is always used.
Because the connection passes to the brokers, the listeners use the TLS certificates signed by the internal cluster CA and not the ingress certificates.
To configure listeners to use your own listener certificates, use the brokerCertChainAndKey
property.
For more information about enabling TLS passthrough, see the TLS passthrough documentation.
-
An Ingress NGINX Controller for Kubernetes is running with TLS passthrough enabled
-
A running Cluster Operator
In this procedure, the Kafka cluster name is my-cluster
.
The name of the listener is external
.
-
Configure a
Kafka
resource with an external listener set to theingress
type.Specify an ingress hostname for the bootstrap service and each of the Kafka brokers in the Kafka cluster. Add any hostname to the
bootstrap
andbroker-<index>
prefixes that identify the bootstrap and brokers.For example:
apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: labels: app: my-cluster name: my-cluster namespace: myproject spec: kafka: # ... listeners: - name: external port: 9094 type: ingress tls: true # (1) 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 class: nginx # (2) # ... zookeeper: # ...
-
For
ingress
type listeners, TLS encryption must be enabled (true
). -
(Optional) Class that specifies the ingress controller to use. You might need to add a class if you have not set up a default and a class name is missing in the ingresses created.
-
-
Create or update the resource.
kubectl apply -f <kafka_configuration_file>
A cluster CA certificate to verify the identity of the kafka brokers is created in the secret
my-cluster-cluster-ca-cert
.ClusterIP
type services are created for each Kafka broker, as well as an external bootstrap service.An
ingress
is also created for each service, with a DNS address to expose them using the Ingress NGINX Controller for Kubernetes.Ingresses created for the bootstrap and brokersNAME CLASS HOSTS ADDRESS PORTS my-cluster-kafka-0 nginx broker-0.myingress.com external.ingress.com 80,443 my-cluster-kafka-1 nginx broker-1.myingress.com external.ingress.com 80,443 my-cluster-kafka-2 nginx broker-2.myingress.com external.ingress.com 80,443 my-cluster-kafka-bootstrap nginx bootstrap.myingress.com external.ingress.com 80,443
The DNS addresses used for client connection are propagated to the
status
of each ingress.Status for the bootstrap ingressstatus: loadBalancer: ingress: - hostname: external.ingress.com # ...
-
Use a target broker to check the client-server TLS connection on port 443 using the OpenSSL
s_client
.openssl s_client -connect broker-0.myingress.com:443 -servername broker-0.myingress.com -showcerts
The server name is the SNI for passing the connection to the broker.
If the connection is successful, the certificates for the broker are returned.
Certificates for the brokerCertificate chain 0 s:O = io.strimzi, CN = my-cluster-kafka i:O = io.strimzi, CN = cluster-ca v0
-
Extract the cluster CA certificate.
kubectl get secret my-cluster-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
-
Configure your client to connect to the brokers.
-
Specify the bootstrap host (from the listener
configuration
) and port 443 in your Kafka client as the bootstrap address to connect to the Kafka cluster. For example,bootstrap.myingress.com:443
. -
Add the extracted certificate to the truststore of your Kafka client to configure a TLS connection.
If you enabled a client authentication mechanism, you will also need to configure it in your client.
-
Note
|
If you are using your own listener certificates, check whether you need to add the CA certificate to the client’s truststore configuration. If it is a public (external) CA, you usually won’t need to add it. |
9.7. Accessing Kafka using OpenShift routes
Use OpenShift routes to access a Strimzi Kafka cluster from clients outside the OpenShift cluster.
To be able to use routes, add configuration for a route
type listener in the Kafka
custom resource.
When applied, the configuration creates a dedicated route and service for an external bootstrap and each broker in the cluster.
Clients connect to the bootstrap route, which routes them through the bootstrap service to connect to a broker.
Per-broker connections are then established using DNS names, which route traffic from the client to the broker through the broker-specific routes and services.
To connect to a broker, you specify a hostname for the route bootstrap address, as well as the certificate used for TLS encryption. For access using routes, the port is always 443.
Warning
|
An OpenShift route address comprises the name of the Kafka cluster, the name of the listener, and the name of the project it is created in.
For example, my-cluster-kafka-external-bootstrap-myproject (<cluster_name>-kafka-<listener_name>-bootstrap-<namespace>). Be careful that the whole length of the address does not exceed a maximum limit of 63 characters.
|
The procedure shows basic listener configuration.
TLS encryption (tls
) must be enabled.
You can also specify a client authentication mechanism (authentication
).
Add additional configuration using configuration
properties.
For example, you can use the host
configuration property with route
listeners to specify the hostnames used by the bootstrap and per-broker services.
For more information on listener configuration, see the GenericKafkaListener
schema reference.
TLS passthrough is enabled for routes created by Strimzi. Kafka uses a binary protocol over TCP, but routes are designed to work with a HTTP protocol. To be able to route TCP traffic through routes, Strimzi uses TLS passthrough with Server Name Indication (SNI).
SNI helps with identifying and passing connection to Kafka brokers.
In passthrough mode, TLS encryption is always used.
Because the connection passes to the brokers, the listeners use TLS certificates signed by the internal cluster CA and not the ingress certificates.
To configure listeners to use your own listener certificates, use the brokerCertChainAndKey
property.
-
A running Cluster Operator
In this procedure, the Kafka cluster name is my-cluster
.
The name of the listener is external
.
-
Configure a
Kafka
resource with an external listener set to theroute
type.For example:
apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: labels: app: my-cluster name: my-cluster namespace: myproject spec: kafka: # ... listeners: - name: external port: 9094 type: route tls: true # (1) authentication: type: tls # ... # ... zookeeper: # ...
-
For
route
type listeners, TLS encryption must be enabled (true
).
-
-
Create or update the resource.
kubectl apply -f <kafka_configuration_file>
A cluster CA certificate to verify the identity of the kafka brokers is created in the secret
my-cluster-cluster-ca-cert
.ClusterIP
type services are created for each Kafka broker, as well as an external bootstrap service.A
route
is also created for each service, with a DNS address (host/port) to expose them using the default OpenShift HAProxy router.The routes are preconfigured with TLS passthrough.
Routes created for the bootstraps and brokersNAME HOST/PORT SERVICES PORT TERMINATION my-cluster-kafka-external-0 my-cluster-kafka-external-0-my-project.router.com my-cluster-kafka-external-0 9094 passthrough my-cluster-kafka-external-1 my-cluster-kafka-external-1-my-project.router.com my-cluster-kafka-external-1 9094 passthrough my-cluster-kafka-external-2 my-cluster-kafka-external-2-my-project.router.com my-cluster-kafka-external-2 9094 passthrough my-cluster-kafka-external-bootstrap my-cluster-kafka-external-bootstrap-my-project.router.com my-cluster-kafka-external-bootstrap 9094 passthrough
The DNS addresses used for client connection are propagated to the
status
of each route.Example status for the bootstrap routestatus: ingress: - host: >- my-cluster-kafka-external-bootstrap-my-project.router.com # ...
-
Use a target broker to check the client-server TLS connection on port 443 using the OpenSSL
s_client
.openssl s_client -connect my-cluster-kafka-external-0-my-project.router.com:443 -servername my-cluster-kafka-external-0-my-project.router.com -showcerts
The server name is the SNI for passing the connection to the broker.
If the connection is successful, the certificates for the broker are returned.
Certificates for the brokerCertificate chain 0 s:O = io.strimzi, CN = my-cluster-kafka i:O = io.strimzi, CN = cluster-ca v0
-
Retrieve the address of the bootstrap service from the status of the
Kafka
resource.kubectl get kafka my-cluster -o=jsonpath='{.status.listeners[?(@.name=="external")].bootstrapServers}{"\n"}' my-cluster-kafka-external-bootstrap-my-project.router.com:443
The address comprises the cluster name, the listener name, the project name and the domain of the router (
router.com
in this example). -
Extract the cluster CA certificate.
kubectl get secret my-cluster-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
-
Configure your client to connect to the brokers.
-
Specify the address for the bootstrap service and port 443 in your Kafka client as the bootstrap address to connect to the Kafka cluster.
-
Add the extracted certificate to the truststore of your Kafka client to configure a TLS connection.
If you enabled a client authentication mechanism, you will also need to configure it in your client.
-
Note
|
If you are using your own listener certificates, check whether you need to add the CA certificate to the client’s truststore configuration. If it is a public (external) CA, you usually won’t need to add it. |
10. Managing secure access to Kafka
Secure your Kafka cluster by managing the access a client has to Kafka brokers. Specify configuration options to secure Kafka brokers and clients
A secure connection between Kafka brokers and clients can encompass the following:
-
Encryption for data exchange
-
Authentication to prove identity
-
Authorization to allow or decline actions executed by users
The authentication and authorization mechanisms specified for a client must match those specified for the Kafka brokers. Strimzi operators automate the configuration process and create the certificates required for authentication.
10.1. Security options for Kafka
Use the Kafka
resource to configure the mechanisms used for Kafka authentication and authorization.
10.1.1. Listener authentication
Configure client authentication for Kafka brokers when creating listeners.
Specify the listener authentication type using the Kafka.spec.kafka.listeners.authentication
property in the Kafka
resource.
For clients inside the Kubernetes cluster, you can create plain
(without encryption) or tls
internal listeners.
The internal
listener type use a headless service and the DNS names given to the broker pods.
As an alternative to the headless service, you can also create a cluster-ip
type of internal listener to expose Kafka using per-broker ClusterIP
services.
For clients outside the Kubernetes cluster, you create external listeners and specify a connection mechanism,
which can be nodeport
, loadbalancer
, ingress
(Kubernetes only), or route
(OpenShift only).
For more information on the configuration options for connecting an external client, see Setting up client access to a Kafka cluster.
Supported authentication options:
-
mTLS authentication (only on the listeners with TLS enabled encryption)
-
SCRAM-SHA-512 authentication
The authentication option you choose depends on how you wish to authenticate client access to Kafka brokers.
Note
|
Try exploring the standard authentication options before using custom authentication. Custom authentication allows for any type of kafka-supported authentication. It can provide more flexibility, but also adds complexity. |
The listener authentication
property is used to specify an authentication mechanism specific to that listener.
If no authentication
property is specified then the listener does not authenticate clients which connect through that listener.
The listener will accept all connections without authentication.
Authentication must be configured when using the User Operator to manage KafkaUsers
.
The following example shows:
-
A
plain
listener configured for SCRAM-SHA-512 authentication -
A
tls
listener with mTLS authentication -
An
external
listener with mTLS authentication
Each listener is configured with a unique name and port within a Kafka cluster.
Note
|
Listeners cannot be configured to use the ports reserved for inter-broker communication (9091 or 9090) and metrics (9404). |
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
namespace: myproject
spec:
kafka:
# ...
listeners:
- name: plain
port: 9092
type: internal
tls: true
authentication:
type: scram-sha-512
- name: tls
port: 9093
type: internal
tls: true
authentication:
type: tls
- name: external
port: 9094
type: loadbalancer
tls: true
authentication:
type: tls
# ...
mTLS authentication
mTLS authentication is always used for the communication between Kafka brokers and ZooKeeper pods.
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. For mutual, or two-way, authentication, both the server and the client present certificates. When you configure mTLS authentication, the broker authenticates the client (client authentication) and the client authenticates the broker (server authentication).
mTLS listener configuration in the Kafka
resource requires the following:
-
tls: true
to specify TLS encryption and server authentication -
authentication.type: tls
to specify the client authentication
When a Kafka cluster is created by the Cluster Operator, it creates a new secret with the name <cluster_name>-cluster-ca-cert
.
The secret contains a CA certificate.
The CA certificate is in PEM and PKCS #12 format.
To verify a Kafka cluster, add the CA certificate to the truststore in your client configuration.
To verify a client, add a user certificate and key to the keystore in your client configuration.
For more information on configuring a client for mTLS, see User authentication.
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 browser obtains proof of the identity of the web server. |
SCRAM-SHA-512 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 encrypted client connections.
When SCRAM-SHA-512 authentication is used with a TLS connection, the TLS protocol provides the encryption, but is not used for authentication.
The following properties of SCRAM make it safe to use SCRAM-SHA-512 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.
When 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.
Network policies
By default, Strimzi automatically creates a NetworkPolicy
resource for every listener that is enabled on a Kafka broker.
This NetworkPolicy
allows applications to connect to listeners in all namespaces.
Use network policies as part of the listener configuration.
If you want to restrict access to a listener at the network level to only selected applications or namespaces, use the networkPolicyPeers
property.
Each listener can have a different networkPolicyPeers
configuration.
For more information on network policy peers, refer to the NetworkPolicyPeer API reference.
If you want to use custom network policies, you can set the STRIMZI_NETWORK_POLICY_GENERATION
environment variable to false
in the Cluster Operator configuration.
For more information, see Configuring the Cluster Operator with environment variables.
Note
|
Your configuration of Kubernetes must support ingress NetworkPolicies in order to use network policies in Strimzi.
|
Providing listener certificates
You can provide your own server certificates, called Kafka listener certificates, for TLS listeners or external listeners which have TLS encryption enabled. For more information, see Providing your own Kafka listener certificates for TLS encryption.
10.1.2. Kafka authorization
Configure authorization for Kafka brokers using the Kafka.spec.kafka.authorization
property in the Kafka
resource.
If the authorization
property is missing, no authorization is enabled and clients have no restrictions.
When enabled, authorization is applied to all enabled listeners.
The authorization method is defined in the type
field.
Supported authorization options:
-
OAuth 2.0 authorization (if you are using OAuth 2.0 token based authentication)
Super users
Super users can access all resources in your Kafka cluster regardless of any access restrictions, and are supported by all authorization mechanisms.
To designate super users for a Kafka cluster, add a list of user principals to the superUsers
property.
If a user uses mTLS authentication, the username is the common name from the TLS certificate subject prefixed with CN=
.
If you are not using the User Operator and using your own certificates for mTLS, the username is the full certificate subject.
A full certificate subject can have the following fields: CN=user,OU=my_ou,O=my_org,L=my_location,ST=my_state,C=my_country_code
.
Omit any fields that are not present.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
namespace: myproject
spec:
kafka:
# ...
authorization:
type: simple
superUsers:
- CN=client_1
- user_2
- CN=client_3
- CN=client_4,OU=my_ou,O=my_org,L=my_location,ST=my_state,C=US
- CN=client_5,OU=my_ou,O=my_org,C=GB
- CN=client_6,O=my_org
# ...
10.2. Security options for Kafka clients
Use the KafkaUser
resource to configure the authentication mechanism, authorization mechanism, and access rights for Kafka clients.
In terms of configuring security, clients are represented as users.
You can authenticate and authorize user access to Kafka brokers. Authentication permits access, and authorization constrains the access to permissible actions.
You can also create super users that have unconstrained access to Kafka brokers.
The authentication and authorization mechanisms must match the specification for the listener used to access the Kafka brokers.
For more information on configuring a KafkaUser
resource to access Kafka brokers securely, see Setting up client access to a Kafka cluster using listeners.
10.2.1. 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/v1beta2
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.
If the status of the KafkaUser
resource remains empty, check your label.
10.2.2. User authentication
Use the KafkaUser
custom resource to configure authentication credentials for users (clients) that require access to a Kafka cluster.
Configure the credentials using the authentication
property in KafkaUser.spec
.
By specifying a type
, you control what credentials are generated.
Supported authentication types:
-
tls
for mTLS authentication -
tls-external
for mTLS authentication using external certificates -
scram-sha-512
for SCRAM-SHA-512 authentication
If tls
or scram-sha-512
is specified, the User Operator creates authentication credentials when it creates the user.
If tls-external
is specified, the user still uses mTLS, but no authentication credentials are created.
Use this option when you’re providing your own certificates.
When no authentication type is specified, the User Operator does not create the user or its credentials.
You can use tls-external
to authenticate with mTLS using a certificate issued outside the User Operator.
The User Operator does not generate a TLS certificate or a secret.
You can still manage ACL rules and quotas through the User Operator in the same way as when you’re using the tls
mechanism.
This means that you use the CN=USER-NAME
format when specifying ACL rules and quotas.
USER-NAME is the common name given in a TLS certificate.
mTLS authentication
To use mTLS authentication, you set the type
field in the KafkaUser
resource to tls
.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
name: my-user
labels:
strimzi.io/cluster: my-cluster
spec:
authentication:
type: tls
# ...
The authentication type must match the equivalent configuration for the Kafka
listener used to access the Kafka cluster.
When the user is created by the User Operator, it creates a new secret with the same name as the KafkaUser
resource.
The secret contains a private and public key for mTLS.
The public key is contained in a user certificate, which is signed by a clients CA (certificate authority) when it is created.
All keys are in X.509 format.
Note
|
If you are using the clients CA generated by the Cluster Operator, the user certificates generated by the User Operator are also renewed when the clients CA is renewed by the Cluster Operator. |
The user secret provides keys and certificates in PEM and PKCS #12 formats.
apiVersion: v1
kind: Secret
metadata:
name: my-user
labels:
strimzi.io/kind: KafkaUser
strimzi.io/cluster: my-cluster
type: Opaque
data:
ca.crt: <public_key> # Public key of the clients CA
user.crt: <user_certificate> # Public key of the user
user.key: <user_private_key> # Private key of the user
user.p12: <store> # PKCS #12 store for user certificates and keys
user.password: <password_for_store> # Protects the PKCS #12 store
When you configure a client, you specify the following:
-
Truststore properties for the public cluster CA certificate to verify the identity of the Kafka cluster
-
Keystore properties for the user authentication credentials to verify the client
The configuration depends on the file format (PEM or PKCS #12). This example uses PKCS #12 stores, and the passwords required to access the credentials in the stores.
bootstrap.servers=<kafka_cluster_name>-kafka-bootstrap:9093 # (1)
security.protocol=SSL # (2)
ssl.truststore.location=/tmp/ca.p12 # (3)
ssl.truststore.password=<truststore_password> # (4)
ssl.keystore.location=/tmp/user.p12 # (5)
ssl.keystore.password=<keystore_password> # (6)
-
The bootstrap server address to connect to the Kafka cluster.
-
The security protocol option when using TLS for encryption.
-
The truststore location contains the public key certificate (
ca.p12
) for the Kafka cluster. A cluster CA certificate and password is generated by the Cluster Operator in the<cluster_name>-cluster-ca-cert
secret when the Kafka cluster is created. -
The password (
ca.password
) for accessing the truststore. -
The keystore location contains the public key certificate (
user.p12
) for the Kafka user. -
The password (
user.password
) for accessing the keystore.
mTLS authentication using a certificate issued outside the User Operator
To use mTLS authentication using a certificate issued outside the User Operator, you set the type
field in the KafkaUser
resource to tls-external
.
A secret and credentials are not created for the user.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
name: my-user
labels:
strimzi.io/cluster: my-cluster
spec:
authentication:
type: tls-external
# ...
SCRAM-SHA-512 authentication
To use the SCRAM-SHA-512 authentication mechanism, you set the type
field in the KafkaUser
resource to scram-sha-512
.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
name: my-user
labels:
strimzi.io/cluster: my-cluster
spec:
authentication:
type: scram-sha-512
# ...
When the user is created by the User Operator, it creates a new secret with the same name as the KafkaUser
resource.
The secret contains the generated password in the password
key, which is encoded with base64.
In order to use the password, it must be decoded.
apiVersion: v1
kind: Secret
metadata:
name: my-user
labels:
strimzi.io/kind: KafkaUser
strimzi.io/cluster: my-cluster
type: Opaque
data:
password: Z2VuZXJhdGVkcGFzc3dvcmQ= (1)
sasl.jaas.config: b3JnLmFwYWNoZS5rYWZrYS5jb21tb24uc2VjdXJpdHkuc2NyYW0uU2NyYW1Mb2dpbk1vZHVsZSByZXF1aXJlZCB1c2VybmFtZT0ibXktdXNlciIgcGFzc3dvcmQ9ImdlbmVyYXRlZHBhc3N3b3JkIjsK (2)
-
The generated password, base64 encoded.
-
The JAAS configuration string for SASL SCRAM-SHA-512 authentication, base64 encoded.
Decoding the generated password:
echo "Z2VuZXJhdGVkcGFzc3dvcmQ=" | base64 --decode
Custom password configuration
When a user is created, Strimzi generates a random password.
You can use your own password instead of the one generated by Strimzi. To do so, create a secret with the password and reference it in the KafkaUser
resource.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
name: my-user
labels:
strimzi.io/cluster: my-cluster
spec:
authentication:
type: scram-sha-512
password:
valueFrom:
secretKeyRef:
name: my-secret (1)
key: my-password (2)
# ...
-
The name of the secret containing the predefined password.
-
The key for the password stored inside the secret.
10.2.3. User authorization
Use the KafkaUser
custom resource to configure authorization rules for users (clients) that require access to a Kafka cluster.
Configure the rules using the authorization
property in KafkaUser.spec
.
By specifying a type
, you control what rules are used.
To use simple authorization, you set the type
property to simple
in KafkaUser.spec.authorization
.
The simple authorization uses the Kafka Admin API to manage the ACL rules inside your Kafka cluster.
Whether ACL management in the User Operator is enabled or not depends on your authorization configuration in the Kafka cluster.
-
For simple authorization, ACL management is always enabled.
-
For OPA authorization, ACL management is always disabled. Authorization rules are configured in the OPA server.
-
For Keycloak authorization, you can manage the ACL rules directly in Keycloak. You can also delegate authorization to the simple authorizer as a fallback option in the configuration. When delegation to the simple authorizer is enabled, the User Operator will enable management of ACL rules as well.
-
For custom authorization using a custom authorization plugin, use the
supportsAdminApi
property in the.spec.kafka.authorization
configuration of theKafka
custom resource to enable or disable the support.
Authorization is cluster-wide.
The authorization type must match the equivalent configuration in the Kafka
custom resource.
If ACL management is not enabled, Strimzi rejects a resource if it contains any ACL rules.
If you’re using a standalone deployment of the User Operator, ACL management is enabled by default.
You can disable it using the STRIMZI_ACLS_ADMIN_API_SUPPORTED
environment variable.
If no authorization is specified, the User Operator does not provision any access rights for the user.
Whether such a KafkaUser
can still access resources depends on the authorizer being used.
For example, for the AclAuthorizer
this is determined by its allow.everyone.if.no.acl.found
configuration.
ACL rules
AclAuthorizer
uses ACL rules to manage access to Kafka brokers.
ACL rules grant access rights to the user, which you specify in the acls
property.
For more information about the AclRule
object, see the AclRule
schema reference.
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 in KafkaUser
.
For more information on configuring super user access to brokers, see Kafka authorization.
User quotas
You can configure the spec
for the KafkaUser
resource to enforce quotas so that a user does not exceed a configured level of access to Kafka brokers.
You can set size-based network usage and time-based CPU utilization thresholds.
You can also add a partition mutation quota to control the rate at which requests to change partitions are accepted for user requests.
KafkaUser
with user quotasapiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
name: my-user
labels:
strimzi.io/cluster: my-cluster
spec:
# ...
quotas:
producerByteRate: 1048576 (1)
consumerByteRate: 2097152 (2)
requestPercentage: 55 (3)
controllerMutationRate: 10 (4)
-
Byte-per-second quota on the amount of data the user can push to a Kafka broker
-
Byte-per-second quota on the amount of data the user can fetch from a Kafka broker
-
CPU utilization limit as a percentage of time for a client group
-
Number of concurrent partition creation and deletion operations (mutations) allowed per second
For more information on these properties, see the KafkaUserQuotas
schema reference.
10.3. Securing access to Kafka brokers
To establish secure access to Kafka brokers, you configure and apply:
-
A
Kafka
resource to:-
Create listeners with a specified authentication type
-
Configure authorization for the whole Kafka cluster
-
-
A
KafkaUser
resource to access the Kafka brokers securely through the listeners
Configure the Kafka
resource to set up:
-
Listener authentication
-
Network policies that restrict access to Kafka listeners
-
Kafka authorization
-
Super users for unconstrained access to brokers
Authentication is configured independently for each listener. Authorization is always configured for the whole Kafka cluster.
The Cluster Operator creates the listeners and sets up the cluster and client certificate authority (CA) certificates to enable authentication within the Kafka cluster.
You can replace the certificates generated by the Cluster Operator by installing your own certificates.
You can also provide your own server certificates and private keys for any listener with TLS encryption enabled. These user-provided certificates are called Kafka listener certificates. Providing Kafka listener certificates allows you to leverage existing security infrastructure, such as your organization’s private CA or a public CA. Kafka clients will need to trust the CA which was used to sign the listener certificate. You must manually renew Kafka listener certificates when needed. Certificates are available in PKCS #12 format (.p12) and PEM (.crt) formats.
Use KafkaUser
to enable the authentication and authorization mechanisms that a specific client uses to access Kafka.
Configure the KafkaUser
resource to set up:
-
Authentication to match the enabled listener authentication
-
Authorization to match the enabled Kafka authorization
-
Quotas to control the use of resources by clients
The User Operator creates the user representing the client and the security credentials used for client authentication, based on the chosen authentication type.
Refer to the schema reference for more information on access configuration properties:
10.3.1. Securing Kafka brokers
This procedure shows the steps involved in securing Kafka brokers when running Strimzi.
The security implemented for Kafka brokers must be compatible with the security implemented for the clients requiring access.
-
Kafka.spec.kafka.listeners[*].authentication
matchesKafkaUser.spec.authentication
-
Kafka.spec.kafka.authorization
matchesKafkaUser.spec.authorization
The steps show the configuration for simple authorization and a listener using mTLS authentication.
For more information on listener configuration, see the GenericKafkaListener
schema reference.
Alternatively, you can use SCRAM-SHA or OAuth 2.0 for listener authentication, and OAuth 2.0 or OPA for Kafka authorization.
-
Configure the
Kafka
resource.-
Configure the
authorization
property for authorization. -
Configure the
listeners
property to create a listener with authentication.For example:
apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka spec: kafka: # ... authorization: (1) type: simple superUsers: (2) - CN=client_1 - user_2 - CN=client_3 listeners: - name: tls port: 9093 type: internal tls: true authentication: type: tls (3) # ... zookeeper: # ...
-
Authorization enables
simple
authorization on the Kafka broker using theAclAuthorizer
Kafka plugin. -
List of user principals with unlimited access to Kafka. CN is the common name from the client certificate when mTLS authentication is used.
-
Listener authentication mechanisms may be configured for each listener, and specified as mTLS, SCRAM-SHA-512, or token-based OAuth 2.0.
If you are configuring an external listener, the configuration is dependent on the chosen connection mechanism.
-
-
-
Create or update the
Kafka
resource.kubectl apply -f <kafka_configuration_file>
The Kafka cluster is configured with a Kafka broker listener using mTLS authentication.
A service is created for each Kafka broker pod.
A service is created to serve as the bootstrap address for connection to the Kafka cluster.
The cluster CA certificate to verify the identity of the kafka brokers is also created in the secret
<cluster_name>-cluster-ca-cert
.
10.3.2. Securing user access to Kafka
Create or modify a KafkaUser
to represent a client that requires secure access to the Kafka cluster.
When you configure the KafkaUser
authentication and authorization mechanisms, ensure they match the equivalent Kafka
configuration:
-
KafkaUser.spec.authentication
matchesKafka.spec.kafka.listeners[*].authentication
-
KafkaUser.spec.authorization
matchesKafka.spec.kafka.authorization
This procedure shows how a user is created with mTLS authentication. You can also create a user with SCRAM-SHA authentication.
The authentication required depends on the type of authentication configured for the Kafka broker listener.
Note
|
Authentication between Kafka users and Kafka brokers depends on the authentication settings for each. For example, it is not possible to authenticate a user with mTLS if it is not also enabled in the Kafka configuration. |
-
A running Kafka cluster configured with a Kafka broker listener using mTLS authentication and TLS encryption.
-
A running User Operator (typically deployed with the Entity Operator).
The authentication type in KafkaUser
should match the authentication configured in Kafka
brokers.
-
Configure the
KafkaUser
resource.For example:
apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaUser metadata: name: my-user labels: strimzi.io/cluster: my-cluster spec: authentication: (1) type: tls authorization: type: simple (2) acls: - resource: type: topic name: my-topic patternType: literal operations: - Describe - Read - resource: type: group name: my-group patternType: literal operations: - Read
-
User authentication mechanism, defined as mutual
tls
orscram-sha-512
. -
Simple authorization, which requires an accompanying list of ACL rules.
-
-
Create or update the
KafkaUser
resource.kubectl apply -f <user_config_file>
The user is created, as well as a Secret with the same name as the
KafkaUser
resource. The Secret contains a private and public key for mTLS authentication.
For information on configuring a Kafka client with properties for secure connection to Kafka brokers, see Setting up client access to a Kafka cluster using listeners.
10.3.3. Restricting access to Kafka listeners using network policies
You can restrict access to a listener to only selected applications by using the networkPolicyPeers
property.
-
A Kubernetes cluster with support for Ingress NetworkPolicies.
-
The Cluster Operator is running.
-
Open the
Kafka
resource. -
In the
networkPolicyPeers
property, 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/v1beta2 kind: Kafka spec: kafka: # ... listeners: - name: tls port: 9093 type: internal tls: true authentication: type: tls networkPolicyPeers: - podSelector: matchLabels: app: kafka-client # ... zookeeper: # ...
-
Create or update the resource.
Use
kubectl apply
:kubectl apply -f your-file
10.3.4. Providing your own Kafka listener certificates for TLS encryption
Listeners provide client access to Kafka brokers.
Configure listeners in the Kafka
resource, including the configuration required for client access using TLS.
By default, the listeners use certificates signed by the internal CA (certificate authority) certificates generated by Strimzi.
A CA certificate is generated by the Cluster Operator when it creates a Kafka cluster.
When you configure a client for TLS, you add the CA certificate to its truststore configuration to verify the Kafka cluster.
You can also install and use your own CA certificates.
Or you can configure a listener using brokerCertChainAndKey
properties and use a custom server certificate.
The brokerCertChainAndKey
properties allow you to access Kafka brokers using your own custom certificates at the listener-level.
You create a secret with your own private key and server certificate, then specify the key and certificate in the listener’s brokerCertChainAndKey
configuration.
You can use a certificate signed by a public (external) CA or a private CA.
If signed by a public CA, you usually won’t need to add it to a client’s truststore configuration.
Custom certificates are not managed by Strimzi, so you need to renew them manually.
Note
|
Listener certificates are used for TLS encryption and server authentication only. They are not used for TLS client authentication. If you want to use your own certificate for TLS client authentication as well, you must install and use your own clients CA. |
-
The Cluster Operator is running.
-
Each listener requires the following:
-
A compatible server certificate signed by an external CA. (Provide an X.509 certificate in PEM format.)
You can use one listener certificate for multiple listeners.
-
Subject Alternative Names (SANs) are specified in the certificate for each listener. For more information, see Alternative subjects in server certificates for Kafka listeners.
-
If you are not using a self-signed certificate, you can provide a certificate that includes the whole CA chain in the certificate.
You can only use the brokerCertChainAndKey
properties if TLS encryption (tls: true
) is configured for the listener.
Note
|
Strimzi does not support the use of encrypted private keys for TLS. The private key stored in the secret must be unencrypted for this to work. |
-
Create a
Secret
containing your private key and server certificate:kubectl create secret generic my-secret --from-file=my-listener-key.key --from-file=my-listener-certificate.crt
-
Edit the
Kafka
resource for your cluster.Configure the listener to use your
Secret
, certificate file, and private key file in theconfiguration.brokerCertChainAndKey
property.Example configuration for aloadbalancer
external listener with TLS encryption enabled# ... listeners: - name: plain port: 9092 type: internal tls: false - name: external port: 9094 type: loadbalancer tls: true configuration: brokerCertChainAndKey: secretName: my-secret certificate: my-listener-certificate.crt key: my-listener-key.key # ...
Example configuration for a TLS listener# ... listeners: - name: plain port: 9092 type: internal tls: false - name: tls port: 9093 type: internal tls: true configuration: brokerCertChainAndKey: secretName: my-secret certificate: my-listener-certificate.crt key: my-listener-key.key # ...
-
Apply the new configuration to create or update the resource:
kubectl apply -f kafka.yaml
The Cluster Operator starts a rolling update of the Kafka cluster, which updates the configuration of the listeners.
NoteA rolling update is also started if you update a Kafka listener certificate in a Secret
that is already used by a listener.
10.3.5. Alternative subjects in server certificates for Kafka listeners
In order to use TLS hostname verification with your own Kafka listener certificates, you must use the correct Subject Alternative Names (SANs) for each listener. The certificate SANs must specify hostnames for the following:
-
All of the Kafka brokers in your cluster
-
The Kafka cluster bootstrap service
You can use wildcard certificates if they are supported by your CA.
Examples of SANs for internal listeners
Use the following examples to help you specify hostnames of the SANs in your certificates for your internal listeners.
Replace <cluster-name>
with the name of the Kafka cluster and <namespace>
with the Kubernetes namespace where the cluster is running.
type: internal
listener//Kafka brokers
*.<cluster-name>-kafka-brokers
*.<cluster-name>-kafka-brokers.<namespace>.svc
// Bootstrap service
<cluster-name>-kafka-bootstrap
<cluster-name>-kafka-bootstrap.<namespace>.svc
type: internal
listener// Kafka brokers
<cluster-name>-kafka-0.<cluster-name>-kafka-brokers
<cluster-name>-kafka-0.<cluster-name>-kafka-brokers.<namespace>.svc
<cluster-name>-kafka-1.<cluster-name>-kafka-brokers
<cluster-name>-kafka-1.<cluster-name>-kafka-brokers.<namespace>.svc
# ...
// Bootstrap service
<cluster-name>-kafka-bootstrap
<cluster-name>-kafka-bootstrap.<namespace>.svc
type: cluster-ip
listener// Kafka brokers
<cluster-name>-kafka-<listener-name>-0
<cluster-name>-kafka-<listener-name>-0.<namespace>.svc
<cluster-name>-kafka-<listener-name>-1
<cluster-name>-kafka-<listener-name>-1.<namespace>.svc
# ...
// Bootstrap service
<cluster-name>-kafka-<listener-name>-bootstrap
<cluster-name>-kafka-<listener-name>-bootstrap.<namespace>.svc
Examples of SANs for external listeners
For external listeners which have TLS encryption enabled, the hostnames you need to specify in certificates depends on the external listener type
.
External listener type | In the SANs, specify… |
---|---|
|
Addresses of all Kafka broker You can use a matching wildcard name. |
|
Addresses of all Kafka broker You can use a matching wildcard name. |
|
Addresses of all Kafka broker You can use a matching wildcard name. |
|
Addresses of all Kubernetes worker nodes that the Kafka broker pods might be scheduled to. You can use a matching wildcard name. |
10.4. Using OAuth 2.0 token-based authentication
Strimzi supports the use of OAuth 2.0 authentication using the OAUTHBEARER and PLAIN mechanisms.
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.
Kafka brokers and clients both need to be configured to use OAuth 2.0. You can configure OAuth 2.0 authentication, then OAuth 2.0 authorization.
Note
|
OAuth 2.0 authentication can be used in conjunction with Kafka authorization. |
Using OAuth 2.0 authentication, application clients can access resources on application servers (called resource servers) without exposing account credentials.
The application client passes an access token as a means of authenticating, which application servers can also use to determine the level of access to grant. The authorization server handles the granting of access and inquiries about access.
In the context of Strimzi:
-
Kafka brokers act as OAuth 2.0 resource servers
-
Kafka clients act as OAuth 2.0 application clients
Kafka clients authenticate to Kafka brokers. The brokers and clients communicate with the OAuth 2.0 authorization server, as necessary, to obtain or validate access tokens.
For a deployment of Strimzi, OAuth 2.0 integration provides:
-
Server-side OAuth 2.0 support for Kafka brokers
-
Client-side OAuth 2.0 support for Kafka MirrorMaker, Kafka Connect, and the Kafka Bridge
10.4.1. OAuth 2.0 authentication mechanisms
Strimzi supports the OAUTHBEARER and PLAIN mechanisms for OAuth 2.0 authentication. Both mechanisms allow Kafka clients to establish authenticated sessions with Kafka brokers. The authentication flow between clients, the authorization server, and Kafka brokers is different for each mechanism.
We recommend that you configure clients to use OAUTHBEARER whenever possible. OAUTHBEARER provides a higher level of security than PLAIN because client credentials are never shared with Kafka brokers. Consider using PLAIN only with Kafka clients that do not support OAUTHBEARER.
You configure Kafka broker listeners to use OAuth 2.0 authentication for connecting clients.
If necessary, you can use the OAUTHBEARER and PLAIN mechanisms on the same oauth
listener.
The properties to support each mechanism must be explicitly specified in the oauth
listener configuration.
OAUTHBEARER is automatically enabled in the oauth
listener configuration for the Kafka broker.
You can set the enableOauthBearer
property to true
, though this is not required.
# ...
authentication:
type: oauth
# ...
enableOauthBearer: true
Many Kafka client tools use libraries that provide basic support for OAUTHBEARER at the protocol level. To support application development, Strimzi provides an OAuth callback handler for the upstream Kafka Client Java libraries (but not for other libraries). Therefore, you do not need to write your own callback handlers. An application client can use the callback handler to provide the access token. Clients written in other languages, such as Go, must use custom code to connect to the authorization server and obtain the access token.
With OAUTHBEARER, the client initiates a session with the Kafka broker for credentials exchange, where credentials take the form of a bearer token provided by the callback handler. Using the callbacks, you can configure token provision in one of three ways:
-
Client ID and Secret (by using the OAuth 2.0 client credentials mechanism)
-
A long-lived access token, obtained manually at configuration time
-
A long-lived refresh token, obtained manually at configuration time
Note
|
OAUTHBEARER authentication can only be used by Kafka clients that support the OAUTHBEARER mechanism at the protocol level. |
To use PLAIN, you must enable it in the oauth
listener configuration for the Kafka broker.
In the following example, PLAIN is enabled in addition to OAUTHBEARER, which is enabled by default.
If you want to use PLAIN only, you can disable OAUTHBEARER by setting enableOauthBearer
to false
.
# ...
authentication:
type: oauth
# ...
enablePlain: true
tokenEndpointUri: https://OAUTH-SERVER-ADDRESS/auth/realms/external/protocol/openid-connect/token
PLAIN is a simple authentication mechanism used by all Kafka client tools. To enable PLAIN to be used with OAuth 2.0 authentication, Strimzi provides OAuth 2.0 over PLAIN server-side callbacks.
With the Strimzi implementation of PLAIN, the client credentials are not stored in ZooKeeper. Instead, client credentials are handled centrally behind a compliant authorization server, similar to when OAUTHBEARER authentication is used.
When used with the OAuth 2.0 over PLAIN callbacks, Kafka clients authenticate with Kafka brokers using either of the following methods:
-
Client ID and secret (by using the OAuth 2.0 client credentials mechanism)
-
A long-lived access token, obtained manually at configuration time
For both methods, the client must provide the PLAIN username
and password
properties to pass credentials to the Kafka broker.
The client uses these properties to pass a client ID and secret or username and access token.
Client IDs and secrets are used to obtain access tokens.
Access tokens are passed as password
property values.
You pass the access token with or without an $accessToken:
prefix.
-
If you configure a token endpoint (
tokenEndpointUri
) in the listener configuration, you need the prefix. -
If you don’t configure a token endpoint (
tokenEndpointUri
) in the listener configuration, you don’t need the prefix. The Kafka broker interprets the password as a raw access token.
If the password
is set as the access token, the username
must be set to the same principal name that the Kafka broker obtains from the access token.
You can specify username extraction options in your listener using the userNameClaim
, fallbackUserNameClaim
, fallbackUsernamePrefix
, and userInfoEndpointUri
properties.
The username extraction process also depends on your authorization server; in particular, how it maps client IDs to account names.
Note
|
OAuth over PLAIN does not support |
10.4.2. OAuth 2.0 Kafka broker configuration
Kafka broker configuration for OAuth 2.0 involves:
-
Creating the OAuth 2.0 client in the authorization server
-
Configuring OAuth 2.0 authentication in the Kafka custom resource
Note
|
In relation to the authorization server, Kafka brokers and Kafka clients are both regarded as OAuth 2.0 clients. |
OAuth 2.0 client configuration on an authorization server
To configure a Kafka broker to validate the token received during session initiation, the recommended approach is to create an OAuth 2.0 client definition in an authorization server, configured as confidential, with the following client credentials enabled:
-
Client ID of
kafka
(for example) -
Client ID and Secret as the authentication mechanism
Note
|
You only need to use a client ID and secret when using a non-public introspection endpoint of the authorization server. The credentials are not typically required when using public authorization server endpoints, as with fast local JWT token validation. |
OAuth 2.0 authentication configuration in the Kafka cluster
To use OAuth 2.0 authentication in the Kafka cluster, you specify, for example, a tls
listener configuration for your Kafka cluster custom resource with the authentication method oauth
:
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
kafka:
# ...
listeners:
- name: tls
port: 9093
type: internal
tls: true
authentication:
type: oauth
#...
You can configure OAuth 2.0 authentication in your listeners.
We recommend using OAuth 2.0 authentication together with TLS encryption (tls: true
).
Without encryption, the connection is vulnerable to network eavesdropping and unauthorized access through token theft.
You configure an external
listener with type: oauth
for a secure transport layer to communicate with the client.
# ...
listeners:
- name: external
port: 9094
type: loadbalancer
tls: true
authentication:
type: oauth
#...
The tls
property is false by default, so it must be enabled.
When you have defined the type of authentication as OAuth 2.0, you add configuration based on the type of validation, either as fast local JWT validation or token validation using an introspection endpoint.
The procedure to configure OAuth 2.0 for listeners, with descriptions and examples, is described in Configuring OAuth 2.0 support for Kafka brokers.
Fast local JWT token validation configuration
Fast local JWT token validation checks a JWT token signature locally.
The local check ensures that a token:
-
Conforms to type by containing a (typ) claim value of
Bearer
for an access token -
Is valid (not expired)
-
Has an issuer that matches a
validIssuerURI
You specify a validIssuerURI
attribute when you configure the listener, so that any tokens not issued by the authorization server are rejected.
