1. Configuration overview
Strimzi simplifies the process of running Apache Kafka in a Kubernetes cluster.
This guide describes how to configure and manage a Strimzi deployment.
1.1. Configuring custom resources
Use custom resources to configure your Strimzi deployment.
You can use custom resources to configure and create instances of the following components:
-
Kafka clusters
-
Kafka Connect clusters
-
Kafka MirrorMaker
-
Kafka Bridge
-
Cruise Control
You can also use custom resource configuration to manage your instances or modify your deployment to introduce additional features. This might include configuration that supports the following:
-
Securing client access to Kafka brokers
-
Accessing Kafka brokers from outside the cluster
-
Creating topics
-
Creating users (clients)
-
Controlling feature gates
-
Changing logging frequency
-
Allocating resource limits and requests
-
Introducing features, such as Strimzi Drain Cleaner, Cruise Control, or distributed tracing.
The Custom resource API reference describes the properties you can use in your configuration.
1.2. Using ConfigMaps to add configuration
Use ConfigMap
resources to add specific configuration to your Strimzi deployment.
ConfigMaps use key-value pairs to store non-confidential data.
Configuration data added to ConfigMaps is maintained in one place and can be reused amongst components.
ConfigMaps can only store configuration data related to the following:
-
Logging configuration
-
Metrics configuration
-
External configuration for Kafka Connect connectors
You can’t use ConfigMaps for other areas of configuration.
When you configure a component, you can add a reference to a ConfigMap using the configMapKeyRef
property.
For example, you can use configMapKeyRef
to reference a ConfigMap that provides configuration for logging.
You might use a ConfigMap to pass a Log4j configuration file.
You add the reference to the logging
configuration.
spec:
# ...
logging:
type: external
valueFrom:
configMapKeyRef:
name: my-config-map
key: my-config-map-key
To use a ConfigMap for metrics configuration, you add a reference to the metricsConfig
configuration of the component in the same way.
ExternalConfiguration
properties make data from a ConfigMap (or Secret) mounted to a pod available as environment variables or volumes.
You can use external configuration data for the connectors used by Kafka Connect.
The data might be related to an external data source, providing the values needed for the connector to communicate with that data source.
For example, you can use the configMapKeyRef
property to pass configuration data from a ConfigMap as an environment variable.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
name: my-connect
spec:
# ...
externalConfiguration:
env:
- name: MY_ENVIRONMENT_VARIABLE
valueFrom:
configMapKeyRef:
name: my-config-map
key: my-key
If you are using ConfigMaps that are managed externally, use configuration providers to load the data in the ConfigMaps. For more information on using configuration providers, see Loading configuration values from external sources.
1.2.1. Naming custom ConfigMaps
Strimzi creates its own ConfigMaps and other resources when it is deployed to Kubernetes. The ConfigMaps contain data necessary for running components. The ConfigMaps created by Strimzi must not be edited.
Make sure that any custom ConfigMaps you create do not have the same name as these default ConfigMaps. If they have the same name, they will be overwritten. For example, if your ConfigMap has the same name as the ConfigMap for the Kafka cluster, it will be overwritten when there is an update to the Kafka cluster.
1.3. 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. Configuring a Strimzi deployment
Configure your Strimzi deployment using custom resources. Strimzi provides example configuration files, which can serve as a starting point when building your own Kafka component configuration for deployment.
Note
|
Labels applied to a custom resource are also applied to the Kubernetes resources making up its cluster. This provides a convenient mechanism for resources to be labeled as required. |
You can use Prometheus and Grafana to monitor your Strimzi deployment. For more information, see Introducing metrics to Kafka.
2.1. Kafka cluster configuration
Configure a Kafka deployment using the Kafka
resource.
A Kafka cluster is deployed with a ZooKeeper cluster, so configuration options are also available for ZooKeeper within the Kafka
resource.
The Entity Operator comprises the Topic Operator and User Operator.
You can also configure entityOperator
properties in the Kafka
resource to include the Topic Operator and User Operator in the deployment.
Kafka
schema reference describes the full schema of the Kafka
resource.
For more information about Apache Kafka, see the Apache Kafka documentation.
You configure listeners for connecting clients to Kafka brokers.
For more information on configuring listeners, see GenericKafkaListener
schema reference.
When deploying Kafka, the Cluster Operator automatically sets up and renews TLS certificates to enable encryption and authentication within your cluster. If required, you can manually renew the cluster and clients CA certificates before their renewal period starts. You can also replace the keys used by the cluster and clients CA certificates. For more information, see Renewing CA certificates manually and Replacing private keys.
2.1.1. Configuring Kafka
Use the properties of the Kafka
resource to configure your Kafka deployment.
As well as configuring Kafka, you can add configuration for ZooKeeper and the Strimzi Operators. Common configuration properties, such as logging and healthchecks, are configured independently for each component.
This procedure shows only some of the possible configuration options, but those that are particularly important include:
-
Resource requests (CPU / Memory)
-
JVM options for maximum and minimum memory allocation
-
Listeners (and authentication of clients)
-
Authentication
-
Storage
-
Rack awareness
-
Metrics
-
Cruise Control for cluster rebalancing
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 your Kafka version.
For more information, see Upgrading Kafka.
-
A Kubernetes cluster
-
A running Cluster Operator
See the Deploying and Upgrading Strimzi guide for instructions on deploying a:
-
Edit the
spec
properties 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: kafka: replicas: 3 (1) version: 3.3.2 (2) logging: (3) type: inline loggers: kafka.root.logger.level: "INFO" resources: (4) requests: memory: 64Gi cpu: "8" limits: memory: 64Gi cpu: "12" readinessProbe: (5) initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 jvmOptions: (6) -Xms: 8192m -Xmx: 8192m image: my-org/my-image:latest (7) listeners: (8) - name: plain (9) port: 9092 (10) type: internal (11) tls: false (12) configuration: useServiceDnsDomain: true (13) - name: tls port: 9093 type: internal tls: true authentication: (14) type: tls - name: external (15) port: 9094 type: route tls: true configuration: brokerCertChainAndKey: (16) secretName: my-secret certificate: my-certificate.crt key: my-key.key authorization: (17) type: simple config: (18) auto.create.topics.enable: "false" offsets.topic.replication.factor: 3 transaction.state.log.replication.factor: 3 transaction.state.log.min.isr: 2 default.replication.factor: 3 min.insync.replicas: 2 inter.broker.protocol.version: "3.3" ssl.cipher.suites: "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384" (19) ssl.enabled.protocols: "TLSv1.2" ssl.protocol: "TLSv1.2" storage: (20) type: persistent-claim (21) size: 10000Gi (22) rack: (23) topologyKey: topology.kubernetes.io/zone metricsConfig: (24) type: jmxPrometheusExporter valueFrom: configMapKeyRef: (25) name: my-config-map key: my-key # ... zookeeper: (26) replicas: 3 (27) logging: (28) type: inline loggers: zookeeper.root.logger: "INFO" resources: requests: memory: 8Gi cpu: "2" limits: memory: 8Gi cpu: "2" jvmOptions: -Xms: 4096m -Xmx: 4096m storage: type: persistent-claim size: 1000Gi metricsConfig: # ... entityOperator: (29) tlsSidecar: (30) resources: requests: cpu: 200m memory: 64Mi limits: cpu: 500m memory: 128Mi topicOperator: watchedNamespace: my-topic-namespace reconciliationIntervalSeconds: 60 logging: (31) type: inline loggers: rootLogger.level: "INFO" resources: requests: memory: 512Mi cpu: "1" limits: memory: 512Mi cpu: "1" userOperator: watchedNamespace: my-topic-namespace reconciliationIntervalSeconds: 60 logging: (32) type: inline loggers: rootLogger.level: INFO resources: requests: memory: 512Mi cpu: "1" limits: memory: 512Mi cpu: "1" kafkaExporter: (33) # ... cruiseControl: (34) # ...
-
The number of replica nodes. If your cluster already has topics defined, you can scale clusters.
-
Kafka version, which can be changed to a supported version by following the upgrade procedure.
-
Kafka loggers and log levels added directly (
inline
) or indirectly (external
) through a ConfigMap. A custom ConfigMap must be placed under thelog4j.properties
key. For the Kafkakafka.root.logger.level
logger, you can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. -
Requests for reservation of supported resources, currently
cpu
andmemory
, and limits to specify the maximum resources that can be consumed. -
Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).
-
JVM configuration options to optimize performance for the Virtual Machine (VM) running Kafka.
-
ADVANCED OPTION: Container image configuration, which is recommended only in special situations.
-
Listeners configure how clients connect to the Kafka cluster via bootstrap addresses. Listeners are configured as internal or external listeners for connection from inside or outside the Kubernetes cluster.
-
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.
-
Listener type specified as
internal
orcluster-ip
(to expose Kafka using per-brokerClusterIP
services), or for external listeners, asroute
,loadbalancer
,nodeport
oringress
. -
Enables TLS encryption for each listener. Default is
false
. TLS encryption is not required forroute
listeners. -
Defines whether the fully-qualified DNS names including the cluster service suffix (usually
.cluster.local
) are assigned. -
Listener authentication mechanism specified as mTLS, SCRAM-SHA-512, or token-based OAuth 2.0.
-
External listener configuration specifies how the Kafka cluster is exposed outside Kubernetes, such as through a
route
,loadbalancer
ornodeport
. -
Optional configuration for a Kafka listener certificate managed by an external CA (certificate authority). The
brokerCertChainAndKey
specifies aSecret
that contains a server certificate and a private key. You can configure Kafka listener certificates on any listener with enabled TLS encryption. -
Authorization enables simple, OAUTH 2.0, or OPA authorization on the Kafka broker. Simple authorization uses the
AclAuthorizer
Kafka plugin. -
Broker configuration. Standard Apache Kafka configuration may be provided, restricted to those properties not managed directly by Strimzi.
-
Storage is configured as
ephemeral
,persistent-claim
orjbod
. -
Storage size for persistent volumes may be increased and additional volumes may be added to JBOD storage.
-
Persistent storage has additional configuration options, such as a storage
id
andclass
for dynamic volume provisioning. -
Rack awareness configuration to spread replicas across different racks, data centers, or availability zones. The
topologyKey
must match a node label containing the rack ID. The example used in this configuration specifies a zone using the standardtopology.kubernetes.io/zone
label. -
Prometheus metrics enabled. In this example, metrics are configured for the Prometheus JMX Exporter (the default metrics exporter).
-
Prometheus rules for exporting metrics to a Grafana dashboard through the Prometheus JMX Exporter, which are enabled by referencing a ConfigMap containing configuration for the Prometheus JMX exporter. You can enable metrics without further configuration using a reference to a ConfigMap containing an empty file under
metricsConfig.valueFrom.configMapKeyRef.key
. -
ZooKeeper-specific configuration, which contains properties similar to the Kafka configuration.
-
The number of ZooKeeper nodes. ZooKeeper clusters or ensembles usually run with an odd number of nodes, typically three, five, or seven. The majority of nodes must be available in order to maintain an effective quorum. If the ZooKeeper cluster loses its quorum, it will stop responding to clients and the Kafka brokers will stop working. Having a stable and highly available ZooKeeper cluster is crucial for Strimzi.
-
Specified ZooKeeper loggers and log levels.
-
Entity Operator configuration, which specifies the configuration for the Topic Operator and User Operator.
-
Entity Operator TLS sidecar configuration. Entity Operator uses the TLS sidecar for secure communication with ZooKeeper.
-
Specified Topic Operator loggers and log levels. This example uses
inline
logging. -
Specified User Operator loggers and log levels.
-
Kafka Exporter configuration. Kafka Exporter is an optional component for extracting metrics data from Kafka brokers, in particular consumer lag data. For Kafka Exporter to be able to work properly, consumer groups need to be in use.
-
Optional configuration for Cruise Control, which is used to rebalance the Kafka cluster.
-
-
Create or update the resource:
kubectl apply -f <kafka_configuration_file>
2.1.2. Configuring the Entity Operator
The Entity Operator is responsible for managing Kafka-related entities in a running Kafka cluster.