The authorization server does not need to be contacted during fast local JWT token validation.
You activate fast local JWT token validation by specifying a jwksEndpointUri
attribute, the endpoint exposed by the OAuth 2.0 authorization server.
The endpoint contains the public keys used to validate signed JWT tokens, which are sent as credentials by Kafka clients.
Note
|
All communication with the authorization server should be performed using TLS encryption. |
You can configure a certificate truststore as a Kubernetes Secret in your Strimzi project namespace, and use a tlsTrustedCertificates
attribute to point to the Kubernetes Secret containing the truststore file.
You might want to configure a userNameClaim
to properly extract a username from the JWT token.
If you want to use Kafka ACL authorization, you need to identify the user by their username during authentication.
(The sub
claim in JWT tokens is typically a unique ID, not a username.)
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
kafka:
#...
listeners:
- name: tls
port: 9093
type: internal
tls: true
authentication:
type: oauth
validIssuerUri: <https://<auth-server-address>/auth/realms/tls>
jwksEndpointUri: <https://<auth-server-address>/auth/realms/tls/protocol/openid-connect/certs>
userNameClaim: preferred_username
maxSecondsWithoutReauthentication: 3600
tlsTrustedCertificates:
- secretName: oauth-server-cert
certificate: ca.crt
#...
OAuth 2.0 introspection endpoint configuration
Token validation using an OAuth 2.0 introspection endpoint treats a received access token as opaque. The Kafka broker sends an access token to the introspection endpoint, which responds with the token information necessary for validation. Importantly, it returns up-to-date information if the specific access token is valid, and also information about when the token expires.
To configure OAuth 2.0 introspection-based validation, you specify an introspectionEndpointUri
attribute rather than the jwksEndpointUri
attribute specified for fast local JWT token validation.
Depending on the authorization server, you typically have to specify a clientId
and clientSecret
, because the introspection endpoint is usually protected.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
kafka:
listeners:
- name: tls
port: 9093
type: internal
tls: true
authentication:
type: oauth
clientId: kafka-broker
clientSecret:
secretName: my-cluster-oauth
key: clientSecret
validIssuerUri: <https://<auth-server-address>/auth/realms/tls>
introspectionEndpointUri: <https://<auth-server-address>/auth/realms/tls/protocol/openid-connect/token/introspect>
userNameClaim: preferred_username
maxSecondsWithoutReauthentication: 3600
tlsTrustedCertificates:
- secretName: oauth-server-cert
certificate: ca.crt
10.4.3. Session re-authentication for Kafka brokers
You can configure oauth
listeners to use Kafka session re-authentication for OAuth 2.0 sessions between Kafka clients and Kafka brokers.
This mechanism enforces the expiry of an authenticated session between the client and the broker after a defined period of time.
When a session expires, the client immediately starts a new session by reusing the existing connection rather than dropping it.
Session re-authentication is disabled by default.
To enable it, you set a time value for maxSecondsWithoutReauthentication
in the oauth
listener configuration.
The same property is used to configure session re-authentication for OAUTHBEARER and PLAIN authentication.
For an example configuration, see Configuring OAuth 2.0 support for Kafka brokers.
Session re-authentication must be supported by the Kafka client libraries used by the client.
Session re-authentication can be used with fast local JWT or introspection endpoint token validation.
When the broker’s authenticated session expires, the client must re-authenticate to the existing session by sending a new, valid access token to the broker, without dropping the connection.
If token validation is successful, a new client session is started using the existing connection. If the client fails to re-authenticate, the broker will close the connection if further attempts are made to send or receive messages. Java clients that use Kafka client library 2.2 or later automatically re-authenticate if the re-authentication mechanism is enabled on the broker.
Session re-authentication also applies to refresh tokens, if used. When the session expires, the client refreshes the access token by using its refresh token. The client then uses the new access token to re-authenticate to the existing session.
When session re-authentication is configured, session expiry works differently for OAUTHBEARER and PLAIN authentication.
For OAUTHBEARER and PLAIN, using the client ID and secret method:
-
The broker’s authenticated session will expire at the configured
maxSecondsWithoutReauthentication
. -
The session will expire earlier if the access token expires before the configured time.
For PLAIN using the long-lived access token method:
-
The broker’s authenticated session will expire at the configured
maxSecondsWithoutReauthentication
. -
Re-authentication will fail if the access token expires before the configured time. Although session re-authentication is attempted, PLAIN has no mechanism for refreshing tokens.
If maxSecondsWithoutReauthentication
is not configured, OAUTHBEARER and PLAIN clients can remain connected to brokers indefinitely, without needing to re-authenticate.
Authenticated sessions do not end with access token expiry.
However, this can be considered when configuring authorization, for example, by using keycloak
authorization or installing a custom authorizer.
10.4.4. OAuth 2.0 Kafka client configuration
A Kafka client is configured with either:
-
The credentials required to obtain a valid access token from an authorization server (client ID and Secret)
-
A valid long-lived access token or refresh token, obtained using tools provided by an authorization server
The only information ever sent to the Kafka broker is an access token. The credentials used to authenticate with the authorization server to obtain the access token are never sent to the broker.
When a client obtains an access token, no further communication with the authorization server is needed.
The simplest mechanism is authentication with a client ID and Secret. Using a long-lived access token, or a long-lived refresh token, adds more complexity because there is an additional dependency on authorization server tools.
Note
|
If you are using long-lived access tokens, you may need to configure the client in the authorization server to increase the maximum lifetime of the token. |
If the Kafka client is not configured with an access token directly, the client exchanges credentials for an access token during Kafka session initiation by contacting the authorization server. The Kafka client exchanges either:
-
Client ID and Secret
-
Client ID, refresh token, and (optionally) a secret
-
Username and password, with client ID and (optionally) a secret
10.4.5. OAuth 2.0 client authentication flows
OAuth 2.0 authentication flows depend on the underlying Kafka client and Kafka broker configuration. The flows must also be supported by the authorization server used.
The Kafka broker listener configuration determines how clients authenticate using an access token. The client can pass a client ID and secret to request an access token.
If a listener is configured to use PLAIN authentication, the client can authenticate with a client ID and secret or username and access token.
These values are passed as the username
and password
properties of the PLAIN mechanism.
Listener configuration supports the following token validation options:
-
You can use fast local token validation based on JWT signature checking and local token introspection, without contacting an authorization server. The authorization server provides a JWKS endpoint with public certificates that are used to validate signatures on the tokens.
-
You can use a call to a token introspection endpoint provided by an authorization server. Each time a new Kafka broker connection is established, the broker passes the access token received from the client to the authorization server. The Kafka broker checks the response to confirm whether or not the token is valid.
Note
|
An authorization server might only allow the use of opaque access tokens, which means that local token validation is not possible. |
Kafka client credentials can also be configured for the following types of authentication:
-
Direct local access using a previously generated long-lived access token
-
Contact with the authorization server for a new access token to be issued (using a client ID and a secret, or a refresh token, or a username and a password)
Example client authentication flows using the SASL OAUTHBEARER mechanism
You can use the following communication flows for Kafka authentication using the SASL OAUTHBEARER mechanism.
-
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
-
The Kafka client requests an access token from the authorization server using a client ID and secret, and optionally a refresh token. Alternatively, the client may authenticate using a username and a password.
-
The authorization server generates a new access token.
-
The Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the access token.
-
The Kafka broker validates the access token by calling a token introspection endpoint on the authorization server using its own client ID and secret.
-
A Kafka client session is established if the token is valid.
-
The Kafka client authenticates with the authorization server from the token endpoint, using a client ID and secret, and optionally a refresh token. Alternatively, the client may authenticate using a username and a password.
-
The authorization server generates a new access token.
-
The Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the access token.
-
The Kafka broker validates the access token locally using a JWT token signature check, and local token introspection.
-
The Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the long-lived access token.
-
The Kafka broker validates the access token by calling a token introspection endpoint on the authorization server, using its own client ID and secret.
-
A Kafka client session is established if the token is valid.
-
The Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the long-lived access token.
-
The Kafka broker validates the access token locally using a 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. |
Example client authentication flows using the SASL PLAIN mechanism
You can use the following communication flows for Kafka authentication using the OAuth PLAIN mechanism.
-
The Kafka client passes a
clientId
as a username and asecret
as a password. -
The Kafka broker uses a token endpoint to pass the
clientId
andsecret
to the authorization server. -
The authorization server returns a fresh access token or an error if the client credentials are not valid.
-
The Kafka broker validates the token in one of the following ways:
-
If a token introspection endpoint is specified, the Kafka broker validates the access token by calling the endpoint on the authorization server. A session is established if the token validation is successful.
-
If local token introspection is used, a request is not made to the authorization server. The Kafka broker validates the access token locally using a JWT token signature check.
-
-
The Kafka client passes a username and password. The password provides the value of an access token that was obtained manually and configured before running the client.
-
The password is passed with or without an
$accessToken:
string prefix depending on whether or not the Kafka broker listener is configured with a token endpoint for authentication.-
If the token endpoint is configured, the password should be prefixed by
$accessToken:
to let the broker know that the password parameter contains an access token rather than a client secret. The Kafka broker interprets the username as the account username. -
If the token endpoint is not configured on the Kafka broker listener (enforcing a
no-client-credentials mode
), the password should provide the access token without the prefix. The Kafka broker interprets the username as the account username. In this mode, the client doesn’t use a client ID and secret, and thepassword
parameter is always interpreted as a raw access token.
-
-
The Kafka broker validates the token in one of the following ways:
-
If a token introspection endpoint is specified, the Kafka broker validates the access token by calling the endpoint on the authorization server. A session is established if token validation is successful.
-
If local token introspection is used, there is no request made to the authorization server. Kafka broker validates the access token locally using a JWT token signature check.
-
10.4.6. 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.
We advise use of OAuth 2.0 over an encrypted interface through through a listener with tls: true
.
Plain listeners are not recommended.
If the authorization server is using certificates signed by the trusted CA and matching the OAuth 2.0 server hostname, TLS connection works using the default settings. Otherwise, you may need to configure the truststore with proper certificates or disable the certificate hostname validation.
When configuring the Kafka broker you have two options for the mechanism used to validate the access token during OAuth 2.0 authentication of the newly connected Kafka client:
For more information on the configuration of OAuth 2.0 authentication for 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.
The examples here show the configuration options as configured for external listeners.
Example 1: Configuring fast local JWT token validation#... - name: external port: 9094 type: loadbalancer tls: true authentication: type: oauth (1) validIssuerUri: <https://<auth-server-address>/auth/realms/external> (2) jwksEndpointUri: <https://<auth-server-address>/auth/realms/external/protocol/openid-connect/certs> (3) userNameClaim: preferred_username (4) maxSecondsWithoutReauthentication: 3600 (5) tlsTrustedCertificates: (6) - secretName: oauth-server-cert certificate: ca.crt disableTlsHostnameVerification: true (7) jwksExpirySeconds: 360 (8) jwksRefreshSeconds: 300 (9) jwksMinRefreshPauseSeconds: 1 (10)
-
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 token claim (or key) that contains the actual user name in the token. The user name is the principal used to identify the user. The
userNameClaim
value will depend on the authentication flow and the authorization server used. -
(Optional) Activates the Kafka re-authentication mechanism that enforces session expiry to the same length of time as the access token. If the specified value is less than the time left for the access token to expire, then the client will have to re-authenticate before the actual token expiry. By default, the session does not expire when the access token expires, and the client does not attempt re-authentication.
-
(Optional) Trusted certificates for TLS connection to the authorization server.
-
(Optional) Disable TLS hostname verification. Default is
false
. -
The duration the JWKS certificates are considered valid before they expire. Default is
360
seconds. If you specify a longer time, consider the risk of allowing access to revoked certificates. -
The period between refreshes of JWKS certificates. The interval must be at least 60 seconds shorter than the expiry interval. Default is
300
seconds. -
The minimum pause in seconds between consecutive attempts to refresh JWKS public keys. When an unknown signing key is encountered, the JWKS keys refresh is scheduled outside the regular periodic schedule with at least the specified pause since the last refresh attempt. The refreshing of keys follows the rule of exponential backoff, retrying on unsuccessful refreshes with ever increasing pause, until it reaches
jwksRefreshSeconds
. The default value is 1.
Example 2: Configuring token validation using an introspection endpoint- name: external port: 9094 type: loadbalancer tls: true authentication: type: oauth validIssuerUri: <https://<auth-server-address>/auth/realms/external> introspectionEndpointUri: <https://<auth-server-address>/auth/realms/external/protocol/openid-connect/token/introspect> (1) clientId: kafka-broker (2) clientSecret: (3) secretName: my-cluster-oauth key: clientSecret userNameClaim: preferred_username (4) maxSecondsWithoutReauthentication: 3600 (5)
-
URI of the token introspection endpoint.
-
Client ID to identify the client.
-
Client Secret and client ID is used for authentication.
-
The token claim (or key) that contains the actual user name in the token. The user name is the principal used to identify the user. The
userNameClaim
value will depend on the authorization server used. -
(Optional) Activates the Kafka re-authentication mechanism that enforces session expiry to the same length of time as the access token. If the specified value is less than the time left for the access token to expire, then the client will have to re-authenticate before the actual token expiry. By default, the session does not expire when the access token expires, and the client does not attempt re-authentication.
Depending on how you apply OAuth 2.0 authentication, and the type of authorization server, there are additional (optional) configuration settings you can use:
# ... authentication: type: oauth # ... checkIssuer: false (1) checkAudience: true (2) fallbackUserNameClaim: client_id (3) fallbackUserNamePrefix: client-account- (4) validTokenType: bearer (5) userInfoEndpointUri: https://OAUTH-SERVER-ADDRESS/auth/realms/external/protocol/openid-connect/userinfo (6) enableOauthBearer: false (7) enablePlain: true (8) tokenEndpointUri: https://OAUTH-SERVER-ADDRESS/auth/realms/external/protocol/openid-connect/token (9) customClaimCheck: "@.custom == 'custom-value'" (10) clientAudience: AUDIENCE (11) clientScope: SCOPE (12) connectTimeoutSeconds: 60 (13) readTimeoutSeconds: 60 (14) httpRetries: 2 (15) httpRetryPauseMs: 300 (16) groupsClaim: "$.groups" (17) groupsClaimDelimiter: "," (18)
-
If your authorization server does not provide an
iss
claim, it is not possible to perform an issuer check. In this situation, setcheckIssuer
tofalse
and do not specify avalidIssuerUri
. Default istrue
. -
If your authorization server provides an
aud
(audience) claim, and you want to enforce an audience check, setcheckAudience
totrue
. Audience checks identify the intended recipients of tokens. As a result, the Kafka broker will reject tokens that do not have itsclientId
in theiraud
claim. Default isfalse
. -
An authorization server may not provide a single attribute to identify both regular users and clients. When a client authenticates in its own name, the server might provide a client ID. When a user authenticates using a username and password, to obtain a refresh token or an access token, the server might provide a username attribute in addition to a client ID. Use this fallback option to specify the username claim (attribute) to use if a primary user ID attribute is not available.
-
In situations where
fallbackUserNameClaim
is applicable, it may also be necessary to prevent name collisions between the values of the username claim, and those of the fallback username claim. Consider a situation where a client calledproducer
exists, but also a regular user calledproducer
exists. In order to differentiate between the two, you can use this property to add a prefix to the user ID of the client. -
(Only applicable when using
introspectionEndpointUri
) Depending on the authorization server you are using, the introspection endpoint may or may not return the token type attribute, or it may contain different values. You can specify a valid token type value that the response from the introspection endpoint has to contain. -
(Only applicable when using
introspectionEndpointUri
) The authorization server may be configured or implemented in such a way to not provide any identifiable information in an Introspection Endpoint response. In order to obtain the user ID, you can configure the URI of theuserinfo
endpoint as a fallback. TheuserNameClaim
,fallbackUserNameClaim
, andfallbackUserNamePrefix
settings are applied to the response ofuserinfo
endpoint. -
Set this to
false
to disable the OAUTHBEARER mechanism on the listener. At least one of PLAIN or OAUTHBEARER has to be enabled. Default istrue
. -
Set to
true
to enable PLAIN authentication on the listener, which is supported for clients on all platforms. -
Additional configuration for the PLAIN mechanism. If specified, clients can authenticate over PLAIN by passing an access token as the
password
using an$accessToken:
prefix. For production, always usehttps://
urls. -
Additional custom rules can be imposed on the JWT access token during validation by setting this to a JsonPath filter query. If the access token does not contain the necessary data, it is rejected. When using the
introspectionEndpointUri
, the custom check is applied to the introspection endpoint response JSON. -
An
audience
parameter passed to the token endpoint. An audience is used when obtaining an access token for inter-broker authentication. It is also used in the name of a client for OAuth 2.0 over PLAIN client authentication using aclientId
andsecret
. This only affects the ability to obtain the token, and the content of the token, depending on the authorization server. It does not affect token validation rules by the listener. -
A
scope
parameter passed to the token endpoint. A scope is used when obtaining an access token for inter-broker authentication. It is also used in the name of a client for OAuth 2.0 over PLAIN client authentication using aclientId
andsecret
. This only affects the ability to obtain the token, and the content of the token, depending on the authorization server. It does not affect token validation rules by the listener. -
The connect timeout in seconds when connecting to the authorization server. The default value is 60.
-
The read timeout in seconds when connecting to the authorization server. The default value is 60.
-
The maximum number of times to retry a failed HTTP request to the authorization server. The default value is
0
, meaning that no retries are performed. To use this option effectively, consider reducing the timeout times for theconnectTimeoutSeconds
andreadTimeoutSeconds
options. However, note that retries may prevent the current worker thread from being available to other requests, and if too many requests stall, it could make the Kafka broker unresponsive. -
The time to wait before attempting another retry of a failed HTTP request to the authorization server. By default, this time is set to zero, meaning that no pause is applied. This is because many issues that cause failed requests are per-request network glitches or proxy issues that can be resolved quickly. However, if your authorization server is under stress or experiencing high traffic, you may want to set this option to a value of 100 ms or more to reduce the load on the server and increase the likelihood of successful retries.
-
A JsonPath query that is used to extract groups information from either the JWT token or the introspection endpoint response. This option is not set by default. By configuring this option, a custom authorizer can make authorization decisions based on user groups.
-
A delimiter used to parse groups information when it is returned as a single delimited string. The default value is ',' (comma).
-
-
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 pod -w
The rolling update configures the brokers to use OAuth 2.0 authentication.
Configuring Kafka Java clients to use OAuth 2.0
Configure Kafka producer and consumer APIs to use OAuth 2.0 for interaction with Kafka brokers.
Add a callback plugin to your client pom.xml
file, then configure your client for OAuth 2.0.
Specify the following in your client configuration:
-
A SASL (Simple Authentication and Security Layer) security protocol:
-
SASL_SSL
for authentication over TLS encrypted connections -
SASL_PLAINTEXT
for authentication over unencrypted connectionsUse
SASL_SSL
for production andSASL_PLAINTEXT
for local development only. When usingSASL_SSL
, additionalssl.truststore
configuration is needed. The truststore configuration is required for secure connection (https://
) to the OAuth 2.0 authorization server. To verify the OAuth 2.0 authorization server, add the CA certificate for the authorization server to the truststore in your client configuration. You can configure a truststore in PEM or PKCS #12 format.
-
-
A Kafka SASL mechanism:
-
OAUTHBEARER
for credentials exchange using a bearer token -
PLAIN
to pass client credentials (clientId + secret) or an access token
-
-
A JAAS (Java Authentication and Authorization Service) module that implements the SASL mechanism:
-
org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule
implements the OAUTHBEARER mechanism -
org.apache.kafka.common.security.plain.PlainLoginModule
implements the PLAIN mechanism
-
-
SASL authentication properties, which support the following authentication methods:
-
OAuth 2.0 client credentials
-
OAuth 2.0 password grant (deprecated)
-
Access token
-
Refresh token
-
Add the SASL authentication properties as JAAS configuration (sasl.jaas.config
).
How you configure the authentication properties depends on the authentication method you are using to access the OAuth 2.0 authorization server.
In this procedure, the properties are specified in a properties file, then loaded into the client configuration.
Note
|
You can also specify authentication properties as environment variables, or as Java system properties. For Java system properties, you can set them using setProperty and pass them on the command line using the -D option.
|
-
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>0.12.0</version> </dependency>
-
Configure the client properties by specifying the following configuration in a properties file:
-
The security protocol
-
The SASL mechanism
-
The JAAS module and authentication properties according to the method being used
For example, we can add the following to a
client.properties
file:Client credentials mechanism propertiessecurity.protocol=SASL_SSL # (1) sasl.mechanism=OAUTHBEARER # (2) ssl.truststore.location=/tmp/truststore.p12 (3) ssl.truststore.password=$STOREPASS ssl.truststore.type=PKCS12 sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \ oauth.token.endpoint.uri="<token_endpoint_url>" \ # (4) oauth.client.id="<client_id>" \ # (5) oauth.client.secret="<client_secret>" \ # (6) oauth.ssl.truststore.location="/tmp/oauth-truststore.p12" \ (7) oauth.ssl.truststore.password="$STOREPASS" \ (8) oauth.ssl.truststore.type="PKCS12" \ (9) oauth.scope="<scope>" \ # (10) oauth.audience="<audience>" ; # (11)
-
SASL_SSL
security protocol for TLS-encrypted connections. UseSASL_PLAINTEXT
over unencrypted connections for local development only. -
The SASL mechanism specified as
OAUTHBEARER
orPLAIN
. -
The truststore configuration for secure access to the Kafka cluster.
-
URI of the authorization server token endpoint.
-
Client ID, which is the name used when creating the client in the authorization server.
-
Client secret created when creating the client in the authorization server.
-
The location contains the public key certificate (
truststore.p12
) for the authorization server. -
The password for accessing the truststore.
-
The truststore type.
-
(Optional) The
scope
for requesting the token from the token endpoint. An authorization server may require a client to specify the scope. -
(Optional) The
audience
for requesting the token from the token endpoint. An authorization server may require a client to specify the audience.
Password grants mechanism propertiessecurity.protocol=SASL_SSL sasl.mechanism=OAUTHBEARER ssl.truststore.location=/tmp/truststore.p12 ssl.truststore.password=$STOREPASS ssl.truststore.type=PKCS12 sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \ oauth.token.endpoint.uri="<token_endpoint_url>" \ oauth.client.id="<client_id>" \ # (1) oauth.client.secret="<client_secret>" \ # (2) oauth.password.grant.username="<username>" \ # (3) oauth.password.grant.password="<password>" \ # (4) oauth.ssl.truststore.location="/tmp/oauth-truststore.p12" \ oauth.ssl.truststore.password="$STOREPASS" \ oauth.ssl.truststore.type="PKCS12" \ oauth.scope="<scope>" \ oauth.audience="<audience>" ;
-
Client ID, which is the name used when creating the client in the authorization server.
-
(Optional) Client secret created when creating the client in the authorization server.
-
Username for password grant authentication. OAuth password grant configuration (username and password) uses the OAuth 2.0 password grant method. To use password grants, create a user account for a client on your authorization server with limited permissions. The account should act like a service account. Use in environments where user accounts are required for authentication, but consider using a refresh token first.
-
Password for password grant authentication.
NoteSASL PLAIN does not support passing a username and password (password grants) using the OAuth 2.0 password grant method.
Access token propertiessecurity.protocol=SASL_SSL sasl.mechanism=OAUTHBEARER ssl.truststore.location=/tmp/truststore.p12 ssl.truststore.password=$STOREPASS ssl.truststore.type=PKCS12 sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \ oauth.token.endpoint.uri="<token_endpoint_url>" \ oauth.access.token="<access_token>" ; # (1) oauth.ssl.truststore.location="/tmp/oauth-truststore.p12" \ oauth.ssl.truststore.password="$STOREPASS" \ oauth.ssl.truststore.type="PKCS12" \
-
Long-lived access token for Kafka clients.
Refresh token propertiessecurity.protocol=SASL_SSL sasl.mechanism=OAUTHBEARER ssl.truststore.location=/tmp/truststore.p12 ssl.truststore.password=$STOREPASS ssl.truststore.type=PKCS12 sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \ oauth.token.endpoint.uri="<token_endpoint_url>" \ oauth.client.id="<client_id>" \ # (1) oauth.client.secret="<client_secret>" \ # (2) oauth.refresh.token="<refresh_token>" ; # (3) oauth.ssl.truststore.location="/tmp/oauth-truststore.p12" \ oauth.ssl.truststore.password="$STOREPASS" \ oauth.ssl.truststore.type="PKCS12" \
-
Client ID, which is the name used when creating the client in the authorization server.
-
(Optional) Client secret created when creating the client in the authorization server.
-
Long-lived refresh token for Kafka clients.
-
-
-
Input the client properties for OAUTH 2.0 authentication into the Java client code.
Example showing input of client propertiesProperties props = new Properties(); try (FileReader reader = new FileReader("client.properties", StandardCharsets.UTF_8)) { props.load(reader); }
-
Verify that the Kafka client can access the Kafka brokers.
Configuring OAuth 2.0 for Kafka components
This procedure describes how to configure Kafka components to use OAuth 2.0 authentication using an authorization server.
You can configure authentication for:
-
Kafka Connect
-
Kafka MirrorMaker
-
Kafka Bridge
In this scenario, the Kafka component and the authorization server are running in the same cluster.
For more information on the configuration of OAuth 2.0 authentication for Kafka components, see the KafkaClientAuthenticationOAuth
schema reference.
The schema reference includes examples of configuration options.
-
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/v1beta2 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 OAuth 2.0 authentication, you can use the following options:
-
Client ID and secret
-
Client ID and refresh token
-
Access token
-
Username and password
-
TLS
For example, here OAuth 2.0 is assigned to the Kafka Bridge client using a client ID and secret, and TLS:
apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaBridge metadata: name: my-bridge spec: # ... authentication: type: oauth (1) tokenEndpointUri: https://<auth-server-address>/auth/realms/master/protocol/openid-connect/token (2) clientId: kafka-bridge clientSecret: secretName: my-bridge-oauth key: clientSecret tlsTrustedCertificates: (3) - secretName: oauth-server-cert certificate: tls.crt
-
Authentication type set to
oauth
. -
URI of the token endpoint for authentication.
-
Trusted certificates for TLS connection to the authorization server.
Depending on how you apply OAuth 2.0 authentication, and the type of authorization server, there are additional configuration options you can use:
# ... spec: # ... authentication: # ... disableTlsHostnameVerification: true (1) checkAccessTokenType: false (2) accessTokenIsJwt: false (3) scope: any (4) audience: kafka (5) connectTimeoutSeconds: 60 (6) readTimeoutSeconds: 60 (7) httpRetries: 2 (8) httpRetryPauseMs: 300 (9)
-
(Optional) Disable TLS hostname verification. Default is
false
. -
If the authorization server does not return a
typ
(type) claim inside the JWT token, you can applycheckAccessTokenType: false
to skip the token type check. Default istrue
. -
If you are using opaque tokens, you can apply
accessTokenIsJwt: false
so that access tokens are not treated as JWT tokens. -
(Optional) The
scope
for requesting the token from the token endpoint. An authorization server may require a client to specify the scope. In this case it isany
. -
(Optional) The
audience
for requesting the token from the token endpoint. An authorization server may require a client to specify the audience. In this case it iskafka
. -
(Optional) The connect timeout in seconds when connecting to the authorization server. The default value is 60.
-
(Optional) The read timeout in seconds when connecting to the authorization server. The default value is 60.
-
(Optional) The maximum number of times to retry a failed HTTP request to the authorization server. The default value is
0
, meaning that no retries are performed. To use this option effectively, consider reducing the timeout times for theconnectTimeoutSeconds
andreadTimeoutSeconds
options. However, note that retries may prevent the current worker thread from being available to other requests, and if too many requests stall, it could make the Kafka broker unresponsive. -
(Optional) The time to wait before attempting another retry of a failed HTTP request to the authorization server. By default, this time is set to zero, meaning that no pause is applied. This is because many issues that cause failed requests are per-request network glitches or proxy issues that can be resolved quickly. However, if your authorization server is under stress or experiencing high traffic, you may want to set this option to a value of 100 ms or more to reduce the load on the server and increase the likelihood of successful retries.
-
-
Apply the changes to the deployment of your Kafka resource.
kubectl 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.
10.4.7. Authorization server examples
When choosing an authorization server, consider the features that best support configuration of your chosen authentication flow.
For the purposes of testing OAuth 2.0 with Strimzi, Keycloak and ORY Hydra were implemented as the OAuth 2.0 authorization server.
For more information, see:
10.5. Using OAuth 2.0 token-based authorization
Strimzi supports the use of OAuth 2.0 token-based authorization through Keycloak Keycloak Authorization Services, which allows you to manage security policies and permissions centrally.
Security policies and permissions defined in Keycloak are used to grant access to resources on Kafka brokers. Users and clients are matched against policies that permit access to perform specific actions on Kafka brokers.
Kafka allows all users full access to brokers by default,
and also provides the AclAuthorizer
plugin to configure authorization based on Access Control Lists (ACLs).
ZooKeeper stores ACL rules that grant or deny access to resources based on username. However, OAuth 2.0 token-based authorization with Keycloak offers far greater flexibility on how you wish to implement access control to Kafka brokers. In addition, you can configure your Kafka brokers to use OAuth 2.0 authorization and ACLs.
10.5.1. OAuth 2.0 authorization mechanism
OAuth 2.0 authorization in Strimzi uses Keycloak server Authorization Services REST endpoints to extend token-based authentication with Keycloak by applying defined security policies on a particular user, and providing a list of permissions granted on different resources for that user. Policies use roles and groups to match permissions to users. OAuth 2.0 authorization enforces permissions locally based on the received list of grants for the user from Keycloak Authorization Services.
Kafka broker custom authorizer
A Keycloak authorizer (KeycloakRBACAuthorizer
) is provided with Strimzi.
To be able to use the Keycloak REST endpoints for Authorization Services provided by Keycloak,
you configure a custom authorizer on the Kafka broker.
The authorizer fetches a list of granted permissions from the authorization server as needed, and enforces authorization locally on the Kafka Broker, making rapid authorization decisions for each client request.
10.5.2. Configuring OAuth 2.0 authorization support
This procedure describes how to configure Kafka brokers to use OAuth 2.0 authorization using Keycloak Authorization Services.
Consider the access you require or want to limit for certain users. You can use a combination of Keycloak groups, roles, clients, and users to configure access in Keycloak.
Typically, groups are used to match users based on organizational departments or geographical locations. And roles are used to match users based on their function.
With Keycloak, you can store users and groups in LDAP, whereas clients and roles cannot be stored this way. Storage and access to user data may be a factor in how you choose to configure authorization policies.
Note
|
Super users always have unconstrained access to a Kafka broker regardless of the authorization implemented on the Kafka broker. |
-
Strimzi must be configured to use OAuth 2.0 with Keycloak for token-based authentication. You use the same Keycloak server endpoint when you set up authorization.
-
OAuth 2.0 authentication must be configured with the
maxSecondsWithoutReauthentication
option to enable re-authentication.
-
Access the Keycloak Admin Console or use the Keycloak Admin CLI to enable Authorization Services for the Kafka broker client you created when setting up OAuth 2.0 authentication.
-
Use Authorization Services to define resources, authorization scopes, policies, and permissions for the client.
-
Bind the permissions to users and clients by assigning them roles and groups.
-
Configure the Kafka brokers to use Keycloak authorization by updating the Kafka broker configuration (
Kafka.spec.kafka
) of yourKafka
resource in an editor.kubectl edit kafka my-cluster
-
Configure the Kafka broker
kafka
configuration to usekeycloak
authorization, and to be able to access the authorization server and Authorization Services.For example:
apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: kafka: # ... authorization: type: keycloak (1) tokenEndpointUri: <https://<auth-server-address>/auth/realms/external/protocol/openid-connect/token> (2) clientId: kafka (3) delegateToKafkaAcls: false (4) disableTlsHostnameVerification: false (5) superUsers: (6) - CN=fred - sam - CN=edward tlsTrustedCertificates: (7) - secretName: oauth-server-cert certificate: ca.crt grantsRefreshPeriodSeconds: 60 (8) grantsRefreshPoolSize: 5 (9) connectTimeoutSeconds: 60 (10) readTimeoutSeconds: 60 (11) httpRetries: 2 (12) #...
-
Type
keycloak
enables Keycloak authorization. -
URI of the Keycloak token endpoint. For production, always use
https://
urls. When you configure token-basedoauth
authentication, you specify ajwksEndpointUri
as the URI for local JWT validation. The hostname for thetokenEndpointUri
URI must be the same. -
The client ID of the OAuth 2.0 client definition in Keycloak that has Authorization Services enabled. Typically,
kafka
is used as the ID. -
(Optional) Delegate authorization to Kafka
AclAuthorizer
if access is denied by Keycloak Authorization Services policies. Default isfalse
. -
(Optional) Disable TLS hostname verification. Default is
false
. -
(Optional) Designated super users.
-
(Optional) Trusted certificates for TLS connection to the authorization server.
-
(Optional) The time between two consecutive grants refresh runs. That is the maximum time for active sessions to detect any permissions changes for the user on Keycloak. The default value is 60.
-
(Optional) The number of threads to use to refresh (in parallel) the grants for the active sessions. The default value is 5.
-
(Optional) The connect timeout in seconds when connecting to the Keycloak token endpoint. The default value is 60.
-
(Optional) The read timeout in seconds when connecting to the Keycloak token endpoint. The default value is 60.
-
(Optional) The maximum number of times to retry (without pausing) a failed HTTP request to the authorization server. The default value is
0
, meaning that no retries are performed. To use this option effectively, consider reducing the timeout times for theconnectTimeoutSeconds
andreadTimeoutSeconds
options. However, note that retries may prevent the current worker thread from being available to other requests, and if too many requests stall, it could make the Kafka broker unresponsive.
-
-
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 kafka kubectl get pod -w
The rolling update configures the brokers to use OAuth 2.0 authorization.
-
Verify the configured permissions by accessing Kafka brokers as clients or users with specific roles, making sure they have the necessary access, or do not have the access they are not supposed to have.
10.5.3. Managing policies and permissions in Keycloak Authorization Services
This section describes the authorization models used by Keycloak Authorization Services and Kafka, and defines the important concepts in each model.
To grant permissions to access Kafka, you can map Keycloak Authorization Services objects to Kafka resources by creating an OAuth client specification in Keycloak. Kafka permissions are granted to user accounts or service accounts using Keycloak Authorization Services rules.
Examples are shown of the different user permissions required for common Kafka operations, such as creating and listing topics.
Kafka and Keycloak authorization models overview
Kafka and Keycloak Authorization Services use different authorization models.
Kafka authorization model
Kafka’s authorization model uses resource types.
When a Kafka client performs an action on a broker, the broker uses the configured KeycloakRBACAuthorizer
to check the client’s permissions, based on the action and resource type.
Kafka uses five resource types to control access: Topic
, Group
, Cluster
, TransactionalId
, and DelegationToken
.
Each resource type has a set of available permissions.
Topic
-
Create
-
Write
-
Read
-
Delete
-
Describe
-
DescribeConfigs
-
Alter
-
AlterConfigs
Group
-
Read
-
Describe
-
Delete
Cluster
-
Create
-
Describe
-
Alter
-
DescribeConfigs
-
AlterConfigs
-
IdempotentWrite
-
ClusterAction
TransactionalId
-
Describe
-
Write
DelegationToken
-
Describe
Keycloak Authorization Services model
The Keycloak Authorization Services model has four concepts for defining and granting permissions: resources, authorization scopes, policies, and permissions.
- Resources
-
A resource is a set of resource definitions that are used to match resources with permitted actions. A resource might be an individual topic, for example, or all topics with names starting with the same prefix. A resource definition is associated with a set of available authorization scopes, which represent a set of all actions available on the resource. Often, only a subset of these actions is actually permitted.
- Authorization scopes
-
An authorization scope is a set of all the available actions on a specific resource definition. When you define a new resource, you add scopes from the set of all scopes.
- Policies
-
A policy is an authorization rule that uses criteria to match against a list of accounts. Policies can match:
-
Service accounts based on client ID or roles
-
User accounts based on username, groups, or roles.
-
- Permissions
-
A permission grants a subset of authorization scopes on a specific resource definition to a set of users.
Map Keycloak Authorization Services to the Kafka authorization model
The Kafka authorization model is used as a basis for defining the Keycloak roles and resources that will control access to Kafka.
To grant Kafka permissions to user accounts or service accounts, you first create an OAuth client specification in Keycloak for the Kafka broker.
You then specify Keycloak Authorization Services rules on the client.
Typically, the client id of the OAuth client that represents the broker is kafka
.
The example configuration files provided with Strimzi use kafka
as the OAuth client id.
Note
|
If you have multiple Kafka clusters, you can use a single OAuth client ( |
The kafka
client definition must have the Authorization Enabled option enabled in the Keycloak Admin Console.
All permissions exist within the scope of the kafka
client. If you have different Kafka clusters configured with different OAuth client IDs, they each need a separate set of permissions even though they’re part of the same Keycloak realm.
When the Kafka client uses OAUTHBEARER authentication, the Keycloak authorizer (KeycloakRBACAuthorizer
) uses the access token of the current session to retrieve a list of grants from the Keycloak server.
To retrieve the grants, the authorizer evaluates the Keycloak Authorization Services policies and permissions.
An initial Keycloak configuration usually involves uploading authorization scopes to create a list of all possible actions that can be performed on each Kafka resource type. This step is performed once only, before defining any permissions. You can add authorization scopes manually instead of uploading them.
Authorization scopes must contain all the possible Kafka permissions regardless of the resource type:
-
Create
-
Write
-
Read
-
Delete
-
Describe
-
Alter
-
DescribeConfig
-
AlterConfig
-
ClusterAction
-
IdempotentWrite
Note
|
If you’re certain you won’t need a permission (for example, |
Resource patterns are used for pattern matching against the targeted resources when performing permission checks.
The general pattern format is RESOURCE-TYPE:PATTERN-NAME
.
The resource types mirror the Kafka authorization model. The pattern allows for two matching options:
-
Exact matching (when the pattern does not end with
*
) -
Prefix matching (when the pattern ends with
*
)
Topic:my-topic
Topic:orders-*
Group:orders-*
Cluster:*
Additionally, the general pattern format can be prefixed by kafka-cluster:CLUSTER-NAME
followed by a comma, where CLUSTER-NAME refers to the metadata.name
in the Kafka custom resource.
kafka-cluster:my-cluster,Topic:*
kafka-cluster:*,Group:b_*
When the kafka-cluster
prefix is missing, it is assumed to be kafka-cluster:*
.
When defining a resource, you can associate it with a list of possible authorization scopes which are relevant to the resource. Set whatever actions make sense for the targeted resource type.
Though you may add any authorization scope to any resource, only the scopes supported by the resource type are considered for access control.
Policies are used to target permissions to one or more user accounts or service accounts. Targeting can refer to:
-
Specific user or service accounts
-
Realm roles or client roles
-
User groups
-
JavaScript rules to match a client IP address
A policy is given a unique name and can be reused to target multiple permissions to multiple resources.
Use fine-grained permissions to pull together the policies, resources, and authorization scopes that grant access to users.
The name of each permission should clearly define which permissions it grants to which users.
For example, Dev Team B can read from topics starting with x
.
-
For more information about how to configure permissions through Keycloak Authorization Services, see Trying Keycloak Authorization Services.
Example permissions required for Kafka operations
The following examples demonstrate the user permissions required for performing common operations on Kafka.
To create a topic, the Create
permission is required for the specific topic, or for Cluster:kafka-cluster
.
bin/kafka-topics.sh --create --topic my-topic \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties
If a user has the Describe
permission on a specified topic, the topic is listed.
bin/kafka-topics.sh --list \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties
To display a topic’s details, Describe
and DescribeConfigs
permissions are required on the topic.
bin/kafka-topics.sh --describe --topic my-topic \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties
To produce messages to a topic, Describe
and Write
permissions are required on the topic.
If the topic hasn’t been created yet, and topic auto-creation is enabled, the permissions to create a topic are required.
bin/kafka-console-producer.sh --topic my-topic \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --producer.config=/tmp/config.properties
To consume messages from a topic, Describe
and Read
permissions are required on the topic.
Consuming from the topic normally relies on storing the consumer offsets in a consumer group, which requires additional Describe
and Read
permissions on the consumer group.
Two resources
are needed for matching. For example:
Topic:my-topic
Group:my-group-*
bin/kafka-console-consumer.sh --topic my-topic --group my-group-1 --from-beginning \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --consumer.config /tmp/config.properties
As well as the permissions for producing to a topic, an additional IdempotentWrite
permission is required on the
Cluster:kafka-cluster
resource.
Two resources
are needed for matching. For example:
Topic:my-topic
Cluster:kafka-cluster
bin/kafka-console-producer.sh --topic my-topic \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --producer.config=/tmp/config.properties --producer-property enable.idempotence=true --request-required-acks -1
When listing consumer groups, only the groups on which the user has the Describe
permissions are returned.
Alternatively, if the user has the Describe
permission on the Cluster:kafka-cluster
, all the consumer groups are returned.
bin/kafka-consumer-groups.sh --list \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties
To display a consumer group’s details, the Describe
permission is required on the group and the topics associated with the group.
bin/kafka-consumer-groups.sh --describe --group my-group-1 \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties
To change a topic’s configuration, the Describe
and Alter
permissions are required on the topic.
bin/kafka-topics.sh --alter --topic my-topic --partitions 2 \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties
In order to use kafka-configs.sh
to get a broker’s configuration, the DescribeConfigs
permission is required on the
Cluster:kafka-cluster
.
bin/kafka-configs.sh --entity-type brokers --entity-name 0 --describe --all \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties
To change a Kafka broker’s configuration, DescribeConfigs
and AlterConfigs
permissions are required on Cluster:kafka-cluster
.
bin/kafka-configs --entity-type brokers --entity-name 0 --alter --add-config log.cleaner.threads=2 \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties
To delete a topic, the Describe
and Delete
permissions are required on the topic.
bin/kafka-topics.sh --delete --topic my-topic \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties
To run leader selection for topic partitions, the Alter
permission is required on the Cluster:kafka-cluster
.
bin/kafka-leader-election.sh --topic my-topic --partition 0 --election-type PREFERRED /
--bootstrap-server my-cluster-kafka-bootstrap:9092 --admin.config /tmp/config.properties
To generate a partition reassignment file, Describe
permissions are required on the topics involved.
bin/kafka-reassign-partitions.sh --topics-to-move-json-file /tmp/topics-to-move.json --broker-list "0,1" --generate \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config /tmp/config.properties > /tmp/partition-reassignment.json
To execute the partition reassignment, Describe
and Alter
permissions are required on Cluster:kafka-cluster
. Also,
Describe
permissions are required on the topics involved.
bin/kafka-reassign-partitions.sh --reassignment-json-file /tmp/partition-reassignment.json --execute \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config /tmp/config.properties
To verify partition reassignment, Describe
, and AlterConfigs
permissions are required on Cluster:kafka-cluster
, and on each
of the topics involved.
bin/kafka-reassign-partitions.sh --reassignment-json-file /tmp/partition-reassignment.json --verify \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config /tmp/config.properties
10.5.4. Trying Keycloak Authorization Services
This example explains how to use Keycloak Authorization Services with keycloak
authorization.
Use Keycloak Authorization Services to enforce access restrictions on Kafka clients.
Keycloak Authorization Services use authorization scopes, policies and permissions to define and apply access control to resources.
Keycloak Authorization Services REST endpoints provide a list of granted permissions on resources for authenticated users. The list of grants (permissions) is fetched from the Keycloak server as the first action after an authenticated session is established by the Kafka client. The list is refreshed in the background so that changes to the grants are detected. Grants are cached and enforced locally on the Kafka broker for each user session to provide fast authorization decisions.
Strimzi provides example configuration files. These include the following example files for setting up Keycloak:
kafka-ephemeral-oauth-single-keycloak-authz.yaml
-
An example
Kafka
custom resource configured for OAuth 2.0 token-based authorization using Keycloak. You can use the custom resource to deploy a Kafka cluster that useskeycloak
authorization and token-basedoauth
authentication. kafka-authz-realm.json
-
An example Keycloak realm configured with sample groups, users, roles and clients. You can import the realm into a Keycloak instance to set up fine-grained permissions to access Kafka.
If you want to try the example with Keycloak, use these files to perform the tasks outlined in this section in the order shown.
When you configure token-based oauth
authentication, you specify a jwksEndpointUri
as the URI for local JWT validation.
When you configure keycloak
authorization, you specify a tokenEndpointUri
as the URI of the Keycloak token endpoint.
The hostname for both URIs must be the same.
In Keycloak, confidential clients with service accounts enabled can authenticate to the server in their own name using a client ID and a secret.
This is convenient for microservices that typically act in their own name, and not as agents of a particular user (like a web site).
Service accounts can have roles assigned like regular users.
They cannot, however, have groups assigned.
As a consequence, if you want to target permissions to microservices using service accounts, you cannot use group policies, and should instead use role policies.
Conversely, if you want to limit certain permissions only to regular user accounts where authentication with a username and password is required, you can achieve that as a side effect of using the group policies rather than the role policies.
This is what is used in this example for permissions that start with ClusterManager
.
Performing cluster management is usually done interactively using CLI tools.
It makes sense to require the user to log in before using the resulting access token to authenticate to the Kafka broker.
In this case, the access token represents the specific user, rather than the client application.
Accessing the Keycloak Admin Console
Set up Keycloak, then connect to its Admin Console and add the preconfigured realm.
Use the example kafka-authz-realm.json
file to import the realm.
You can check the authorization rules defined for the realm in the Admin Console.
The rules grant access to the resources on the Kafka cluster configured to use the example Keycloak realm.
-
A running Kubernetes cluster.
-
The Strimzi
examples/security/keycloak-authorization/kafka-authz-realm.json
file that contains the preconfigured realm.
-
Install the Keycloak server using the Keycloak Operator as described in Installing the Keycloak Operator in the Keycloak documentation.
-
Wait until the Keycloak instance is running.
-
Get the external hostname to be able to access the Admin Console.
NS=sso kubectl get ingress keycloak -n $NS
In this example, we assume the Keycloak server is running in the
sso
namespace. -
Get the password for the
admin
user.kubectl get -n $NS pod keycloak-0 -o yaml | less
The password is stored as a secret, so get the configuration YAML file for the Keycloak instance to identify the name of the secret (
secretKeyRef.name
). -
Use the name of the secret to obtain the clear text password.
SECRET_NAME=credential-keycloak kubectl get -n $NS secret $SECRET_NAME -o yaml | grep PASSWORD | awk '{print $2}' | base64 -D
In this example, we assume the name of the secret is
credential-keycloak
. -
Log in to the Admin Console with the username
admin
and the password you obtained.Use
https://HOSTNAME
to access the KubernetesIngress
.You can now upload the example realm to Keycloak using the Admin Console.
-
Click Add Realm to import the example realm.
-
Add the
examples/security/keycloak-authorization/kafka-authz-realm.json
file, and then click Create.You now have
kafka-authz
as your current realm in the Admin Console.The default view displays the Master realm.
-
In the Keycloak Admin Console, go to Clients > kafka > Authorization > Settings and check that Decision Strategy is set to Affirmative.
An affirmative policy means that at least one policy must be satisfied for a client to access the Kafka cluster.
-
In the Keycloak Admin Console, go to Groups, Users, Roles and Clients to view the realm configuration.
- Groups
-
Groups
are used to create user groups and set user permissions. Groups are sets of users with a name assigned. They are used to compartmentalize users into geographical, organizational or departmental units. Groups can be linked to an LDAP identity provider. You can make a user a member of a group through a custom LDAP server admin user interface, for example, to grant permissions on Kafka resources. - Users
-
Users
are used to create users. For this example,alice
andbob
are defined.alice
is a member of theClusterManager
group andbob
is a member ofClusterManager-my-cluster
group. Users can be stored in an LDAP identity provider. - Roles
-
Roles
mark users or clients as having certain permissions. Roles are a concept analogous to groups. They are usually used to tag users with organizational roles and have the requisite permissions. Roles cannot be stored in an LDAP identity provider. If LDAP is a requirement, you can use groups instead, and add Keycloak roles to the groups so that when users are assigned a group they also get a corresponding role. - Clients
-
Clients
can have specific configurations. For this example,kafka
,kafka-cli
,team-a-client
, andteam-b-client
clients are configured.-
The
kafka
client is used by Kafka brokers to perform the necessary OAuth 2.0 communication for access token validation. This client also contains the authorization services resource definitions, policies, and authorization scopes used to perform authorization on the Kafka brokers. The authorization configuration is defined in thekafka
client from the Authorization tab, which becomes visible when Authorization Enabled is switched on from the Settings tab. -
The
kafka-cli
client is a public client that is used by the Kafka command line tools when authenticating with username and password to obtain an access token or a refresh token. -
The
team-a-client
andteam-b-client
clients are confidential clients representing services with partial access to certain Kafka topics.
-
-
In the Keycloak Admin Console, go to Authorization > Permissions to see the granted permissions that use the resources and policies defined for the realm.
For example, the
kafka
client has the following permissions:Dev Team A can write to topics that start with x_ on any cluster Dev Team B can read from topics that start with x_ on any cluster Dev Team B can update consumer group offsets that start with x_ on any cluster ClusterManager of my-cluster Group has full access to cluster config on my-cluster ClusterManager of my-cluster Group has full access to consumer groups on my-cluster ClusterManager of my-cluster Group has full access to topics on my-cluster
- Dev Team A
-
The Dev Team A realm role can write to topics that start with
x_
on any cluster. This combines a resource calledTopic:x_*
,Describe
andWrite
scopes, and theDev Team A
policy. TheDev Team A
policy matches all users that have a realm role calledDev Team A
. - Dev Team B
-
The Dev Team B realm role can read from topics that start with
x_
on any cluster. This combinesTopic:x_*
,Group:x_*
resources,Describe
andRead
scopes, and theDev Team B
policy. TheDev Team B
policy matches all users that have a realm role calledDev Team B
. Matching users and clients have the ability to read from topics, and update the consumed offsets for topics and consumer groups that have names starting withx_
.
Deploying a Kafka cluster with Keycloak authorization
Deploy a Kafka cluster configured to connect to the Keycloak server.
Use the example kafka-ephemeral-oauth-single-keycloak-authz.yaml
file to deploy the Kafka cluster as a Kafka
custom resource.
The example deploys a single-node Kafka cluster with keycloak
authorization and oauth
authentication.
-
The Keycloak authorization server is deployed to your Kubernetes cluster and loaded with the example realm.
-
The Cluster Operator is deployed to your Kubernetes cluster.
-
The Strimzi
examples/security/keycloak-authorization/kafka-ephemeral-oauth-single-keycloak-authz.yaml
custom resource.
-
Use the hostname of the Keycloak instance you deployed to prepare a truststore certificate for Kafka brokers to communicate with the Keycloak server.
SSO_HOST=SSO-HOSTNAME SSO_HOST_PORT=$SSO_HOST:443 STOREPASS=storepass echo "Q" | openssl s_client -showcerts -connect $SSO_HOST_PORT 2>/dev/null | awk ' /BEGIN CERTIFICATE/,/END CERTIFICATE/ { print $0 } ' > /tmp/sso.pem
The certificate is required as Kubernetes
Ingress
is used to make a secure (HTTPS) connection.Usually there is not one single certificate, but a certificate chain. You only have to provide the top-most issuer CA, which is listed last in the
/tmp/sso.pem
file. You can extract it manually or using the following commands:Example command to extract the top CA certificate in a certificate chainsplit -p "-----BEGIN CERTIFICATE-----" sso.pem sso- for f in $(ls sso-*); do mv $f $f.pem; done cp $(ls sso-* | sort -r | head -n 1) sso-ca.crt
NoteA trusted CA certificate is normally obtained from a trusted source, and not by using the openssl
command. -
Deploy the certificate to Kubernetes as a secret.
kubectl create secret generic oauth-server-cert --from-file=/tmp/sso-ca.crt -n $NS
-
Set the hostname as an environment variable
SSO_HOST=SSO-HOSTNAME
-
Create and deploy the example Kafka cluster.
cat examples/security/keycloak-authorization/kafka-ephemeral-oauth-single-keycloak-authz.yaml | sed -E 's#\${SSO_HOST}'"#$SSO_HOST#" | kubectl create -n $NS -f -
Preparing TLS connectivity for a CLI Kafka client session
Create a new pod for an interactive CLI session. Set up a truststore with a Keycloak certificate for TLS connectivity. The truststore is to connect to Keycloak and the Kafka broker.
-
The Keycloak authorization server is deployed to your Kubernetes cluster and loaded with the example realm.
In the Keycloak Admin Console, check the roles assigned to the clients are displayed in Clients > Service Account Roles.
-
The Kafka cluster configured to connect with Keycloak is deployed to your Kubernetes cluster.
-
Run a new interactive pod container using the Strimzi Kafka image to connect to a running Kafka broker.
NS=sso kubectl run -ti --restart=Never --image=quay.io/strimzi/kafka:0.35.1-kafka-3.4.0 kafka-cli -n $NS -- /bin/sh
NoteIf kubectl
times out waiting on the image download, subsequent attempts may result in an AlreadyExists error. -
Attach to the pod container.
kubectl attach -ti kafka-cli -n $NS
-
Use the hostname of the Keycloak instance to prepare a certificate for client connection using TLS.
SSO_HOST=SSO-HOSTNAME SSO_HOST_PORT=$SSO_HOST:443 STOREPASS=storepass echo "Q" | openssl s_client -showcerts -connect $SSO_HOST_PORT 2>/dev/null | awk ' /BEGIN CERTIFICATE/,/END CERTIFICATE/ { print $0 } ' > /tmp/sso.pem
Usually there is not one single certificate, but a certificate chain. You only have to provide the top-most issuer CA, which is listed last in the
/tmp/sso.pem
file. You can extract it manually or using the following command:Example command to extract the top CA certificate in a certificate chainsplit -p "-----BEGIN CERTIFICATE-----" sso.pem sso- for f in $(ls sso-*); do mv $f $f.pem; done cp $(ls sso-* | sort -r | head -n 1) sso-ca.crt
NoteA trusted CA certificate is normally obtained from a trusted source, and not by using the openssl
command. -
Create a truststore for TLS connection to the Kafka brokers.
keytool -keystore /tmp/truststore.p12 -storetype pkcs12 -alias sso -storepass $STOREPASS -import -file /tmp/sso-ca.crt -noprompt
-
Use the Kafka bootstrap address as the hostname of the Kafka broker and the
tls
listener port (9093) to prepare a certificate for the Kafka broker.KAFKA_HOST_PORT=my-cluster-kafka-bootstrap:9093 STOREPASS=storepass echo "Q" | openssl s_client -showcerts -connect $KAFKA_HOST_PORT 2>/dev/null | awk ' /BEGIN CERTIFICATE/,/END CERTIFICATE/ { print $0 } ' > /tmp/my-cluster-kafka.pem
The obtained
.pem
file is usually not one single certificate, but a certificate chain. You only have to provide the top-most issuer CA, which is listed last in the/tmp/my-cluster-kafka.pem
file. You can extract it manually or using the following command:Example command to extract the top CA certificate in a certificate chainsplit -p "-----BEGIN CERTIFICATE-----" /tmp/my-cluster-kafka.pem kafka- for f in $(ls kafka-*); do mv $f $f.pem; done cp $(ls kafka-* | sort -r | head -n 1) my-cluster-kafka-ca.crt
NoteA trusted CA certificate is normally obtained from a trusted source, and not by using the openssl
command. For this example we assume the client is running in a pod in the same namespace where the Kafka cluster was deployed. If the client is accessing the Kafka cluster from outside the Kubernetes cluster, you would have to first determine the bootstrap address. In that case you can also get the cluster certificate directly from the Kubernetes secret, and there is no need foropenssl
. For more information, see Setting up client access to a Kafka cluster. -
Add the certificate for the Kafka broker to the truststore.
keytool -keystore /tmp/truststore.p12 -storetype pkcs12 -alias my-cluster-kafka -storepass $STOREPASS -import -file /tmp/my-cluster-kafka-ca.crt -noprompt
Keep the session open to check authorized access.
Checking authorized access to Kafka using a CLI Kafka client session
Check the authorization rules applied through the Keycloak realm using an interactive CLI session. Apply the checks using Kafka’s example producer and consumer clients to create topics with user and service accounts that have different levels of access.
Use the team-a-client
and team-b-client
clients to check the authorization rules.
Use the alice
admin user to perform additional administrative tasks on Kafka.
The Strimzi Kafka image used in this example contains Kafka producer and consumer binaries.
-
ZooKeeper and Kafka are running in the Kubernetes cluster to be able to send and receive messages.
-
The interactive CLI Kafka client session is started.
-
Prepare a Kafka configuration file with authentication properties for the
team-a-client
client.SSO_HOST=SSO-HOSTNAME cat > /tmp/team-a-client.properties << EOF security.protocol=SASL_SSL ssl.truststore.location=/tmp/truststore.p12 ssl.truststore.password=$STOREPASS ssl.truststore.type=PKCS12 sasl.mechanism=OAUTHBEARER sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \ oauth.client.id="team-a-client" \ oauth.client.secret="team-a-client-secret" \ oauth.ssl.truststore.location="/tmp/truststore.p12" \ oauth.ssl.truststore.password="$STOREPASS" \ oauth.ssl.truststore.type="PKCS12" \ oauth.token.endpoint.uri="https://$SSO_HOST/auth/realms/kafka-authz/protocol/openid-connect/token" ; sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler EOF
The SASL OAUTHBEARER mechanism is used. This mechanism requires a client ID and client secret, which means the client first connects to the Keycloak server to obtain an access token. The client then connects to the Kafka broker and uses the access token to authenticate.
-
Prepare a Kafka configuration file with authentication properties for the
team-b-client
client.cat > /tmp/team-b-client.properties << EOF security.protocol=SASL_SSL ssl.truststore.location=/tmp/truststore.p12 ssl.truststore.password=$STOREPASS ssl.truststore.type=PKCS12 sasl.mechanism=OAUTHBEARER sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \ oauth.client.id="team-b-client" \ oauth.client.secret="team-b-client-secret" \ oauth.ssl.truststore.location="/tmp/truststore.p12" \ oauth.ssl.truststore.password="$STOREPASS" \ oauth.ssl.truststore.type="PKCS12" \ oauth.token.endpoint.uri="https://$SSO_HOST/auth/realms/kafka-authz/protocol/openid-connect/token" ; sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler EOF
-
Authenticate admin user
alice
by usingcurl
and performing a password grant authentication to obtain a refresh token.USERNAME=alice PASSWORD=alice-password GRANT_RESPONSE=$(curl -X POST "https://$SSO_HOST/auth/realms/kafka-authz/protocol/openid-connect/token" -H 'Content-Type: application/x-www-form-urlencoded' -d "grant_type=password&username=$USERNAME&password=$PASSWORD&client_id=kafka-cli&scope=offline_access" -s -k) REFRESH_TOKEN=$(echo $GRANT_RESPONSE | awk -F "refresh_token\":\"" '{printf $2}' | awk -F "\"" '{printf $1}')
The refresh token is an offline token that is long-lived and does not expire.
-
Prepare a Kafka configuration file with authentication properties for the admin user
alice
.cat > /tmp/alice.properties << EOF security.protocol=SASL_SSL ssl.truststore.location=/tmp/truststore.p12 ssl.truststore.password=$STOREPASS ssl.truststore.type=PKCS12 sasl.mechanism=OAUTHBEARER sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \ oauth.refresh.token="$REFRESH_TOKEN" \ oauth.client.id="kafka-cli" \ oauth.ssl.truststore.location="/tmp/truststore.p12" \ oauth.ssl.truststore.password="$STOREPASS" \ oauth.ssl.truststore.type="PKCS12" \ oauth.token.endpoint.uri="https://$SSO_HOST/auth/realms/kafka-authz/protocol/openid-connect/token" ; sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler EOF
The
kafka-cli
public client is used for theoauth.client.id
in thesasl.jaas.config
. Since it’s a public client it does not require a secret. The client authenticates with the refresh token that was authenticated in the previous step. The refresh token requests an access token behind the scenes, which is then sent to the Kafka broker for authentication.
Use the team-a-client
configuration to check that you can produce messages to topics that start with a_
or x_
.
-
Write to topic
my-topic
.bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic my-topic \ --producer.config=/tmp/team-a-client.properties First message
This request returns a
Not authorized to access topics: [my-topic]
error.team-a-client
has aDev Team A
role that gives it permission to perform any supported actions on topics that start witha_
, but can only write to topics that start withx_
. The topic namedmy-topic
matches neither of those rules. -
Write to topic
a_messages
.bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic a_messages \ --producer.config /tmp/team-a-client.properties First message Second message
Messages are produced to Kafka successfully.
-
Press CTRL+C to exit the CLI application.
-
Check the Kafka container log for a debug log of
Authorization GRANTED
for the request.kubectl logs my-cluster-kafka-0 -f -n $NS
Use the team-a-client
configuration to consume messages from topic a_messages
.
-
Fetch messages from topic
a_messages
.bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic a_messages \ --from-beginning --consumer.config /tmp/team-a-client.properties
The request returns an error because the
Dev Team A
role forteam-a-client
only has access to consumer groups that have names starting witha_
. -
Update the
team-a-client
properties to specify the custom consumer group it is permitted to use.bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic a_messages \ --from-beginning --consumer.config /tmp/team-a-client.properties --group a_consumer_group_1
The consumer receives all the messages from the
a_messages
topic.
The team-a-client
is an account without any cluster-level access, but it can be used with some administrative operations.
-
List topics.
bin/kafka-topics.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/team-a-client.properties --list
The
a_messages
topic is returned. -
List consumer groups.
bin/kafka-consumer-groups.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/team-a-client.properties --list
The
a_consumer_group_1
consumer group is returned.Fetch details on the cluster configuration.
bin/kafka-configs.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/team-a-client.properties \ --entity-type brokers --describe --entity-default
The request returns an error because the operation requires cluster level permissions that
team-a-client
does not have.
Use the team-b-client
configuration to produce messages to topics that start with b_
.
-
Write to topic
a_messages
.bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic a_messages \ --producer.config /tmp/team-b-client.properties Message 1
This request returns a
Not authorized to access topics: [a_messages]
error. -
Write to topic
b_messages
.bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic b_messages \ --producer.config /tmp/team-b-client.properties Message 1 Message 2 Message 3
Messages are produced to Kafka successfully.
-
Write to topic
x_messages
.bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \ --producer.config /tmp/team-b-client.properties Message 1
A
Not authorized to access topics: [x_messages]
error is returned, Theteam-b-client
can only read from topicx_messages
. -
Write to topic
x_messages
usingteam-a-client
.bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \ --producer.config /tmp/team-a-client.properties Message 1
This request returns a
Not authorized to access topics: [x_messages]
error. Theteam-a-client
can write to thex_messages
topic, but it does not have a permission to create a topic if it does not yet exist. Beforeteam-a-client
can write to thex_messages
topic, an admin power user must create it with the correct configuration, such as the number of partitions and replicas.
Use admin user alice
to manage Kafka.
alice
has full access to manage everything on any Kafka cluster.
-
Create the
x_messages
topic asalice
.bin/kafka-topics.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/alice.properties \ --topic x_messages --create --replication-factor 1 --partitions 1
The topic is created successfully.
-
List all topics as
alice
.bin/kafka-topics.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/alice.properties --list bin/kafka-topics.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/team-a-client.properties --list bin/kafka-topics.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/team-b-client.properties --list
Admin user
alice
can list all the topics, whereasteam-a-client
andteam-b-client
can only list the topics they have access to.The
Dev Team A
andDev Team B
roles both haveDescribe
permission on topics that start withx_
, but they cannot see the other team’s topics because they do not haveDescribe
permissions on them. -
Use the
team-a-client
to produce messages to thex_messages
topic:bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \ --producer.config /tmp/team-a-client.properties Message 1 Message 2 Message 3
As
alice
created thex_messages
topic, messages are produced to Kafka successfully. -
Use the
team-b-client
to produce messages to thex_messages
topic.bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \ --producer.config /tmp/team-b-client.properties Message 4 Message 5
This request returns a
Not authorized to access topics: [x_messages]
error. -
Use the
team-b-client
to consume messages from thex_messages
topic:bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \ --from-beginning --consumer.config /tmp/team-b-client.properties --group x_consumer_group_b
The consumer receives all the messages from the
x_messages
topic. -
Use the
team-a-client
to consume messages from thex_messages
topic.bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \ --from-beginning --consumer.config /tmp/team-a-client.properties --group x_consumer_group_a
This request returns a
Not authorized to access topics: [x_messages]
error. -
Use the
team-a-client
to consume messages from a consumer group that begins witha_
.bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \ --from-beginning --consumer.config /tmp/team-a-client.properties --group a_consumer_group_a
This request returns a
Not authorized to access topics: [x_messages]
error.Dev Team A
has noRead
access on topics that start with ax_
. -
Use
alice
to produce messages to thex_messages
topic.bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \ --from-beginning --consumer.config /tmp/alice.properties
Messages are produced to Kafka successfully.
alice
can read from or write to any topic. -
Use
alice
to read the cluster configuration.bin/kafka-configs.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/alice.properties \ --entity-type brokers --describe --entity-default
The cluster configuration for this example is empty.
11. Managing TLS certificates
Strimzi supports TLS for encrypted communication between Kafka and Strimzi components.
Communication is always encrypted between the following components:
-
Communication between Kafka and ZooKeeper
-
Interbroker communication between Kafka brokers
-
Internodal communication between ZooKeeper nodes
-
Strimzi operator communication with Kafka brokers and ZooKeeper nodes
Communication between Kafka clients and Kafka brokers is encrypted according to how the cluster is configured. For the Kafka and Strimzi components, TLS certificates are also used for authentication.
The Cluster Operator automatically sets up and renews TLS certificates to enable encryption and authentication within your cluster. It also sets up other TLS certificates if you want to enable encryption or mTLS authentication between Kafka brokers and clients.
CA (certificate authority) certificates are generated by the Cluster Operator to verify the identities of components and clients. If you don’t want to use the CAs generated by the Cluster Operator, you can install your own cluster and clients CA certificates.
You can also provide Kafka listener certificates for TLS listeners or external listeners that have TLS encryption enabled. Use Kafka listener certificates to incorporate the security infrastructure you already have in place.
Note
|
Any certificates you provide are not renewed by the Cluster Operator. |
11.1. Internal cluster CA and clients CA
To support encryption, each Strimzi component needs its own private keys and public key certificates. All component certificates are signed by an internal CA (certificate authority) called the cluster CA.
Similarly, each Kafka client application connecting to Strimzi using mTLS needs to use private keys and certificates. A second internal CA, named the clients CA, is used to sign certificates for the Kafka clients.
Both the cluster CA and clients CA have a self-signed public key certificate.
Kafka brokers are configured to trust certificates signed by either the cluster CA or clients CA. Components that clients do not need to connect to, such as ZooKeeper, only trust certificates signed by the cluster CA. Unless TLS encryption for external listeners is disabled, client applications must trust certificates signed by the cluster CA. This is also true for client applications that perform mTLS authentication.
By default, Strimzi automatically generates and renews CA certificates issued by the cluster CA or clients CA.
You can configure the management of these CA certificates in the Kafka.spec.clusterCa
and Kafka.spec.clientsCa
objects.
You can replace the CA certificates for the cluster CA or clients CA with your own. For more information, see Installing your own CA certificates and private keys. If you provide your own CA certificates, you must renew them before they expire.
11.2. Secrets generated by the operators
Secrets are created when custom resources are deployed, such as Kafka
and KafkaUser
.
Strimzi uses these secrets to store private and public key certificates for Kafka clusters, clients, and users.
The secrets are used for establishing TLS encrypted connections between Kafka brokers, and between brokers and clients.
They are also used for mTLS authentication.
Cluster and clients secrets are always pairs: one contains the public key and one contains the private key.
- Cluster secret
-
A cluster secret contains the cluster CA to sign Kafka broker certificates. Connecting clients use the certificate to establish a TLS encrypted connection with a Kafka cluster. The certificate verifies broker identity.
- Client secret
-
A client secret contains the clients CA for a user to sign its own client certificate. This allows mutual authentication against the Kafka cluster. The broker validates a client’s identity through the certificate.
- User secret
-
A user secret contains a private key and certificate. The secret is created and signed by the clients CA when a new user is created. The key and certificate are used to authenticate and authorize the user when accessing the cluster.
11.2.1. TLS authentication using keys and certificates in PEM or PKCS #12 format
The secrets created by Strimzi provide private keys and certificates in PEM (Privacy Enhanced Mail) and PKCS #12 (Public-Key Cryptography Standards) formats. PEM and PKCS #12 are OpenSSL-generated key formats for TLS communications using the SSL protocol.
You can configure mutual TLS (mTLS) authentication that uses the credentials contained in the secrets generated for a Kafka cluster and user.
To set up mTLS, you must first do the following:
When you deploy a Kafka cluster, a <cluster_name>-cluster-ca-cert
secret is created with public key to verify the cluster.
You use the public key to configure a truststore for the client.
When you create a KafkaUser
, a <kafka_user_name>
secret is created with the keys and certificates to verify the user (client).
Use these credentials to configure a keystore for the client.
With the Kafka cluster and client set up to use mTLS, you extract credentials from the secrets and add them to your client configuration.
- PEM keys and certificates
-
For PEM, you add the following to your client configuration:
- Truststore
-
-
ca.crt
from the<cluster_name>-cluster-ca-cert
secret, which is the CA certificate for the cluster.
-
- Keystore
-
-
user.crt
from the<kafka_user_name>
secret, which is the public certificate of the user. -
user.key
from the<kafka_user_name>
secret, which is the private key of the user.
-
- PKCS #12 keys and certificates
-
For PKCS #12, you add the following to your client configuration:
- Truststore
-
-
ca.p12
from the<cluster_name>-cluster-ca-cert
secret, which is the CA certificate for the cluster. -
ca.password
from the<cluster_name>-cluster-ca-cert
secret, which is the password to access the public cluster CA certificate.
-
- Keystore
-
-
user.p12
from the<kafka_user_name>
secret, which is the public key certificate of the user. -
user.password
from the<kafka_user_name>
secret, which is the password to access the public key certificate of the Kafka user.
-
PKCS #12 is supported by Java, so you can add the values of the certificates directly to your Java client configuration. You can also reference the certificates from a secure storage location. With PEM files, you must add the certificates directly to the client configuration in single-line format. Choose a format that’s suitable for establishing TLS connections between your Kafka cluster and client. Use PKCS #12 if you are unfamiliar with PEM.
Note
|
All keys are 2048 bits in size and, by default, are valid for 365 days from the initial generation. You can change the validity period. |
11.2.2. Secrets generated by the Cluster Operator
The Cluster Operator generates the following certificates, which are saved as secrets in the Kubernetes cluster. Strimzi uses these secrets by default.
The cluster CA and clients CA have separate secrets for the private key and public key.
<cluster_name>-cluster-ca
-
Contains the private key of the cluster CA. Strimzi and Kafka components use the private key to sign server certificates.
<cluster_name>-cluster-ca-cert
-
Contains the public key of the cluster CA. Kafka clients use the public key to verify the identity of the Kafka brokers they are connecting to with TLS server authentication.
<cluster_name>-clients-ca
-
Contains the private key of the clients CA. Kafka clients use the private key to sign new user certificates for mTLS authentication when connecting to Kafka brokers.
<cluster_name>-clients-ca-cert
-
Contains the public key of the clients CA. Kafka brokers use the public key to verify the identity of clients accessing the Kafka brokers when mTLS authentication is used.
Secrets for communication between Strimzi components contain a private key and a public key certificate signed by the cluster CA.
<cluster_name>-kafka-brokers
-
Contains the private and public keys for Kafka brokers.
<cluster_name>-zookeeper-nodes
-
Contains the private and public keys for ZooKeeper nodes.
<cluster_name>-cluster-operator-certs
-
Contains the private and public keys for encrypting communication between the Cluster Operator and Kafka or ZooKeeper.
<cluster_name>-entity-topic-operator-certs
-
Contains the private and public keys for encrypting communication between the Topic Operator and Kafka or ZooKeeper.
<cluster_name>-entity-user-operator-certs
-
Contains the private and public keys for encrypting communication between the User Operator and Kafka or ZooKeeper.
<cluster_name>-cruise-control-certs
-
Contains the private and public keys for encrypting communication between Cruise Control and Kafka or ZooKeeper.
<cluster_name>-kafka-exporter-certs
-
Contains the private and public keys for encrypting communication between Kafka Exporter and Kafka or ZooKeeper.
Note
|
You can provide your own server certificates and private keys to connect to Kafka brokers using Kafka listener certificates rather than certificates signed by the cluster CA. |
11.2.3. Cluster CA secrets
Cluster CA secrets are managed by the Cluster Operator in a Kafka cluster.
Only the <cluster_name>-cluster-ca-cert
secret is required by clients.
All other cluster secrets are accessed by Strimzi components.
You can enforce this using Kubernetes role-based access controls, if necessary.
Note
|
The CA certificates in <cluster_name>-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.
|
Field | Description |
---|---|
|
The current private key for the cluster CA. |
Field | Description |
---|---|
|
PKCS #12 store for storing certificates and keys. |
|
Password for protecting the PKCS #12 store. |
|
The current certificate for the cluster CA. |
Field | Description |
---|---|
|
PKCS #12 store for storing certificates and keys. |
|
Password for protecting the PKCS #12 store. |
|
Certificate for a Kafka broker pod <num>. Signed by a current or former cluster CA private key in |
|
Private key for a Kafka broker pod |
Field | Description |
---|---|
|
PKCS #12 store for storing certificates and keys. |
|
Password for protecting the PKCS #12 store. |
|
Certificate for ZooKeeper node <num>. Signed by a current or former cluster CA private key in |
|
Private key for ZooKeeper pod |
Field | Description |
---|---|
|
PKCS #12 store for storing certificates and keys. |
|
Password for protecting the PKCS #12 store. |
|
Certificate for mTLS communication between the Cluster Operator and Kafka or ZooKeeper.
Signed by a current or former cluster CA private key in |
|
Private key for mTLS communication between the Cluster Operator and Kafka or ZooKeeper. |
Field | Description |
---|---|
|
PKCS #12 store for storing certificates and keys. |
|
Password for protecting the PKCS #12 store. |
|
Certificate for mTLS communication between the Topic Operator and Kafka or ZooKeeper.
Signed by a current or former cluster CA private key in |
|
Private key for mTLS communication between the Topic Operator and Kafka or ZooKeeper. |
Field | Description |
---|---|
|
PKCS #12 store for storing certificates and keys. |
|
Password for protecting the PKCS #12 store. |
|
Certificate for mTLS communication between the User Operator and Kafka or ZooKeeper.
Signed by a current or former cluster CA private key in |
|
Private key for mTLS communication between the User Operator and Kafka or ZooKeeper. |
Field | Description |
---|---|
|
PKCS #12 store for storing certificates and keys. |
|
Password for protecting the PKCS #12 store. |
|
Certificate for mTLS communication between Cruise Control and Kafka or ZooKeeper.
Signed by a current or former cluster CA private key in |
|
Private key for mTLS communication between the Cruise Control and Kafka or ZooKeeper. |
Field | Description |
---|---|
|
PKCS #12 store for storing certificates and keys. |
|
Password for protecting the PKCS #12 store. |
|
Certificate for mTLS communication between Kafka Exporter and Kafka or ZooKeeper.
Signed by a current or former cluster CA private key in |
|
Private key for mTLS communication between the Kafka Exporter and Kafka or ZooKeeper. |
11.2.4. Clients CA secrets
Clients CA secrets are managed by the Cluster Operator in a Kafka cluster.
The certificates in <cluster_name>-clients-ca-cert
are those which the Kafka brokers trust.
The <cluster_name>-clients-ca
secret is used to sign the certificates of client applications.
This secret must 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.
Field | Description |
---|---|
|
The current private key for the clients CA. |
Field | Description |
---|---|
|
PKCS #12 store for storing certificates and keys. |
|
Password for protecting the PKCS #12 store. |
|
The current certificate for the clients CA. |
11.2.5. User secrets generated by the User Operator
User secrets are managed by the User Operator.
When a user is created using the User Operator, a secret is generated using the name of the user.
Secret name | Field within secret | Description |
---|---|---|
|
|
PKCS #12 store for storing certificates and keys. |
|
Password for protecting the PKCS #12 store. |
|
|
Certificate for the user, signed by the clients CA |
|
|
Private key for the user |
11.2.6. Adding labels and annotations to cluster CA secrets
By configuring the clusterCaCert
template property in the Kafka
custom resource, you can add custom labels and annotations to the Cluster CA secrets created by the Cluster Operator.
Labels and annotations are useful for identifying objects and adding contextual information.
You configure template properties in Strimzi custom resources.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
template:
clusterCaCert:
metadata:
labels:
label1: value1
label2: value2
annotations:
annotation1: value1
annotation2: value2
# ...
11.2.7. Disabling ownerReference
in the CA secrets
By default, the cluster and clients CA secrets are created with an ownerReference
property that is set to the Kafka
custom resource.
This means that, when the Kafka
custom resource is deleted, the CA secrets are also deleted (garbage collected) by Kubernetes.
If you want to reuse the CA for a new cluster, you can disable the ownerReference
by setting the generateSecretOwnerReference
property for the cluster and clients CA secrets to false
in the Kafka
configuration.
When the ownerReference
is disabled, CA secrets are not deleted by Kubernetes when the corresponding Kafka
custom resource is deleted.
ownerReference
for cluster and clients CAsapiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
# ...
spec:
# ...
clusterCa:
generateSecretOwnerReference: false
clientsCa:
generateSecretOwnerReference: false
# ...
11.3. Certificate renewal and validity periods
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 CA certificates automatically created by the Cluster Operator, you can configure the validity period of:
-
Cluster CA certificates in
Kafka.spec.clusterCa.validityDays
-
Clients CA certificates in
Kafka.spec.clientsCa.validityDays
The default validity period for both certificates is 365 days. Manually-installed CA certificates should have their own validity periods defined.
When a CA certificate expires, components and clients that still trust that certificate will not accept connections from peers whose certificates were signed by the CA private key. The components and clients need to trust the new CA certificate instead.
To allow the renewal of CA certificates without a loss of service, the Cluster Operator initiates certificate renewal before the old CA certificates expire.
You can configure the renewal period of the certificates created by the Cluster Operator:
-
Cluster CA certificates in
Kafka.spec.clusterCa.renewalDays
-
Clients CA certificates in
Kafka.spec.clientsCa.renewalDays
The default renewal period for both certificates is 30 days.
The renewal period is measured backwards, from the expiry date of the current certificate.
Not Before Not After
| |
|<--------------- validityDays --------------->|
<--- renewalDays --->|
To make a change to the validity and renewal periods after creating the Kafka cluster, you configure and apply the Kafka
custom resource,
and manually renew the CA certificates.
If you do not manually renew the certificates, the new periods will be used the next time the certificate is renewed automatically.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
# ...
spec:
# ...
clusterCa:
renewalDays: 30
validityDays: 365
generateCertificateAuthority: true
clientsCa:
renewalDays: 30
validityDays: 365
generateCertificateAuthority: true
# ...
The behavior of the Cluster Operator during the renewal period depends on the settings for the generateCertificateAuthority
certificate generation properties for the cluster CA and clients CA.
true
-
If the properties are set to
true
, a CA certificate is generated automatically by the Cluster Operator, and renewed automatically within the renewal period. false
-
If the properties are set to
false
, a CA certificate is not generated by the Cluster Operator. Use this option if you are installing your own certificates.
11.3.1. Renewal process with automatically generated CA certificates
The Cluster Operator performs the following processes in this order when renewing CA certificates:
-
Generates a new CA certificate, but retains the existing key.
The new certificate replaces the old one with the name
ca.crt
within the correspondingSecret
. -
Generates new client certificates (for ZooKeeper nodes, Kafka brokers, and the Entity Operator).
This is not strictly necessary because the signing key has not changed, but it keeps the validity period of the client certificate in sync with the CA certificate.
-
Restarts ZooKeeper nodes so that they will trust the new CA certificate and use the new client certificates.
-
Restarts Kafka brokers so that they will trust the new CA certificate and use the new client certificates.
-
Restarts the Topic and User Operators so that they will trust the new CA certificate and use the new client certificates.
User certificates are signed by the clients CA. User certificates generated by the User Operator are renewed when the clients CA is renewed.
11.3.2. Client certificate renewal
The Cluster Operator is not aware of the client applications using the Kafka cluster.
When connecting to the cluster, and to ensure they operate correctly, client applications must:
-
Trust the cluster CA certificate published in the <cluster>-cluster-ca-cert Secret.
-
Use the credentials published in their <user-name> Secret to connect to the cluster.
The User Secret provides credentials in PEM and PKCS #12 format, or it can provide a password when using SCRAM-SHA authentication. The User Operator creates the user credentials when a user is created.
You must ensure clients continue to work after certificate renewal. The renewal process depends on how the clients are configured.
If you are provisioning client certificates and keys manually, you must generate new client certificates and ensure the new certificates are used by clients within the renewal period. Failure to do this by the end of the renewal period could result in client applications being unable to connect to the cluster.
Note
|
For workloads running inside the same Kubernetes cluster and namespace, Secrets can be mounted as a volume so the client Pods construct their keystores and truststores from the current state of the Secrets. For more details on this procedure, see Configuring internal clients to trust the cluster CA. |
11.3.3. Manually renewing the CA certificates generated by the Cluster Operator
Cluster and clients CA certificates generated by the Cluster Operator auto-renew at the start of their respective certificate renewal periods.
However, you can use the strimzi.io/force-renew
annotation to manually renew one or both of these certificates before the certificate renewal period starts.
You might do this for security reasons, or if you have changed the renewal or validity periods for the certificates.
A renewed certificate uses the same private key as the old certificate.
Note
|
If you are using your own CA certificates, the force-renew annotation cannot be used.
Instead, follow the procedure for renewing your own CA certificates.
|
-
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.Table 16. Annotation for the Secret that forces renewal of certificates Certificate Secret Annotate command Cluster CA
KAFKA-CLUSTER-NAME-cluster-ca-cert
kubectl annotate secret KAFKA-CLUSTER-NAME-cluster-ca-cert strimzi.io/force-renew=true
Clients CA
KAFKA-CLUSTER-NAME-clients-ca-cert
kubectl annotate secret KAFKA-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.
-
Check the period the CA certificate is valid:
For example, using an
openssl
command:kubectl get secret CA-CERTIFICATE-SECRET -o 'jsonpath={.data.CA-CERTIFICATE}' | base64 -d | openssl x509 -subject -issuer -startdate -enddate -noout
CA-CERTIFICATE-SECRET is the name of the
Secret
, which isKAFKA-CLUSTER-NAME-cluster-ca-cert
for the cluster CA certificate andKAFKA-CLUSTER-NAME-clients-ca-cert
for the clients CA certificate.CA-CERTIFICATE is the name of the CA certificate, such as
jsonpath={.data.ca\.crt}
.The command returns a
notBefore
andnotAfter
date, which is the validity period for the CA certificate.For example, for a cluster CA certificate:
subject=O = io.strimzi, CN = cluster-ca v0 issuer=O = io.strimzi, CN = cluster-ca v0 notBefore=Jun 30 09:43:54 2020 GMT notAfter=Jun 30 09:43:54 2021 GMT
-
Delete old certificates from the Secret.
When components are using the new certificates, older certificates might still be active. Delete the old certificates to remove any potential security risk.
11.3.4. Replacing private keys used by the CA certificates generated by the Cluster Operator
You can replace the private keys used by the cluster CA and clients CA certificates generated by the Cluster Operator. When a private key is replaced, the Cluster Operator generates a new CA certificate for the new private key.