The Entity Operator comprises the:
-
Topic Operator to manage Kafka topics
-
User Operator to manage Kafka users
Through Kafka
resource configuration, the Cluster Operator can deploy the Entity Operator, including one or both operators, when deploying a Kafka cluster.
The operators are automatically configured to manage the topics and users of the Kafka cluster. The Topic Operator and User Operator can only watch a single namespace. For more information, see Watching namespaces with Strimzi operators.
Note
|
When deployed, the Entity Operator pod contains the operators according to the deployment configuration. |
Entity Operator configuration properties
Use the entityOperator
property in Kafka.spec
to configure the Entity Operator.
The entityOperator
property supports several sub-properties:
-
tlsSidecar
-
topicOperator
-
userOperator
-
template
The tlsSidecar
property contains the configuration of the TLS sidecar container, which is used to communicate with ZooKeeper.
The template
property contains the configuration of the Entity Operator pod, such as labels, annotations, affinity, and tolerations.
For more information on configuring templates, see Customizing Kubernetes resources.
The topicOperator
property contains the configuration of the Topic Operator.
When this option is missing, the Entity Operator is deployed without the Topic Operator.
The userOperator
property contains the configuration of the User Operator.
When this option is missing, the Entity Operator is deployed without the User Operator.
For more information on the properties used to configure the Entity Operator, see the EntityUserOperatorSpec
schema reference.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
zookeeper:
# ...
entityOperator:
topicOperator: {}
userOperator: {}
If an empty object ({}
) is used for the topicOperator
and userOperator
, all properties use their default values.
When both topicOperator
and userOperator
properties are missing, the Entity Operator is not deployed.
Topic Operator configuration properties
Topic Operator deployment can be configured using additional options inside the topicOperator
object.
The following properties are supported:
watchedNamespace
-
The Kubernetes namespace in which the Topic Operator watches for
KafkaTopic
resources. Default is the namespace where the Kafka cluster is deployed. reconciliationIntervalSeconds
-
The interval between periodic reconciliations in seconds. Default
120
. zookeeperSessionTimeoutSeconds
-
The ZooKeeper session timeout in seconds. Default
18
. topicMetadataMaxAttempts
-
The number of attempts at getting topic metadata from Kafka. The time between each attempt is defined as an exponential back-off. Consider increasing this value when topic creation might take more time due to the number of partitions or replicas. Default
6
. image
-
The
image
property can be used to configure the container image which will be used. For more details about configuring custom container images, seeimage
. resources
-
The
resources
property configures the amount of resources allocated to the Topic Operator. For more details about resource request and limit configuration, seeresources
. logging
-
The
logging
property configures the logging of the Topic Operator. For more details, seelogging
.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
zookeeper:
# ...
entityOperator:
# ...
topicOperator:
watchedNamespace: my-topic-namespace
reconciliationIntervalSeconds: 60
# ...
User Operator configuration properties
User Operator deployment can be configured using additional options inside the userOperator
object.
The following properties are supported:
watchedNamespace
-
The Kubernetes namespace in which the User Operator watches for
KafkaUser
resources. Default is the namespace where the Kafka cluster is deployed. reconciliationIntervalSeconds
-
The interval between periodic reconciliations in seconds. Default
120
. image
-
The
image
property can be used to configure the container image which will be used. For more details about configuring custom container images, seeimage
. resources
-
The
resources
property configures the amount of resources allocated to the User Operator. For more details about resource request and limit configuration, seeresources
. logging
-
The
logging
property configures the logging of the User Operator. For more details, seelogging
. secretPrefix
-
The
secretPrefix
property adds a prefix to the name of all Secrets created from the KafkaUser resource. For example,secretPrefix: kafka-
would prefix all Secret names withkafka-
. So a KafkaUser namedmy-user
would create a Secret namedkafka-my-user
.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
zookeeper:
# ...
entityOperator:
# ...
userOperator:
watchedNamespace: my-user-namespace
reconciliationIntervalSeconds: 60
# ...
2.1.3. Configuring Kafka and ZooKeeper storage
As stateful applications, Kafka and ZooKeeper store data on disk. Strimzi supports three storage types for this data:
-
Ephemeral (Recommended for development only)
-
Persistent
-
JBOD (Kafka only not ZooKeeper)
When configuring a Kafka
resource, you can specify the type of storage used by the Kafka broker and its corresponding ZooKeeper node. You configure the storage type using the storage
property in the following resources:
-
Kafka.spec.kafka
-
Kafka.spec.zookeeper
The storage type is configured in the type
field.
Refer to the schema reference for more information on storage configuration properties:
Warning
|
The storage type cannot be changed after a Kafka cluster is deployed. |
Data storage considerations
For Strimzi to work well, an efficient data storage infrastructure is essential. We strongly recommend using block storage. Strimzi is only tested for use with block storage. File storage, such as NFS, is not tested and there is no guarantee it will work.
Choose one of the following options for your block storage:
-
A cloud-based block storage solution, such as Amazon Elastic Block Store (EBS)
-
Persistent storage using local persistent volumes
-
Storage Area Network (SAN) volumes accessed by a protocol such as Fibre Channel or iSCSI
Note
|
Strimzi does not require Kubernetes raw block volumes. |
File systems
Kafka uses a file system for storing messages. Strimzi is compatible with the XFS and ext4 file systems, which are commonly used with Kafka. Consider the underlying architecture and requirements of your deployment when choosing and setting up your file system.
For more information, refer to Filesystem Selection in the Kafka documentation.
Disk usage
Use separate disks for Apache Kafka and ZooKeeper.
Solid-state drives (SSDs), though not essential, can improve the performance of Kafka in large clusters where data is sent to and received from multiple topics asynchronously. SSDs are particularly effective with ZooKeeper, which requires fast, low latency data access.
Note
|
You do not need to provision replicated storage because Kafka and ZooKeeper both have built-in data replication. |
Ephemeral storage
Ephemeral data storage is transient. All pods on a node share a local ephemeral storage space. Data is retained for as long as the pod that uses it is running. The data is lost when a pod is deleted. Although a pod can recover data in a highly available environment.
Because of its transient nature, ephemeral storage is only recommended for development and testing.
Ephemeral storage uses emptyDir
volumes to store data.