Note
|
If you are using your own CA certificates, the force-replace annotation cannot be used.
Instead, follow the procedure for renewing your own CA certificates.
|
-
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.Table 17. Commands for replacing private keys 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.
11.4. TLS connections
11.4.1. ZooKeeper communication
Communication between the ZooKeeper nodes on all ports, as well as between clients and ZooKeeper, is encrypted using TLS.
Communication between Kafka brokers and ZooKeeper nodes is also encrypted.
11.4.2. Kafka inter-broker communication
Communication between Kafka brokers is always encrypted using TLS. The connections between the Kafka controller and brokers use an internal control plane listener on port 9090. Replication of data between brokers, as well as internal connections from Strimzi operators, Cruise Control, or the Kafka Exporter use the replication listener on port 9091. These internal listeners are not available to Kafka clients.
11.4.3. Topic and User Operators
All Operators use encryption for communication with both Kafka and ZooKeeper. In Topic and User Operators, a TLS sidecar is used when communicating with ZooKeeper.
11.4.4. Cruise Control
Cruise Control uses encryption for communication with both Kafka and ZooKeeper. A TLS sidecar is used when communicating with ZooKeeper.
11.4.5. Kafka Client connections
Encrypted or unencrypted communication between Kafka brokers and clients is configured using the tls
property for spec.kafka.listeners
.
11.5. Configuring internal clients to trust the cluster CA
This procedure describes how to configure a Kafka client that resides inside the Kubernetes cluster — connecting to a TLS listener — to trust the cluster CA certificate.
The easiest way to achieve this for an internal client is to use a volume mount to access the Secrets
containing the necessary certificates and keys.
Follow the steps to configure trust certificates that are signed by the cluster CA for Java-based Kafka Producer, Consumer, and Streams APIs.
Choose the steps to follow according to the certificate format of the cluster CA: PKCS #12 (.p12
) or PEM (.crt
).
The steps describe how to mount the Cluster Secret that verifies the identity of the Kafka cluster to the client pod.
-
The Cluster Operator must be running.
-
There needs to be a
Kafka
resource within the Kubernetes cluster. -
You need a Kafka client application inside the Kubernetes cluster that will connect using TLS, and needs to trust the cluster CA certificate.
-
The client application must be running in the same namespace as the
Kafka
resource.
-
Mount the cluster Secret as a volume when defining the client pod.
For example:
kind: Pod apiVersion: v1 metadata: name: client-pod spec: containers: - name: client-name image: client-name volumeMounts: - name: secret-volume mountPath: /data/p12 env: - name: SECRET_PASSWORD valueFrom: secretKeyRef: name: my-secret key: my-password volumes: - name: secret-volume secret: secretName: my-cluster-cluster-ca-cert
Here we’re mounting the following:
-
The PKCS #12 file into an exact path, which can be configured
-
The password into an environment variable, where it can be used for Java configuration
-
-
Configure the Kafka client with the following properties:
-
A security protocol option:
-
security.protocol: SSL
when using TLS for encryption (with or without mTLS authentication). -
security.protocol: SASL_SSL
when using SCRAM-SHA authentication over TLS.
-
-
ssl.truststore.location
with the truststore location where the certificates were imported. -
ssl.truststore.password
with the password for accessing the truststore. -
ssl.truststore.type=PKCS12
to identify the truststore type.
-
-
Mount the cluster Secret as a volume when defining the client pod.
For example:
kind: Pod apiVersion: v1 metadata: name: client-pod spec: containers: - name: client-name image: client-name volumeMounts: - name: secret-volume mountPath: /data/crt volumes: - name: secret-volume secret: secretName: my-cluster-cluster-ca-cert
-
Use the extracted certificate to configure a TLS connection in clients that use certificates in X.509 format.
11.6. Configuring external clients to trust the cluster CA
This procedure describes how to configure a Kafka client that resides outside the Kubernetes cluster – connecting to an external
listener – to trust the cluster CA certificate.
Follow this procedure when setting up the client and during the renewal period, when the old clients CA certificate is replaced.
Follow the steps to configure trust certificates that are signed by the cluster CA for Java-based Kafka Producer, Consumer, and Streams APIs.
Choose the steps to follow according to the certificate format of the cluster CA: PKCS #12 (.p12
) or PEM (.crt
).
The steps describe how to obtain the certificate from the Cluster Secret that verifies the identity of the Kafka cluster.
Important
|
The <cluster_name>-cluster-ca-cert secret contains more than one CA certificate during the CA certificate renewal period.
Clients must add all of them to their truststores.
|
-
The Cluster Operator must be running.
-
There needs to be a
Kafka
resource within the Kubernetes cluster. -
You need a Kafka client application outside the Kubernetes cluster that will connect using TLS, and needs to trust the cluster CA certificate.
-
Extract the cluster CA certificate and password from the
<cluster_name>-cluster-ca-cert
Secret of the Kafka cluster.kubectl get secret <cluster_name>-cluster-ca-cert -o jsonpath='{.data.ca\.p12}' | base64 -d > ca.p12
kubectl get secret <cluster_name>-cluster-ca-cert -o jsonpath='{.data.ca\.password}' | base64 -d > ca.password
Replace <cluster_name> with the name of the Kafka cluster.
-
Configure the Kafka client with the following properties:
-
A security protocol option:
-
security.protocol: SSL
when using TLS. -
security.protocol: SASL_SSL
when using SCRAM-SHA authentication over TLS.
-
-
ssl.truststore.location
with the truststore location where the certificates were imported. -
ssl.truststore.password
with the password for accessing the truststore. This property can be omitted if it is not needed by the truststore. -
ssl.truststore.type=PKCS12
to identify the truststore type.
-
-
Extract the cluster CA certificate from the
<cluster_name>-cluster-ca-cert
secret of the Kafka cluster.kubectl get secret <cluster_name>-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
-
Use the extracted certificate to configure a TLS connection in clients that use certificates in X.509 format.
11.7. Using your own CA certificates and private keys
Install and use your own CA certificates and private keys instead of using the defaults generated by the Cluster Operator. You can replace the cluster and clients CA certificates and private keys.
You can switch to using your own CA certificates and private keys in the following ways:
-
Install your own CA certificates and private keys before deploying your Kafka cluster
-
Replace the default CA certificates and private keys with your own after deploying a Kafka cluster
The steps to replace the default CA certificates and private keys after deploying a Kafka cluster are the same as those used to renew your own CA certificates and private keys.
If you use your own certificates, they won’t be renewed automatically. You need to renew the CA certificates and private keys before they expire.
Renewal options:
-
Renew the CA certificates only
-
Renew CA certificates and private keys (or replace the defaults)
11.7.1. Installing your own CA certificates and private keys
Install your own CA certificates and private keys instead of using the cluster and clients CA certificates and private keys generated by the Cluster Operator.
By default, Strimzi uses the following cluster CA and clients CA secrets, which are renewed automatically.
-
Cluster CA secrets
-
<cluster_name>-cluster-ca
-
<cluster_name>-cluster-ca-cert
-
-
Clients CA secrets
-
<cluster_name>-clients-ca
-
<cluster_name>-clients-ca-cert
-
To install your own certificates, use the same names.
-
The Cluster Operator is running.
-
A Kafka cluster is not yet deployed.
If you have already deployed a Kafka cluster, you can replace the default CA certificates with your own.
-
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 using the X509v3 Basic Constraints extension. Basic Constraints limit the path length of a certificate chain.
-
-
The OpenSSL TLS management tool for converting certificates.
The Cluster Operator generates keys and certificates in PEM (Privacy Enhanced Mail) and PKCS #12 (Public-Key Cryptography Standards) formats. You can add your own certificates in either format.
Some applications cannot use PEM certificates and support only PKCS #12 certificates.
If you don’t have a cluster certificate in PKCS #12 format, use the OpenSSL TLS management tool to generate one from your ca.crt
file.
openssl pkcs12 -export -in ca.crt -nokeys -out ca.p12 -password pass:<P12_password> -caname ca.crt
Replace <P12_password> with your own password.
-
Create a new secret that contains the CA certificate.
Client secret creation with a certificate in PEM format onlykubectl create secret generic <cluster_name>-clients-ca-cert --from-file=ca.crt=ca.crt
Cluster secret creation with certificates in PEM and PKCS #12 formatkubectl create secret generic <cluster_name>-cluster-ca-cert \ --from-file=ca.crt=ca.crt \ --from-file=ca.p12=ca.p12 \ --from-literal=ca.password=P12-PASSWORD
Replace <cluster_name> with the name of your Kafka cluster.
-
Create a new secret that contains the private key.
kubectl create secret generic CA-KEY-SECRET --from-file=ca.key=ca.key
-
Label the secrets.
kubectl label secret CA-CERTIFICATE-SECRET strimzi.io/kind=Kafka strimzi.io/cluster=<cluster_name>
kubectl label secret CA-KEY-SECRET strimzi.io/kind=Kafka strimzi.io/cluster=<cluster_name>
-
Label
strimzi.io/kind=Kafka
identifies the Kafka custom resource. -
Label
strimzi.io/cluster=<cluster_name>
identifies the Kafka cluster.
-
-
Annotate the secrets
kubectl annotate secret CA-CERTIFICATE-SECRET strimzi.io/ca-cert-generation=CA-CERTIFICATE-GENERATION
kubectl annotate secret CA-KEY-SECRET strimzi.io/ca-key-generation=CA-KEY-GENERATION
-
Annotation
strimzi.io/ca-cert-generation=CA-CERTIFICATE-GENERATION
defines the generation of a new CA certificate. -
Annotation
strimzi.io/ca-key-generation=CA-KEY-GENERATION
defines the generation of a new CA key.Start from 0 (zero) as the incremental value (
strimzi.io/ca-cert-generation=0
) for your own CA certificate. Set a higher incremental value when you renew the certificates.
-
-
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/v1beta2 spec: # ... clusterCa: generateCertificateAuthority: false
11.7.2. Renewing your own CA certificates
If you are using your own CA certificates, you need to renew them manually. The Cluster Operator will not renew them automatically. Renew the CA certificates in the renewal period before they expire.
Perform the steps in this procedure when you are renewing CA certificates and continuing with the same private key. If you are renewing your own CA certificates and private keys, see Renewing or replacing CA certificates and private keys with your own.
The procedure describes the renewal of CA certificates in PEM format.
-
The Cluster Operator is running.
-
You have new cluster or clients X.509 certificates in PEM format.
-
Update the
Secret
for the CA certificate.Edit the existing secret to add the new CA certificate and update the certificate generation annotation value.
kubectl edit secret <ca_certificate_secret_name>
<ca_certificate_secret_name> is the name of the
Secret
, which is<kafka_cluster_name>-cluster-ca-cert
for the cluster CA certificate and<kafka_cluster_name>-clients-ca-cert
for the clients CA certificate.The following example shows a secret for a cluster CA certificate that’s associated with a Kafka cluster named
my-cluster
.Example secret configuration for a cluster CA certificateapiVersion: v1 kind: Secret data: ca.crt: LS0tLS1CRUdJTiBDRVJUSUZJQ0F... (1) metadata: annotations: strimzi.io/ca-cert-generation: "0" (2) labels: strimzi.io/cluster: my-cluster strimzi.io/kind: Kafka name: my-cluster-cluster-ca-cert #... type: Opaque
-
Current base64-encoded CA certificate
-
Current CA certificate generation annotation value
-
-
Encode your new CA certificate into base64.
cat <path_to_new_certificate> | base64
-
Update the CA certificate.
Copy the base64-encoded CA certificate from the previous step as the value for the
ca.crt
property underdata
. -
Increase the value of the CA certificate generation annotation.
Update the
strimzi.io/ca-cert-generation
annotation with a higher incremental value. For example, changestrimzi.io/ca-cert-generation=0
tostrimzi.io/ca-cert-generation=1
. If theSecret
is missing the annotation, the value is treated as0
, so add the annotation with a value of1
.When Strimzi generates certificates, the certificate generation annotation is automatically incremented by the Cluster Operator. For your own CA certificates, set the annotations with a higher incremental value. The annotation needs a higher value than the one from the current secret so that the Cluster Operator can roll the pods and update the certificates. The
strimzi.io/ca-cert-generation
has to be incremented on each CA certificate renewal. -
Save the secret with the new CA certificate and certificate generation annotation value.
Example secret configuration updated with a new CA certificateapiVersion: v1 kind: Secret data: ca.crt: GCa6LS3RTHeKFiFDGBOUDYFAZ0F... (1) metadata: annotations: strimzi.io/ca-cert-generation: "1" (2) labels: strimzi.io/cluster: my-cluster strimzi.io/kind: Kafka name: my-cluster-cluster-ca-cert #... type: Opaque
-
New base64-encoded CA certificate
-
New CA certificate generation annotation value
-
On the next reconciliation, the Cluster Operator performs a rolling update of ZooKeeper, Kafka, and other components to trust the new CA certificate.
If maintenance time windows are configured, the Cluster Operator will roll the pods at the first reconciliation within the next maintenance time window.
11.7.3. Renewing or replacing CA certificates and private keys with your own
If you are using your own CA certificates and private keys, you need to renew them manually. The Cluster Operator will not renew them automatically. Renew the CA certificates in the renewal period before they expire. You can also use the same procedure to replace the CA certificates and private keys generated by the Strimzi operators with your own.
Perform the steps in this procedure when you are renewing or replacing CA certificates and private keys. If you are only renewing your own CA certificates, see Renewing your own CA certificates.
The procedure describes the renewal of CA certificates and private keys in PEM format.
Before going through the following steps, make sure that the CN (Common Name) of the new CA certificate is different from the current one.
For example, when the Cluster Operator renews certificates automatically it adds a v<version_number> suffix to identify a version.
Do the same with your own CA certificate by adding a different suffix on each renewal.
By using a different key to generate a new CA certificate, you retain the current CA certificate stored in the Secret
.
-
The Cluster Operator is running.
-
You have new cluster or clients X.509 certificates and keys in PEM format.
-
Pause the reconciliation of the
Kafka
custom resource.-
Annotate the custom resource in Kubernetes, setting the
pause-reconciliation
annotation totrue
:kubectl annotate Kafka <name_of_custom_resource> strimzi.io/pause-reconciliation="true"
For example, for a
Kafka
custom resource namedmy-cluster
:kubectl annotate Kafka my-cluster strimzi.io/pause-reconciliation="true"
-
Check that the status conditions of the custom resource show a change to
ReconciliationPaused
:kubectl describe Kafka <name_of_custom_resource>
The
type
condition changes toReconciliationPaused
at thelastTransitionTime
.
-
-
Update the
Secret
for the CA certificate.-
Edit the existing secret to add the new CA certificate and update the certificate generation annotation value.
kubectl edit secret <ca_certificate_secret_name>
<ca_certificate_secret_name> is the name of the
Secret
, which isKAFKA-CLUSTER-NAME-cluster-ca-cert
for the cluster CA certificate andKAFKA-CLUSTER-NAME-clients-ca-cert
for the clients CA certificate.The following example shows a secret for a cluster CA certificate that’s associated with a Kafka cluster named
my-cluster
.Example secret configuration for a cluster CA certificateapiVersion: v1 kind: Secret data: ca.crt: LS0tLS1CRUdJTiBDRVJUSUZJQ0F... (1) metadata: annotations: strimzi.io/ca-cert-generation: "0" (2) labels: strimzi.io/cluster: my-cluster strimzi.io/kind: Kafka name: my-cluster-cluster-ca-cert #... type: Opaque
-
Current base64-encoded CA certificate
-
Current CA certificate generation annotation value
-
-
Rename the current CA certificate to retain it.
Rename the current
ca.crt
property underdata
asca-<date>.crt
, where <date> is the certificate expiry date in the format YEAR-MONTH-DAYTHOUR-MINUTE-SECONDZ. For exampleca-2022-01-26T17-32-00Z.crt:
. Leave the value for the property as it is to retain the current CA certificate. -
Encode your new CA certificate into base64.
cat <path_to_new_certificate> | base64
-
Update the CA certificate.
Create a new
ca.crt
property underdata
and copy the base64-encoded CA certificate from the previous step as the value forca.crt
property. -
Increase the value of the CA certificate generation annotation.
Update the
strimzi.io/ca-cert-generation
annotation with a higher incremental value. For example, changestrimzi.io/ca-cert-generation=0
tostrimzi.io/ca-cert-generation=1
. If theSecret
is missing the annotation, the value is treated as0
, so add the annotation with a value of1
.When Strimzi generates certificates, the certificate generation annotation is automatically incremented by the Cluster Operator. For your own CA certificates, set the annotations with a higher incremental value. The annotation needs a higher value than the one from the current secret so that the Cluster Operator can roll the pods and update the certificates. The
strimzi.io/ca-cert-generation
has to be incremented on each CA certificate renewal. -
Save the secret with the new CA certificate and certificate generation annotation value.
Example secret configuration updated with a new CA certificateapiVersion: v1 kind: Secret data: ca.crt: GCa6LS3RTHeKFiFDGBOUDYFAZ0F... (1) ca-2022-01-26T17-32-00Z.crt: LS0tLS1CRUdJTiBDRVJUSUZJQ0F... (2) metadata: annotations: strimzi.io/ca-cert-generation: "1" (3) labels: strimzi.io/cluster: my-cluster strimzi.io/kind: Kafka name: my-cluster-cluster-ca-cert #... type: Opaque
-
New base64-encoded CA certificate
-
Old base64-encoded CA certificate
-
New CA certificate generation annotation value
-
-
-
Update the
Secret
for the CA key used to sign your new CA certificate.-
Edit the existing secret to add the new CA key and update the key generation annotation value.
kubectl edit secret <ca_key_name>
<ca_key_name> is the name of CA key, which is
<kafka_cluster_name>-cluster-ca
for the cluster CA key and<kafka_cluster_name>-clients-ca
for the clients CA key.The following example shows a secret for a cluster CA key that’s associated with a Kafka cluster named
my-cluster
.Example secret configuration for a cluster CA keyapiVersion: v1 kind: Secret data: ca.key: SA1cKF1GFDzOIiPOIUQBHDNFGDFS... (1) metadata: annotations: strimzi.io/ca-key-generation: "0" (2) labels: strimzi.io/cluster: my-cluster strimzi.io/kind: Kafka name: my-cluster-cluster-ca #... type: Opaque
-
Current base64-encoded CA key
-
Current CA key generation annotation value
-
-
Encode the CA key into base64.
cat <path_to_new_key> | base64
-
Update the CA key.
Copy the base64-encoded CA key from the previous step as the value for the
ca.key
property underdata
. -
Increase the value of the CA key generation annotation.
Update the
strimzi.io/ca-key-generation
annotation with a higher incremental value. For example, changestrimzi.io/ca-key-generation=0
tostrimzi.io/ca-key-generation=1
. If theSecret
is missing the annotation, it is treated as0
, so add the annotation with a value of1
.When Strimzi generates certificates, the key generation annotation is automatically incremented by the Cluster Operator. For your own CA certificates together with a new CA key, set the annotation with a higher incremental value. The annotation needs a higher value than the one from the current secret so that the Cluster Operator can roll the pods and update the certificates and keys. The
strimzi.io/ca-key-generation
has to be incremented on each CA certificate renewal.
-
-
Save the secret with the new CA key and key generation annotation value.
Example secret configuration updated with a new CA keyapiVersion: v1 kind: Secret data: ca.key: AB0cKF1GFDzOIiPOIUQWERZJQ0F... (1) metadata: annotations: strimzi.io/ca-key-generation: "1" (2) labels: strimzi.io/cluster: my-cluster strimzi.io/kind: Kafka name: my-cluster-cluster-ca #... type: Opaque
-
New base64-encoded CA key
-
New CA key generation annotation value
-
-
Resume from the pause.
To resume the
Kafka
custom resource reconciliation, set thepause-reconciliation
annotation tofalse
.kubectl annotate --overwrite Kafka <name_of_custom_resource> strimzi.io/pause-reconciliation="false"
You can also do the same by removing the
pause-reconciliation
annotation.kubectl annotate Kafka <name_of_custom_resource> strimzi.io/pause-reconciliation-
On the next reconciliation, the Cluster Operator performs a rolling update of ZooKeeper, Kafka, and other components to trust the new CA certificate. When the rolling update is complete, the Cluster Operator will start a new one to generate new server certificates signed by the new CA key.
If maintenance time windows are configured, the Cluster Operator will roll the pods at the first reconciliation within the next maintenance time window.
12. Scaling clusters by adding or removing brokers
Scaling Kafka clusters by adding brokers can increase the performance and reliability of the cluster. Adding more brokers increases available resources, allowing the cluster to handle larger workloads and process more messages. It can also improve fault tolerance by providing more replicas and backups. Conversely, removing underutilized brokers can reduce resource consumption and improve efficiency. Scaling must be done carefully to avoid disruption or data loss. By redistributing partitions across all brokers in the cluster, the resource utilization of each broker is reduced, which can increase the overall throughput of the cluster.
Note
|
To increase the throughput of a Kafka topic, you can increase the number of partitions for that topic. This allows the load of the topic to be shared between different brokers in the cluster. However, if every broker is constrained by a specific resource (such as I/O), adding more partitions will not increase the throughput. In this case, you need to add more brokers to the cluster. |
Adjusting the Kafka.spec.kafka.replicas
configuration affects the number of brokers in the cluster that act as replicas.
The actual replication factor for topics is determined by settings for the default.replication.factor
and min.insync.replicas
, and the number of available brokers.
For example, a replication factor of 3 means that each partition of a topic is replicated across three brokers, ensuring fault tolerance in the event of a broker failure.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
replicas: 3
# ...
config: (18)
# ...
default.replication.factor: 3
min.insync.replicas: 2
# ...
When you add add or remove brokers, Kafka does not automatically reassign partitions.
The best way to do this is using Cruise Control.
You can use Cruise Control’s add-brokers
and remove-brokers
modes when scaling a cluster up or down.
-
Use the
add-brokers
mode after scaling up a Kafka cluster to move partition replicas from existing brokers to the newly added brokers. -
Use the
remove-brokers
mode before scaling down a Kafka cluster to move partition replicas off the brokers that are going to be removed.
Note
|
When scaling down brokers, you cannot specify which specific pod to remove from the cluster. Instead, the broker removal process starts from the highest numbered pod. |
13. Rebalancing clusters using Cruise Control
Cruise Control is an open source system that supports the following Kafka operations:
-
Monitoring cluster workload
-
Rebalancing a cluster based on predefined constraints
The operations help with running a more balanced Kafka cluster that uses broker pods more efficiently.
A typical cluster can become unevenly loaded over time. Partitions that handle large amounts of message traffic might not be evenly distributed across the available brokers. To rebalance the cluster, administrators must monitor the load on brokers and manually reassign busy partitions to brokers with spare capacity.
Cruise Control automates the cluster rebalancing process. It constructs a workload model of resource utilization for the cluster—based on CPU, disk, and network load—and generates optimization proposals (that you can approve or reject) for more balanced partition assignments. A set of configurable optimization goals is used to calculate these proposals.
You can generate optimization proposals in specific modes.
The default full
mode rebalances partitions across all brokers.
You can also use the add-brokers
and remove-brokers
modes to accommodate changes when scaling a cluster up or down.
When you approve an optimization proposal, Cruise Control applies it to your Kafka cluster.
You configure and generate optimization proposals using a KafkaRebalance
resource.
You can configure the resource using an annotation so that optimization proposals are approved automatically or manually.
Note
|
Strimzi provides example configuration files for Cruise Control. |
13.1. Cruise Control components and features
Cruise Control consists of four main components—the Load Monitor, the Analyzer, the Anomaly Detector, and the Executor—and a REST API for client interactions. Strimzi utilizes the REST API to support the following Cruise Control features:
-
Generating optimization proposals from optimization goals.
-
Rebalancing a Kafka cluster based on an optimization proposal.
- Optimization goals
-
An optimization goal describes a specific objective to achieve from a rebalance. For example, a goal might be to distribute topic replicas across brokers more evenly. You can change what goals to include through configuration. A goal is defined as a hard goal or soft goal. You can add hard goals through Cruise Control deployment configuration. You also have main, default, and user-provided goals that fit into each of these categories.
-
Hard goals are preset and must be satisfied for an optimization proposal to be successful.
-
Soft goals do not need to be satisfied for an optimization proposal to be successful. They can be set aside if it means that all hard goals are met.
-
Main goals are inherited from Cruise Control. Some are preset as hard goals. Main goals are used in optimization proposals by default.
-
Default goals are the same as the main goals by default. You can specify your own set of default goals.
-
User-provided goals are a subset of default goals that are configured for generating a specific optimization proposal.
-
- Optimization proposals
-
Optimization proposals comprise the goals you want to achieve from a rebalance. You generate an optimization proposal to create a summary of proposed changes and the results that are possible with the rebalance. The goals are assessed in a specific order of priority. You can then choose to approve or reject the proposal. You can reject the proposal to run it again with an adjusted set of goals.
You can generate an optimization proposal in one of three modes.
-
full
is the default mode and runs a full rebalance. -
add-brokers
is the mode you use after adding brokers when scaling up a Kafka cluster. -
remove-brokers
is the mode you use before removing brokers when scaling down a Kafka cluster.
-
Other Cruise Control features are not currently supported, including self healing, notifications, write-your-own goals, and changing the topic replication factor.
13.2. Optimization goals overview
Optimization goals are constraints on workload redistribution and resource utilization across a Kafka cluster. To rebalance a Kafka cluster, Cruise Control uses optimization goals to generate optimization proposals, which you can approve or reject.
13.2.1. Goals order of priority
Strimzi supports most of the optimization goals developed in the Cruise Control project. The supported goals, in the default descending order of priority, are as follows:
-
Rack-awareness
-
Minimum number of leader replicas per broker for a set of topics
-
Replica capacity
-
Capacity goals
-
Disk capacity
-
Network inbound capacity
-
Network outbound capacity
-
CPU capacity
-
-
Replica distribution
-
Potential network output
-
Resource distribution goals
-
Disk utilization distribution
-
Network inbound utilization distribution
-
Network outbound utilization distribution
-
CPU utilization distribution
-
-
Leader bytes-in rate distribution
-
Topic replica distribution
-
Leader replica distribution
-
Preferred leader election
-
Intra-broker disk capacity
-
Intra-broker disk usage distribution
For more information on each optimization goal, see Goals in the Cruise Control Wiki.
Note
|
"Write your own" goals and Kafka assigner goals are not yet supported. |
13.2.2. Goals configuration in Strimzi custom resources
You configure optimization goals in Kafka
and KafkaRebalance
custom resources.
Cruise Control has configurations for hard optimization goals that must be satisfied, as well as main, default, and user-provided optimization goals.
You can specify optimization goals in the following configuration:
-
Main goals —
Kafka.spec.cruiseControl.config.goals
-
Hard goals —
Kafka.spec.cruiseControl.config.hard.goals
-
Default goals —
Kafka.spec.cruiseControl.config.default.goals
-
User-provided goals —
KafkaRebalance.spec.goals
Note
|
Resource distribution goals are subject to capacity limits on broker resources. |
13.2.3. Hard and soft optimization goals
Hard goals are goals that must be satisfied in optimization proposals. Goals that are not configured as hard goals are known as soft goals. You can think of soft goals as best effort goals: they do not need to be satisfied in optimization proposals, but are included in optimization calculations. An optimization proposal that violates one or more soft goals, but satisfies all hard goals, is valid.
Cruise Control will calculate optimization proposals that satisfy all the hard goals and as many soft goals as possible (in their priority order). An optimization proposal that does not satisfy all the hard goals is rejected by Cruise Control and not sent to the user for approval.
Note
|
For example, you might have a soft goal to distribute a topic’s replicas evenly across the cluster (the topic replica distribution goal). Cruise Control will ignore this goal if doing so enables all the configured hard goals to be met. |
In Cruise Control, the following main optimization goals are preset as hard goals:
RackAwareGoal; MinTopicLeadersPerBrokerGoal; ReplicaCapacityGoal; DiskCapacityGoal; NetworkInboundCapacityGoal; NetworkOutboundCapacityGoal; CpuCapacityGoal
You configure hard goals in the Cruise Control deployment configuration, by editing the hard.goals
property in Kafka.spec.cruiseControl.config
.
-
To inherit the preset hard goals from Cruise Control, do not specify the
hard.goals
property inKafka.spec.cruiseControl.config
-
To change the preset hard goals, specify the desired goals in the
hard.goals
property, using their fully-qualified domain names.
Kafka
configuration for hard optimization goalsapiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
zookeeper:
# ...
entityOperator:
topicOperator: {}
userOperator: {}
cruiseControl:
brokerCapacity:
inboundNetwork: 10000KB/s
outboundNetwork: 10000KB/s
config:
# Note that `default.goals` (superset) must also include all `hard.goals` (subset)
default.goals: >
com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkInboundCapacityGoal,
com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkOutboundCapacityGoal
hard.goals: >
com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkInboundCapacityGoal,
com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkOutboundCapacityGoal
# ...
Increasing the number of configured hard goals will reduce the likelihood of Cruise Control generating valid optimization proposals.
If skipHardGoalCheck: true
is specified in the KafkaRebalance
custom resource, Cruise Control does not check that the list of user-provided optimization goals (in KafkaRebalance.spec.goals
) contains all the configured hard goals (hard.goals
).
Therefore, if some, but not all, of the user-provided optimization goals are in the hard.goals
list, Cruise Control will still treat them as hard goals even if skipHardGoalCheck: true
is specified.
13.2.4. Main optimization goals
The main optimization goals are available to all users. Goals that are not listed in the main optimization goals are not available for use in Cruise Control operations.
Unless you change the Cruise Control deployment configuration, Strimzi will inherit the following main optimization goals from Cruise Control, in descending priority order:
RackAwareGoal; ReplicaCapacityGoal; DiskCapacityGoal; NetworkInboundCapacityGoal; NetworkOutboundCapacityGoal; CpuCapacityGoal; ReplicaDistributionGoal; PotentialNwOutGoal; DiskUsageDistributionGoal; NetworkInboundUsageDistributionGoal; NetworkOutboundUsageDistributionGoal; CpuUsageDistributionGoal; TopicReplicaDistributionGoal; LeaderReplicaDistributionGoal; LeaderBytesInDistributionGoal; PreferredLeaderElectionGoal
Some of these goals are preset as hard goals.
To reduce complexity, we recommend that you use the inherited main optimization goals, unless you need to completely exclude one or more goals from use in KafkaRebalance
resources. The priority order of the main optimization goals can be modified, if desired, in the configuration for default optimization goals.
You configure main optimization goals, if necessary, in the Cruise Control deployment configuration: Kafka.spec.cruiseControl.config.goals
-
To accept the inherited main optimization goals, do not specify the
goals
property inKafka.spec.cruiseControl.config
. -
If you need to modify the inherited main optimization goals, specify a list of goals, in descending priority order, in the
goals
configuration option.
Note
|
To avoid errors when generating optimization proposals, make sure that any changes you make to the goals or default.goals in Kafka.spec.cruiseControl.config include all of the hard goals specified for the hard.goals property. To clarify, the hard goals must also be specified (as a subset) for the main optimization goals and default goals.
|
13.2.5. Default optimization goals
Cruise Control uses the default optimization goals to generate the cached optimization proposal. For more information about the cached optimization proposal, see Optimization proposals overview.
You can override the default optimization goals by setting user-provided optimization goals in a KafkaRebalance
custom resource.
Unless you specify default.goals
in the Cruise Control deployment configuration, the main optimization goals are used as the default optimization goals.
In this case, the cached optimization proposal is generated using the main optimization goals.
-
To use the main optimization goals as the default goals, do not specify the
default.goals
property inKafka.spec.cruiseControl.config
. -
To modify the default optimization goals, edit the
default.goals
property inKafka.spec.cruiseControl.config
. You must use a subset of the main optimization goals.
Kafka
configuration for default optimization goalsapiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
zookeeper:
# ...
entityOperator:
topicOperator: {}
userOperator: {}
cruiseControl:
brokerCapacity:
inboundNetwork: 10000KB/s
outboundNetwork: 10000KB/s
config:
# Note that `default.goals` (superset) must also include all `hard.goals` (subset)
default.goals: >
com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal,
com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal,
com.linkedin.kafka.cruisecontrol.analyzer.goals.DiskCapacityGoal
hard.goals: >
com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal
# ...
If no default optimization goals are specified, the cached proposal is generated using the main optimization goals.
13.2.6. User-provided optimization goals
User-provided optimization goals narrow down the configured default goals for a particular optimization proposal.
You can set them, as required, in spec.goals
in a KafkaRebalance
custom resource:
KafkaRebalance.spec.goals
User-provided optimization goals can generate optimization proposals for different scenarios.
For example, you might want to optimize leader replica distribution across the Kafka cluster without considering disk capacity or disk utilization.
So, you create a KafkaRebalance
custom resource containing a single user-provided goal for leader replica distribution.
User-provided optimization goals must:
-
Include all configured hard goals, or an error occurs
-
Be a subset of the main optimization goals
To ignore the configured hard goals when generating an optimization proposal, add the skipHardGoalCheck: true
property to the KafkaRebalance
custom resource. See Generating optimization proposals.
-
Configurations in the Cruise Control Wiki.
13.3. Optimization proposals overview
Configure a KafkaRebalance
resource to generate optimization proposals and apply the suggested changes.
An optimization proposal is a summary of proposed changes that would produce a more balanced Kafka cluster, with partition workloads distributed more evenly among the brokers.
Each optimization proposal is based on the set of optimization goals that was used to generate it, subject to any configured capacity limits on broker resources.
All optimization proposals are estimates of the impact of a proposed rebalance. You can approve or reject a proposal. You cannot approve a cluster rebalance without first generating the optimization proposal.
You can run optimization proposals in one of the following rebalancing modes:
-
full
-
add-brokers
-
remove-brokers
13.3.1. Rebalancing modes
You specify a rebalancing mode using the spec.mode
property of the KafkaRebalance
custom resource.
full
-
The
full
mode runs a full rebalance by moving replicas across all the brokers in the cluster. This is the default mode if thespec.mode
property is not defined in theKafkaRebalance
custom resource. add-brokers
-
The
add-brokers
mode is used after scaling up a Kafka cluster by adding one or more brokers. Normally, after scaling up a Kafka cluster, new brokers are used to host only the partitions of newly created topics. If no new topics are created, the newly added brokers are not used and the existing brokers remain under the same load. By using theadd-brokers
mode immediately after adding brokers to the cluster, the rebalancing operation moves replicas from existing brokers to the newly added brokers. You specify the new brokers as a list using thespec.brokers
property of theKafkaRebalance
custom resource. remove-brokers
-
The
remove-brokers
mode is used before scaling down a Kafka cluster by removing one or more brokers. If you scale down a Kafka cluster, brokers are shut down even if they host replicas. This can lead to under-replicated partitions and possibly result in some partitions being under their minimum ISR (in-sync replicas). To avoid this potential problem, theremove-brokers
mode moves replicas off the brokers that are going to be removed. When these brokers are not hosting replicas anymore, you can safely run the scaling down operation. You specify the brokers you’re removing as a list in thespec.brokers
property in theKafkaRebalance
custom resource.
In general, use the full
rebalance mode to rebalance a Kafka cluster by spreading the load across brokers.
Use the add-brokers
and remove-brokers
modes only if you want to scale your cluster up or down and rebalance the replicas accordingly.
The procedure to run a rebalance is actually the same across the three different modes.
The only difference is with specifying a mode through the spec.mode
property and, if needed, listing brokers that have been added or will be removed through the spec.brokers
property.
13.3.2. The results of an optimization proposal
When an optimization proposal is generated, a summary and broker load is returned.
- Summary
-
The summary is contained in the
KafkaRebalance
resource. The summary provides an overview of the proposed cluster rebalance and indicates the scale of the changes involved. A summary of a successfully generated optimization proposal is contained in theStatus.OptimizationResult
property of theKafkaRebalance
resource. The information provided is a summary of the full optimization proposal. - Broker load
-
The broker load is stored in a ConfigMap that contains data as a JSON string. The broker load shows before and after values for the proposed rebalance, so you can see the impact on each of the brokers in the cluster.
13.3.3. Manually approving or rejecting an optimization proposal
An optimization proposal summary shows the proposed scope of changes.
You can use the name of the KafkaRebalance
resource to return a summary from the command line.
kubectl describe kafkarebalance <kafka_rebalance_resource_name> -n <namespace>
You can also use the jq
command line JSON parser tool.
kubectl get kafkarebalance -o json | jq <jq_query>
.
Use the summary to decide whether to approve or reject an optimization proposal.
- Approving an optimization proposal
-
You approve the optimization proposal by setting the
strimzi.io/rebalance
annotation of theKafkaRebalance
resource toapprove
. Cruise Control applies the proposal to the Kafka cluster and starts a cluster rebalance operation. - Rejecting an optimization proposal
-
If you choose not to approve an optimization proposal, you can change the optimization goals or update any of the rebalance performance tuning options, and then generate another proposal. You can generate a new optimization proposal for a
KafkaRebalance
resource by setting thestrimzi.io/rebalance
annotation torefresh
.
Use optimization proposals to assess the movements required for a rebalance. For example, a summary describes inter-broker and intra-broker movements. Inter-broker rebalancing moves data between separate brokers. Intra-broker rebalancing moves data between disks on the same broker when you are using a JBOD storage configuration. Such information can be useful even if you don’t go ahead and approve the proposal.
You might reject an optimization proposal, or delay its approval, because of the additional load on a Kafka cluster when rebalancing.
In the following example, the proposal suggests the rebalancing of data between separate brokers. The rebalance involves the movement of 55 partition replicas, totaling 12MB of data, across the brokers. Though the inter-broker movement of partition replicas has a high impact on performance, the total amount of data is not large. If the total data was much larger, you could reject the proposal, or time when to approve the rebalance to limit the impact on the performance of the Kafka cluster.
Rebalance performance tuning options can help reduce the impact of data movement. If you can extend the rebalance period, you can divide the rebalance into smaller batches. Fewer data movements at a single time reduces the load on the cluster.
Name: my-rebalance
Namespace: myproject
Labels: strimzi.io/cluster=my-cluster
Annotations: API Version: kafka.strimzi.io/v1alpha1
Kind: KafkaRebalance
Metadata:
# ...
Status:
Conditions:
Last Transition Time: 2022-04-05T14:36:11.900Z
Status: ProposalReady
Type: State
Observed Generation: 1
Optimization Result:
Data To Move MB: 0
Excluded Brokers For Leadership:
Excluded Brokers For Replica Move:
Excluded Topics:
Intra Broker Data To Move MB: 12
Monitored Partitions Percentage: 100
Num Intra Broker Replica Movements: 0
Num Leader Movements: 24
Num Replica Movements: 55
On Demand Balancedness Score After: 82.91290759174306
On Demand Balancedness Score Before: 78.01176356230222
Recent Windows: 5
Session Id: a4f833bd-2055-4213-bfdd-ad21f95bf184
The proposal will also move 24 partition leaders to different brokers. This requires a change to the ZooKeeper configuration, which has a low impact on performance.
The balancedness scores are measurements of the overall balance of the Kafka cluster before and after the optimization proposal is approved. A balancedness score is based on optimization goals. If all goals are satisfied, the score is 100. The score is reduced for each goal that will not be met. Compare the balancedness scores to see whether the Kafka cluster is less balanced than it could be following a rebalance.
13.3.4. Automatically approving an optimization proposal
To save time, you can automate the process of approving optimization proposals. With automation, when you generate an optimization proposal it goes straight into a cluster rebalance.
To enable the optimization proposal auto-approval mechanism, create the KafkaRebalance
resource with the strimzi.io/rebalance-auto-approval
annotation set to true
.
If the annotation is not set or set to false
, the optimization proposal requires manual approval.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaRebalance
metadata:
name: my-rebalance
labels:
strimzi.io/cluster: my-cluster
annotations:
strimzi.io/rebalance-auto-approval: "true"
spec:
mode: # any mode
# ...
You can still check the status when automatically approving an optimization proposal.
The status of the KafkaRebalance
resource moves to Ready
when the rebalance is complete.
13.3.5. Optimization proposal summary properties
The following table explains the properties contained in the optimization proposal’s summary section.
JSON property | Description |
---|---|
|
The total number of partition replicas that will be transferred between the disks of the cluster’s brokers. Performance impact during rebalance operation: Relatively high, but lower than |
|
Not yet supported. An empty list is returned. |
|
The number of partition replicas that will be moved between separate brokers. Performance impact during rebalance operation: Relatively high. |
|
A measurement of the overall balancedness of a Kafka Cluster, before and after the optimization proposal was generated. The score is calculated by subtracting the sum of the The |
|
The sum of the size of each partition replica that will be moved between disks on the same broker (see also Performance impact during rebalance operation: Variable. The larger the number, the longer the cluster rebalance will take to complete. Moving a large amount of data between disks on the same broker has less impact than between separate brokers (see |
|
The number of metrics windows upon which the optimization proposal is based. |
|
The sum of the size of each partition replica that will be moved to a separate broker (see also Performance impact during rebalance operation: Variable. The larger the number, the longer the cluster rebalance will take to complete. |
|
The percentage of partitions in the Kafka cluster covered by the optimization proposal. Affected by the number of |
|
If you specified a regular expression in the |
|
The number of partitions whose leaders will be switched to different replicas. This involves a change to ZooKeeper configuration. Performance impact during rebalance operation: Relatively low. |
|
Not yet supported. An empty list is returned. |
13.3.6. Broker load properties
The broker load is stored in a ConfigMap (with the same name as the KafkaRebalance custom resource) as a JSON formatted string. This JSON string consists of a JSON object with keys for each broker IDs linking to a number of metrics for each broker. Each metric consist of three values. The first is the metric value before the optimization proposal is applied, the second is the expected value of the metric after the proposal is applied, and the third is the difference between the first two values (after minus before).
Note
|
The ConfigMap appears when the KafkaRebalance resource is in the ProposalReady state and remains after the rebalance is complete.
|
You can use the name of the ConfigMap to view its data from the command line.
kubectl describe configmaps <my_rebalance_configmap_name> -n <namespace>
You can also use the jq
command line JSON parser tool to extract the JSON string from the ConfigMap.
kubectl get configmaps <my_rebalance_configmap_name> -o json | jq '.["data"]["brokerLoad.json"]|fromjson|.'
The following table explains the properties contained in the optimization proposal’s broker load ConfigMap:
JSON property | Description |
---|---|
|
The number of replicas on this broker that are partition leaders. |
|
The number of replicas on this broker. |
|
The CPU utilization as a percentage of the defined capacity. |
|
The disk utilization as a percentage of the defined capacity. |
|
The absolute disk usage in MB. |
|
The total network output rate for the broker. |
|
The network input rate for all partition leader replicas on this broker. |
|
The network input rate for all follower replicas on this broker. |
|
The hypothetical maximum network output rate that would be realized if this broker became the leader of all the replicas it currently hosts. |
13.3.7. Cached optimization proposal
Cruise Control maintains a cached optimization proposal based on the configured default optimization goals. Generated from the workload model, the cached optimization proposal is updated every 15 minutes to reflect the current state of the Kafka cluster. If you generate an optimization proposal using the default optimization goals, Cruise Control returns the most recent cached proposal.
To change the cached optimization proposal refresh interval, edit the proposal.expiration.ms
setting in the Cruise Control deployment configuration.
Consider a shorter interval for fast changing clusters, although this increases the load on the Cruise Control server.
13.4. Rebalance performance tuning overview
You can adjust several performance tuning options for cluster rebalances. These options control how partition replica and leadership movements in a rebalance are executed, as well as the bandwidth that is allocated to a rebalance operation.
13.4.1. Partition reassignment commands
Optimization proposals are comprised of separate partition reassignment commands. When you approve a proposal, the Cruise Control server applies these commands to the Kafka cluster.
A partition reassignment command consists of either of the following types of operations:
-
Partition movement: Involves transferring the partition replica and its data to a new location. Partition movements can take one of two forms:
-
Inter-broker movement: The partition replica is moved to a log directory on a different broker.
-
Intra-broker movement: The partition replica is moved to a different log directory on the same broker.
-
-
Leadership movement: This involves switching the leader of the partition’s replicas.
Cruise Control issues partition reassignment commands to the Kafka cluster in batches. The performance of the cluster during the rebalance is affected by the number of each type of movement contained in each batch.
13.4.2. Replica movement strategies
Cluster rebalance performance is also influenced by the replica movement strategy that is applied to the batches of partition reassignment commands.
By default, Cruise Control uses the BaseReplicaMovementStrategy
, which simply applies the commands in the order they were generated.
However, if there are some very large partition reassignments early in the proposal, this strategy can slow down the application of the other reassignments.
Cruise Control provides four alternative replica movement strategies that can be applied to optimization proposals:
-
PrioritizeSmallReplicaMovementStrategy
: Order reassignments in order of ascending size. -
PrioritizeLargeReplicaMovementStrategy
: Order reassignments in order of descending size. -
PostponeUrpReplicaMovementStrategy
: Prioritize reassignments for replicas of partitions which have no out-of-sync replicas. -
PrioritizeMinIsrWithOfflineReplicasStrategy
: Prioritize reassignments with (At/Under)MinISR partitions with offline replicas. This strategy will only work ifcruiseControl.config.concurrency.adjuster.min.isr.check.enabled
is set totrue
in theKafka
custom resource’s spec.
These strategies can be configured as a sequence. The first strategy attempts to compare two partition reassignments using its internal logic. If the reassignments are equivalent, then it passes them to the next strategy in the sequence to decide the order, and so on.
13.4.3. Intra-broker disk balancing
Moving a large amount of data between disks on the same broker has less impact than between separate brokers. If you are running a Kafka deployment that uses JBOD storage with multiple disks on the same broker, Cruise Control can balance partitions between the disks.
Note
|
If you are using JBOD storage with a single disk, intra-broker disk balancing will result in a proposal with 0 partition movements since there are no disks to balance between. |
To perform an intra-broker disk balance, set rebalanceDisk
to true
under the KafkaRebalance.spec
.
When setting rebalanceDisk
to true
, do not set a goals
field in the KafkaRebalance.spec
, as Cruise Control will automatically set the intra-broker goals and ignore the inter-broker goals.
Cruise Control does not perform inter-broker and intra-broker balancing at the same time.
13.4.4. Rebalance tuning options
Cruise Control provides several configuration options for tuning the rebalance parameters discussed above. You can set these tuning options when configuring and deploying Cruise Control with Kafka or optimization proposal levels:
-
The Cruise Control server setting can be set in the Kafka custom resource under
Kafka.spec.cruiseControl.config
. -
The individual rebalance performance configurations can be set under
KafkaRebalance.spec
.
The relevant configurations are summarized in the following table.
Cruise Control properties | KafkaRebalance properties | Default | Description |
---|---|---|---|
|
|
5 |
The maximum number of inter-broker partition movements in each partition reassignment batch |
|
|
2 |
The maximum number of intra-broker partition movements in each partition reassignment batch |
|
|
1000 |
The maximum number of partition leadership changes in each partition reassignment batch |
|
|
Null (no limit) |
The bandwidth (in bytes per second) to assign to partition reassignment |
|
|
|
The list of strategies (in priority order) used to determine the order in which partition reassignment commands are executed for generated proposals.
For the server setting, use a comma separated string with the fully qualified names of the strategy class (add |
- |
|
false |
Enables intra-broker disk balancing, which balances disk space utilization between disks on the same broker. Only applies to Kafka deployments that use JBOD storage with multiple disks. |
Changing the default settings affects the length of time that the rebalance takes to complete, as well as the load placed on the Kafka cluster during the rebalance. Using lower values reduces the load but increases the amount of time taken, and vice versa.
13.5. Configuring and deploying Cruise Control with Kafka
Configure a Kafka
resource to deploy Cruise Control alongside a Kafka cluster.
You can use the cruiseControl
properties of the Kafka
resource to configure the deployment.
Deploy one instance of Cruise Control per Kafka cluster.
Use goals
configuration in the Cruise Control config
to specify optimization goals for generating optimization proposals.
You can use brokerCapacity
to change the default capacity limits for goals related to resource distribution.
If brokers are running on nodes with heterogeneous network resources, you can use overrides
to set network capacity limits for each broker.
If an empty object ({}
) is used for the cruiseControl
configuration, all properties use their default values.
For more information on the configuration options for Cruise Control, see the Custom resource API reference.
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
cruiseControl
property for theKafka
resource.The properties you can configure are shown in this example configuration:
apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: # ... cruiseControl: brokerCapacity: # (1) inboundNetwork: 10000KB/s outboundNetwork: 10000KB/s overrides: # (2) - brokers: [0] inboundNetwork: 20000KiB/s outboundNetwork: 20000KiB/s - brokers: [1, 2] inboundNetwork: 30000KiB/s outboundNetwork: 30000KiB/s # ... config: # (3) # Note that `default.goals` (superset) must also include all `hard.goals` (subset) default.goals: > # (4) com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.DiskCapacityGoal # ... hard.goals: > com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal # ... cpu.balance.threshold: 1.1 metadata.max.age.ms: 300000 send.buffer.bytes: 131072 webserver.http.cors.enabled: true # (5) webserver.http.cors.origin: "*" webserver.http.cors.exposeheaders: "User-Task-ID,Content-Type" # ... resources: # (6) requests: cpu: 1 memory: 512Mi limits: cpu: 2 memory: 2Gi logging: # (7) type: inline loggers: rootLogger.level: "INFO" template: # (8) pod: metadata: labels: label1: value1 securityContext: runAsUser: 1000001 fsGroup: 0 terminationGracePeriodSeconds: 120 readinessProbe: # (9) initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 metricsConfig: # (10) type: jmxPrometheusExporter valueFrom: configMapKeyRef: name: cruise-control-metrics key: metrics-config.yml # ...
-
Capacity limits for broker resources.
-
Overrides set network capacity limits for specific brokers when running on nodes with heterogeneous network resources.
-
Cruise Control configuration. Standard Cruise Control configuration may be provided, restricted to those properties not managed directly by Strimzi.
-
Optimization goals configuration, which can include configuration for default optimization goals (
default.goals
), main optimization goals (goals
), and hard goals (hard.goals
). -
CORS enabled and configured for read-only access to the Cruise Control API.
-
Requests for reservation of supported resources, currently
cpu
andmemory
, and limits to specify the maximum resources that can be consumed. -
Cruise Control loggers and log levels added directly (
inline
) or indirectly (external
) through a ConfigMap. A custom ConfigMap must be placed under thelog4j.properties
key. Cruise Control has a single logger namedrootLogger.level
. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. -
Template customization. Here a pod is scheduled with additional security attributes.
-
Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).
-
Prometheus metrics enabled. In this example, metrics are configured for the Prometheus JMX Exporter (the default metrics exporter).
-
-
Create or update the resource:
kubectl apply -f <kafka_configuration_file>
-
Check the status of the deployment:
kubectl get deployments -n <my_cluster_operator_namespace>
Output shows the deployment name and readinessNAME READY UP-TO-DATE AVAILABLE my-cluster-cruise-control 1/1 1 1
my-cluster
is the name of the Kafka cluster.READY
shows the number of replicas that are ready/expected. The deployment is successful when theAVAILABLE
output shows1
.
Auto-created topics
The following table shows the three topics that are automatically created when Cruise Control is deployed. These topics are required for Cruise Control to work properly and must not be deleted or changed. You can change the name of the topic using the specified configuration option.
Auto-created topic configuration | Default topic name | Created by | Function |
---|---|---|---|
|
|
Strimzi Metrics Reporter |
Stores the raw metrics from the Metrics Reporter in each Kafka broker. |
|
|
Cruise Control |
Stores the derived metrics for each partition. These are created by the Metric Sample Aggregator. |
|
|
Cruise Control |
Stores the metrics samples used to create the Cluster Workload Model. |
To prevent the removal of records that are needed by Cruise Control, log compaction is disabled in the auto-created topics.
Note
|
If the names of the auto-created topics are changed in a Kafka cluster that already has Cruise Control enabled, the old topics will not be deleted and should be manually removed. |
After configuring and deploying Cruise Control, you can generate optimization proposals.
13.6. Generating optimization proposals
When you create or update a KafkaRebalance
resource, Cruise Control generates an optimization proposal for the Kafka cluster based on the configured optimization goals.
Analyze the information in the optimization proposal and decide whether to approve it.
You can use the results of the optimization proposal to rebalance your Kafka cluster.
You can run the optimization proposal in one of the following modes:
-
full
(default) -
add-brokers
-
remove-brokers
The mode you use depends on whether you are rebalancing across all the brokers already running in the Kafka cluster; or you want to rebalance after scaling up or before scaling down your Kafka cluster. For more information, see Rebalancing modes with broker scaling.
-
You have deployed Cruise Control to your Strimzi cluster.
-
You have configured optimization goals and, optionally, capacity limits on broker resources.
For more information on configuring Cruise Control, see Configuring and deploying Cruise Control with Kafka.
-
Create a
KafkaRebalance
resource and specify the appropriate mode.full
mode (default)-
To use the default optimization goals defined in the
Kafka
resource, leave thespec
property empty. Cruise Control rebalances a Kafka cluster infull
mode by default.Example configuration with full rebalancing by defaultapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: {}
You can also run a full rebalance by specifying the
full
mode through thespec.mode
property.Example configuration specifyingfull
modeapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: mode: full
add-brokers
mode-
If you want to rebalance a Kafka cluster after scaling up, specify the
add-brokers
mode.In this mode, existing replicas are moved to the newly added brokers. You need to specify the brokers as a list.
Example configuration specifyingadd-brokers
modeapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: mode: add-brokers brokers: [3, 4] (1)
-
List of newly added brokers added by the scale up operation. This property is mandatory.
-
remove-brokers
mode-
If you want to rebalance a Kafka cluster before scaling down, specify the
remove-brokers
mode.In this mode, replicas are moved off the brokers that are going to be removed. You need to specify the brokers that are being removed as a list.
Example configuration specifyingremove-brokers
modeapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: mode: remove-brokers brokers: [3, 4] (1)
-
List of brokers to be removed by the scale down operation. This property is mandatory.
NoteThe following steps and the steps to approve or stop a rebalance are the same regardless of the rebalance mode you are using. -
-
To configure user-provided optimization goals instead of using the default goals, add the
goals
property and enter one or more goals.In the following example, rack awareness and replica capacity are configured as user-provided optimization goals:
apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: goals: - RackAwareGoal - ReplicaCapacityGoal
-
To ignore the configured hard goals, add the
skipHardGoalCheck: true
property:apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: goals: - RackAwareGoal - ReplicaCapacityGoal skipHardGoalCheck: true
-
(Optional) To approve the optimization proposal automatically, set the
strimzi.io/rebalance-auto-approval
annotation totrue
:apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster annotations: strimzi.io/rebalance-auto-approval: "true" spec: goals: - RackAwareGoal - ReplicaCapacityGoal skipHardGoalCheck: true
-
Create or update the resource:
kubectl apply -f <kafka_rebalance_configuration_file>
The Cluster Operator requests the optimization proposal from Cruise Control. This might take a few minutes depending on the size of the Kafka cluster.
-
If you used the automatic approval mechanism, wait for the status of the optimization proposal to change to
Ready
. If you haven’t enabled the automatic approval mechanism, wait for the status of the optimization proposal to change toProposalReady
:kubectl get kafkarebalance -o wide -w -n <namespace>
PendingProposal
-
A
PendingProposal
status means the rebalance operator is polling the Cruise Control API to check if the optimization proposal is ready. ProposalReady
-
A
ProposalReady
status means the optimization proposal is ready for review and approval.
When the status changes to
ProposalReady
, the optimization proposal is ready to approve. -
Review the optimization proposal.
The optimization proposal is contained in the
Status.Optimization Result
property of theKafkaRebalance
resource.kubectl describe kafkarebalance <kafka_rebalance_resource_name>
Example optimization proposalStatus: Conditions: Last Transition Time: 2020-05-19T13:50:12.533Z Status: ProposalReady Type: State Observed Generation: 1 Optimization Result: Data To Move MB: 0 Excluded Brokers For Leadership: Excluded Brokers For Replica Move: Excluded Topics: Intra Broker Data To Move MB: 0 Monitored Partitions Percentage: 100 Num Intra Broker Replica Movements: 0 Num Leader Movements: 0 Num Replica Movements: 26 On Demand Balancedness Score After: 81.8666802863978 On Demand Balancedness Score Before: 78.01176356230222 Recent Windows: 1 Session Id: 05539377-ca7b-45ef-b359-e13564f1458c
The properties in the
Optimization Result
section describe the pending cluster rebalance operation. For descriptions of each property, see Contents of optimization proposals.
If a Kafka cluster is overloaded in terms of CPU utilization, you might see an insufficient CPU capacity error in the KafkaRebalance
status.
It’s worth noting that this utilization value is unaffected by the excludedTopics
configuration.
Although optimization proposals will not reassign replicas of excluded topics, their load is still considered in the utilization calculation.
com.linkedin.kafka.cruisecontrol.exception.OptimizationFailureException:
[CpuCapacityGoal] Insufficient capacity for cpu (Utilization 615.21,
Allowed Capacity 420.00, Threshold: 0.70). Add at least 3 brokers with
the same cpu capacity (100.00) as broker-0. Add at least 3 brokers with
the same cpu capacity (100.00) as broker-0.
Note
|
The error shows CPU capacity as a percentage rather than the number of CPU cores.
For this reason, it does not directly map to the number of CPUs configured in the Kafka custom resource.
It is like having a single virtual CPU per broker, which has the cycles of the CPUs configured in |
13.7. Approving an optimization proposal
You can approve an optimization proposal generated by Cruise Control, if its status is ProposalReady
.
Cruise Control will then apply the optimization proposal to the Kafka cluster, reassigning partitions to brokers and changing partition leadership.
Caution
|
This is not a dry run. Before you approve an optimization proposal, you must:
|
-
You have generated an optimization proposal from Cruise Control.
-
The
KafkaRebalance
custom resource status isProposalReady
.
Perform these steps for the optimization proposal that you want to approve.
-
Unless the optimization proposal is newly generated, check that it is based on current information about the state of the Kafka cluster. To do so, refresh the optimization proposal to make sure it uses the latest cluster metrics:
-
Annotate the
KafkaRebalance
resource in Kubernetes withstrimzi.io/rebalance=refresh
:kubectl annotate kafkarebalance <kafka_rebalance_resource_name> strimzi.io/rebalance=refresh
-
-
Wait for the status of the optimization proposal to change to
ProposalReady
:kubectl get kafkarebalance -o wide -w -n <namespace>
PendingProposal
-
A
PendingProposal
status means the rebalance operator is polling the Cruise Control API to check if the optimization proposal is ready. ProposalReady
-
A
ProposalReady
status means the optimization proposal is ready for review and approval.
When the status changes to
ProposalReady
, the optimization proposal is ready to approve. -
Approve the optimization proposal that you want Cruise Control to apply.
Annotate the
KafkaRebalance
resource in Kubernetes withstrimzi.io/rebalance=approve
:kubectl annotate kafkarebalance <kafka_rebalance_resource_name> strimzi.io/rebalance=approve
-
The Cluster Operator detects the annotated resource and instructs Cruise Control to rebalance the Kafka cluster.
-
Wait for the status of the optimization proposal to change to
Ready
:kubectl get kafkarebalance -o wide -w -n <namespace>
Rebalancing
-
A
Rebalancing
status means the rebalancing is in progress. Ready
-
A
Ready
status means the rebalance is complete. NotReady
-
A
NotReady
status means an error occurred—see Fixing problems with aKafkaRebalance
resource.
When the status changes to
Ready
, the rebalance is complete.To use the same
KafkaRebalance
custom resource to generate another optimization proposal, apply therefresh
annotation to the custom resource. This moves the custom resource to thePendingProposal
orProposalReady
state. You can then review the optimization proposal and approve it, if desired.
13.8. Stopping a cluster rebalance
Once started, a cluster rebalance operation might take some time to complete and affect the overall performance of the Kafka cluster.
If you want to stop a cluster rebalance operation that is in progress, apply the stop
annotation to the KafkaRebalance
custom resource.
This instructs Cruise Control to finish the current batch of partition reassignments and then stop the rebalance.
When the rebalance has stopped, completed partition reassignments have already been applied; therefore, the state of the Kafka cluster is different when compared to prior to the start of the rebalance operation.
If further rebalancing is required, you should generate a new optimization proposal.
Note
|
The performance of the Kafka cluster in the intermediate (stopped) state might be worse than in the initial state. |
-
You have approved the optimization proposal by annotating the
KafkaRebalance
custom resource withapprove
. -
The status of the
KafkaRebalance
custom resource isRebalancing
.
-
Annotate the
KafkaRebalance
resource in Kubernetes:kubectl annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=stop
-
Check the status of the
KafkaRebalance
resource:kubectl describe kafkarebalance rebalance-cr-name
-
Wait until the status changes to
Stopped
.
13.9. Fixing problems with a KafkaRebalance
resource
If an issue occurs when creating a KafkaRebalance
resource or interacting with Cruise Control, the error is reported in the resource status, along with details of how to fix it.
The resource also moves to the NotReady
state.
To continue with the cluster rebalance operation, you must fix the problem in the KafkaRebalance
resource itself or with the overall Cruise Control deployment.
Problems might include the following:
-
A misconfigured parameter in the
KafkaRebalance
resource. -
The
strimzi.io/cluster
label for specifying the Kafka cluster in theKafkaRebalance
resource is missing. -
The Cruise Control server is not deployed as the
cruiseControl
property in theKafka
resource is missing. -
The Cruise Control server is not reachable.
After fixing the issue, you need to add the refresh
annotation to the KafkaRebalance
resource.
During a “refresh”, a new optimization proposal is requested from the Cruise Control server.
-
You have approved an optimization proposal.
-
The status of the
KafkaRebalance
custom resource for the rebalance operation isNotReady
.
-
Get information about the error from the
KafkaRebalance
status:kubectl describe kafkarebalance rebalance-cr-name
-
Attempt to resolve the issue in the
KafkaRebalance
resource. -
Annotate the
KafkaRebalance
resource in Kubernetes:kubectl annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=refresh
-
Check the status of the
KafkaRebalance
resource:kubectl describe kafkarebalance rebalance-cr-name
-
Wait until the status changes to
PendingProposal
, or directly toProposalReady
.
14. Using the partition reassignment tool
When scaling a Kafka cluster, you may need to add or remove brokers and update the distribution of partitions or the replication factor of topics.
To update partitions and topics, you can use the kafka-reassign-partitions.sh
tool.
Neither the Strimzi Cruise Control integration nor the Topic Operator support changing the replication factor of a topic.
However, you can change the replication factor of a topic using the kafka-reassign-partitions.sh
tool.
The tool can also be used to reassign partitions and balance the distribution of partitions across brokers to improve performance. However, it is recommended to use Cruise Control for automated partition reassignments and cluster rebalancing. Cruise Control can move topics from one broker to another without any downtime, and it is the most efficient way to reassign partitions.
It is recommended to run the kafka-reassign-partitions.sh
tool as a separate interactive pod rather than within the broker container.
Running the Kafka bin/
scripts within the broker container may cause a JVM to start with the same settings as the Kafka broker, which can potentially cause disruptions.
By running the kafka-reassign-partitions.sh
tool in a separate pod, you can avoid this issue.
Running a pod with the -ti
option creates an interactive pod with a terminal for running shell commands inside the pod.
kubectl run helper-pod -ti --image=quay.io/strimzi/kafka:0.35.1-kafka-3.4.0 --rm=true --restart=Never -- bash
14.1. Partition reassignment tool overview
The partition reassignment tool provides the following capabilities for managing Kafka partitions and brokers:
- Redistributing partition replicas
-
Scale your cluster up and down by adding or removing brokers, and move Kafka partitions from heavily loaded brokers to under-utilized brokers. To do this, you must create a partition reassignment plan that identifies which topics and partitions to move and where to move them. Cruise Control is recommended for this type of operation as it automates the cluster rebalancing process.
- Scaling topic replication factor up and down
-
Increase or decrease the replication factor of your Kafka topics. To do this, you must create a partition reassignment plan that identifies the existing replication assignment across partitions and an updated assignment with the replication factor changes.
- Changing the preferred leader
-
Change the preferred leader of a Kafka partition. This can be useful if the current preferred leader is unavailable or if you want to redistribute load across the brokers in the cluster. To do this, you must create a partition reassignment plan that specifies the new preferred leader for each partition by changing the order of replicas.
- Changing the log directories to use a specific JBOD volume
-
Change the log directories of your Kafka brokers to use a specific JBOD volume. This can be useful if you want to move your Kafka data to a different disk or storage device. To do this, you must create a partition reassignment plan that specifies the new log directory for each topic.
14.1.1. Generating a partition reassignment plan
The partition reassignment tool (kafka-reassign-partitions.sh
) works by generating a partition assignment plan that specifies which partitions should be moved from their current broker to a new broker.
If you are satisfied with the plan, you can execute it. The tool then does the following:
-
Migrates the partition data to the new broker
-
Updates the metadata on the Kafka brokers to reflect the new partition assignments
-
Triggers a rolling restart of the Kafka brokers to ensure that the new assignments take effect
The partition reassignment tool 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 only want to reassign some 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 the partitions in the file have been moved to their intended brokers. If the reassignment is complete,--verify
also removes any traffic throttles (--throttle
) 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 must 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.
14.1.2. Specifying topics in a partition reassignment JSON file
The kafka-reassign-partitions.sh
tool uses a reassignment JSON file that specifies the topics to reassign.
You can generate a reassignment JSON file or create a file manually if you want to move specific partitions.
A basic reassignment JSON file has the structure presented in the following example, which describes three partitions belonging to two Kafka topics.
Each partition is reassigned to a new set of replicas, which are identified by their broker IDs.
The version
, topic
, partition
, and replicas
properties are all required.
{
"version": 1, # (1)
"partitions": [ # (2)
{
"topic": "example-topic-1", # (3)
"partition": 0, # (4)
"replicas": [1, 2, 3] # (5)
},
{
"topic": "example-topic-1",
"partition": 1,
"replicas": [2, 3, 4]
},
{
"topic": "example-topic-2",
"partition": 0,
"replicas": [3, 4, 5]
}
]
}
-
The version of the reassignment JSON file format. Currently, only version 1 is supported, so this should always be 1.
-
An array that specifies the partitions to be reassigned.
-
The name of the Kafka topic that the partition belongs to.
-
The ID of the partition being reassigned.
-
An ordered array of the IDs of the brokers that should be assigned as replicas for this partition. The first broker in the list is the leader replica.
Note
|
Partitions not included in the JSON are not changed. |
If you specify only topics using a topics
array, the partition reassignment tool reassigns all the partitions belonging to the specified topics.
{
"version": 1,
"topics": [
{ "topic": "my-topic"}
]
}
14.1.3. Reassigning partitions between JBOD volumes
When using JBOD storage in your Kafka cluster, you can 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 log_dirs
values for each partition in the reassignment JSON file.
Each log_dirs
array contains the same number of entries as the replicas
array, since each replica should be assigned to a specific log directory.
The log_dirs
array contains either an absolute path to a log directory or the special value any
.
The any
value indicates that Kafka can choose any available log directory for that replica, which can be useful when reassigning partitions between JBOD volumes.
{
"version": 1,
"partitions": [
{
"topic": "example-topic-1",
"partition": 0,
"replicas": [1, 2, 3]
"log_dirs": ["/var/lib/kafka/data-0/kafka-log1", "any", "/var/lib/kafka/data-1/kafka-log2"]
},
{
"topic": "example-topic-1",
"partition": 1,
"replicas": [2, 3, 4]
"log_dirs": ["any", "/var/lib/kafka/data-2/kafka-log3", "/var/lib/kafka/data-3/kafka-log4"]
},
{
"topic": "example-topic-2",
"partition": 0,
"replicas": [3, 4, 5]
"log_dirs": ["/var/lib/kafka/data-4/kafka-log5", "any", "/var/lib/kafka/data-5/kafka-log6"]
}
]
}
14.1.4. Throttling partition reassignment
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.
Use the --throttle
parameter with the kafka-reassign-partitions.sh
tool to throttle a reassignment.
You specify a maximum threshold in bytes per second for the movement of partitions between brokers.
For example, --throttle 5000000
sets a maximum threshold for moving partitions of 50 MBps.
Throttling might cause the reassignment to take longer to complete.
-
If the throttle is too low, 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, clients will be impacted.
For example, for producers, this could manifest as higher than normal latency waiting for acknowledgment. For consumers, this could manifest as a drop in throughput caused by higher latency between polls.
14.2. Generating a reassignment JSON file to reassign partitions
Generate a reassignment JSON file with the kafka-reassign-partitions.sh
tool to reassign partitions after scaling a Kafka cluster.
Adding or removing brokers does not automatically redistribute the existing partitions.
To balance the partition distribution and take full advantage of the new brokers, you can reassign the partitions using the kafka-reassign-partitions.sh
tool.
You run the tool from an interactive pod container connected to the Kafka cluster.
The following procedure describes a secure reassignment process that uses mTLS. You’ll need a Kafka cluster that uses TLS encryption and mTLS authentication.
You’ll need the following to establish a connection:
-
The cluster CA certificate and password generated by the Cluster Operator when the Kafka cluster is created
-
The user CA certificate and password generated by the User Operator when a user is created for client access to the Kafka cluster
In this procedure, the CA certificates and corresponding passwords are extracted from the cluster and user secrets that contain them in PKCS #12 (.p12
and .password
) format.
The passwords allow access to the .p12
stores that contain the certificates.
You use the .p12
stores to specify a truststore and keystore to authenticate connection to the Kafka cluster.
-
You have a running Cluster Operator.
-
You have a running Kafka cluster based on a
Kafka
resource configured with internal TLS encryption and mTLS authentication.Kafka configuration with TLS encryption and mTLS authenticationapiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: kafka: # ... listeners: # ... - name: tls port: 9093 type: internal tls: true (1) authentication: type: tls (2) # ...
-
Enables TLS encryption for the internal listener.
-
Listener authentication mechanism specified as mutual
tls
.
-
-
The running Kafka cluster contains a set of topics and partitions to reassign.
Example topic configuration formy-topic
apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaTopic metadata: name: my-topic labels: strimzi.io/cluster: my-cluster spec: partitions: 10 replicas: 3 config: retention.ms: 7200000 segment.bytes: 1073741824 # ...
-
You have a
KafkaUser
configured with ACL rules that specify permission to produce and consume topics from the Kafka brokers.Example Kafka user configuration with ACL rules to allow operations onmy-topic
andmy-cluster
apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaUser metadata: name: my-user labels: strimzi.io/cluster: my-cluster spec: authentication: (1) type: tls authorization: type: simple (2) acls: # access to the topic - resource: type: topic name: my-topic operations: - Create - Describe - Read - AlterConfigs host: "*" # access to the cluster - resource: type: cluster operations: - Alter - AlterConfigs host: "*" # ... # ...
-
User authentication mechanism defined as mutual
tls
. -
Simple authorization and accompanying list of ACL rules.
-
-
Extract the cluster CA certificate and password from the
<cluster_name>-cluster-ca-cert
secret of the Kafka cluster.kubectl get secret <cluster_name>-cluster-ca-cert -o jsonpath='{.data.ca\.p12}' | base64 -d > ca.p12
kubectl get secret <cluster_name>-cluster-ca-cert -o jsonpath='{.data.ca\.password}' | base64 -d > ca.password
Replace <cluster_name> with the name of the Kafka cluster. When you deploy Kafka using the
Kafka
resource, a secret with the cluster CA certificate is created with the Kafka cluster name (<cluster_name>-cluster-ca-cert
). For example,my-cluster-cluster-ca-cert
. -
Run a new interactive pod container using the Strimzi Kafka image to connect to a running Kafka broker.
kubectl run --restart=Never --image=quay.io/strimzi/kafka:0.35.1-kafka-3.4.0 <interactive_pod_name> -- /bin/sh -c "sleep 3600"
Replace <interactive_pod_name> with the name of the pod.
-
Copy the cluster CA certificate to the interactive pod container.
kubectl cp ca.p12 <interactive_pod_name>:/tmp
-
Extract the user CA certificate and password from the secret of the Kafka user that has permission to access the Kafka brokers.
kubectl get secret <kafka_user> -o jsonpath='{.data.user\.p12}' | base64 -d > user.p12
kubectl get secret <kafka_user> -o jsonpath='{.data.user\.password}' | base64 -d > user.password
Replace <kafka_user> with the name of the Kafka user. When you create a Kafka user using the
KafkaUser
resource, a secret with the user CA certificate is created with the Kafka user name. For example,my-user
. -
Copy the user CA certificate to the interactive pod container.
kubectl cp user.p12 <interactive_pod_name>:/tmp
The CA certificates allow the interactive pod container to connect to the Kafka broker using TLS.
-
Create a
config.properties
file to specify the truststore and keystore used to authenticate connection to the Kafka cluster.Use the certificates and passwords you extracted in the previous steps.
bootstrap.servers=<kafka_cluster_name>-kafka-bootstrap:9093 (1) security.protocol=SSL (2) ssl.truststore.location=/tmp/ca.p12 (3) ssl.truststore.password=<truststore_password> (4) ssl.keystore.location=/tmp/user.p12 (5) ssl.keystore.password=<keystore_password> (6)
-
The bootstrap server address to connect to the Kafka cluster. Use your own Kafka cluster name to replace <kafka_cluster_name>.
-
The security protocol option when using TLS for encryption.
-
The truststore location contains the public key certificate (
ca.p12
) for the Kafka cluster. -
The password (
ca.password
) for accessing the truststore. -
The keystore location contains the public key certificate (
user.p12
) for the Kafka user. -
The password (
user.password
) for accessing the keystore.
-
-
Copy the
config.properties
file to the interactive pod container.kubectl cp config.properties <interactive_pod_name>:/tmp/config.properties
-
Prepare a JSON file named
topics.json
that specifies the topics to move.Specify topic names as a comma-separated list.
Example JSON file to reassign all the partitions ofmy-topic
{ "version": 1, "topics": [ { "topic": "my-topic"} ] }
You can also use this file to change the replication factor of a topic.
-
Copy the
topics.json
file to the interactive pod container.kubectl cp topics.json <interactive_pod_name>:/tmp/topics.json
-
Start a shell process in the interactive pod container.
kubectl exec -n <namespace> -ti <interactive_pod_name> /bin/bash
Replace <namespace> with the Kubernetes namespace where the pod is running.
-
Use the
kafka-reassign-partitions.sh
command to generate the reassignment JSON.Example command to move the partitions ofmy-topic
to specified brokersbin/kafka-reassign-partitions.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 \ --command-config /tmp/config.properties \ --topics-to-move-json-file /tmp/topics.json \ --broker-list 0,1,2,3,4 \ --generate
14.3. Reassigning partitions after adding brokers
Use a reassignment file generated by the kafka-reassign-partitions.sh
tool to reassign partitions after increasing the number of brokers in a Kafka cluster.
The reassignment file should describe how partitions are reassigned to brokers in the enlarged Kafka cluster.
You apply the reassignment specified in the file to the brokers and then verify the new partition assignments.
This procedure describes a secure scaling process that uses TLS. You’ll need a Kafka cluster that uses TLS encryption and mTLS authentication.
Note
|
Though you can use the kafka-reassign-partitions.sh tool, Cruise Control is recommended for automated partition reassignments and cluster rebalancing.
Cruise Control can move topics from one broker to another without any downtime, and it is the most efficient way to reassign partitions.
|
-
You have a running Kafka cluster based on a
Kafka
resource configured with internal TLS encryption and mTLS authentication. -
You have generated a reassignment JSON file named
reassignment.json
. -
You are running an interactive pod container that is connected to the running Kafka broker.
-
You are connected as a
KafkaUser
configured with ACL rules that specify permission to manage the Kafka cluster and its topics.
-
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.
-
If you haven’t done so, run an interactive pod container to generate a reassignment JSON file named
reassignment.json
. -
Copy the
reassignment.json
file to the interactive pod container.kubectl cp reassignment.json <interactive_pod_name>:/tmp/reassignment.json
Replace <interactive_pod_name> with the name of the pod.
-
Start a shell process in the interactive pod container.
kubectl exec -n <namespace> -ti <interactive_pod_name> /bin/bash
Replace <namespace> with the Kubernetes namespace where the pod is running.
-
Run the partition reassignment using the
kafka-reassign-partitions.sh
script from the interactive pod container.bin/kafka-reassign-partitions.sh --bootstrap-server <cluster_name>-kafka-bootstrap:9093 \ --command-config /tmp/config.properties \ --reassignment-json-file /tmp/reassignment.json \ --execute
Replace <cluster_name> with the name of your Kafka cluster. For example,
my-cluster-kafka-bootstrap:9093
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:bin/kafka-reassign-partitions.sh --bootstrap-server <cluster_name>-kafka-bootstrap:9093 \ --command-config /tmp/config.properties \ --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 with a different throttled rate. For example:
bin/kafka-reassign-partitions.sh --bootstrap-server <cluster_name>-kafka-bootstrap:9093 \ --command-config /tmp/config.properties \ --reassignment-json-file /tmp/reassignment.json \ --throttle 10000000 \ --execute
-
Verify that 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.bin/kafka-reassign-partitions.sh --bootstrap-server <cluster_name>-kafka-bootstrap:9093 \ --command-config /tmp/config.properties \ --reassignment-json-file /tmp/reassignment.json \ --verify
The reassignment has finished when the
--verify
command reports that each of the partitions being moved has 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.
14.4. Reassigning partitions before removing brokers
Use a reassignment file generated by the kafka-reassign-partitions.sh
tool to reassign partitions before decreasing the number of brokers in a Kafka cluster.
The reassignment file must describe how partitions are reassigned to the remaining brokers in the Kafka cluster.
You apply the reassignment specified in the file to the brokers and then verify the new partition assignments.
Brokers in the highest numbered pods are removed first.
This procedure describes a secure scaling process that uses TLS. You’ll need a Kafka cluster that uses TLS encryption and mTLS authentication.
Note
|
Though you can use the kafka-reassign-partitions.sh tool, Cruise Control is recommended for automated partition reassignments and cluster rebalancing.
Cruise Control can move topics from one broker to another without any downtime, and it is the most efficient way to reassign partitions.
|
-
You have a running Kafka cluster based on a
Kafka
resource configured with internal TLS encryption and mTLS authentication. -
You have generated a reassignment JSON file named
reassignment.json
. -
You are running an interactive pod container that is connected to the running Kafka broker.
-
You are connected as a
KafkaUser
configured with ACL rules that specify permission to manage the Kafka cluster and its topics.
-
If you haven’t done so, run an interactive pod container to generate a reassignment JSON file named
reassignment.json
. -
Copy the
reassignment.json
file to the interactive pod container.kubectl cp reassignment.json <interactive_pod_name>:/tmp/reassignment.json
Replace <interactive_pod_name> with the name of the pod.
-
Start a shell process in the interactive pod container.
kubectl exec -n <namespace> -ti <interactive_pod_name> /bin/bash
Replace <namespace> with the Kubernetes namespace where the pod is running.
-
Run the partition reassignment using the
kafka-reassign-partitions.sh
script from the interactive pod container.bin/kafka-reassign-partitions.sh --bootstrap-server <cluster_name>-kafka-bootstrap:9093 \ --command-config /tmp/config.properties \ --reassignment-json-file /tmp/reassignment.json \ --execute
Replace <cluster_name> with the name of your Kafka cluster. For example,
my-cluster-kafka-bootstrap:9093
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:bin/kafka-reassign-partitions.sh --bootstrap-server <cluster_name>-kafka-bootstrap:9093 \ --command-config /tmp/config.properties \ --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 with a different throttled rate. For example:
bin/kafka-reassign-partitions.sh --bootstrap-server <cluster_name>-kafka-bootstrap:9093 \ --command-config /tmp/config.properties \ --reassignment-json-file /tmp/reassignment.json \ --throttle 10000000 \ --execute
-
Verify that 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.bin/kafka-reassign-partitions.sh --bootstrap-server <cluster_name>-kafka-bootstrap:9093 \ --command-config /tmp/config.properties \ --reassignment-json-file /tmp/reassignment.json \ --verify
The reassignment has finished when the
--verify
command reports that each of the partitions being moved has 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.
-
When all the partition reassignments have finished, the brokers 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-z0-9]-delete$
, the broker still has live partitions and 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 pods 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.
-
When you have confirmed that the broker has no live partitions, you can edit the
Kafka.spec.kafka.replicas
property of yourKafka
resource to reduce the number of brokers.
14.5. Changing the replication factor of topics
To change the replication factor of topics in a Kafka cluster, use the kafka-reassign-partitions.sh
tool.
This can be done by running the tool from an interactive pod container that is connected to the Kafka cluster,
and using a reassignment file to describe how the topic replicas should be changed.
This procedure describes a secure process that uses TLS. You’ll need a Kafka cluster that uses TLS encryption and mTLS authentication.
-
You have a running Kafka cluster based on a
Kafka
resource configured with internal TLS encryption and mTLS authentication. -
You are running an interactive pod container that is connected to the running Kafka broker.
-
You have generated a reassignment JSON file named
reassignment.json
. -
You are connected as a
KafkaUser
configured with ACL rules that specify permission to manage the Kafka cluster and its topics.
In this procedure, a topic called my-topic
has 4 replicas and we want to reduce it to 3.
A JSON file named topics.json
specifies the topic, and was used to generate the reassignment.json
file.
my-topic
{
"version": 1,
"topics": [
{ "topic": "my-topic"}
]
}
-
If you haven’t done so, run an interactive pod container to generate a reassignment JSON file named
reassignment.json
.Example reassignment JSON file showing the current and proposed replica assignmentCurrent partition replica assignment {"version":1,"partitions":[{"topic":"my-topic","partition":0,"replicas":[3,4,2,0],"log_dirs":["any","any","any","any"]},{"topic":"my-topic","partition":1,"replicas":[0,2,3,1],"log_dirs":["any","any","any","any"]},{"topic":"my-topic","partition":2,"replicas":[1,3,0,4],"log_dirs":["any","any","any","any"]}]} Proposed partition reassignment configuration {"version":1,"partitions":[{"topic":"my-topic","partition":0,"replicas":[0,1,2,3],"log_dirs":["any","any","any","any"]},{"topic":"my-topic","partition":1,"replicas":[1,2,3,4],"log_dirs":["any","any","any","any"]},{"topic":"my-topic","partition":2,"replicas":[2,3,4,0],"log_dirs":["any","any","any","any"]}]}
Save a copy of this file locally in case you need to revert the changes later on.
-
Edit the
reassignment.json
to remove a replica from each partition.For example use
jq
to remove the last replica in the list for each partition of the topic:Removing the last topic replica for each partitionjq '.partitions[].replicas |= del(.[-1])' reassignment.json > reassignment.json
Example reassignment file showing the updated replicas{"version":1,"partitions":[{"topic":"my-topic","partition":0,"replicas":[0,1,2],"log_dirs":["any","any","any","any"]},{"topic":"my-topic","partition":1,"replicas":[1,2,3],"log_dirs":["any","any","any","any"]},{"topic":"my-topic","partition":2,"replicas":[2,3,4],"log_dirs":["any","any","any","any"]}]}
-
Copy the
reassignment.json
file to the interactive pod container.kubectl cp reassignment.json <interactive_pod_name>:/tmp/reassignment.json
Replace <interactive_pod_name> with the name of the pod.
-
Start a shell process in the interactive pod container.
kubectl exec -n <namespace> -ti <interactive_pod_name> /bin/bash
Replace <namespace> with the Kubernetes namespace where the pod is running.