An emptyDir
volume is created when a pod is assigned to a node.
You can set the total amount of storage for the emptyDir
using the sizeLimit
property .
Important
|
Ephemeral storage is not suitable for single-node ZooKeeper clusters or Kafka topics with a replication factor of 1. |
To use ephemeral storage, you set the storage type configuration in the Kafka
or ZooKeeper
resource to ephemeral
.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
storage:
type: ephemeral
# ...
zookeeper:
# ...
storage:
type: ephemeral
# ...
Mount path of Kafka log directories
The ephemeral volume is used by Kafka brokers as log directories mounted into the following path:
/var/lib/kafka/data/kafka-logIDX
Where IDX
is the Kafka broker pod index. For example /var/lib/kafka/data/kafka-log0
.
Persistent storage
Persistent data storage retains data in the event of system disruption. For pods that use persistent data storage, data is persisted across pod failures and restarts.
A dynamic provisioning framework enables clusters to be created with persistent storage. Pod configuration uses Persistent Volume Claims (PVCs) to make storage requests on persistent volumes (PVs). PVs are storage resources that represent a storage volume. PVs are independent of the pods that use them. The PVC requests the amount of storage required when a pod is being created. The underlying storage infrastructure of the PV does not need to be understood. If a PV matches the storage criteria, the PVC is bound to the PV.
Because of its permanent nature, persistent storage is recommended for production.
PVCs can request different types of persistent storage by specifying a StorageClass. Storage classes define storage profiles and dynamically provision PVs. If a storage class is not specified, the default storage class is used. Persistent storage options might include SAN storage types or local persistent volumes.
To use persistent storage, you set the storage type configuration in the Kafka
or ZooKeeper
resource to persistent-claim
.
In the production environment, the following configuration is recommended:
-
For Kafka, configure
type: jbod
with one or moretype: persistent-claim
volumes -
For ZooKeeper, configure
type: persistent-claim
Persistent storage also has the following configuration options:
id
(optional)-
A storage identification number. This option is mandatory for storage volumes defined in a JBOD storage declaration. Default is
0
. size
(required)-
The size of the persistent volume claim, for example, "1000Gi".
class
(optional)-
The Kubernetes StorageClass to use for dynamic volume provisioning. Storage
class
configuration includes parameters that describe the profile of a volume in detail. selector
(optional)-
Configuration to specify a specific PV. Provides key:value pairs representing the labels of the volume selected.
deleteClaim
(optional)-
Boolean value to specify whether the PVC is deleted when the cluster is uninstalled. Default is
false
.
Warning
|
Increasing the size of persistent volumes in an existing Strimzi cluster is only supported in Kubernetes versions that support persistent volume resizing. The persistent volume to be resized must use a storage class that supports volume expansion. For other versions of Kubernetes and storage classes that do not support volume expansion, you must decide the necessary storage size before deploying the cluster. Decreasing the size of existing persistent volumes is not possible. |
# ...
spec:
kafka:
# ...
storage:
type: jbod
volumes:
- id: 0
type: persistent-claim
size: 100Gi
deleteClaim: false
- id: 1
type: persistent-claim
size: 100Gi
deleteClaim: false
- id: 2
type: persistent-claim
size: 100Gi
deleteClaim: false
# ...
zookeeper:
storage:
type: persistent-claim
size: 1000Gi
# ...
If you do not specify a storage class, the default is used. The following example specifies a storage class.
# ...
storage:
type: persistent-claim
size: 1Gi
class: my-storage-class
# ...
Use a selector
to specify a labeled persistent volume that provides certain features, such as an SSD.
# ...
storage:
type: persistent-claim
size: 1Gi
selector:
hdd-type: ssd
deleteClaim: true
# ...
Storage class overrides
Instead of using the default storage class, you can specify a different storage class for one or more Kafka brokers or ZooKeeper nodes.
This is useful, for example, when storage classes are restricted to different availability zones or data centers.
You can use the overrides
field for this purpose.
In this example, the default storage class is named my-storage-class
:
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
labels:
app: my-cluster
name: my-cluster
namespace: myproject
spec:
# ...
kafka:
replicas: 3
storage:
type: jbod
volumes:
- id: 0
type: persistent-claim
size: 100Gi
deleteClaim: false
class: my-storage-class
overrides:
- broker: 0
class: my-storage-class-zone-1a
- broker: 1
class: my-storage-class-zone-1b
- broker: 2
class: my-storage-class-zone-1c
# ...
# ...
zookeeper:
replicas: 3
storage:
deleteClaim: true
size: 100Gi
type: persistent-claim
class: my-storage-class
overrides:
- broker: 0
class: my-storage-class-zone-1a
- broker: 1
class: my-storage-class-zone-1b
- broker: 2
class: my-storage-class-zone-1c
# ...
As a result of the configured overrides
property, the volumes use the following storage classes:
-
The persistent volumes of ZooKeeper node 0 use
my-storage-class-zone-1a
. -
The persistent volumes of ZooKeeper node 1 use
my-storage-class-zone-1b
. -
The persistent volumes of ZooKeeepr node 2 use
my-storage-class-zone-1c
. -
The persistent volumes of Kafka broker 0 use
my-storage-class-zone-1a
. -
The persistent volumes of Kafka broker 1 use
my-storage-class-zone-1b
. -
The persistent volumes of Kafka broker 2 use
my-storage-class-zone-1c
.
The overrides
property is currently used only to override storage class configurations.
Overrides for other storage configuration properties is not currently supported.
Other storage configuration properties are currently not supported.
PVC resources for persistent storage
When persistent storage is used, it creates PVCs with the following names:
data-cluster-name-kafka-idx
-
PVC for the volume used for storing data for the Kafka broker pod
idx
. data-cluster-name-zookeeper-idx
-
PVC for the volume used for storing data for the ZooKeeper node pod
idx
.
Mount path of Kafka log directories
The persistent volume is used by the Kafka brokers as log directories mounted into the following path:
/var/lib/kafka/data/kafka-logIDX
Where IDX
is the Kafka broker pod index. For example /var/lib/kafka/data/kafka-log0
.
Resizing persistent volumes
You can provision increased storage capacity by increasing the size of the persistent volumes used by an existing Strimzi cluster. Resizing persistent volumes is supported in clusters that use either a single persistent volume or multiple persistent volumes in a JBOD storage configuration.
Note
|
You can increase but not decrease the size of persistent volumes. Decreasing the size of persistent volumes is not currently supported in Kubernetes. |
-
A Kubernetes cluster with support for volume resizing.
-
The Cluster Operator is running.
-
A Kafka cluster using persistent volumes created using a storage class that supports volume expansion.
-
Edit the
Kafka
resource for your cluster.Change the
size
property to increase the size of the persistent volume allocated to a Kafka cluster, a ZooKeeper cluster, or both.-
For Kafka clusters, update the
size
property underspec.kafka.storage
. -
For ZooKeeper clusters, update the
size
property underspec.zookeeper.storage
.
Kafka configuration to increase the volume size to2000Gi
apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: kafka: # ... storage: type: persistent-claim size: 2000Gi class: my-storage-class # ... zookeeper: # ...
-
-
Create or update the resource:
kubectl apply -f <kafka_configuration_file>
Kubernetes increases the capacity of the selected persistent volumes in response to a request from the Cluster Operator. When the resizing is complete, the Cluster Operator restarts all pods that use the resized persistent volumes. This happens automatically.
-
Verify that the storage capacity has increased for the relevant pods on the cluster:
kubectl get pv
Kafka broker pods with increased storageNAME CAPACITY CLAIM pvc-0ca459ce-... 2000Gi my-project/data-my-cluster-kafka-2 pvc-6e1810be-... 2000Gi my-project/data-my-cluster-kafka-0 pvc-82dc78c9-... 2000Gi my-project/data-my-cluster-kafka-1
The output shows the names of each PVC associated with a broker pod.
-
For more information about resizing persistent volumes in Kubernetes, see Resizing Persistent Volumes using Kubernetes.
JBOD storage
You can configure Strimzi to use JBOD, a data storage configuration of multiple disks or volumes. JBOD is one approach to providing increased data storage for Kafka brokers. It can also improve performance.
Note
|
JBOD storage is supported for Kafka only not ZooKeeper. |
A JBOD configuration is described by one or more volumes, each of which can be either ephemeral or persistent. The rules and constraints for JBOD volume declarations are the same as those for ephemeral and persistent storage. For example, you cannot decrease the size of a persistent storage volume after it has been provisioned, or you cannot change the value of sizeLimit
when the type is ephemeral
.
To use JBOD storage, you set the storage type configuration in the Kafka
resource to jbod
.
The volumes
property allows you to describe the disks that make up your JBOD storage array or configuration.
# ...
storage:
type: jbod
volumes:
- id: 0
type: persistent-claim
size: 100Gi
deleteClaim: false
- id: 1
type: persistent-claim
size: 100Gi
deleteClaim: false
# ...
The IDs cannot be changed once the JBOD volumes are created. You can add or remove volumes from the JBOD configuration.
PVC resource for JBOD storage
When persistent storage is used to declare JBOD volumes, it creates a PVC with the following name:
data-id-cluster-name-kafka-idx
-
PVC for the volume used for storing data for the Kafka broker pod
idx
. Theid
is the ID of the volume used for storing data for Kafka broker pod.
Mount path of Kafka log directories
The JBOD volumes are used by Kafka brokers as log directories mounted into the following path:
/var/lib/kafka/data-id/kafka-logidx
Where id
is the ID of the volume used for storing data for Kafka broker pod idx
. For example /var/lib/kafka/data-0/kafka-log0
.
Adding volumes to JBOD storage
This procedure describes how to add volumes to a Kafka cluster configured to use JBOD storage. It cannot be applied to Kafka clusters configured to use any other storage type.
Note
|
When adding a new volume under an id which was already used in the past and removed, you have to make sure that the previously used PersistentVolumeClaims have been deleted.
|
-
A Kubernetes cluster
-
A running Cluster Operator
-
A Kafka cluster with JBOD storage
-
Edit the
spec.kafka.storage.volumes
property in theKafka
resource. Add the new volumes to thevolumes
array. For example, add the new volume with id2
:apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: kafka: # ... storage: type: jbod volumes: - id: 0 type: persistent-claim size: 100Gi deleteClaim: false - id: 1 type: persistent-claim size: 100Gi deleteClaim: false - id: 2 type: persistent-claim size: 100Gi deleteClaim: false # ... zookeeper: # ...
-
Create or update the resource:
kubectl apply -f <kafka_configuration_file>
-
Create new topics or reassign existing partitions to the new disks.
For more information about reassigning topics, see Partition reassignment tool.
Removing volumes from JBOD storage
This procedure describes how to remove volumes from Kafka cluster configured to use JBOD storage. It cannot be applied to Kafka clusters configured to use any other storage type. The JBOD storage always has to contain at least one volume.
Important
|
To avoid data loss, you have to move all partitions before removing the volumes. |
-
A Kubernetes cluster
-
A running Cluster Operator
-
A Kafka cluster with JBOD storage with two or more volumes
-
Reassign all partitions from the disks which are you going to remove. Any data in partitions still assigned to the disks which are going to be removed might be lost.
-
Edit the
spec.kafka.storage.volumes
property in theKafka
resource. Remove one or more volumes from thevolumes
array. For example, remove the volumes with ids1
and2
:apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: kafka: # ... storage: type: jbod volumes: - id: 0 type: persistent-claim size: 100Gi deleteClaim: false # ... zookeeper: # ...
-
Create or update the resource:
kubectl apply -f <kafka_configuration_file>
For more information about reassigning topics, see Partition reassignment tool.
2.1.4. Scaling clusters
Scale Kafka clusters by adding or removing brokers. If a cluster already has topics defined, you also have to reassign partitions.
Use the kafka-reassign-partitions.sh
tool to reassign partitions.
The 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.
Broker scaling configuration
You configure the Kafka.spec.kafka.replicas
configuration to add or reduce the number of brokers.