-
Make the topic replica change using the
kafka-reassign-partitions.sh
script from the interactive pod container.bin/kafka-reassign-partitions.sh --bootstrap-server <cluster_name>-kafka-bootstrap:9093 \ --command-config /tmp/config.properties \ --reassignment-json-file /tmp/reassignment.json \ --execute
NoteRemoving replicas from a broker does not require any inter-broker data movement, so there is no need to throttle replication. If you are adding replicas, then you may want to change the throttle rate. -
Verify that the change to the topic replicas 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.bin/kafka-reassign-partitions.sh --bootstrap-server <cluster_name>-kafka-bootstrap:9093 \ --command-config /tmp/config.properties \ --reassignment-json-file /tmp/reassignment.json \ --verify
The reassignment has finished when the
--verify
command reports that each of the partitions being moved has completed successfully. This final--verify
will also have the effect of removing any reassignment throttles. -
Run the
bin/kafka-topics.sh
command with the--describe
option to see the results of the change to the topics.bin/kafka-topics.sh --bootstrap-server <cluster_name>-kafka-bootstrap:9093 \ --command-config /tmp/config.properties \ --describe
Results of reducing the number of replicas for a topicmy-topic Partition: 0 Leader: 0 Replicas: 0,1,2 Isr: 0,1,2 my-topic Partition: 1 Leader: 2 Replicas: 1,2,3 Isr: 1,2,3 my-topic Partition: 2 Leader: 3 Replicas: 2,3,4 Isr: 2,3,4
15. Using Strimzi Operators
Use the Strimzi operators to manage your Kafka cluster, and Kafka topics and users.
15.1. Watching namespaces with Strimzi operators
Operators watch and manage Strimzi resources in namespaces. The Cluster Operator can watch a single namespace, multiple namespaces, or all namespaces in a Kubernetes cluster. The Topic Operator and User Operator can watch a single namespace.
-
The Cluster Operator watches for
Kafka
resources -
The Topic Operator watches for
KafkaTopic
resources -
The User Operator watches for
KafkaUser
resources
The Topic Operator and the User Operator can only watch a single Kafka cluster in a namespace. And they can only be connected to a single Kafka cluster.
If multiple Topic Operators watch the same namespace, name collisions and topic deletion can occur.
This is because each Kafka cluster uses Kafka topics that have the same name (such as __consumer_offsets
).
Make sure that only one Topic Operator watches a given namespace.
When using multiple User Operators with a single namespace, a user with a given username can exist in more than one Kafka cluster.
If you deploy the Topic Operator and User Operator using the Cluster Operator, they watch the Kafka cluster deployed by the Cluster Operator by default.
You can also specify a namespace using watchedNamespace
in the operator configuration.
For a standalone deployment of each operator, you specify a namespace and connection to the Kafka cluster to watch in the configuration.
15.2. Using the Cluster Operator
Use the Cluster Operator to deploy a Kafka cluster and other Kafka components.
15.2.1. Role-Based Access Control (RBAC) resources
The Cluster Operator creates and manages RBAC resources for Strimzi components that need access to Kubernetes resources.
For the Cluster Operator to function, it needs permission within the Kubernetes cluster to interact with Kafka resources, such as Kafka
and KafkaConnect
, as well as managed resources like ConfigMap
, Pod
, Deployment
, and Service
.
Permission is specified through Kubernetes role-based access control (RBAC) resources:
-
ServiceAccount
-
Role
andClusterRole
-
RoleBinding
andClusterRoleBinding
Delegating privileges to Strimzi components
The Cluster Operator runs under a service account called strimzi-cluster-operator
.
It is assigned cluster roles that give it permission to create the RBAC resources for Strimzi components.
Role bindings associate the cluster roles with the service account.
Kubernetes prevents components operating under one ServiceAccount
from granting another ServiceAccount
privileges that the granting ServiceAccount
does not have.
Because the Cluster Operator creates the RoleBinding
and ClusterRoleBinding
RBAC resources needed by the resources it manages, it requires a role that gives it the same privileges.
The following tables describe the RBAC resources created by the Cluster Operator.
Name | Used by |
---|---|
|
Kafka broker pods |
|
ZooKeeper pods |
|
Kafka Connect pods |
|
MirrorMaker pods |
|
MirrorMaker 2 pods |
|
Kafka Bridge pods |
|
Entity Operator |
Name | Used by |
---|---|
|
Cluster Operator |
|
Cluster Operator |
|
Cluster Operator |
|
Cluster Operator, rack feature (when used) |
|
Cluster Operator, Topic Operator, User Operator |
|
Cluster Operator, Kafka clients for rack awareness |
Name | Used by |
---|---|
|
Cluster Operator |
|
Cluster Operator, Kafka brokers for rack awareness |
|
Cluster Operator, Kafka clients for rack awareness |
Name | Used by |
---|---|
|
Cluster Operator |
|
Cluster Operator, Kafka brokers for rack awareness |
Running the Cluster Operator using a 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 strimzi-cluster-operator
is specified as the serviceAccountName
.
ClusterRole
resources
The Cluster Operator uses ClusterRole
resources to provide the necessary access to resources.
Depending on the Kubernetes cluster setup, a cluster administrator might be needed to create the cluster roles.
Note
|
Cluster administrator rights are only needed for the creation of ClusterRole resources.
The Cluster Operator will not run under a cluster admin account.
|
ClusterRole
resources follow the principle of least privilege and contain only those privileges needed by the Cluster Operator to operate the cluster of the Kafka component. The first set of assigned privileges allow the Cluster Operator to manage Kubernetes resources such as Deployment
, Pod
, and ConfigMap
.
All cluster roles are required by the Cluster Operator in order to delegate privileges.
The Cluster Operator uses the strimzi-cluster-operator-namespaced
and strimzi-cluster-operator-global
cluster roles 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:
# Resources in this role are used by the operator based on an operand being deployed in some namespace. When needed, you
# can deploy the operator as a cluster-wide operator. But grant the rights listed in this role only on the namespaces
# where the operands will be deployed. That way, you can limit the access the operator has to other namespaces where it
# does not manage any clusters.
- apiGroups:
- "rbac.authorization.k8s.io"
resources:
# The cluster operator needs to access and manage rolebindings to grant Strimzi components cluster permissions
- rolebindings
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
- apiGroups:
- "rbac.authorization.k8s.io"
resources:
# The cluster operator needs to access and manage roles to grant the entity operator permissions
- roles
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
- apiGroups:
- ""
resources:
# The cluster operator needs to access and delete pods, this is to allow it to monitor pod health and coordinate rolling updates
- pods
# The cluster operator needs to access and manage service accounts to grant Strimzi components cluster permissions
- serviceaccounts
# The cluster operator needs to access and manage config maps for Strimzi components configuration
- configmaps
# The cluster operator needs to access and manage services and endpoints to expose Strimzi components to network traffic
- services
- endpoints
# The cluster operator needs to access and manage secrets to handle credentials
- secrets
# The cluster operator needs to access and manage persistent volume claims to bind them to Strimzi components for persistent data
- persistentvolumeclaims
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
- apiGroups:
- "apps"
resources:
# The cluster operator needs to access and manage deployments to run deployment based Strimzi components
- deployments
- deployments/scale
- deployments/status
# The cluster operator needs to access and manage stateful sets to run stateful sets based Strimzi components
- statefulsets
# The cluster operator needs to access replica-sets to manage Strimzi components and to determine error states
- replicasets
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
- apiGroups:
- "" # legacy core events api, used by topic operator
- "events.k8s.io" # new events api, used by cluster operator
resources:
# The cluster operator needs to be able to create events and delegate permissions to do so
- events
verbs:
- create
- apiGroups:
# Kafka Connect Build on OpenShift requirement
- build.openshift.io
resources:
- buildconfigs
- buildconfigs/instantiate
- builds
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
- apiGroups:
- networking.k8s.io
resources:
# The cluster operator needs to access and manage network policies to lock down communication between Strimzi components
- networkpolicies
# The cluster operator needs to access and manage ingresses which allow external access to the services in a cluster
- ingresses
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
- apiGroups:
- route.openshift.io
resources:
# The cluster operator needs to access and manage routes to expose Strimzi components for external access
- routes
- routes/custom-host
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
- apiGroups:
- image.openshift.io
resources:
# The cluster operator needs to verify the image stream when used for Kafka Connect image build
- imagestreams
verbs:
- get
- apiGroups:
- policy
resources:
# The cluster operator needs to access and manage pod disruption budgets this limits the number of concurrent disruptions
# that a Strimzi component experiences, allowing for higher availability
- poddisruptionbudgets
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
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:
# The cluster operator needs to create and manage cluster role bindings in the case of an install where a user
# has specified they want their cluster role bindings generated
- clusterrolebindings
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
- apiGroups:
- storage.k8s.io
resources:
# The cluster operator requires "get" permissions to view storage class details
# This is because only a persistent volume of a supported storage class type can be resized
- storageclasses
verbs:
- get
- apiGroups:
- ""
resources:
# The cluster operator requires "list" permissions to view all nodes in a cluster
# The listing is used to determine the node addresses when NodePort access is configured
# These addresses are then exposed in the custom resource states
- nodes
verbs:
- list
The strimzi-cluster-operator-leader-election
cluster role represents the permissions needed for the leader election.
ClusterRole
with leader election permissionsapiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: strimzi-cluster-operator-leader-election
labels:
app: strimzi
rules:
- apiGroups:
- coordination.k8s.io
resources:
# The cluster operator needs to access and manage leases for leader election
# The "create" verb cannot be used with "resourceNames"
- leases
verbs:
- create
- apiGroups:
- coordination.k8s.io
resources:
# The cluster operator needs to access and manage leases for leader election
- leases
resourceNames:
# The default RBAC files give the operator only access to the Lease resource names strimzi-cluster-operator
# If you want to use another resource name or resource namespace, you have to configure the RBAC resources accordingly
- strimzi-cluster-operator
verbs:
- get
- list
- watch
- delete
- patch
- update
The strimzi-kafka-broker
cluster role represents the access needed by the init container in Kafka pods that use rack awareness.
A role binding named strimzi-<cluster_name>-kafka-init
grants the <cluster_name>-kafka
service account access to nodes within a cluster using the strimzi-kafka-broker
role.
If the rack feature is not used and the cluster is not exposed through nodeport
, no binding is created.
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:
# The Kafka Brokers require "get" permissions to view the node they are on
# This information is used to generate a Rack ID that is used for High Availability configurations
- nodes
verbs:
- get
The strimzi-entity-operator
cluster role represents the access needed by the Topic Operator and User Operator.
The Topic Operator produces Kubernetes events with status information, so the <cluster_name>-entity-operator
service account is bound to the strimzi-entity-operator
role, which grants this access via the strimzi-entity-operator
role binding.
ClusterRole
for the Cluster Operator allowing it to delegate access to events to the Topic and User OperatorsapiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: strimzi-entity-operator
labels:
app: strimzi
rules:
- apiGroups:
- "kafka.strimzi.io"
resources:
# The entity operator runs the KafkaTopic assembly operator, which needs to access and manage KafkaTopic resources
- kafkatopics
- kafkatopics/status
# The entity operator runs the KafkaUser assembly operator, which needs to access and manage KafkaUser resources
- kafkausers
- kafkausers/status
verbs:
- get
- list
- watch
- create
- patch
- update
- delete
- apiGroups:
- ""
resources:
- events
verbs:
# The entity operator needs to be able to create events
- create
- apiGroups:
- ""
resources:
# The entity operator user-operator needs to access and manage secrets to store generated credentials
- secrets
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
The strimzi-kafka-client
cluster role represents the access needed by Kafka clients that use rack awareness.
ClusterRole
for the Cluster Operator allowing it to delegate access to Kubernetes nodes to the Kafka client-based podsapiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: strimzi-kafka-client
labels:
app: strimzi
rules:
- apiGroups:
- ""
resources:
# The Kafka clients (Connect, Mirror Maker, etc.) require "get" permissions to view the node they are on
# This information is used to generate a Rack ID (client.rack option) that is used for consuming from the closest
# replicas when enabled
- nodes
verbs:
- get
ClusterRoleBinding
resources
The Cluster Operator uses ClusterRoleBinding
and RoleBinding
resources to associate its ClusterRole
with its ServiceAccount
:
Cluster role bindings are required by cluster roles 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
Cluster role bindings are also needed for the cluster roles used in delegating privileges:
ClusterRoleBinding
for the Cluster Operator and Kafka broker rack awarenessapiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
name: strimzi-cluster-operator-kafka-broker-delegation
labels:
app: strimzi
# The Kafka broker cluster role must be bound to the cluster operator service account so that it can delegate the cluster role to the Kafka brokers.
# This must be done to avoid escalating privileges which would be blocked by Kubernetes.
subjects:
- kind: ServiceAccount
name: strimzi-cluster-operator
namespace: myproject
roleRef:
kind: ClusterRole
name: strimzi-kafka-broker
apiGroup: rbac.authorization.k8s.io
ClusterRoleBinding
for the Cluster Operator and Kafka client rack awarenessapiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
name: strimzi-cluster-operator-kafka-client-delegation
labels:
app: strimzi
# The Kafka clients cluster role must be bound to the cluster operator service account so that it can delegate the
# cluster role to the Kafka clients using it for consuming from closest replica.
# This must be done to avoid escalating privileges which would be blocked by Kubernetes.
subjects:
- kind: ServiceAccount
name: strimzi-cluster-operator
namespace: myproject
roleRef:
kind: ClusterRole
name: strimzi-kafka-client
apiGroup: rbac.authorization.k8s.io
Cluster roles containing only namespaced resources are bound using role bindings only.
RoleBinding
for the Cluster OperatorapiVersion: 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
RoleBinding
for the Cluster Operator and Kafka broker rack awarenessapiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
name: strimzi-cluster-operator-entity-operator-delegation
labels:
app: strimzi
# The Entity Operator cluster role must be bound to the cluster operator service account so that it can delegate the cluster role to the Entity Operator.
# This must be done to avoid escalating privileges which would be blocked by Kubernetes.
subjects:
- kind: ServiceAccount
name: strimzi-cluster-operator
namespace: myproject
roleRef:
kind: ClusterRole
name: strimzi-entity-operator
apiGroup: rbac.authorization.k8s.io
15.2.2. ConfigMap for Cluster Operator logging
Cluster Operator logging is configured through a ConfigMap
named strimzi-cluster-operator
.
A ConfigMap
containing logging configuration is created when installing the Cluster Operator.
This ConfigMap
is described in the file install/cluster-operator/050-ConfigMap-strimzi-cluster-operator.yaml
.
You configure Cluster Operator logging by changing the data field log4j2.properties
in this ConfigMap
.
To update the logging configuration, you can edit the 050-ConfigMap-strimzi-cluster-operator.yaml
file and then run the following command:
kubectl create -f install/cluster-operator/050-ConfigMap-strimzi-cluster-operator.yaml
Alternatively, edit the ConfigMap
directly:
kubectl edit configmap strimzi-cluster-operator
To change the frequency of the reload interval, set a time in seconds in the monitorInterval
option in the created ConfigMap
.
If the ConfigMap
is missing when the Cluster Operator is deployed, the default logging values are used.
If the ConfigMap
is accidentally deleted after the Cluster Operator is deployed, the most recently loaded logging configuration is used.
Create a new ConfigMap
to load a new logging configuration.
Note
|
Do not remove the monitorInterval option from the ConfigMap .
|
15.2.3. Configuring the Cluster Operator with environment variables
You can configure the Cluster Operator using environment variables. The supported environment variables are listed here.
Note
|
The environment variables are specified for the container image of the Cluster Operator in its Deployment configuration file.
(install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml )
|
STRIMZI_NAMESPACE
-
A comma-separated list of namespaces that the operator operates in. When not set, set to empty string, or set to
*
, the Cluster Operator operates in all namespaces.The Cluster Operator deployment might use the downward API to set this automatically to the namespace the Cluster Operator is deployed in.
Example configuration for Cluster Operator namespacesenv: - 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_OPERATION_TIMEOUT_MS
-
Optional, default 300000 ms. The timeout for internal operations, in milliseconds. Increase this value when using Strimzi on clusters where regular Kubernetes operations take longer than usual (because of slow downloading of Docker images, for example).
STRIMZI_ZOOKEEPER_ADMIN_SESSION_TIMEOUT_MS
-
Optional, default 10000 ms. The session timeout for the Cluster Operator’s ZooKeeper admin client, in milliseconds. Increase the value if ZooKeeper requests from the Cluster Operator are regularly failing due to timeout issues. There is a maximum allowed session time set on the ZooKeeper server side via the
maxSessionTimeout
config. By default, the maximum session timeout value is 20 times the defaulttickTime
(whose default is 2000) at 40000 ms. If you require a higher timeout, change themaxSessionTimeout
ZooKeeper server configuration value. STRIMZI_OPERATIONS_THREAD_POOL_SIZE
-
Optional, default 10. The worker thread pool size, which is used for various asynchronous and blocking operations that are run by the Cluster Operator.
STRIMZI_OPERATOR_NAME
-
Optional, defaults to the pod’s hostname. The operator name identifies the Strimzi instance when emitting Kubernetes events.
STRIMZI_OPERATOR_NAMESPACE
-
The name of the namespace where the Cluster Operator is running. Do not configure this variable manually. Use the downward API.
env: - name: STRIMZI_OPERATOR_NAMESPACE valueFrom: fieldRef: fieldPath: metadata.namespace
STRIMZI_OPERATOR_NAMESPACE_LABELS
-
Optional. The labels of the namespace where the Strimzi Cluster Operator is running. Use namespace labels to configure the namespace selector in network policies. Network policies allow the Strimzi Cluster Operator access only to the operands from the namespace with these labels. When not set, the namespace selector in network policies is configured to allow access to the Cluster Operator from any namespace in the Kubernetes cluster.
env: - name: STRIMZI_OPERATOR_NAMESPACE_LABELS value: label1=value1,label2=value2
STRIMZI_LABELS_EXCLUSION_PATTERN
-
Optional, default regex pattern is
^app.kubernetes.io/(?!part-of).*
. The regex exclusion pattern used to filter labels propagation from the main custom resource to its subresources. The labels exclusion filter is not applied to labels in template sections such asspec.kafka.template.pod.metadata.labels
.env: - name: STRIMZI_LABELS_EXCLUSION_PATTERN value: "^key1.*"
STRIMZI_CUSTOM_{COMPONENT_NAME}_LABELS
-
Optional. One or more custom labels to apply to all the pods created by the
{COMPONENT_NAME}
custom resource. The Cluster Operator labels the pods when the custom resource is created or is next reconciled.Labels can be applied to the following components:
-
KAFKA
-
KAFKA_CONNECT
-
KAFKA_CONNECT_BUILD
-
ZOOKEEPER
-
ENTITY_OPERATOR
-
KAFKA_MIRROR_MAKER2
-
KAFKA_MIRROR_MAKER
-
CRUISE_CONTROL
-
KAFKA_BRIDGE
-
KAFKA_EXPORTER
-
STRIMZI_CUSTOM_RESOURCE_SELECTOR
-
Optional. The label selector to filter the custom resources handled by the Cluster Operator. The operator will operate only on those custom resources that have the specified labels set. Resources without these labels will not be seen by the operator. The label selector applies to
Kafka
,KafkaConnect
,KafkaBridge
,KafkaMirrorMaker
, andKafkaMirrorMaker2
resources.KafkaRebalance
andKafkaConnector
resources are operated only when their corresponding Kafka and Kafka Connect clusters have the matching labels.env: - name: STRIMZI_CUSTOM_RESOURCE_SELECTOR value: label1=value1,label2=value2
STRIMZI_KAFKA_IMAGES
-
Required. The mapping from the Kafka version to the corresponding Docker image containing a Kafka broker for that version. The required syntax is whitespace or comma-separated
<version>=<image>
pairs. For example3.3.1=quay.io/strimzi/kafka:0.35.1-kafka-3.3.1, 3.4.0=quay.io/strimzi/kafka:0.35.1-kafka-3.4.0
. This is used when aKafka.spec.kafka.version
property is specified but not theKafka.spec.kafka.image
in theKafka
resource. STRIMZI_DEFAULT_KAFKA_INIT_IMAGE
-
Optional, default
quay.io/strimzi/operator:0.35.1
. The image name to use as default for the init container if no image is specified as thekafka-init-image
in theKafka
resource. The init container is started before the broker for initial configuration work, such as rack support. STRIMZI_KAFKA_CONNECT_IMAGES
-
Required. The mapping from the Kafka version to the corresponding Docker image of Kafka Connect for that version. The required syntax is whitespace or comma-separated
<version>=<image>
pairs. For example3.3.1=quay.io/strimzi/kafka:0.35.1-kafka-3.3.1, 3.4.0=quay.io/strimzi/kafka:0.35.1-kafka-3.4.0
. This is used when aKafkaConnect.spec.version
property is specified but not theKafkaConnect.spec.image
. STRIMZI_KAFKA_MIRROR_MAKER_IMAGES
-
Required. The mapping from the Kafka version to the corresponding Docker image of MirrorMaker for that version. The required syntax is whitespace or comma-separated
<version>=<image>
pairs. For example3.3.1=quay.io/strimzi/kafka:0.35.1-kafka-3.3.1, 3.4.0=quay.io/strimzi/kafka:0.35.1-kafka-3.4.0
. This is used when aKafkaMirrorMaker.spec.version
property is specified but not theKafkaMirrorMaker.spec.image
. STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE
-
Optional, default
quay.io/strimzi/operator:0.35.1
. 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 theKafka
resource. STRIMZI_DEFAULT_USER_OPERATOR_IMAGE
-
Optional, default
quay.io/strimzi/operator:0.35.1
. 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 theKafka
resource. STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGE
-
Optional, default
quay.io/strimzi/kafka:0.35.1-kafka-3.4.0
. The image name to use as the default when deploying the sidecar container for the Entity Operator if no image is specified as theKafka.spec.entityOperator.tlsSidecar.image
in theKafka
resource. The sidecar provides TLS support. STRIMZI_IMAGE_PULL_POLICY
-
Optional. The
ImagePullPolicy
that is applied to containers in all pods managed by the Cluster Operator. The valid values areAlways
,IfNotPresent
, andNever
. If not specified, the Kubernetes defaults are used. Changing the policy will result in a rolling update of all your Kafka, Kafka Connect, and Kafka MirrorMaker clusters. STRIMZI_IMAGE_PULL_SECRETS
-
Optional. A comma-separated list of
Secret
names. The secrets referenced here contain the credentials to the container registries where the container images are pulled from. The secrets are specified in theimagePullSecrets
property for all pods created by the Cluster Operator. Changing this list results in a rolling update of all your Kafka, Kafka Connect, and Kafka MirrorMaker clusters. STRIMZI_KUBERNETES_VERSION
-
Optional. Overrides the Kubernetes version information detected from the API server.
Example configuration for Kubernetes version overrideenv: - name: STRIMZI_KUBERNETES_VERSION value: | major=1 minor=16 gitVersion=v1.16.2 gitCommit=c97fe5036ef3df2967d086711e6c0c405941e14b gitTreeState=clean buildDate=2019-10-15T19:09:08Z goVersion=go1.12.10 compiler=gc platform=linux/amd64
KUBERNETES_SERVICE_DNS_DOMAIN
-
Optional. Overrides the default Kubernetes DNS domain name suffix.
By default, services assigned in the Kubernetes cluster have a DNS domain name that uses the default suffix
cluster.local
.For example, for broker kafka-0:
<cluster-name>-kafka-0.<cluster-name>-kafka-brokers.<namespace>.svc.cluster.local
The DNS domain name is added to the Kafka broker certificates used for hostname verification.
If you are using a different DNS domain name suffix in your cluster, change the
KUBERNETES_SERVICE_DNS_DOMAIN
environment variable from the default to the one you are using in order to establish a connection with the Kafka brokers. STRIMZI_CONNECT_BUILD_TIMEOUT_MS
-
Optional, default 300000 ms. The timeout for building new Kafka Connect images with additional connectors, in milliseconds. Consider increasing this value when using Strimzi to build container images containing many connectors or using a slow container registry.
STRIMZI_NETWORK_POLICY_GENERATION
-
Optional, default
true
. Network policy for resources. Network policies allow connections between Kafka components.Set this environment variable to
false
to disable network policy generation. You might do this, for example, if you want to use custom network policies. Custom network policies allow more control over maintaining the connections between components. STRIMZI_DNS_CACHE_TTL
-
Optional, default
30
. Number of seconds to cache successful name lookups in local DNS resolver. Any negative value means cache forever. Zero means do not cache, which can be useful for avoiding connection errors due to long caching policies being applied. STRIMZI_POD_SET_RECONCILIATION_ONLY
-
Optional, default
false
. When set totrue
, the Cluster Operator reconciles only theStrimziPodSet
resources and any changes to the other custom resources (Kafka
,KafkaConnect
, and so on) are ignored. This mode is useful for ensuring that your pods are recreated if needed, but no other changes happen to the clusters. STRIMZI_FEATURE_GATES
-
Optional. Enables or disables the features and functionality controlled by feature gates.
STRIMZI_POD_SECURITY_PROVIDER_CLASS
-
Optional. Configuration for the pluggable
PodSecurityProvider
class, which can be used to provide the security context configuration for Pods and containers.
Leader election environment variables
Use leader election environment variables when running additional Cluster Operator replicas. You might run additional replicas to safeguard against disruption caused by major failure.
STRIMZI_LEADER_ELECTION_ENABLED
-
Optional, disabled (
false
) by default. Enables or disables leader election, which allows additional Cluster Operator replicas to run on standby.
Note
|
Leader election is disabled by default. It is only enabled when applying this environment variable on installation. |
STRIMZI_LEADER_ELECTION_LEASE_NAME
-
Required when leader election is enabled. The name of the Kubernetes
Lease
resource that is used for the leader election. STRIMZI_LEADER_ELECTION_LEASE_NAMESPACE
-
Required when leader election is enabled. The namespace where the Kubernetes
Lease
resource used for leader election is created. You can use the downward API to configure it to the namespace where the Cluster Operator is deployed.env: - name: STRIMZI_LEADER_ELECTION_LEASE_NAMESPACE valueFrom: fieldRef: fieldPath: metadata.namespace
STRIMZI_LEADER_ELECTION_IDENTITY
-
Required when leader election is enabled. Configures the identity of a given Cluster Operator instance used during the leader election. The identity must be unique for each operator instance. You can use the downward API to configure it to the name of the pod where the Cluster Operator is deployed.
env: - name: STRIMZI_LEADER_ELECTION_IDENTITY valueFrom: fieldRef: fieldPath: metadata.name
STRIMZI_LEADER_ELECTION_LEASE_DURATION_MS
-
Optional, default 15000 ms. Specifies the duration the acquired lease is valid.
STRIMZI_LEADER_ELECTION_RENEW_DEADLINE_MS
-
Optional, default 10000 ms. Specifies the period the leader should try to maintain leadership.
STRIMZI_LEADER_ELECTION_RETRY_PERIOD_MS
-
Optional, default 2000 ms. Specifies the frequency of updates to the lease lock by the leader.
Restricting Cluster Operator access with network policy
Use the STRIMZI_OPERATOR_NAMESPACE_LABELS
environment variable to establish network policy for the Cluster Operator using namespace labels.
The Cluster Operator can run in the same namespace as the resources it manages, or in a separate namespace.
By default, the STRIMZI_OPERATOR_NAMESPACE
environment variable is configured to use the downward API to find the namespace the Cluster Operator is running in.
If the Cluster Operator is running in the same namespace as the resources, only local access is required and allowed by Strimzi.
If the Cluster Operator is running in a separate namespace to the resources it manages, any namespace in the Kubernetes cluster is allowed access to the Cluster Operator unless network policy is configured. By adding namespace labels, access to the Cluster Operator is restricted to the namespaces specified.
#...
env:
# ...
- name: STRIMZI_OPERATOR_NAMESPACE_LABELS
value: label1=value1,label2=value2
#...
Setting the time interval for periodic reconciliation
Use the STRIMZI_FULL_RECONCILIATION_INTERVAL_MS
variable to set the time interval for periodic reconciliations.
The Cluster Operator reacts to all notifications about applicable cluster resources received from the Kubernetes cluster. If the operator is not running, or if a notification is not received for any reason, 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 resources with the current cluster deployments in order to have a consistent state across all of them.
15.2.4. Configuring the Cluster Operator with default proxy settings
If you are running a Kafka cluster behind a HTTP proxy, you can still pass data in and out of the cluster. For example, you can run Kafka Connect with connectors that push and pull data from outside the proxy. Or you can use a proxy to connect with an authorization server.
Configure the Cluster Operator deployment to specify the proxy environment variables.
The Cluster Operator accepts standard proxy configuration (HTTP_PROXY
, HTTPS_PROXY
and NO_PROXY
) as environment variables.
The proxy settings are applied to all Strimzi containers.
The format for a proxy address is http://IP-ADDRESS:PORT-NUMBER. To set up a proxy with a name and password, the format is http://USERNAME:PASSWORD@IP-ADDRESS:PORT-NUMBER.
-
You need an account with permission to create and manage
CustomResourceDefinition
and RBAC (ClusterRole
, andRoleBinding
) resources.
-
To add proxy environment variables to the Cluster Operator, update its
Deployment
configuration (install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml
).Example proxy configuration for the Cluster OperatorapiVersion: apps/v1 kind: Deployment spec: # ... template: spec: serviceAccountName: strimzi-cluster-operator containers: # ... env: # ... - name: "HTTP_PROXY" value: "http://proxy.com" (1) - name: "HTTPS_PROXY" value: "https://proxy.com" (2) - name: "NO_PROXY" value: "internal.com, other.domain.com" (3) # ...
-
Address of the proxy server.
-
Secure address of the proxy server.
-
Addresses for servers that are accessed directly as exceptions to the proxy server. The URLs are comma-separated.
Alternatively, edit the
Deployment
directly:kubectl edit deployment strimzi-cluster-operator
-
-
If you updated the YAML file instead of editing the
Deployment
directly, apply the changes:kubectl create -f install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml
15.2.5. Running multiple Cluster Operator replicas with leader election
The default Cluster Operator configuration enables leader election. Use leader election to run multiple parallel replicas of the Cluster Operator. One replica is elected as the active leader and operates the deployed resources. The other replicas run in standby mode. When the leader stops or fails, one of the standby replicas is elected as the new leader and starts operating the deployed resources.
By default, Strimzi runs with a single Cluster Operator replica that is always the leader replica. When a single Cluster Operator replica stops or fails, Kubernetes starts a new replica.
Running the Cluster Operator with multiple replicas is not essential. But it’s useful to have replicas on standby in case of large-scale disruptions. For example, suppose multiple worker nodes or an entire availability zone fails. This failure might cause the Cluster Operator pod and many Kafka pods to go down at the same time. If subsequent pod scheduling causes congestion through lack of resources, this can delay operations when running a single Cluster Operator.
Configuring Cluster Operator replicas
To run additional Cluster Operator replicas in standby mode, you will need to increase the number of replicas and enable leader election. To configure leader election, use the leader election environment variables.
To make the required changes, configure the following Cluster Operator installation files located in install/cluster-operator/
:
-
060-Deployment-strimzi-cluster-operator.yaml
-
022-ClusterRole-strimzi-cluster-operator-role.yaml
-
022-RoleBinding-strimzi-cluster-operator.yaml
Leader election has its own ClusterRole
and RoleBinding
RBAC resources that target the namespace where the Cluster Operator is running, rather than the namespace it is watching.
The default deployment configuration creates a Lease
resource called strimzi-cluster-operator
in the same namespace as the Cluster Operator.
The Cluster Operator uses leases to manage leader election.
The RBAC resources provide the permissions to use the Lease
resource.
If you use a different Lease
name or namespace, update the ClusterRole
and RoleBinding
files accordingly.
-
You need an account with permission to create and manage
CustomResourceDefinition
and RBAC (ClusterRole
, andRoleBinding
) resources.
Edit the Deployment
resource that is used to deploy the Cluster Operator, which is defined in the 060-Deployment-strimzi-cluster-operator.yaml
file.
-
Change the
replicas
property from the default (1) to a value that matches the required number of replicas.Increasing the number of Cluster Operator replicasapiVersion: apps/v1 kind: Deployment metadata: name: strimzi-cluster-operator labels: app: strimzi spec: replicas: 3
-
Check that the leader election
env
properties are set.If they are not set, configure them.
To enable leader election,
STRIMZI_LEADER_ELECTION_ENABLED
must be set totrue
(default).In this example, the name of the lease is changed to
my-strimzi-cluster-operator
.Configuring leader election environment variables for the Cluster Operator# ... spec containers: - name: strimzi-cluster-operator # ... env: - name: STRIMZI_LEADER_ELECTION_ENABLED value: "true" - name: STRIMZI_LEADER_ELECTION_LEASE_NAME value: "my-strimzi-cluster-operator" - name: STRIMZI_LEADER_ELECTION_LEASE_NAMESPACE valueFrom: fieldRef: fieldPath: metadata.namespace - name: STRIMZI_LEADER_ELECTION_IDENTITY valueFrom: fieldRef: fieldPath: metadata.name
For a description of the available environment variables, see Leader election environment variables.
If you specified a different name or namespace for the
Lease
resource used in leader election, update the RBAC resources. -
(optional) Edit the
ClusterRole
resource in the022-ClusterRole-strimzi-cluster-operator-role.yaml
file.Update
resourceNames
with the name of theLease
resource.Updating the ClusterRole references to the leaseapiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRole metadata: name: strimzi-cluster-operator-leader-election labels: app: strimzi rules: - apiGroups: - coordination.k8s.io resourceNames: - my-strimzi-cluster-operator # ...
-
(optional) Edit the
RoleBinding
resource in the022-RoleBinding-strimzi-cluster-operator.yaml
file.Update
subjects.name
andsubjects.namespace
with the name of theLease
resource and the namespace where it was created.Updating the RoleBinding references to the leaseapiVersion: rbac.authorization.k8s.io/v1 kind: RoleBinding metadata: name: strimzi-cluster-operator-leader-election labels: app: strimzi subjects: - kind: ServiceAccount name: my-strimzi-cluster-operator namespace: myproject # ...
-
Deploy the Cluster Operator:
kubectl create -f install/cluster-operator -n myproject
-
Check the status of the deployment:
kubectl get deployments -n myproject
Output shows the deployment name and readinessNAME READY UP-TO-DATE AVAILABLE strimzi-cluster-operator 3/3 3 3
READY
shows the number of replicas that are ready/expected. The deployment is successful when theAVAILABLE
output shows the correct number of replicas.
15.2.6. FIPS support
Federal Information Processing Standards (FIPS) are standards for computer security and interoperability. When running Strimzi on a FIPS-enabled Kubernetes cluster, the OpenJDK used in Strimzi container images automatically switches to FIPS mode. From version 0.33, Strimzi can run on FIPS-enabled Kubernetes clusters without any changes or special configuration. It uses only the FIPS-compliant security libraries from the OpenJDK.
When running in the FIPS mode, SCRAM-SHA-512 passwords need to be at least 32 characters long. From Strimzi 0.33, the default password length in Strimzi User Operator is set to 32 characters as well. If you have a Kafka cluster with custom configuration that uses a password length that is less than 32 characters, you need to update your configuration. If you have any users with passwords shorter than 32 characters, you need to regenerate a password with the required length. You can do that, for example, by deleting the user secret and waiting for the User Operator to create a new password with the appropriate length.
Important
|
If you are using FIPS-enabled Kubernetes clusters, you may experience higher memory consumption compared to regular Kubernetes clusters. To avoid any issues, we suggest increasing the memory request to at least 512Mi. |
Disabling FIPS mode
Strimzi automatically switches to FIPS mode when running on a FIPS-enabled Kubernetes cluster.
Disable FIPS mode by setting the FIPS_MODE
environment variable to disabled
in the deployment configuration for the Cluster Operator.
With FIPS mode disabled, Strimzi automatically disables FIPS in the OpenJDK for all components.
With FIPS mode disabled, Strimzi is not FIPS compliant.
The Strimzi operators, as well as all operands, run in the same way as if they were running on an Kubernetes cluster without FIPS enabled.
-
To disable the FIPS mode in the Cluster Operator, update its
Deployment
configuration (install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml
) and add theFIPS_MODE
environment variable.Example FIPS configuration for the Cluster OperatorapiVersion: apps/v1 kind: Deployment spec: # ... template: spec: serviceAccountName: strimzi-cluster-operator containers: # ... env: # ... - name: "FIPS_MODE" value: "disabled" # (1) # ...
-
Disables the FIPS mode.
Alternatively, edit the
Deployment
directly:kubectl edit deployment strimzi-cluster-operator
-
-
If you updated the YAML file instead of editing the
Deployment
directly, apply the changes:kubectl apply -f install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml
15.3. Using the Topic Operator
When you create, modify or delete a topic using the KafkaTopic
resource,
the Topic Operator ensures those changes are reflected in the Kafka cluster.
For more information on the KafkaTopic
resource, see the KafkaTopic
schema reference.
You can deploy the Topic Operator using the Cluster Operator or as a standalone operator. You would use a standalone Topic Operator with a Kafka cluster that is not managed by the Cluster Operator.
For deployment instructions, see the following:
Important
|
To deploy the standalone Topic Operator, you need to set environment variables to connect to a Kafka cluster. These environment variables do not need to be set if you are deploying the Topic Operator using the Cluster Operator as they will be set by the Cluster Operator. |
15.3.1. Kafka topic resource
The KafkaTopic
resource is used to configure topics, including the number of partitions and replicas.
The full schema for KafkaTopic
is described in KafkaTopic
schema reference.
Identifying a Kafka cluster for topic handling
A KafkaTopic
resource includes a label that specifies the name of the Kafka cluster (derived from the name of the Kafka
resource) to which it belongs.
For example:
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
name: topic-name-1
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.
Kafka topic usage recommendations
When working with topics, be consistent.
Always operate on either KafkaTopic
resources or topics directly in Kubernetes.
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
|
For information on the requirements for identifiers and names in Kubernetes, refer to Object Names and IDs. |
Kafka topic naming conventions
Kafka and Kubernetes impose their own validation rules for the naming of topics in Kafka and KafkaTopic.metadata.name
respectively.
There are valid names for each which are invalid in the other.
Using the spec.topicName
property, it is possible to create a valid topic in Kafka with a name that would be invalid for the Kafka topic 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:
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
name: topic-name-1
spec:
topicName: topicName-1 (1)
# ...
-
Upper case is invalid in Kubernetes.
cannot be changed to:
apiVersion: kafka.strimzi.io/v1beta2
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 a valid
|
15.3.2. Topic Operator topic store
The Topic Operator uses Kafka to store topic metadata describing topic configuration as key-value pairs. The topic store is based on the Kafka Streams key-value mechanism, which uses Kafka topics to persist the state.
Topic metadata is cached in-memory and accessed locally within the Topic Operator.
Updates from operations applied to the local in-memory cache are persisted to a backup topic store on disk.
The topic store is continually synchronized with updates from Kafka topics or Kubernetes KafkaTopic
custom resources.
Operations are handled rapidly with the topic store set up this way,
but should the in-memory cache crash it is automatically repopulated from the persistent storage.
Internal topic store topics
Internal topics support the handling of topic metadata in the topic store.
__strimzi_store_topic
-
Input topic for storing the topic metadata
__strimzi-topic-operator-kstreams-topic-store-changelog
-
Retains a log of compacted topic store values
Warning
|
Do not delete these topics, as they are essential to the running of the Topic Operator. |
Migrating topic metadata from ZooKeeper
In previous releases of Strimzi, topic metadata was stored in ZooKeeper. The new process removes this requirement, bringing the metadata into the Kafka cluster, and under the control of the Topic Operator.
When upgrading to Strimzi 0.35.1, the transition to Topic Operator control of the topic store is seamless. Metadata is found and migrated from ZooKeeper, and the old store is deleted.
Downgrading to a Strimzi version that uses ZooKeeper to store topic metadata
If you are reverting back to a version of Strimzi earlier than 0.22, which uses ZooKeeper for the storage of topic metadata, you still downgrade your Cluster Operator to the previous version, then downgrade Kafka brokers and client applications to the previous Kafka version as standard.
However, you must also delete the topics that were created for the topic store using a kafka-admin
command, specifying the bootstrap address of the Kafka cluster.
For example:
kubectl run kafka-admin -ti --image=quay.io/strimzi/kafka:0.35.1-kafka-3.4.0 --rm=true --restart=Never -- ./bin/kafka-topics.sh --bootstrap-server localhost:9092 --topic __strimzi-topic-operator-kstreams-topic-store-changelog --delete && ./bin/kafka-topics.sh --bootstrap-server localhost:9092 --topic __strimzi_store_topic --delete
The command must correspond to the type of listener and authentication used to access the Kafka cluster.
The Topic Operator will reconstruct the ZooKeeper topic metadata from the state of the topics in Kafka.
Topic Operator topic replication and scaling
The recommended configuration for topics managed by the Topic Operator is a topic replication factor of 3, and a minimum of 2 in-sync replicas.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
name: my-topic
labels:
strimzi.io/cluster: my-cluster
spec:
partitions: 10 (1)
replicas: 3 (2)
config:
min.insync.replicas: 2 (3)
#...
-
The number of partitions for the topic.
-
The number of replica topic partitions. Currently, this cannot be changed in the
KafkaTopic
resource, but it can be changed using thekafka-reassign-partitions.sh
tool. -
The minimum number of replica partitions that a message must be successfully written to, or an exception is raised.
Note
|
In-sync replicas are used in conjunction with the acks configuration for producer applications.
The acks configuration determines the number of follower partitions a message must be replicated to before the message is acknowledged as successfully received.
The Topic Operator runs with acks=all , whereby messages must be acknowledged by all in-sync replicas.
|
When scaling Kafka clusters by adding or removing brokers, replication factor configuration is not changed and replicas are not reassigned automatically.
However, you can use the kafka-reassign-partitions.sh
tool to change the replication factor, and manually reassign replicas to brokers.
Alternatively, though the integration of Cruise Control for Strimzi cannot change the replication factor for topics, the optimization proposals it generates for rebalancing Kafka include commands that transfer partition replicas and change partition leadership.