Broker addition
The primary way of increasing throughput for a topic is to increase the number of partitions for that topic. That works because the extra partitions allow the load of the topic to be shared between the different brokers in the cluster. However, in situations where every broker is constrained by a particular resource (typically I/O) using more partitions will not result in increased throughput. Instead, you need to add brokers to the cluster.
When you add an extra broker to the cluster, Kafka does not assign any partitions to it automatically. You must decide which partitions to reassign from the existing brokers to the new broker.
Once the partitions have been redistributed between all the brokers, the resource utilization of each broker is reduced.
Broker removal
If you are using StatefulSets
to manage broker pods, you cannot remove any pod from the cluster.
You can only remove one or more of the highest numbered pods from the cluster.
For example, in a cluster of 12 brokers the pods are named cluster-name-kafka-0
up to cluster-name-kafka-11
.
If you decide to scale down by one broker, the cluster-name-kafka-11
will be removed.
Before you remove a broker from a cluster, ensure that it is not assigned to any partitions. You should also decide which of the remaining brokers will be responsible for each of the partitions on the broker being decommissioned. Once the broker has no assigned partitions, you can scale the cluster down safely.
Partition reassignment tool
The Topic Operator does not currently support reassigning replicas to different brokers, so it is necessary to connect directly to broker pods to reassign replicas to brokers.
Within a broker pod, the kafka-reassign-partitions.sh
tool allows you to reassign partitions to different brokers.
It has three different modes:
--generate
-
Takes a set of topics and brokers and generates a reassignment JSON file which will result in the partitions of those topics being assigned to those brokers. Because this operates on whole topics, it cannot be used when you 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 need to cancel a reassignment, wait for it to complete and then perform another reassignment to revert the effects of the first reassignment.
The kafka-reassign-partitions.sh
will print the reassignment JSON for this reversion as part of its output.
Very large reassignments should be broken down into a number of smaller reassignments in case there is a need to stop in-progress reassignment.
Partition reassignment JSON file
The reassignment JSON file has a specific structure:
{
"version": 1,
"partitions": [
<PartitionObjects>
]
}
Where <PartitionObjects> is a comma-separated list of objects like:
{
"topic": <TopicName>,
"partition": <Partition>,
"replicas": [ <AssignedBrokerIds> ]
}
Note
|
Although Kafka also supports a "log_dirs" property this should not be used in Strimzi.
|
The following is an example reassignment JSON file that assigns partition 4
of topic topic-a
to brokers 2
, 4
and 7
, and partition 2
of topic topic-b
to brokers 1
, 5
and 7
:
{
"version": 1,
"partitions": [
{
"topic": "topic-a",
"partition": 4,
"replicas": [2,4,7]
},
{
"topic": "topic-b",
"partition": 2,
"replicas": [1,5,7]
}
]
}
Partitions not included in the JSON are not changed.
Partition reassignment between JBOD volumes
When using JBOD storage in your Kafka cluster, you can choose to reassign the partitions between specific volumes and their log directories (each volume has a single log directory).
To reassign a partition to a specific volume, add the log_dirs
option to <PartitionObjects> in the reassignment JSON file.
{
"topic": <TopicName>,
"partition": <Partition>,
"replicas": [ <AssignedBrokerIds> ],
"log_dirs": [ <AssignedLogDirs> ]
}
The log_dirs
object should contain the same number of log directories as the number of replicas specified in the replicas
object.
The value should be either an absolute path to the log directory, or the any
keyword.
{
"topic": "topic-a",
"partition": 4,
"replicas": [2,4,7].
"log_dirs": [ "/var/lib/kafka/data-0/kafka-log2", "/var/lib/kafka/data-0/kafka-log4", "/var/lib/kafka/data-0/kafka-log7" ]
}
Partition reassignment throttles
Partition reassignment can be a slow process because it involves transferring large amounts of data between brokers.
To avoid a detrimental impact on clients, you can throttle the reassignment process.
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.
Generating reassignment JSON files
This procedure describes how to generate a reassignment JSON file.
Use the reassignment file with the kafka-reassign-partitions.sh
tool to reassign partitions after scaling a Kafka cluster.
You run the tool from an interactive pod container connected to the Kafka cluster.
The steps describe 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:latest-kafka-3.3.2 <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 oftopic-a
andtopic-b
{ "version": 1, "topics": [ { "topic": "topic-a"}, { "topic": "topic-b"} ] }
-
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 all the partitions oftopic-a
andtopic-b
to brokers0
,1
and2
bin/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 \ --generate
Scaling up a Kafka cluster
Use a reassignment file to increase the number of brokers in a Kafka cluster.
The reassignment file should describe how partitions are reassigned to brokers in the enlarged Kafka cluster.
This procedure describes a secure scaling 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 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.
Scaling down a Kafka cluster
Use a reassignment file to decrease 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. 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.
-
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-zA-Z0-9.-]+\.[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.
2.1.5. Retrieving JMX metrics with JmxTrans
JmxTrans is a tool for retrieving JMX metrics data from Java processes and pushing that data, in various formats, to remote sinks inside or outside the cluster. JmxTrans can communicate with a secure JMX port.
Important
|
Support for JmxTrans in Strimzi is deprecated. It is currently planned to be removed in Strimzi 0.35.0. |
JmxTrans reads JMX metrics data from secure or insecure Kafka brokers and pushes the data to remote sinks in various data formats. For example, JmxTrans can obtain JMX metrics about the request rate of each Kafka broker’s network and then push the data to a Logstash database outside the Kubernetes cluster.
For more information about JmxTrans, see the JmxTrans GitHub.
Configuring a JmxTrans deployment
-
A running Kubernetes cluster
You can configure a JmxTrans deployment by using the Kafka.spec.jmxTrans
property.
A JmxTrans deployment can read from a secure or insecure Kafka broker.
To configure a JmxTrans deployment, define the following properties:
-
Kafka.spec.jmxTrans.outputDefinitions
-
Kafka.spec.jmxTrans.kafkaQueries
For more information on these properties, see the JmxTransSpec
schema reference.
Note
|
To use JMXTrans, jmxOptions must be configured on the Kafka broker.
|
Configuring JmxTrans output definitions
Output definitions specify where JMX metrics are pushed to, and in which data format.
For information about supported data formats, see Data formats.
How many seconds JmxTrans agent waits for before pushing new data can be configured through the flushDelay
property.
The host
and port
properties define the target host address and target port the data is pushed to.
The name
property is a required property that is referenced by the Kafka.spec.jmxTrans.kafkaQueries
property.
Here is an example configuration pushing JMX data in the Graphite format every 5 seconds to a Logstash database on http://myLogstash:9999, and another pushing to standardOut
(standard output):
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
jmxTrans:
outputDefinitions:
- outputType: "com.googlecode.jmxtrans.model.output.GraphiteWriter"
host: "http://myLogstash"
port: 9999
flushDelay: 5
name: "logstash"
- outputType: "com.googlecode.jmxtrans.model.output.StdOutWriter"
name: "standardOut"
# ...
# ...
zookeeper:
# ...
Configuring JmxTrans queries
JmxTrans queries specify what JMX metrics are read from the Kafka brokers.
Currently JmxTrans queries can only be sent to the Kafka Brokers.
Configure the targetMBean
property to specify which target MBean on the Kafka broker is addressed.
Configuring the attributes
property specifies which MBean attribute is read as JMX metrics from the target MBean.
JmxTrans supports wildcards to read from target MBeans, and filter by specifying the typenames
.
The outputs
property defines where the metrics are pushed to by specifying the name of the output definitions.
The following JmxTrans deployment reads from all MBeans that match the pattern kafka.server:type=BrokerTopicMetrics,name=*
and have name
in the target MBean’s name.
From those Mbeans, it obtains JMX metrics about the Count
attribute and pushes the metrics to standard output as defined by outputs
.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
# ...
jmxTrans:
kafkaQueries:
- targetMBean: "kafka.server:type=BrokerTopicMetrics,*"
typeNames: ["name"]
attributes: ["Count"]
outputs: ["standardOut"]
zookeeper:
# ...
2.1.6. Maintenance time windows for rolling updates
Maintenance time windows allow you to schedule certain rolling updates of your Kafka and ZooKeeper clusters to start at a convenient time.
Maintenance time windows overview
In most cases, the Cluster Operator only updates your Kafka or ZooKeeper clusters in response to changes to the corresponding Kafka
resource.
This enables you to plan when to apply changes to a Kafka
resource to minimize the impact on Kafka client applications.
However, some updates to your Kafka and ZooKeeper clusters can happen without any corresponding change to the Kafka
resource.
For example, the Cluster Operator will need to perform a rolling restart if a CA (certificate authority) certificate that it manages is close to expiry.
While a rolling restart of the pods should not affect availability of the service (assuming correct broker and topic configurations), it could affect performance of the Kafka client applications. Maintenance time windows allow you to schedule such spontaneous rolling updates of your Kafka and ZooKeeper clusters to start at a convenient time. If maintenance time windows are not configured for a cluster then it is possible that such spontaneous rolling updates will happen at an inconvenient time, such as during a predictable period of high load.
Maintenance time window definition
You configure maintenance time windows by entering an array of strings in the Kafka.spec.maintenanceTimeWindows
property.
Each string is a cron expression interpreted as being in UTC (Coordinated Universal Time, which for practical purposes is the same as Greenwich Mean Time).
The following example configures a single maintenance time window that starts at midnight and ends at 01:59am (UTC), on Sundays, Mondays, Tuesdays, Wednesdays, and Thursdays:
# ...
maintenanceTimeWindows:
- "* * 0-1 ? * SUN,MON,TUE,WED,THU *"
# ...
In practice, maintenance windows should be set in conjunction with the Kafka.spec.clusterCa.renewalDays
and Kafka.spec.clientsCa.renewalDays
properties of the Kafka
resource, to ensure that the necessary CA certificate renewal can be completed in the configured maintenance time windows.
Note
|
Strimzi does not schedule maintenance operations exactly according to the given windows. Instead, for each reconciliation, it checks whether a maintenance window is currently "open". This means that the start of maintenance operations within a given time window can be delayed by up to the Cluster Operator reconciliation interval. Maintenance time windows must therefore be at least this long. |
-
For more information about the Cluster Operator configuration, see Configuring the Cluster Operator with environment variables.
Configuring a maintenance time window
You can configure a maintenance time window for rolling updates triggered by supported processes.
-
A Kubernetes cluster.
-
The Cluster Operator is running.
-
Add or edit the
maintenanceTimeWindows
property in theKafka
resource. For example to allow maintenance between 0800 and 1059 and between 1400 and 1559 you would set themaintenanceTimeWindows
as shown below:apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... maintenanceTimeWindows: - "* * 8-10 * * ?" - "* * 14-15 * * ?"
-
Create or update the resource:
kubectl apply -f <kafka_configuration_file>
Performing rolling updates:
2.1.7. Connecting to ZooKeeper from a terminal
Most Kafka CLI tools can connect directly to Kafka, so under normal circumstances you should not need to connect to ZooKeeper. ZooKeeper services are secured with encryption and authentication and are not intended to be used by external applications that are not part of Strimzi.
However, if you want to use Kafka CLI tools that require a connection to ZooKeeper, you can use a terminal inside a ZooKeeper container and connect to localhost:12181
as the ZooKeeper address.
-
A Kubernetes cluster is available.
-
A Kafka cluster is running.
-
The Cluster Operator is running.
-
Open the terminal using the Kubernetes console or run the
exec
command from your CLI.For example:
kubectl exec -ti my-cluster-zookeeper-0 -- bin/kafka-topics.sh --list --zookeeper localhost:12181
Be sure to use
localhost:12181
.You can now run Kafka commands to ZooKeeper.
2.1.8. Deleting Kafka nodes manually
This procedure describes how to delete an existing Kafka node by using a Kubernetes annotation.
Deleting a Kafka node consists of deleting both the Pod
on which the Kafka broker is running and the related PersistentVolumeClaim
(if the cluster was deployed with persistent storage).
After deletion, the Pod
and its related PersistentVolumeClaim
are recreated automatically.