Handling changes to topics
A fundamental problem that the Topic Operator needs to solve is that there is no single source of truth:
both the KafkaTopic
resource and the Kafka topic can be modified independently of the Topic Operator.
Complicating this, the Topic Operator might not always be able to observe changes at each end in real time.
For example, when the Topic Operator is down.
To resolve this, the Topic Operator maintains information about each topic in the topic store. When a change happens in the Kafka cluster or Kubernetes, it looks at both the state of the other system and the topic store in order to determine what needs to change to keep everything in sync. The same thing happens whenever the Topic Operator starts, and periodically while it is running.
For example, suppose the Topic Operator is not running, and a KafkaTopic
called my-topic is created.
When the Topic Operator starts, the topic store does not contain information on my-topic, so it can infer that the KafkaTopic
was created after it was last running.
The Topic Operator creates the topic corresponding to my-topic, and also stores metadata for my-topic in the topic store.
If you update Kafka topic configuration or apply a change through the KafkaTopic
custom resource,
the topic store is updated after the Kafka cluster is reconciled.
The topic store also allows the Topic Operator to manage scenarios where the topic configuration is changed in Kafka topics and updated through Kubernetes KafkaTopic
custom resources,
as long as the changes are not incompatible.
For example, it is possible to make changes to the same topic config key, but to different values.
For incompatible changes, the Kafka configuration takes priority, and the KafkaTopic
is updated accordingly.
Note
|
You can also use the KafkaTopic resource to delete topics using a kubectl delete -f KAFKA-TOPIC-CONFIG-FILE command.
To be able to do this, delete.topic.enable must be set to true (default) in the spec.kafka.config of the Kafka resource.
|
15.3.3. Configuring Kafka topics
Use the properties of the KafkaTopic
resource to configure Kafka topics.
You can use kubectl apply
to create or modify topics, and kubectl delete
to delete existing topics.
For example:
-
kubectl apply -f <topic_config_file>
-
kubectl delete KafkaTopic <topic_name>
This procedure shows how to create a topic with 10 partitions and 2 replicas.
It is important that you consider the following before making your changes:
-
Kafka does not support decreasing the number of partitions.
-
Increasing
spec.partitions
for topics with keys will change how records are partitioned, which can be particularly problematic when the topic uses semantic partitioning. -
Strimzi does not support making the following changes through the
KafkaTopic
resource:-
Using
spec.replicas
to change the number of replicas that were initially specified -
Changing topic names using
spec.topicName
-
-
A running Kafka cluster configured with a Kafka broker listener using mTLS authentication and TLS encryption.
-
A running Topic Operator (typically deployed with the Entity Operator).
-
For deleting a topic,
delete.topic.enable=true
(default) in thespec.kafka.config
of theKafka
resource.
-
Configure the
KafkaTopic
resource.Example Kafka topic configurationapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaTopic metadata: name: orders labels: strimzi.io/cluster: my-cluster spec: partitions: 10 replicas: 2
TipWhen modifying a topic, you can get the current version of the resource using kubectl get kafkatopic orders -o yaml
. -
Create the
KafkaTopic
resource in Kubernetes.kubectl apply -f <topic_config_file>
-
Wait for the ready status of the topic to change to
True
:kubectl get kafkatopics -o wide -w -n <namespace>
Kafka topic statusNAME CLUSTER PARTITIONS REPLICATION FACTOR READY my-topic-1 my-cluster 10 3 True my-topic-2 my-cluster 10 3 my-topic-3 my-cluster 10 3 True
Topic creation is successful when the
READY
output showsTrue
. -
If the
READY
column stays blank, get more details on the status from the resource YAML or from the Topic Operator logs.Messages provide details on the reason for the current status.
oc get kafkatopics my-topic-2 -o yaml
Details on a topic with aNotReady
status# ... status: conditions: - lastTransitionTime: "2022-06-13T10:14:43.351550Z" message: Number of partitions cannot be decreased reason: PartitionDecreaseException status: "True" type: NotReady
In this example, the reason the topic is not ready is because the original number of partitions was reduced in the
KafkaTopic
configuration. Kafka does not support this.After resetting the topic configuration, the status shows the topic is ready.
kubectl get kafkatopics my-topic-2 -o wide -w -n <namespace>
Status update of the topicNAME CLUSTER PARTITIONS REPLICATION FACTOR READY my-topic-2 my-cluster 10 3 True
Fetching the details shows no messages
kubectl get kafkatopics my-topic-2 -o yaml
Details on a topic with aREADY
status# ... status: conditions: - lastTransitionTime: '2022-06-13T10:15:03.761084Z' status: 'True' type: Ready
15.3.4. Configuring the Topic Operator with resource requests and limits
You can allocate resources, such as CPU and memory, to the Topic Operator and set a limit on the amount of resources it can consume.
-
The Cluster Operator is running.
-
Update the Kafka cluster configuration in an editor, as required:
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/v1beta2 kind: Kafka spec: # Kafka and ZooKeeper sections... entityOperator: topicOperator: resources: requests: cpu: "1" memory: 500Mi limits: cpu: "1" memory: 500Mi
-
Apply the new configuration to create or update the resource.
kubectl apply -f <kafka_configuration_file>
15.4. Using the User Operator
When you create, modify or delete a user using the KafkaUser
resource,
the User Operator ensures those changes are reflected in the Kafka cluster.
For more information on the KafkaUser
resource, see the KafkaUser
schema reference.
You can deploy the User Operator using the Cluster Operator or as a standalone operator. You would use a standalone User Operator with a Kafka cluster that is not managed by the Cluster Operator.
For deployment instructions, see the following:
Important
|
To deploy the standalone User Operator, you need to set environment variables to connect to a Kafka cluster. These environment variables do not need to be set if you are deploying the User Operator using the Cluster Operator as they will be set by the Cluster Operator. |
15.4.1. Configuring Kafka users
Use the properties of the KafkaUser
resource to configure Kafka users.
You can use kubectl apply
to create or modify users, and kubectl delete
to delete existing users.
For example:
-
kubectl apply -f <user_config_file>
-
kubectl delete KafkaUser <user_name>
Users represent Kafka clients.
When you configure Kafka users, you enable the user authentication and authorization mechanisms required by clients to access Kafka.
The mechanism used must match the equivalent Kafka
configuration.
For more information on using Kafka
and KafkaUser
resources to secure access to Kafka brokers, see Securing access to Kafka brokers.
-
A running Kafka cluster configured with a Kafka broker listener using mTLS authentication and TLS encryption.
-
A running User Operator (typically deployed with the Entity Operator).
-
Configure the
KafkaUser
resource.This example specifies mTLS authentication and simple authorization using ACLs.
Example Kafka user configurationapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaUser metadata: name: my-user labels: strimzi.io/cluster: my-cluster spec: authentication: type: tls authorization: type: simple acls: # Example consumer Acls for topic my-topic using consumer group my-group - resource: type: topic name: my-topic patternType: literal operations: - Describe - Read host: "*" - resource: type: group name: my-group patternType: literal operations: - Read host: "*" # Example Producer Acls for topic my-topic - resource: type: topic name: my-topic patternType: literal operations: - Create - Describe - Write host: "*"
-
Create the
KafkaUser
resource in Kubernetes.kubectl apply -f <user_config_file>
-
Wait for the ready status of the user to change to
True
:kubectl get kafkausers -o wide -w -n <namespace>
Kafka user statusNAME CLUSTER AUTHENTICATION AUTHORIZATION READY my-user-1 my-cluster tls simple True my-user-2 my-cluster tls simple my-user-3 my-cluster tls simple True
User creation is successful when the
READY
output showsTrue
. -
If the
READY
column stays blank, get more details on the status from the resource YAML or User Operator logs.Messages provide details on the reason for the current status.
kubectl get kafkausers my-user-2 -o yaml
Details on a user with aNotReady
status# ... status: conditions: - lastTransitionTime: "2022-06-10T10:07:37.238065Z" message: Simple authorization ACL rules are configured but not supported in the Kafka cluster configuration. reason: InvalidResourceException status: "True" type: NotReady
In this example, the reason the user is not ready is because simple authorization is not enabled in the
Kafka
configuration.Kafka configuration for simple authorizationapiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: kafka: # ... authorization: type: simple
After updating the Kafka configuration, the status shows the user is ready.
kubectl get kafkausers my-user-2 -o wide -w -n <namespace>
Status update of the userNAME CLUSTER AUTHENTICATION AUTHORIZATION READY my-user-2 my-cluster tls simple True
Fetching the details shows no messages.
kubectl get kafkausers my-user-2 -o yaml
Details on a user with aREADY
status# ... status: conditions: - lastTransitionTime: "2022-06-10T10:33:40.166846Z" status: "True" type: Ready
15.4.2. 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/v1beta2 kind: Kafka spec: # Kafka and ZooKeeper sections... entityOperator: userOperator: resources: requests: cpu: "1" memory: 500Mi limits: cpu: "1" memory: 500Mi
Save the file and exit the editor. The Cluster Operator applies the changes automatically.
15.5. Configuring feature gates
Strimzi operators support feature gates to enable or disable certain features and functionality. Enabling a feature gate changes the behavior of the relevant operator and introduces the feature to your Strimzi deployment.
Feature gates have a default state of either enabled or disabled.
To modify a feature gate’s default state, use the STRIMZI_FEATURE_GATES
environment variable in the operator’s configuration.
You can modify multiple feature gates using this single environment variable.
Specify a comma-separated list of feature gate names and prefixes.
A +
prefix enables the feature gate and a -
prefix disables it.
FeatureGate1
and disables FeatureGate2
env:
- name: STRIMZI_FEATURE_GATES
value: +FeatureGate1,-FeatureGate2
15.5.1. ControlPlaneListener feature gate
The ControlPlaneListener
feature gate has moved to GA, which means it is now permanently enabled and cannot be disabled.
With ControlPlaneListener
enabled, the connections between the Kafka controller and brokers use an internal control plane listener on port 9090.
Replication of data between brokers, as well as internal connections from Strimzi operators, Cruise Control, or the Kafka Exporter use the replication listener on port 9091.
Important
|
With the ControlPlaneListener feature gate permanently enabled, it is no longer possible to upgrade or downgrade directly between Strimzi 0.22 and earlier and Strimzi 0.32 and newer.
You have to first upgrade or downgrade through one of the Strimzi versions in-between, disable the ControlPlaneListener feature gate, and then downgrade or upgrade (with the feature gate enabled) to the target version.
|
15.5.2. ServiceAccountPatching feature gate
The ServiceAccountPatching
feature gate has moved to GA, which means it is now permanently enabled and cannot be disabled.
With ServiceAccountPatching
enabled, the Cluster Operator always reconciles service accounts and updates them when needed.
For example, when you change service account labels or annotations using the template
property of a custom resource, the operator automatically updates them on the existing service account resources.
15.5.3. UseStrimziPodSets feature gate
The UseStrimziPodSets
feature gate has moved to GA, which means it is now permanently enabled and cannot be disabled.
Support for StatefulSets
has been removed and Strimzi is now always using StrimziPodSets
to manage Kafka and ZooKeeper pods.
Important
|
With the UseStrimziPodSets feature gate permanently enabled, it is no longer possible to downgrade directly from Strimzi 0.35 and newer to Strimzi 0.27 or earlier.
You have to first downgrade through one of the Strimzi versions in-between, disable the USeStrimziPodSets feature gate, and then downgrade to Strimzi 0.27 or earlier.
|
15.5.4. (Preview) UseKRaft feature gate
The UseKRaft
feature gate has a default state of disabled.
The UseKRaft
feature gate deploys the Kafka cluster in the KRaft (Kafka Raft metadata) mode without ZooKeeper.
This feature gate is currently intended only for development and testing.
Important
|
The KRaft mode is not ready for production in Apache Kafka or in Strimzi. |
When the UseKRaft
feature gate is enabled, the Kafka cluster is deployed without ZooKeeper.
The .spec.zookeeper
properties in the Kafka custom resource will be ignored, but still need to be present.
The UseKRaft
feature gate provides an API that configures Kafka cluster nodes and their roles.
The API is still in development and is expected to change before the KRaft mode is production-ready.
Currently, the KRaft mode in Strimzi has the following major limitations:
-
Moving from Kafka clusters with ZooKeeper to KRaft clusters or the other way around is not supported.
-
Upgrades and downgrades of Apache Kafka versions or the Strimzi operator are not supported. Users might need to delete the cluster, upgrade the operator and deploy a new Kafka cluster.
-
The Topic Operator is not supported. The
spec.entityOperator.topicOperator
property must be removed from theKafka
custom resource. -
SCRAM-SHA-512 authentication is not supported.
-
JBOD storage is not supported. The
type: jbod
storage can be used, but the JBOD array can contain only one disk. -
All Kafka nodes have both the
controller
andbroker
KRaft roles. Kafka clusters with separatecontroller
andbroker
nodes are not supported.
To enable the UseKRaft
feature gate, specify +UseKRaft
in the STRIMZI_FEATURE_GATES
environment variable in the Cluster Operator configuration.
15.5.5. StableConnectIdentities feature gate
The StableConnectIdentities
feature gate has a default state of disabled.
The StableConnectIdentities
feature gate uses StrimziPodSet
resources to manage Kafka Connect and Kafka MirrorMaker 2 pods instead of using Kubernetes Deployment
resources.
StrimziPodSets
give the pods stable names and stable addresses, which do not change during rolling upgrades.
This helps to minimize the number of rebalances of connector tasks.
StableConnectIdentities
feature gateTo enable the StableConnectIdentities
feature gate, specify +StableConnectIdentities
in the STRIMZI_FEATURE_GATES
environment variable in the Cluster Operator configuration.
Important
|
The StableConnectIdentities feature gate must be disabled when downgrading to Strimzi 0.33 and earlier versions.
|
15.5.6. Feature gate releases
Feature gates have three stages of maturity:
-
Alpha — typically disabled by default
-
Beta — typically enabled by default
-
General Availability (GA) — typically always enabled
Alpha stage features might be experimental or unstable, subject to change, or not sufficiently tested for production use. Beta stage features are well tested and their functionality is not likely to change. GA stage features are stable and should not change in the future. Alpha and beta stage features are removed if they do not prove to be useful.
-
The
ControlPlaneListener
feature gate moved to GA stage in Strimzi 0.32. It is now permanently enabled and cannot be disabled. -
The
ServiceAccountPatching
feature gate moved to GA stage in Strimzi 0.30. It is now permanently enabled and cannot be disabled. -
The
UseStrimziPodSets
feature gate moved to GA stage in Strimzi 0.35 and the support for StatefulSets is completely removed. It is now permanently enabled and cannot be disabled. -
The
UseKRaft
feature gate is available for development only and does not currently have a planned release for moving to the beta phase. -
The
StableConnectIdentities
feature gate is in alpha stage and is disabled by default. It is expected to move to beta phase and be enabled by default from Strimzi 0.36.
Note
|
Feature gates might be removed when they reach GA. This means that the feature was incorporated into the Strimzi core features and can no longer be disabled. |
Feature gate | Alpha | Beta | GA |
---|---|---|---|
|
0.23 |
0.27 |
0.32 |
|
0.24 |
0.27 |
0.30 |
|
0.28 |
0.30 |
0.35 |
|
0.29 |
- |
- |
|
0.34 |
0.36 (planned) |
- |
If a feature gate is enabled, you may need to disable it before upgrading or downgrading from a specific Strimzi version. The following table shows which feature gates you need to disable when upgrading or downgrading Strimzi versions.
Disable Feature gate | Upgrading from Strimzi version | Downgrading to Strimzi version |
---|---|---|
|
0.22 and earlier |
0.22 and earlier |
|
- |
0.27 and earlier |
|
- |
0.33 and earlier |
15.6. Monitoring operators using Prometheus metrics
Strimzi operators expose Prometheus metrics. The metrics are automatically enabled and contain information about the following:
-
Number of reconciliations
-
Number of Custom Resources the operator is processing
-
Duration of reconciliations
-
JVM metrics from the operators
Additionally, Strimzi provides an example Grafana dashboard for the operator.
16. Introducing metrics
You can use Prometheus and Grafana to monitor your Strimzi deployment.
You can monitor your Strimzi deployment by viewing key metrics on dashboards and setting up alerts that trigger under certain conditions. Metrics are available for each of the components of Strimzi.
You can also collect metrics specific to oauth
authentication and opa
or keycloak
authorization.
You do this by setting the enableMetrics
property to true
in the listener configuration of the Kafka
resource.
For example, set enableMetrics
to true
in spec.kafka.listeners.authentication
and spec.kafka.authorization
.
Similarly, you can enable metrics for oauth
authentication in the KafkaBridge
, KafkaConnect
, KafkaMirrorMaker
, and KafkaMirrorMaker2
custom resources.
To provide metrics information, Strimzi uses Prometheus rules and Grafana dashboards.
When configured with a set of rules for each component of Strimzi, Prometheus consumes key metrics from the pods that are running in your cluster. Grafana then visualizes those metrics on dashboards. Strimzi includes example Grafana dashboards that you can customize to suit your deployment.
Depending on your requirements, you can:
With Prometheus and Grafana set up, you can use the example Grafana dashboards provided by Strimzi for monitoring.
Additionally, you can configure your deployment to track messages end-to-end by setting up distributed tracing.
Note
|
Strimzi provides example installation files for Prometheus and Grafana. You can use these files as a starting point when trying out monitoring of Strimzi. For further support, try engaging with the Prometheus and Grafana developer communities. |
For more information on the metrics and monitoring tools, refer to the supporting documentation:
-
Apache Kafka Monitoring describes JMX metrics exposed by Apache Kafka
-
ZooKeeper JMX describes JMX metrics exposed by Apache ZooKeeper
16.1. Monitoring consumer lag with Kafka Exporter
Kafka Exporter is an open source project to enhance monitoring of Apache Kafka brokers and clients.
You can configure the Kafka
resource to deploy Kafka Exporter with your Kafka cluster.
Kafka Exporter extracts 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.
Kafka Exporter reads from the __consumer_offsets
topic, which stores information on committed offsets for consumer groups.
For Kafka Exporter to be able to work properly, consumer groups needs to be in use.
A Grafana dashboard for Kafka Exporter is one of a number of example Grafana dashboards provided by Strimzi.
Important
|
Kafka Exporter provides only additional metrics related to consumer lag and consumer offsets. For regular Kafka metrics, you have to configure the Prometheus metrics in Kafka brokers. |
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.
The importance of monitoring consumer lag
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.
Reducing consumer lag
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.
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.
16.2. Monitoring Cruise Control operations
Cruise Control monitors Kafka brokers in order to track the utilization of brokers, topics, and partitions. Cruise Control also provides a set of metrics for monitoring its own performance.
The Cruise Control metrics reporter collects raw metrics data from Kafka brokers. The data is produced to topics that are automatically created by Cruise Control. The metrics are used to generate optimization proposals for Kafka clusters.
Cruise Control metrics are available for real-time monitoring of Cruise Control operations. For example, you can use Cruise Control metrics to monitor the status of rebalancing operations that are running or provide alerts on any anomalies that are detected in an operation’s performance.
You expose Cruise Control metrics by enabling the Prometheus JMX Exporter in the Cruise Control configuration.
Note
|
For a full list of available Cruise Control metrics, which are known as sensors, see the Cruise Control documentation. |
16.2.1. Exposing Cruise Control metrics
If you want to expose metrics on Cruise Control operations, configure the Kafka
resource to deploy Cruise Control and enable Prometheus metrics in the deployment.
You can use your own configuration or use the example kafka-cruise-control-metrics.yaml
file provided by Strimzi.
You add the configuration to the metricsConfig
of the CruiseControl
property in the Kafka
resource.
The configuration enables the Prometheus JMX Exporter to expose Cruise Control metrics through an HTTP endpoint.
The HTTP endpoint is scraped by the Prometheus server.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
Spec:
# ...
cruiseControl:
# ...
metricsConfig:
type: jmxPrometheusExporter
valueFrom:
configMapKeyRef:
name: cruise-control-metrics
key: metrics-config.yml
---
kind: ConfigMap
apiVersion: v1
metadata:
name: cruise-control-metrics
labels:
app: strimzi
data:
metrics-config.yml: |
# metrics configuration...
16.2.2. Viewing Cruise Control metrics
After you expose the Cruise Control metrics, you can use Prometheus or another suitable monitoring system to view information on the metrics data.
Strimzi provides an example Grafana dashboard to display visualizations of Cruise Control metrics.
The dashboard is a JSON file called strimzi-cruise-control.json
.
The exposed metrics provide the monitoring data when you enable the Grafana dashboard.
Monitoring balancedness scores
Cruise Control metrics include a balancedness score. Balancedness is the measure of how evenly a workload is distributed in a Kafka cluster.
The Cruise Control metric for balancedness score (balancedness-score
) might differ from the balancedness score in the KafkaRebalance
resource.
Cruise Control calculates each score using anomaly.detection.goals
which might not be the same as the default.goals
used in the KafkaRebalance
resource.
The anomaly.detection.goals
are specified in the spec.cruiseControl.config
of the Kafka
custom resource.
Note
|
Refreshing the
Otherwise, Cruise Control generates a new optimization proposal based on KafkaRebalance |
Alerts on anomaly detection
Cruise control’s anomaly detector provides metrics data for conditions that block the generation of optimization goals, such as broker failures. If you want more visibility, you can use the metrics provided by the anomaly detector to set up alerts and send out notifications. You can set up Cruise Control’s anomaly notifier to route alerts based on these metrics through a specified notification channel. Alternatively, you can set up Prometheus to scrape the metrics data provided by the anomaly detector and generate alerts. Prometheus Alertmanager can then route the alerts generated by Prometheus.
The Cruise Control documentation provides information on AnomalyDetector
metrics and the anomaly notifier.
16.3. Example metrics files
You can find example Grafana dashboards and other metrics configuration files in the example configuration files provided by Strimzi.
metrics
├── grafana-dashboards (1)
│ ├── strimzi-cruise-control.json
│ ├── strimzi-kafka-bridge.json
│ ├── strimzi-kafka-connect.json
│ ├── strimzi-kafka-exporter.json
│ ├── strimzi-kafka-mirror-maker-2.json
│ ├── strimzi-kafka.json
│ ├── strimzi-operators.json
│ └── strimzi-zookeeper.json
├── grafana-install
│ └── grafana.yaml (2)
├── prometheus-additional-properties
│ └── prometheus-additional.yaml (3)
├── prometheus-alertmanager-config
│ └── alert-manager-config.yaml (4)
├── prometheus-install
│ ├── alert-manager.yaml (5)
│ ├── prometheus-rules.yaml (6)
│ ├── prometheus.yaml (7)
│ ├── strimzi-pod-monitor.yaml (8)
├── kafka-bridge-metrics.yaml (9)
├── kafka-connect-metrics.yaml (10)
├── kafka-cruise-control-metrics.yaml (11)
├── kafka-metrics.yaml (12)
└── kafka-mirror-maker-2-metrics.yaml (13)
-
Example Grafana dashboards for the different Strimzi components.
-
Installation file for the Grafana image.
-
Additional configuration to scrape metrics for CPU, memory and disk volume usage, which comes directly from the Kubernetes cAdvisor agent and kubelet on the nodes.
-
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 resource file for the Prometheus image.
-
PodMonitor definitions translated by the Prometheus Operator into jobs for the Prometheus server to be able to scrape metrics data directly from pods.
-
Kafka Bridge resource with metrics enabled.
-
Metrics configuration that defines Prometheus JMX Exporter relabeling rules for Kafka Connect.
-
Metrics configuration that defines Prometheus JMX Exporter relabeling rules for Cruise Control.
-
Metrics configuration that defines Prometheus JMX Exporter relabeling rules for Kafka and ZooKeeper.
-
Metrics configuration that defines Prometheus JMX Exporter relabeling rules for Kafka Mirror Maker 2.0.
16.3.1. Example Prometheus metrics configuration
Strimzi uses the Prometheus JMX Exporter to expose metrics through an HTTP endpoint, which can be scraped by the Prometheus server.
Grafana dashboards are dependent on Prometheus JMX Exporter relabeling rules, which are defined for Strimzi components in the custom resource configuration.
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.
Strimzi provides example custom resource configuration YAML files with relabeling rules. When deploying Prometheus metrics configuration, you can can deploy the example custom resource or copy the metrics configuration to your own custom resource definition.
Component | Custom resource | Example YAML file |
---|---|---|
Kafka and ZooKeeper |
|
|
Kafka Connect |
|
|
Kafka MirrorMaker 2 |
|
|
Kafka Bridge |
|
|
Cruise Control |
|
|
16.3.2. Example Prometheus rules for alert notifications
Example Prometheus rules for alert notifications are provided with the example metrics configuration files provided by Strimzi.
The rules are specified in the example prometheus-rules.yaml
file for use in a Prometheus deployment.
Alerting rules provide notifications about specific conditions observed in 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.
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.
Alertmanager can be configured to use email, chat messages or other notification methods. Adapt the default configuration of the example rules according to your specific needs.
Example altering rules
The prometheus-rules.yaml
file contains example rules for the following components:
-
Kafka
-
ZooKeeper
-
Entity Operator
-
Kafka Connect
-
Kafka Bridge
-
MirrorMaker
-
Kafka Exporter
A description of each of the example rules is provided in the file.
16.3.3. Example Grafana dashboards
If you deploy Prometheus to provide metrics, you can use the example Grafana dashboards provided with Strimzi to monitor Strimzi components.
Example dashboards are provided in the examples/metrics/grafana-dashboards
directory as JSON files.
All dashboards provide JVM metrics, as well as metrics specific to the component. For example, the Grafana dashboard for Strimzi operators provides information on the number of reconciliations or custom resources they are processing.
The example dashboards don’t show all the metrics supported by Kafka. The dashboards are populated with a representative set of metrics for monitoring.
Component | Example JSON file |
---|---|
Strimzi operators |
|
Kafka |
|
ZooKeeper |
|
Kafka Connect |
|
Kafka MirrorMaker 2 |
|
Kafka Bridge |
|
Cruise Control |
|
Kafka Exporter |
|
Note
|
When metrics are not available to the Kafka Exporter, because there is no traffic in the cluster yet, the Kafka Exporter Grafana dashboard will show N/A for numeric fields and No data to show for graphs.
|
16.4. Using Prometheus with Strimzi
You can use Prometheus to provide monitoring data for the example Grafana dashboards provided with Strimzi.
To expose metrics in Prometheus format, you add configuration to a custom resource. You will also need to make sure that the metrics are scraped by your monitoring stack. Prometheus and Prometheus Alertmanager are used in the examples provided by Strimzi, but you can use also other compatible tools.
16.4.1. Deploying Prometheus metrics configuration
Deploy Prometheus metrics configuration to use Prometheus with Strimzi.
Use the metricsConfig
property to enable and configure Prometheus metrics.
You can use your own configuration or the example custom resource configuration files provided with Strimzi.
-
kafka-metrics.yaml
-
kafka-connect-metrics.yaml
-
kafka-mirror-maker-2-metrics.yaml
-
kafka-bridge-metrics.yaml
-
kafka-cruise-control-metrics.yaml
The example configuration files have relabeling rules and the configuration required to enable Prometheus metrics. Prometheus scrapes metrics from target HTTP endpoints. The example files are a good way to try Prometheus with Strimzi.
To apply the relabeling rules and metrics configuration, do one of the following:
-
Copy the example configuration to your own custom resources
-
Deploy the custom resource with the metrics configuration
If you want to include Kafka Exporter metrics, add kafkaExporter
configuration to your Kafka
resource.
Important
|
Kafka Exporter provides only additional metrics related to consumer lag and consumer offsets. For regular Kafka metrics, you have to configure the Prometheus metrics in Kafka brokers. |
This procedure shows how to deploy Prometheus metrics configuration in the Kafka
resource.
The process is the same when using the example files for other resources.
-
Deploy the example custom resource with the Prometheus configuration.
For example, for each
Kafka
resource you apply thekafka-metrics.yaml
file.Deploying the example configurationkubectl apply -f kafka-metrics.yaml
Alternatively, you can copy the example configuration in
kafka-metrics.yaml
to your ownKafka
resource.Copying the example configurationkubectl edit kafka <kafka-configuration-file>
Copy the
metricsConfig
property and theConfigMap
it references to yourKafka
resource.Example metrics configuration for KafkaapiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: kafka: # ... metricsConfig: (1) type: jmxPrometheusExporter valueFrom: configMapKeyRef: name: kafka-metrics key: kafka-metrics-config.yml --- kind: ConfigMap (2) apiVersion: v1 metadata: name: kafka-metrics labels: app: strimzi data: kafka-metrics-config.yml: | # metrics configuration...
-
Copy the
metricsConfig
property that references the ConfigMap that contains metrics configuration. -
Copy the whole
ConfigMap
that specifies the metrics configuration.
NoteFor Kafka Bridge, you specify the
enableMetrics
property and set it totrue
.apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaBridge metadata: name: my-bridge spec: # ... bootstrapServers: my-cluster-kafka:9092 http: # ... enableMetrics: true # ...
-
-
To deploy Kafka Exporter, add
kafkaExporter
configuration.kafkaExporter
configuration is only specified in theKafka
resource.Example configuration for deploying Kafka ExporterapiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: # ... kafkaExporter: image: my-registry.io/my-org/my-exporter-cluster: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.
-
CPU and memory resources to reserve.
-
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.
-
Customization of deployment templates and pods.
-
Healthcheck readiness probes.
-
Healthcheck liveness probes.
-
Note
|
For Kafka Exporter to be able to work properly, consumer groups need to be in use. |
16.4.2. Setting up 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. |
Prometheus configuration
Strimzi provides example configuration files for the Prometheus server.
A Prometheus YAML file is provided for deployment:
-
prometheus.yaml
Additional Prometheus-related configuration is also provided in the following files:
-
prometheus-additional.yaml
-
prometheus-rules.yaml
-
strimzi-pod-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.
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
PodMonitor
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).
Deploying the CoreOS Prometheus Operator
To deploy the Prometheus Operator to your Kafka cluster, apply the YAML bundle resources file from the Prometheus CoreOS repository.
-
Download the
bundle.yaml
resources file from the repository.On Linux, use:
curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/bundle.yaml | sed -e '/[[:space:]]*namespace: [a-zA-Z0-9-]*$/s/namespace:[[:space:]]*[a-zA-Z0-9-]*$/namespace: my-namespace/' > prometheus-operator-deployment.yaml
On MacOS, use:
curl -s https://raw.githubusercontent.com/coreos/prometheus-operator/master/bundle.yaml | sed -e '' '/[[:space:]]*namespace: [a-zA-Z0-9-]*$/s/namespace:[[:space:]]*[a-zA-Z0-9-]*$/namespace: my-namespace/' > prometheus-operator-deployment.yaml
-
Replace the example
namespace
with your own. -
Use the latest
master
release as shown, or choose a release that is compatible with your version of Kubernetes (see the Kubernetes compatibility matrix). Themaster
release of the Prometheus Operator works with Kubernetes 1.18+.NoteIf using OpenShift, specify a release of the OpenShift fork of the Prometheus Operator repository.
-
-
(Optional) If 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 create -f prometheus-operator-deployment.yaml
Deploying Prometheus
Use Prometheus to obtain monitoring data in your Kafka cluster.
You can use your own Prometheus deployment or deploy Prometheus using the example metrics configuration files provided by Strimzi. The example files include a configuration file for a Prometheus deployment and files for Prometheus-related resources:
-
examples/metrics/prometheus-install/prometheus.yaml
-
examples/metrics/prometheus-install/prometheus-rules.yaml
-
examples/metrics/prometheus-install/strimzi-pod-monitor.yaml
-
examples/metrics/prometheus-additional-properties/prometheus-additional.yaml
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 into: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
PodMonitor
resource instrimzi-pod-monitor.yaml
to define Prometheus jobs that will scrape the metrics data from pods.Update the
namespaceSelector.matchNames
property with the namespace where the pods to scrape the metrics from are running.PodMonitor
is used to scrape data directly from pods for Apache Kafka, ZooKeeper, Operators, the Kafka Bridge and Cruise Control. -
Edit the
prometheus.yaml
installation file to include additional configuration for scraping metrics directly from nodes.The Grafana dashboards provided show metrics for CPU, memory and disk volume usage, which come directly from the Kubernetes cAdvisor agent and kubelet on the nodes.
The Prometheus Operator does not have a monitoring resource like
PodMonitor
for scraping the nodes, so theprometheus-additional.yaml
file contains the additional configuration needed.-
Create a
Secret
resource from the configuration file (prometheus-additional.yaml
in theexamples/metrics/prometheus-additional-properties
directory):kubectl apply -f prometheus-additional.yaml
-
Edit the
additionalScrapeConfigs
property in theprometheus.yaml
file to include the name of theSecret
and theprometheus-additional.yaml
file.
-
-
Deploy the Prometheus resources:
kubectl apply -f strimzi-pod-monitor.yaml kubectl apply -f prometheus-rules.yaml kubectl apply -f prometheus.yaml
16.4.3. Deploying Alertmanager
Use Alertmanager to route alerts to a notification service. Prometheus Alertmanager is a component 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.
You can use the example metrics configuration files provided by Strimzi to deploy Alertmanager to send notifications to a Slack channel. A configuration file defines the resources for deploying Alertmanager:
-
examples/metrics/prometheus-install/alert-manager.yaml
An additional configuration file provides the hook definitions for sending notifications from your Kafka cluster:
-
examples/metrics/prometheus-alertmanager-config/alert-manager-config.yaml
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
in theexamples/metrics/prometheus-alertmanager-config
directory):kubectl apply -f 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
16.4.4. Using metrics with Minikube
When adding Prometheus and Grafana servers to an Apache Kafka deployment using Minikube, 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 Installing a local Kubernetes cluster with Minikube
-
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.
16.5. Enabling the example Grafana dashboards
Use Grafana to provide visualizations of Prometheus metrics on customizable dashboards.
You can use your own Grafana deployment or deploy Grafana using the example metrics configuration files provided by Strimzi. The example files include a configuration file for a Grafana deployment
-
examples/metrics/grafana-install/grafana.yaml
Strimzi also provides example dashboard configuration files for Grafana in JSON format.
-
examples/metrics/grafana-dashboards
This procedure uses the example Grafana configuration file and example dashboards.
The example dashboards are a good starting point for monitoring key metrics, but they don’t show all the metrics supported by Kafka. You can modify the example dashboards or add other metrics, depending on your infrastructure.
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.
The name of the Grafana pod is different for each user.
-
Deploy Grafana.
kubectl apply -f grafana.yaml
-
Get the details of the Grafana service.
kubectl get service grafana
For example:
NAME TYPE CLUSTER-IP PORT(S) grafana
ClusterIP
172.30.123.40
3000/TCP
Note the port number for port forwarding.
-
Use
port-forward
to redirect the Grafana user interface tolocalhost:3000
:kubectl port-forward svc/grafana 3000:3000
-
In a web browser, access the Grafana login screen using the URL
http://localhost:3000
.The Grafana Log In page appears.
-
Enter your user name and password, and then click Log In.
The default Grafana user name and password are both
admin
. After logging in for the first time, you can change the password. -
In Configuration > Data Sources, add Prometheus as a data source.
-
Specify a name
-
Add Prometheus as the type
-
Specify a Prometheus server URL (http://prometheus-operated:9090)
Save and test the connection when you have added the details.
-
-
Click the + icon and then click Import.
-
In
examples/metrics/grafana-dashboards
, copy the JSON of the dashboard to import. -
Paste the JSON into the text box, and then click Load.
-
Repeat steps 7-9 for the other example Grafana dashboards.
The imported Grafana dashboards are available to view from the Dashboards home page.
17. Introducing distributed tracing
Distributed tracing tracks the progress of transactions between applications in a distributed system. In a microservices architecture, tracing tracks the progress of transactions between services. Trace data is useful for monitoring application performance and investigating issues with target systems and end-user applications.
In Strimzi, tracing facilitates the end-to-end tracking of messages: from source systems to Kafka, and then from Kafka to target systems and applications. Distributed tracing complements the monitoring of metrics in Grafana dashboards, as well as the component loggers.
Support for tracing is built in to the following Kafka components:
-
MirrorMaker to trace messages from a source cluster to a target cluster
-
Kafka Connect to trace messages consumed and produced by Kafka Connect
-
Kafka Bridge to trace messages between Kafka and HTTP client applications
Tracing is not supported for Kafka brokers.
You enable and configure tracing for these components through their custom resources.
You add tracing configuration using spec.template
properties.
You enable tracing by specifying a tracing type using the spec.tracing.type
property:
opentelemetry
-
Specify
type: opentelemetry
to use OpenTelemetry. By Default, OpenTelemetry uses the OTLP (OpenTelemetry Protocol) exporter and endpoint to get trace data. You can specify other tracing systems supported by OpenTelemetry, including Jaeger tracing. To do this, you change the OpenTelemetry exporter and endpoint in the tracing configuration. jaeger
-
Specify
type:jaeger
to use OpenTracing and the Jaeger client to get trace data.
Note
|
Support for |
17.1. Tracing options
Use OpenTelemetry or OpenTracing (deprecated) with the Jaeger tracing system.
OpenTelemetry and OpenTracing provide API specifications that are independent from the tracing or monitoring system.
You use the APIs to instrument application code for tracing.
-
Instrumented applications generate traces for individual requests across the distributed system.
-
Traces are composed of spans that define specific units of work over time.
Jaeger is a tracing system for microservices-based distributed systems.
-
Jaeger implements the tracing APIs and provides client libraries for instrumentation.
-
The Jaeger user interface allows you to query, filter, and analyze trace data.
17.2. Environment variables for tracing
Use environment variables when you are enabling tracing for Kafka components or initializing a tracer for Kafka clients.
Tracing environment variables are subject to change. For the latest information, see the OpenTelemetry documentation and OpenTracing documentation.
The following tables describe the key environment variables for setting up a tracer.
Property | Required | Description |
---|---|---|
|
Yes |
The name of the Jaeger tracing service for OpenTelemetry. |
|
Yes |
The exporter used for tracing. |
|
Yes |
The exporter used for tracing.
Set to |
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 |
17.3. Setting up distributed tracing
Enable distributed tracing in Kafka components by specifying a tracing type in the custom resource. Instrument tracers in Kafka clients for end-to-end tracking of messages.
To set up distributed tracing, follow these procedures in order:
-
Enable tracing for MirrorMaker, Kafka Connect, and the Kafka Bridge
-
Set up tracing for clients:
-
Instrument clients with tracers:
17.3.1. Prerequisites
Before setting up distributed tracing, make sure Jaeger backend components are deployed to your Kubernetes cluster. We recommend using the Jaeger operator for deploying Jaeger on your Kubernetes cluster.
For deployment instructions, see the Jaeger documentation.
Note
|
Setting up tracing for applications and systems beyond Strimzi is outside the scope of this content. |
17.3.2. Enabling tracing in MirrorMaker, Kafka Connect, and Kafka Bridge resources
Distributed tracing is supported for MirrorMaker, MirrorMaker 2, Kafka Connect, and the Strimzi Kafka Bridge. Configure the custom resource of the component to specify and enable a tracer service.
Enabling tracing in a resource triggers the following events:
-
Interceptor classes are updated in the integrated consumers and producers of the component.
-
For MirrorMaker, MirrorMaker 2, and Kafka Connect, the tracing agent initializes a tracer based on the tracing configuration defined in the resource.
-
For the Kafka Bridge, a tracer based on the tracing configuration defined in the resource is initialized by the Kafka Bridge itself.
You can enable tracing that uses OpenTelemetry or OpenTracing.
For MirrorMaker and MirrorMaker 2, messages are traced from the source cluster to the target cluster. The trace data records messages entering and leaving the MirrorMaker or MirrorMaker 2 component.
For Kafka Connect, only messages produced and consumed by Kafka Connect are traced. To trace messages sent between Kafka Connect and external systems, you must configure tracing in the connectors for those systems.
For the Kafka Bridge, messages produced and consumed by the Kafka Bridge are traced. Incoming HTTP requests from client applications to send and receive messages through the Kafka Bridge are also traced. To have end-to-end tracing, you must configure tracing in your HTTP clients.
Perform these steps for each KafkaMirrorMaker
, KafkaMirrorMaker2
, KafkaConnect
, and KafkaBridge
resource.
-
In the
spec.template
property, configure the tracer service.-
Use the tracing environment variables as template configuration properties.
-
For OpenTelemetry, set the
spec.tracing.type
property toopentelemetry
. -
For OpenTracing, set the
spec.tracing.type
property tojaeger
.
Example tracing configuration for Kafka Connect using OpenTelemetryapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnect metadata: name: my-connect-cluster spec: #... template: connectContainer: env: - name: OTEL_SERVICE_NAME value: my-otel-service - name: OTEL_EXPORTER_OTLP_ENDPOINT value: "http://otlp-host:4317" tracing: type: opentelemetry #...
Example tracing configuration for MirrorMaker using OpenTelemetryapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaMirrorMaker metadata: name: my-mirror-maker spec: #... template: mirrorMakerContainer: env: - name: OTEL_SERVICE_NAME value: my-otel-service - name: OTEL_EXPORTER_OTLP_ENDPOINT value: "http://otlp-host:4317" tracing: type: opentelemetry #...
Example tracing configuration for MirrorMaker 2 using OpenTelemetryapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaMirrorMaker2 metadata: name: my-mm2-cluster spec: #... template: connectContainer: env: - name: OTEL_SERVICE_NAME value: my-otel-service - name: OTEL_EXPORTER_OTLP_ENDPOINT value: "http://otlp-host:4317" tracing: type: opentelemetry #...
Example tracing configuration for the Kafka Bridge using OpenTelemetryapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaBridge metadata: name: my-bridge spec: #... template: bridgeContainer: env: - name: OTEL_SERVICE_NAME value: my-otel-service - name: OTEL_EXPORTER_OTLP_ENDPOINT value: "http://otlp-host:4317" tracing: type: opentelemetry #...
Example tracing configuration for Kafka Connect using OpenTracingapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnect metadata: name: my-connect-cluster spec: #... template: connectContainer: 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 #...
Example tracing configuration for MirrorMaker using OpenTracingapiVersion: kafka.strimzi.io/v1beta2 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 #...