Warning
|
Deleting a PersistentVolumeClaim can cause permanent data loss. The following procedure should only be performed if you have encountered storage issues.
|
See the Deploying and Upgrading Strimzi guide for instructions on running a:
-
Find the name of the
Pod
that you want to delete.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
. -
Annotate the
Pod
resource in Kubernetes.Use
kubectl annotate
:kubectl annotate pod cluster-name-kafka-index strimzi.io/delete-pod-and-pvc=true
-
Wait for the next reconciliation, when the annotated pod with the underlying persistent volume claim will be deleted and then recreated.
2.1.9. Deleting ZooKeeper nodes manually
This procedure describes how to delete an existing ZooKeeper node by using a Kubernetes annotation.
Deleting a ZooKeeper node consists of deleting both the Pod
on which ZooKeeper is running and the related PersistentVolumeClaim
(if the cluster was deployed with persistent storage).
After deletion, the Pod
and its related PersistentVolumeClaim
are recreated automatically.
Warning
|
Deleting a PersistentVolumeClaim can cause permanent data loss. The following procedure should only be performed if you have encountered storage issues.
|
See the Deploying and Upgrading Strimzi guide for instructions on running a:
-
Find the name of the
Pod
that you want to delete.ZooKeeper pods are named <cluster-name>-zookeeper-<index>, where <index> starts at zero and ends at the total number of replicas minus one. For example,
my-cluster-zookeeper-0
. -
Annotate the
Pod
resource in Kubernetes.Use
kubectl annotate
:kubectl annotate pod cluster-name-zookeeper-index strimzi.io/delete-pod-and-pvc=true
-
Wait for the next reconciliation, when the annotated pod with the underlying persistent volume claim will be deleted and then recreated.
2.1.10. List of Kafka cluster resources
The following resources are created by the Cluster Operator in the Kubernetes cluster:
cluster-name-cluster-ca
-
Secret with the Cluster CA private key used to encrypt the cluster communication.
cluster-name-cluster-ca-cert
-
Secret with the Cluster CA public key. This key can be used to verify the identity of the Kafka brokers.
cluster-name-clients-ca
-
Secret with the Clients CA private key used to sign user certificates
cluster-name-clients-ca-cert
-
Secret with the Clients CA public key. This key can be used to verify the identity of the Kafka users.
cluster-name-cluster-operator-certs
-
Secret with Cluster operators keys for communication with Kafka and ZooKeeper.
cluster-name-zookeeper
-
Name given to the following ZooKeeper resources:
-
StatefulSet or StrimziPodSet (if the UseStrimziPodSets feature gate is enabled) for managing the ZooKeeper node pods.
-
Service account used by the ZooKeeper nodes.
-
PodDisruptionBudget configured for the ZooKeeper nodes.
-
cluster-name-zookeeper-idx
-
Pods created by the ZooKeeper StatefulSet or StrimziPodSet.
cluster-name-zookeeper-nodes
-
Headless Service needed to have DNS resolve the ZooKeeper pods IP addresses directly.
cluster-name-zookeeper-client
-
Service used by Kafka brokers to connect to ZooKeeper nodes as clients.
cluster-name-zookeeper-config
-
ConfigMap that contains the ZooKeeper ancillary configuration, and is mounted as a volume by the ZooKeeper node pods.
cluster-name-zookeeper-nodes
-
Secret with ZooKeeper node keys.
cluster-name-network-policy-zookeeper
-
Network policy managing access to the ZooKeeper services.
data-cluster-name-zookeeper-idx
-
Persistent Volume Claim for the volume used for storing data for the ZooKeeper node pod
idx
. This resource will be created only if persistent storage is selected for provisioning persistent volumes to store data.
cluster-name-kafka
-
Name given to the following Kafka resources:
-
StatefulSet or StrimziPodSet (if the UseStrimziPodSets feature gate is enabled) for managing the Kafka broker pods.
-
Service account used by the Kafka pods.
-
PodDisruptionBudget configured for the Kafka brokers.
-
cluster-name-kafka-idx
-
Name given to the following Kafka resources:
-
Pods created by the Kafka StatefulSet or StrimziPodSet.
-
ConfigMap with Kafka broker configuration (if the UseStrimziPodSets feature gate is enabled).
-
cluster-name-kafka-brokers
-
Service needed to have DNS resolve the Kafka broker pods IP addresses directly.
cluster-name-kafka-bootstrap
-
Service can be used as bootstrap servers for Kafka clients connecting from within the Kubernetes cluster.
cluster-name-kafka-external-bootstrap
-
Bootstrap service for clients connecting from outside the Kubernetes cluster. This resource is created only when an external listener is enabled. The old service name will be used for backwards compatibility when the listener name is
external
and port is9094
. cluster-name-kafka-pod-id
-
Service used to route traffic from outside the Kubernetes cluster to individual pods. This resource is created only when an external listener is enabled. The old service name will be used for backwards compatibility when the listener name is
external
and port is9094
. cluster-name-kafka-external-bootstrap
-
Bootstrap route for clients connecting from outside the Kubernetes cluster. This resource is created only when an external listener is enabled and set to type
route
. The old route name will be used for backwards compatibility when the listener name isexternal
and port is9094
. cluster-name-kafka-pod-id
-
Route for traffic from outside the Kubernetes cluster to individual pods. This resource is created only when an external listener is enabled and set to type
route
. The old route name will be used for backwards compatibility when the listener name isexternal
and port is9094
. cluster-name-kafka-listener-name-bootstrap
-
Bootstrap service for clients connecting from outside the Kubernetes cluster. This resource is created only when an external listener is enabled. The new service name will be used for all other external listeners.
cluster-name-kafka-listener-name-pod-id
-
Service used to route traffic from outside the Kubernetes cluster to individual pods. This resource is created only when an external listener is enabled. The new service name will be used for all other external listeners.
cluster-name-kafka-listener-name-bootstrap
-
Bootstrap route for clients connecting from outside the Kubernetes cluster. This resource is created only when an external listener is enabled and set to type
route
. The new route name will be used for all other external listeners. cluster-name-kafka-listener-name-pod-id
-
Route for traffic from outside the Kubernetes cluster to individual pods. This resource is created only when an external listener is enabled and set to type
route
. The new route name will be used for all other external listeners. cluster-name-kafka-config
-
ConfigMap which contains the Kafka ancillary configuration