Example tracing configuration for MirrorMaker 2 using OpenTracingapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaMirrorMaker2 metadata: name: my-mm2-cluster spec: #... template: connectContainer: 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 #...
Example tracing configuration for the Kafka Bridge using OpenTracingapiVersion: kafka.strimzi.io/v1beta2 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 #...
-
-
Create or update the resource:
kubectl apply -f <resource_configuration_file>
17.3.3. Initializing tracing for Kafka clients
Initialize a tracer, then instrument your client applications for distributed tracing. You can instrument Kafka producer and consumer clients, and Kafka Streams API applications. You can initialize a tracer for OpenTracing or OpenTelemetry.
Configure and initialize a tracer using a set of tracing environment variables.
In each client application add the dependencies for the tracer:
-
Add the Maven dependencies to the
pom.xml
file for the client application:Dependencies for OpenTelemetry<dependency> <groupId>io.opentelemetry</groupId> <artifactId>opentelemetry-sdk-extension-autoconfigure</artifactId> <version>1.18.0-alpha</version> </dependency> <dependency> <groupId>io.opentelemetry.instrumentation</groupId> <artifactId>opentelemetry-kafka-clients-{OpenTelemetryKafkaClient}</artifactId> <version>1.18.0-alpha</version> </dependency> <dependency> <groupId>io.opentelemetry</groupId> <artifactId>opentelemetry-exporter-otlp</artifactId> <version>1.18.0</version> </dependency>
Dependencies for OpenTracing<dependency> <groupId>io.jaegertracing</groupId> <artifactId>jaeger-client</artifactId> <version>1.3.2</version> </dependency> <dependency> <groupId>io.opentracing.contrib</groupId> <artifactId>opentracing-kafka-client</artifactId> <version>0.1.15</version> </dependency>
-
Define the configuration of the tracer using the tracing environment variables.
-
Create a tracer, which is initialized with the environment variables:
Creating a tracer for OpenTelemetryOpenTelemetry ot = GlobalOpenTelemetry.get();
Creating a tracer for OpenTracingTracer tracer = Configuration.fromEnv().getTracer();
-
Register the tracer as a global tracer:
GlobalTracer.register(tracer);
-
Instrument your client:
17.3.4. Instrumenting producers and consumers for tracing
Instrument application code to enable tracing in Kafka producers and consumers. Use a decorator pattern or interceptors to instrument your Java producer and consumer application code for tracing. You can then record traces when messages are produced or retrieved from a topic.
OpenTelemetry and OpenTracing instrumentation projects provide classes that support instrumentation of producers and consumers.
- Decorator instrumentation
-
For decorator instrumentation, create a modified producer or consumer instance for tracing. Decorator instrumentation is different for OpenTelemetry and OpenTracing.
- Interceptor instrumentation
-
For interceptor instrumentation, add the tracing capability to the consumer or producer configuration. Interceptor instrumentation is the same for OpenTelemetry and OpenTracing.
-
You have initialized tracing for the client.
You enable instrumentation in producer and consumer applications by adding the tracing JARs as dependencies to your project.
Perform these steps in the application code of each producer and consumer application. Instrument your client application code using either a decorator pattern or interceptors.
-
To use a decorator pattern, create a modified producer or consumer instance to send or receive messages.
You pass the original
KafkaProducer
orKafkaConsumer
class.Example decorator instrumentation for OpenTelemetry// Producer instance Producer < String, String > op = new KafkaProducer < > ( configs, new StringSerializer(), new StringSerializer() ); Producer < String, String > producer = tracing.wrap(op); KafkaTracing tracing = KafkaTracing.create(GlobalOpenTelemetry.get()); producer.send(...); //consumer instance Consumer<String, String> oc = new KafkaConsumer<>( configs, new StringDeserializer(), new StringDeserializer() ); Consumer<String, String> consumer = tracing.wrap(oc); consumer.subscribe(Collections.singleton("mytopic")); ConsumerRecords<Integer, String> records = consumer.poll(1000); ConsumerRecord<Integer, String> record = ... SpanContext spanContext = TracingKafkaUtils.extractSpanContext(record.headers(), tracer);
Example decorator instrumentation for OpenTracing//producer instance KafkaProducer<Integer, String> producer = new KafkaProducer<>(senderProps); TracingKafkaProducer<Integer, String> tracingProducer = new TracingKafkaProducer<>(producer, tracer); TracingKafkaProducer.send(...) //consumer instance KafkaConsumer<Integer, String> consumer = new KafkaConsumer<>(consumerProps); TracingKafkaConsumer<Integer, String> tracingConsumer = new TracingKafkaConsumer<>(consumer, tracer); tracingConsumer.subscribe(Collections.singletonList("mytopic")); ConsumerRecords<Integer, String> records = tracingConsumer.poll(1000); ConsumerRecord<Integer, String> record = ... SpanContext spanContext = TracingKafkaUtils.extractSpanContext(record.headers(), tracer);
-
To use interceptors, set the interceptor class in the producer or consumer configuration.
You use the
KafkaProducer
andKafkaConsumer
classes in the usual way. TheTracingProducerInterceptor
andTracingConsumerInterceptor
interceptor classes take care of the tracing capability.Example producer configuration using interceptorssenderProps.put(ProducerConfig.INTERCEPTOR_CLASSES_CONFIG, TracingProducerInterceptor.class.getName()); KafkaProducer<Integer, String> producer = new KafkaProducer<>(senderProps); producer.send(...);
Example consumer configuration using interceptorsconsumerProps.put(ConsumerConfig.INTERCEPTOR_CLASSES_CONFIG, TracingConsumerInterceptor.class.getName()); KafkaConsumer<Integer, String> consumer = new KafkaConsumer<>(consumerProps); consumer.subscribe(Collections.singletonList("messages")); ConsumerRecords<Integer, String> records = consumer.poll(1000); ConsumerRecord<Integer, String> record = ... SpanContext spanContext = TracingKafkaUtils.extractSpanContext(record.headers(), tracer);
17.3.5. Instrumenting Kafka Streams applications for tracing
Instrument application code to enable tracing in Kafka Streams API applications. Use a decorator pattern or interceptors to instrument your Kafka Streams API applications for tracing. You can then record traces when messages are produced or retrieved from a topic.
- Decorator instrumentation
-
For decorator instrumentation, create a modified Kafka Streams instance for tracing. The OpenTracing instrumentation project provides a
TracingKafkaClientSupplier
class that supports instrumentation of Kafka Streams. You create a wrapped instance of theTracingKafkaClientSupplier
supplier interface, which provides tracing instrumentation for Kafka Streams. For OpenTelemetry, the process is the same but you need to create a customTracingKafkaClientSupplier
class to provide the support. - Interceptor instrumentation
-
For interceptor instrumentation, add the tracing capability to the Kafka Streams producer and consumer configuration.
-
You have initialized tracing for the client.
You enable instrumentation in Kafka Streams applications by adding the tracing JARs as dependencies to your project.
-
To instrument Kafka Streams with OpenTelemetry, you’ll need to write a custom
TracingKafkaClientSupplier
. -
The custom
TracingKafkaClientSupplier
can extend Kafka’sDefaultKafkaClientSupplier
, overriding the producer and consumer creation methods to wrap the instances with the telemetry-related code.Example customTracingKafkaClientSupplier
private class TracingKafkaClientSupplier extends DefaultKafkaClientSupplier { @Override public Producer<byte[], byte[]> getProducer(Map<String, Object> config) { KafkaTelemetry telemetry = KafkaTelemetry.create(GlobalOpenTelemetry.get()); return telemetry.wrap(super.getProducer(config)); } @Override public Consumer<byte[], byte[]> getConsumer(Map<String, Object> config) { KafkaTelemetry telemetry = KafkaTelemetry.create(GlobalOpenTelemetry.get()); return telemetry.wrap(super.getConsumer(config)); } @Override public Consumer<byte[], byte[]> getRestoreConsumer(Map<String, Object> config) { return this.getConsumer(config); } @Override public Consumer<byte[], byte[]> getGlobalConsumer(Map<String, Object> config) { return this.getConsumer(config); } }
Perform these steps for each Kafka Streams API application.
-
To use a decorator pattern, create an instance of the
TracingKafkaClientSupplier
supplier interface, then provide the supplier interface toKafkaStreams
.Example decorator instrumentationKafkaClientSupplier supplier = new TracingKafkaClientSupplier(tracer); KafkaStreams streams = new KafkaStreams(builder.build(), new StreamsConfig(config), supplier); streams.start();
-
To use interceptors, set the interceptor class in the Kafka Streams producer and consumer configuration.
The
TracingProducerInterceptor
andTracingConsumerInterceptor
interceptor classes take care of the tracing capability.Example producer and consumer configuration using interceptorsprops.put(StreamsConfig.PRODUCER_PREFIX + ProducerConfig.INTERCEPTOR_CLASSES_CONFIG, TracingProducerInterceptor.class.getName()); props.put(StreamsConfig.CONSUMER_PREFIX + ConsumerConfig.INTERCEPTOR_CLASSES_CONFIG, TracingConsumerInterceptor.class.getName());
17.3.6. Introducing a different OpenTelemetry tracing system
Instead of the default OTLP system, you can specify other tracing systems that are supported by OpenTelemetry.
You do this by adding the required artifacts to the Kafka image provided with Strimzi.
Any required implementation specific environment variables must also be set.
You then enable the new tracing implementation using the OTEL_TRACES_EXPORTER
environment variable.
This procedure shows how to implement Zipkin tracing.
-
Add the tracing artifacts to the
/opt/kafka/libs/
directory of the Strimzi Kafka image.You can use the Kafka container image on the Container Registry as a base image for creating a new custom image.
OpenTelemetry artifact for Zipkinio.opentelemetry:opentelemetry-exporter-zipkin
-
Set the tracing exporter and endpoint for the new tracing implementation.
Example Zikpin tracer configurationapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaMirrorMaker2 metadata: name: my-mm2-cluster spec: #... template: connectContainer: env: - name: OTEL_SERVICE_NAME value: my-zipkin-service - name: OTEL_EXPORTER_ZIPKIN_ENDPOINT value: http://zipkin-exporter-host-name:9411/api/v2/spans # (1) - name: OTEL_TRACES_EXPORTER value: zipkin # (2) tracing: type: opentelemetry #...
-
Specifies the Zipkin endpoint to connect to.
-
The Zipkin exporter.
-
17.3.7. Custom span names
A tracing span is a logical unit of work in Jaeger, with an operation name, start time, and duration. Spans have built-in names, but you can specify custom span names in your Kafka client instrumentation where used.
Specifying custom span names is optional and only applies when using a decorator pattern in producer and consumer client instrumentation or Kafka Streams instrumentation.
Specifying span names for OpenTelemetry
Custom span names cannot be specified directly with OpenTelemetry. Instead, you retrieve span names by adding code to your client application to extract additional tags and attributes.
//Defines attribute extraction for a producer
private static class ProducerAttribExtractor implements AttributesExtractor < ProducerRecord < ? , ? > , Void > {
@Override
public void onStart(AttributesBuilder attributes, ProducerRecord < ? , ? > producerRecord) {
set(attributes, AttributeKey.stringKey("prod_start"), "prod1");
}
@Override
public void onEnd(AttributesBuilder attributes, ProducerRecord < ? , ? > producerRecord, @Nullable Void unused, @Nullable Throwable error) {
set(attributes, AttributeKey.stringKey("prod_end"), "prod2");
}
}
//Defines attribute extraction for a consumer
private static class ConsumerAttribExtractor implements AttributesExtractor < ConsumerRecord < ? , ? > , Void > {
@Override
public void onStart(AttributesBuilder attributes, ConsumerRecord < ? , ? > producerRecord) {
set(attributes, AttributeKey.stringKey("con_start"), "con1");
}
@Override
public void onEnd(AttributesBuilder attributes, ConsumerRecord < ? , ? > producerRecord, @Nullable Void unused, @Nullable Throwable error) {
set(attributes, AttributeKey.stringKey("con_end"), "con2");
}
}
//Extracts the attributes
public static void main(String[] args) throws Exception {
Map < String, Object > configs = new HashMap < > (Collections.singletonMap(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092"));
System.setProperty("otel.traces.exporter", "jaeger");
System.setProperty("otel.service.name", "myapp1");
KafkaTracing tracing = KafkaTracing.newBuilder(GlobalOpenTelemetry.get())
.addProducerAttributesExtractors(new ProducerAttribExtractor())
.addConsumerAttributesExtractors(new ConsumerAttribExtractor())
.build();
Specifying span names for OpenTracing
To specify custom span names for OpenTracing, pass a BiFunction
object as an additional argument when instrumenting producers and consumers.
For more information on built-in names and specifying custom span names to instrument client application code in a decorator pattern, see the OpenTracing Apache Kafka client instrumentation.
18. Upgrading Strimzi
Strimzi can be upgraded to version 0.35.1 to take advantage of new features and enhancements, performance improvements, and security options.
As part of the upgrade, you upgrade Kafka to the latest supported version. Each Kafka release introduces new features, improvements, and bug fixes to your Strimzi deployment.
Strimzi can be downgraded to the previous version if you encounter issues with the newer version.
Released versions of Strimzi are available from the GitHub releases page.
If topics are configured for high availability, upgrading Strimzi should not cause any downtime for consumers and producers that publish and read data from those topics. Highly available topics have a replication factor of at least 3 and partitions distributed evenly among the brokers.
Upgrading Strimzi triggers rolling updates, where all brokers are restarted in turn, at different stages of the process. During rolling updates, not all brokers are online, so overall cluster availability is temporarily reduced. A reduction in cluster availability increases the chance that a broker failure will result in lost messages.
18.1. Strimzi upgrade paths
Two upgrade paths are available for Strimzi.
- Incremental upgrade
-
An incremental upgrade involves upgrading Strimzi from the previous minor version to version 0.35.1.
- Multi-version upgrade
-
A multi-version upgrade involves upgrading an older version of Strimzi to version 0.35.1 within a single upgrade, skipping one or more intermediate versions. For example, upgrading directly from Strimzi 0.30.0 to Strimzi 0.35.1 is possible.
18.1.1. Support for Kafka versions when upgrading
When upgrading Strimzi, it is important to ensure compatibility with the Kafka version being used.
Multi-version upgrades are possible even if the supported Kafka versions differ between the old and new versions.
However, if you attempt to upgrade to a new Strimzi version that does not support the current Kafka version, an error indicating that the Kafka version is not supported is generated.
In this case, you must upgrade the Kafka version as part of the Strimzi upgrade by changing the spec.kafka.version
in the Kafka
custom resource to the supported version for the new Strimzi version.
Note
|
You can review supported Kafka versions in the Supported versions table.
|
18.1.2. Upgrading from a Strimzi version earlier than 0.22
If you are upgrading to the latest version of Strimzi from a version prior to version 0.22, do the following:
-
Upgrade Strimzi to version 0.22 following the standard sequence.
-
Convert Strimzi custom resources to
v1beta2
using the API conversion tool provided with Strimzi. -
Do one of the following:
-
Upgrade Strimzi to a version between 0.23 and 0.26 (where the
ControlPlaneListener
feature gate is disabled by default). -
Upgrade Strimzi to a version between 0.27 and 0.31 (where the
ControlPlaneListener
feature gate is enabled by default) with theControlPlaneListener
feature gate disabled.
-
-
Enable the
ControlPlaneListener
feature gate. -
Upgrade to Strimzi 0.35.1 following the standard sequence.
Strimzi custom resources started using the v1beta2
API version in release 0.22.
CRDs and custom resources must be converted before upgrading to Strimzi 0.23 or newer.
For information on using the API conversion tool, see the Strimzi 0.24.0 upgrade documentation.
Note
|
As an alternative to first upgrading to version 0.22, you can install the custom resources from version 0.22 and then convert the resources. |
The ControlPlaneListener
feature is now permanently enabled in Strimzi.
You must upgrade to a version of Strimzi where it is disabled, then enable it using the
STRIMZI_FEATURE_GATES
environment variable in the Cluster Operator configuration.
ControlPlaneListener
feature gateenv:
- name: STRIMZI_FEATURE_GATES
value: -ControlPlaneListener
ControlPlaneListener
feature gateenv:
- name: STRIMZI_FEATURE_GATES
value: +ControlPlaneListener
18.2. Required upgrade sequence
To upgrade brokers and clients without downtime, you must complete the Strimzi upgrade procedures in the following order:
-
Make sure your Kubernetes cluster version is supported.
Strimzi 0.35.1 requires Kubernetes 1.19 and later.
-
Upgrade all Kafka brokers and client applications to the latest supported Kafka version.
-
Optional: Upgrade consumers and Kafka Streams applications to use the incremental cooperative rebalance protocol for partition rebalances.
18.3. Upgrading Kubernetes with minimal downtime
If you are upgrading Kubernetes, refer to the Kubernetes upgrade documentation to check the upgrade path and the steps to upgrade your nodes correctly. Before upgrading Kubernetes, check the supported versions for your version of Strimzi.
When performing your upgrade, you’ll want to keep your Kafka clusters available.
You can employ one of the following strategies:
-
Configuring pod disruption budgets
-
Rolling pods by one of these methods:
-
Using the Strimzi Drain Cleaner
-
Manually by applying an annotation to your pod
-
When using either of the methods to roll the pods, you must set a pod disruption budget of zero using the maxUnavailable
property.
Note
|
StrimziPodSet custom resources manage Kafka and ZooKeeper pods using a custom controller that cannot use the maxUnavailable value directly.
Instead, the maxUnavailable value is converted to a minAvailable value.
If there are three broker pods and the maxUnavailable property is set to 0 (zero), the minAvailable setting is 3 , requiring all three broker pods to be available and allowing zero pods to be unavailable.
|
For Kafka to stay operational, topics must also be replicated for high availability. This requires topic configuration that specifies a replication factor of at least 3 and a minimum number of in-sync replicas to 1 less than the replication factor.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
name: my-topic
labels:
strimzi.io/cluster: my-cluster
spec:
partitions: 1
replicas: 3
config:
# ...
min.insync.replicas: 2
# ...
In a highly available environment, the Cluster Operator maintains a minimum number of in-sync replicas for topics during the upgrade process so that there is no downtime.
18.3.1. Rolling pods using the Strimzi Drain Cleaner
You can use the Strimzi Drain Cleaner to evict nodes during an upgrade. The Strimzi Drain Cleaner annotates pods with a rolling update pod annotation. This informs the Cluster Operator to perform a rolling update of an evicted pod.
A pod disruption budget allows only a specified number of pods to be unavailable at a given time. During planned maintenance of Kafka broker pods, a pod disruption budget ensures Kafka continues to run in a highly available environment.
You specify a pod disruption budget using a template
customization for a Kafka component.
By default, pod disruption budgets allow only a single pod to be unavailable at a given time.
In order to use the Drain Cleaner to roll pods, you set maxUnavailable
to 0
(zero).
Reducing the pod disruption budget to zero prevents voluntary disruptions, so pods must be evicted manually.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
namespace: myproject
spec:
kafka:
# ...
template:
podDisruptionBudget:
maxUnavailable: 0
# ...
18.3.2. Rolling pods manually while keeping topics available
During an upgrade, you can trigger a manual rolling update of pods through the Cluster Operator.
Using Pod
resources, rolling updates restart the pods of resources with new pods.
As with using the Strimzi Drain Cleaner, you’ll need to set the maxUnavailable
value to zero for the pod disruption budget.
You need to watch the pods that need to be drained. You then add a pod annotation to make the update.
Here, the annotation updates a Kafka broker.
kubectl annotate pod <cluster_name>-kafka-<index> strimzi.io/manual-rolling-update=true
You replace <cluster_name> with the name of the cluster.
Kafka broker pods are named <cluster-name>-kafka-<index>, where <index> starts at zero and ends at the total number of replicas minus one.
For example, my-cluster-kafka-0
.
18.4. Upgrading the Cluster Operator
Use the same method to upgrade the Cluster Operator as the initial method of deployment.
- Using installation files
-
If you deployed the Cluster Operator using the installation YAML files, perform your upgrade by modifying the Operator installation files, as described in Upgrading the Cluster Operator.
- Using the OperatorHub.io
-
If you deployed Strimzi from OperatorHub.io, use the Operator Lifecycle Manager (OLM) to change the update channel for the Strimzi operators to a new Strimzi version.
Updating the channel starts one of the following types of upgrade, depending on your chosen upgrade strategy:
-
An automatic upgrade is initiated
-
A manual upgrade that requires approval before installation begins
NoteIf you subscribe to the stable channel, you can get automatic updates without changing channels. However, enabling automatic updates is not recommended because of the potential for missing any pre-installation upgrade steps. Use automatic upgrades only on version-specific channels. For more information on using OperatorHub.io to upgrade Operators, see the Operator Lifecycle Manager documentation.
-
- Using a Helm chart
-
If you deployed the Cluster Operator using a Helm chart, use
helm upgrade
.The
helm upgrade
command does not upgrade the Custom Resource Definitions for Helm. Install the new CRDs manually after upgrading the Cluster Operator. You can access the CRDs from the GitHub releases page or find them in thecrd
subdirectory inside the Helm Chart.
18.4.1. Upgrading the Cluster Operator returns Kafka version error
If you upgrade the Cluster Operator to a version that does not support the current version of Kafka you are using, you get an unsupported Kafka version error.
This error applies to all installation methods and means that you must upgrade Kafka to a supported Kafka version.
Change the spec.kafka.version
in the Kafka
resource to the supported version.
You can use kubectl
to check for error messages like this in the status
of the Kafka
resource.
kubectl get kafka <kafka_cluster_name> -n <namespace> -o jsonpath='{.status.conditions}'
Replace <kafka_cluster_name> with the name of your Kafka cluster and <namespace> with the Kubernetes namespace where the pod is running.
18.4.2. Upgrading the Cluster Operator using installation files
This procedure describes how to upgrade a Cluster Operator deployment to use Strimzi 0.35.1.
Follow this procedure if you deployed the Cluster Operator using the installation YAML files.
The availability of Kafka clusters managed by the Cluster Operator is not affected by the upgrade operation.
Note
|
Refer to the documentation supporting a specific version of Strimzi for information on how to upgrade to that version. |
-
An existing Cluster Operator deployment is available.
-
You have downloaded the release artifacts for Strimzi 0.35.1.
-
Take note of any configuration changes made to the existing Cluster Operator resources (in the
/install/cluster-operator
directory). Any changes will be overwritten by the new version of the Cluster Operator. -
Update your custom resources to reflect the supported configuration options available for Strimzi version 0.35.1.
-
Update the Cluster Operator.
-
Modify the installation files for the new Cluster Operator version according to the namespace the Cluster Operator is running in.
On Linux, use:
sed -i 's/namespace: .*/namespace: my-cluster-operator-namespace/' install/cluster-operator/*RoleBinding*.yaml
On MacOS, use:
sed -i '' 's/namespace: .*/namespace: my-cluster-operator-namespace/' install/cluster-operator/*RoleBinding*.yaml
-
If you modified one or more environment variables in your existing Cluster Operator
Deployment
, edit theinstall/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml
file to use those environment variables.
-
-
When you have an updated configuration, deploy it along with the rest of the installation resources:
kubectl replace -f install/cluster-operator
Wait for the rolling updates to complete.
-
If the new Operator version no longer supports the Kafka version you are upgrading from, the Cluster Operator returns an error message to say the version is not supported. Otherwise, no error message is returned.
-
If the error message is returned, upgrade to a Kafka version that is supported by the new Cluster Operator version:
-
Edit the
Kafka
custom resource. -
Change the
spec.kafka.version
property to a supported Kafka version.
-
-
If the error message is not returned, go to the next step. You will upgrade the Kafka version later.
-
-
Get the image for the Kafka pod to ensure the upgrade was successful:
kubectl get pods my-cluster-kafka-0 -o jsonpath='{.spec.containers[0].image}'
The image tag shows the new Operator version. For example:
quay.io/strimzi/kafka:0.35.1-kafka-3.4.0
Your Cluster Operator was upgraded to version 0.35.1 but the version of Kafka running in the cluster it manages is unchanged.
Following the Cluster Operator upgrade, you must perform a Kafka upgrade.
18.5. Upgrading Kafka
After you have upgraded your Cluster Operator to 0.35.1, the next step is to upgrade all Kafka brokers to the latest supported version of Kafka.
Kafka upgrades are performed by the Cluster Operator through rolling updates of the Kafka brokers.
The Cluster Operator initiates rolling updates based on the Kafka cluster configuration.
If Kafka.spec.kafka.config contains… |
The Cluster Operator initiates… |
---|---|
Both the |
A single rolling update. After the update, the |
Either the |
Two rolling updates. |
No configuration for the |
Two rolling updates. |
Important
|
From Kafka 3.0.0, when the inter.broker.protocol.version is set to 3.0 or higher, the log.message.format.version option is ignored and doesn’t need to be set.
The log.message.format.version property for brokers and the message.format.version property for topics are deprecated and will be removed in a future release of Kafka.
|
As part of the Kafka upgrade, the Cluster Operator initiates rolling updates for ZooKeeper.
-
A single rolling update occurs even if the ZooKeeper version is unchanged.
-
Additional rolling updates occur if the new version of Kafka requires a new ZooKeeper version.
18.5.1. Kafka versions
Kafka’s log message format version and inter-broker protocol version specify, respectively, the log format version appended to messages and the version of the Kafka protocol used in a cluster. To ensure the correct versions are used, the upgrade process involves making configuration changes to existing Kafka brokers and code changes to client applications (consumers and producers).
The following table shows the differences between Kafka versions:
Kafka version | Inter-broker protocol version | Log message format version | ZooKeeper version |
---|---|---|---|
3.3.1 |
3.3 |
3.3 |
3.6.3 |
3.3.2 |
3.3 |
3.3 |
3.6.3 |
3.4.0 |
3.4 |
3.4 |
3.6.3 |
In Kafka, the network protocol used for inter-broker communication is called the inter-broker protocol. Each version of Kafka has a compatible version of the inter-broker protocol. The minor version of the protocol typically increases to match the minor version of Kafka, as shown in the preceding table.
The inter-broker protocol version is set cluster wide in the Kafka
resource.
To change it, you edit the inter.broker.protocol.version
property in Kafka.spec.kafka.config
.
When a producer sends a message to a Kafka broker, the message is encoded using a specific format. The format can change between Kafka releases, so messages specify which version of the message format they were encoded with.
The properties used to set a specific message format version are as follows:
-
message.format.version
property for topics -
log.message.format.version
property for Kafka brokers
From Kafka 3.0.0, the message format version values are assumed to match the inter.broker.protocol.version
and don’t need to be set.
The values reflect the Kafka version used.
When upgrading to Kafka 3.0.0 or higher, you can remove these settings when you update the inter.broker.protocol.version
.
Otherwise, set the message format version based on the Kafka version you are upgrading to.
The default value of message.format.version
for a topic is defined by the log.message.format.version
that is set on the Kafka broker.
You can manually set the message.format.version
of a topic by modifying its topic configuration.
18.5.2. Strategies for upgrading clients
Upgrading Kafka clients ensures that they benefit from the features, fixes, and improvements that are introduced in new versions of Kafka. Upgraded clients maintain compatibility with other upgraded Kafka components. The performance and stability of the clients might also be improved.
Consider the best approach for upgrading Kafka clients and brokers to ensure a smooth transition. The chosen upgrade strategy depends on whether you are upgrading brokers or clients first. Since Kafka 3.0, you can upgrade brokers and client independently and in any order. The decision to upgrade clients or brokers first depends on several factors, such as the number of applications that need to be upgraded and how much downtime is tolerable.
If you upgrade clients before brokers, some new features may not work as they are not yet supported by brokers. However, brokers can handle producers and consumers running with different versions and supporting different log message versions.
Before Kafka 3.0, you would configure a specific message format for brokers using the log.message.format.version
property (or the message.format.version
property at the topic level).
This allowed brokers to support older Kafka clients that were using an outdated message format.
Otherwise, the brokers would need to convert the messages from the older clients, which came with a significant performance cost.
Apache Kafka Java clients have supported the latest message format version since version 0.11.
If all of your clients are using the latest message version, you can remove the log.message.format.version
or message.format.version
overrides when upgrading your brokers.
However, if you still have clients that are using an older message format version, we recommend upgrading your clients first.
Start with the consumers, then upgrade the producers before removing the log.message.format.version
or message.format.version
overrides when upgrading your brokers.
This will ensure that all of your clients can support the latest message format version and that the upgrade process goes smoothly.
You can track Kafka client names and versions using this metric:
-
kafka.server:type=socket-server-metrics,clientSoftwareName=<name>,clientSoftwareVersion=<version>,listener=<listener>,networkProcessor=<processor>
Tip
|
The following Kafka broker metrics help monitor the performance of message down-conversion:
|
18.5.3. Kafka version and image mappings
When upgrading Kafka, consider your settings for the STRIMZI_KAFKA_IMAGES
environment variable and the Kafka.spec.kafka.version
property.
-
Each
Kafka
resource can be configured with aKafka.spec.kafka.version
. -
The Cluster Operator’s
STRIMZI_KAFKA_IMAGES
environment variable provides a mapping between the Kafka version and the image to be used when that version is requested in a givenKafka
resource.-
If
Kafka.spec.kafka.image
is not configured, the default image for the given version is used. -
If
Kafka.spec.kafka.image
is configured, the default image is overridden.
-
Warning
|
The Cluster Operator cannot validate that an image actually contains a Kafka broker of the expected version. Take care to ensure that the given image corresponds to the given Kafka version. |
18.5.4. Upgrading Kafka brokers and client applications
Upgrade a Strimzi Kafka cluster to the latest supported Kafka version and inter-broker protocol version.
You should also choose a strategy for upgrading clients. Kafka clients are upgraded in step 6 of this procedure.
-
The Cluster Operator is up and running.
-
Before you upgrade the Strimzi Kafka cluster, check that the
Kafka.spec.kafka.config
properties of theKafka
resource do not contain configuration options that are not supported in the new Kafka version.
-
Update the Kafka cluster configuration:
kubectl edit kafka <my_cluster>
-
If configured, check that the
inter.broker.protocol.version
andlog.message.format.version
properties are set to the current version.For example, the current version is 3.3 if upgrading from Kafka version 3.3.1 to 3.4.0:
kind: Kafka spec: # ... kafka: version: 3.3.1 config: log.message.format.version: "3.3" inter.broker.protocol.version: "3.3" # ...
If
log.message.format.version
andinter.broker.protocol.version
are not configured, Strimzi automatically updates these versions to the current defaults after the update to the Kafka version in the next step.NoteThe value of log.message.format.version
andinter.broker.protocol.version
must be strings to prevent them from being interpreted as floating point numbers. -
Change the
Kafka.spec.kafka.version
to specify the new Kafka version; leave thelog.message.format.version
andinter.broker.protocol.version
at the defaults for the current Kafka version.NoteChanging the
kafka.version
ensures that all brokers in the cluster will be upgraded to start using the new broker binaries. During this process, some brokers are using the old binaries while others have already upgraded to the new ones. Leaving theinter.broker.protocol.version
unchanged at the current setting ensures that the brokers can continue to communicate with each other throughout the upgrade.For example, if upgrading from Kafka 3.3.1 to 3.4.0:
apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka spec: # ... kafka: version: 3.4.0 (1) config: log.message.format.version: "3.3" (2) inter.broker.protocol.version: "3.3" (3) # ...
-
Kafka version is changed to the new version.
-
Message format version is unchanged.
-
Inter-broker protocol version is unchanged.
WarningYou cannot downgrade Kafka if the inter.broker.protocol.version
for the new Kafka version changes. The inter-broker protocol version determines the schemas used for persistent metadata stored by the broker, including messages written to__consumer_offsets
. The downgraded cluster will not understand the messages. -
-
If the image for the Kafka cluster is defined in the Kafka custom resource, in
Kafka.spec.kafka.image
, update theimage
to point to a container image with the new Kafka version. -
Save and exit the editor, then wait for rolling updates to complete.
Check the progress of the rolling updates by watching the pod state transitions:
kubectl get pods my-cluster-kafka-0 -o jsonpath='{.spec.containers[0].image}'
The rolling updates ensure that each pod is using the broker binaries for the new version of Kafka.
-
Depending on your chosen strategy for upgrading clients, upgrade all client applications to use the new version of the client binaries.
If required, set the
version
property for Kafka Connect and MirrorMaker as the new version of Kafka:-
For Kafka Connect, update
KafkaConnect.spec.version
. -
For MirrorMaker, update
KafkaMirrorMaker.spec.version
. -
For MirrorMaker 2, update
KafkaMirrorMaker2.spec.version
.
-
-
If configured, update the Kafka resource to use the new
inter.broker.protocol.version
version. Otherwise, go to step 9.For example, if upgrading to Kafka 3.4.0:
apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka spec: # ... kafka: version: 3.4.0 config: log.message.format.version: "3.3" inter.broker.protocol.version: "3.4" # ...
-
Wait for the Cluster Operator to update the cluster.
-
If configured, update the Kafka resource to use the new
log.message.format.version
version. Otherwise, go to step 10.For example, if upgrading to Kafka 3.4.0:
apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka spec: # ... kafka: version: 3.4.0 config: log.message.format.version: "3.4" inter.broker.protocol.version: "3.4" # ...
ImportantFrom Kafka 3.0.0, when the inter.broker.protocol.version
is set to3.0
or higher, thelog.message.format.version
option is ignored and doesn’t need to be set. -
Wait for the Cluster Operator to update the cluster.
-
The Kafka cluster and clients are now using the new Kafka version.
-
The brokers are configured to send messages using the inter-broker protocol version and message format version of the new version of Kafka.
-
Following the Kafka upgrade, if required, you can upgrade consumers to use the incremental cooperative rebalance protocol.
18.6. Switching to FIPS mode when upgrading Strimzi
Upgrade Strimzi to run in FIPS mode on FIPS-enabled Kubernetes clusters.
Until Strimzi 0.33, running on FIPS-enabled Kubernetes clusters was possible only by disabling FIPS mode using the FIPS_MODE
environment variable.
From release 0.33, Strimzi supports FIPS mode.
If you run Strimzi on a FIPS-enabled Kubernetes cluster with the FIPS_MODE
set to disabled
, you can enable it by following this procedure.
-
FIPS-enabled Kubernetes cluster
-
An existing Cluster Operator deployment with the
FIPS_MODE
environment variable set todisabled
-
Upgrade the Cluster Operator to version 0.33 or newer but keep the
FIPS_MODE
environment variable set todisabled
. -
If you initially deployed a Strimzi version older than 0.30, it might use old encryption and digest algorithms in its PKCS #12 stores, which are not supported with FIPS enabled. To recreate the certificates with updated algorithms, renew the cluster and clients CA certificates.
-
To renew the CAs generated by the Cluster Operator, add the
force-renew
annotation to the CA secrets to trigger a renewal. -
To renew your own CAs, add the new certificate to the CA secret and update the
ca-cert-generation
annotation with a higher incremental value to capture the update.
-
-
If you use SCRAM-SHA-512 authentication, check the password length of your users. If they are less than 32 characters long, generate a new password in one of the following ways:
-
Delete the user secret so that the User Operator generates a new one with a new password of sufficient length.
-
If you provided your password using the
.spec.authentication.password
properties of theKafkaUser
custom resource, update the password in the Kubernetes secret referenced in the same password configuration. Don’t forget to update your clients to use the new passwords.
-
-
Ensure that the CA certificates are using the correct algorithms and the SCRAM-SHA-512 passwords are of sufficient length. You can then enable the FIPS mode.
-
Remove the
FIPS_MODE
environment variable from the Cluster Operator deployment. This restarts the Cluster Operator and rolls all the operands to enable the FIPS mode. After the restart is complete, all Kafka clusters now run with FIPS mode enabled.
18.7. Upgrading consumers to cooperative rebalancing
You can upgrade Kafka consumers and Kafka Streams applications to use the incremental cooperative rebalance protocol for partition rebalances instead of the default eager rebalance protocol. The new protocol was added in Kafka 2.4.0.
Consumers keep their partition assignments in a cooperative rebalance and only revoke them at the end of the process, if needed to achieve a balanced cluster. This reduces the unavailability of the consumer group or Kafka Streams application.
Note
|
Upgrading to the incremental cooperative rebalance protocol is optional. The eager rebalance protocol is still supported. |
-
You have upgraded Kafka brokers and client applications to Kafka 3.4.0.
To upgrade a Kafka consumer to use the incremental cooperative rebalance protocol:
-
Replace the Kafka clients
.jar
file with the new version. -
In the consumer configuration, append
cooperative-sticky
to thepartition.assignment.strategy
. For example, if therange
strategy is set, change the configuration torange, cooperative-sticky
. -
Restart each consumer in the group in turn, waiting for the consumer to rejoin the group after each restart.
-
Reconfigure each consumer in the group by removing the earlier
partition.assignment.strategy
from the consumer configuration, leaving only thecooperative-sticky
strategy. -
Restart each consumer in the group in turn, waiting for the consumer to rejoin the group after each restart.
To upgrade a Kafka Streams application to use the incremental cooperative rebalance protocol:
-
Replace the Kafka Streams
.jar
file with the new version. -
In the Kafka Streams configuration, set the
upgrade.from
configuration parameter to the Kafka version you are upgrading from (for example, 2.3). -
Restart each of the stream processors (nodes) in turn.
-
Remove the
upgrade.from
configuration parameter from the Kafka Streams configuration. -
Restart each consumer in the group in turn.
19. Downgrading Strimzi
If you are encountering issues with the version of Strimzi you upgraded to, you can revert your installation to the previous version.
If you used the YAML installation files to install Strimzi, you can use the YAML installation files from the previous release to perform the following downgrade procedures:
If the previous version of Strimzi does not support the version of Kafka you are using, you can also downgrade Kafka as long as the log message format versions appended to messages match.
Warning
|
The following downgrade instructions are only suitable if you installed Strimzi using the installation files. If you installed Strimzi using another method, like OperatorHub.io, downgrade may not be supported by that method unless specified in their documentation. To ensure a successful downgrade process, it is essential to use a supported approach. |
19.1. Downgrading the Cluster Operator to a previous version
If you are encountering issues with Strimzi, you can revert your installation.
This procedure describes how to downgrade a Cluster Operator deployment to a previous version.
-
An existing Cluster Operator deployment is available.
-
You have downloaded the installation files for the previous version.
Check the downgrade requirements of the Strimzi feature gates. If a feature gate is permanently enabled, you may need to downgrade to a version that allows you to disable it before downgrading to your target version.
-
Take note of any configuration changes made to the existing Cluster Operator resources (in the
/install/cluster-operator
directory). Any changes will be overwritten by the previous version of the Cluster Operator. -
Revert your custom resources to reflect the supported configuration options available for the version of Strimzi you are downgrading to.
-
Update the Cluster Operator.
-
Modify the installation files for the previous version according to the namespace the Cluster Operator is running in.
On Linux, use:
sed -i 's/namespace: .*/namespace: my-cluster-operator-namespace/' install/cluster-operator/*RoleBinding*.yaml
On MacOS, use:
sed -i '' 's/namespace: .*/namespace: my-cluster-operator-namespace/' install/cluster-operator/*RoleBinding*.yaml
-
If you modified one or more environment variables in your existing Cluster Operator
Deployment
, edit theinstall/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml
file to use those environment variables.
-
-
When you have an updated configuration, deploy it along with the rest of the installation resources:
kubectl replace -f install/cluster-operator
Wait for the rolling updates to complete.
-
Get the image for the Kafka pod to ensure the downgrade was successful:
kubectl get pod my-cluster-kafka-0 -o jsonpath='{.spec.containers[0].image}'
The image tag shows the new Strimzi version followed by the Kafka version. For example,
NEW-STRIMZI-VERSION-kafka-CURRENT-KAFKA-VERSION
.
Your Cluster Operator was downgraded to the previous version.
19.2. Downgrading Kafka
Kafka version downgrades are performed by the Cluster Operator.
19.2.1. Kafka version compatibility for downgrades
Kafka downgrades are dependent on compatible current and target Kafka versions, and the state at which messages have been logged.
You cannot revert to the previous Kafka version if that version does not support any of the inter.broker.protocol.version
settings which have ever been used in that cluster,
or messages have been added to message logs that use a newer log.message.format.version
.
The inter.broker.protocol.version
determines the schemas used for persistent metadata stored by the broker, such as the schema for messages written to __consumer_offsets
.
If you downgrade to a version of Kafka that does not understand an inter.broker.protocol.version
that has ever been previously used in the cluster the broker will encounter data it cannot understand.
If the target downgrade version of Kafka has:
-
The same
log.message.format.version
as the current version, the Cluster Operator downgrades by performing a single rolling restart of the brokers. -
A different
log.message.format.version
, downgrading is only possible if the running cluster has always hadlog.message.format.version
set to the version used by the downgraded version. This is typically only the case if the upgrade procedure was aborted before thelog.message.format.version
was changed. In this case, the downgrade requires:-
Two rolling restarts of the brokers if the interbroker protocol of the two versions is different
-
A single rolling restart if they are the same
-
Downgrading is not possible if the new version has ever used a log.message.format.version
that is not supported by the previous version, including when the default value for log.message.format.version
is used. For example, this resource can be downgraded to Kafka version 3.3.1 because the log.message.format.version
has not been changed:
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
# ...
kafka:
version: 3.4.0
config:
log.message.format.version: "3.3"
# ...
The downgrade would not be possible if the log.message.format.version
was set at "3.4"
or a value was absent, so that the parameter took the default value for a 3.4.0 broker of 3.4.
Important
|
From Kafka 3.0.0, when the inter.broker.protocol.version is set to 3.0 or higher, the log.message.format.version option is ignored and doesn’t need to be set.
|
19.2.2. Downgrading Kafka brokers and client applications
Downgrade a Strimzi Kafka cluster to a lower (previous) version of Kafka, such as downgrading from 3.4.0 to 3.3.1.
-
The Cluster Operator is up and running.
-
Before you downgrade the Strimzi Kafka cluster, check the following for the
Kafka
resource: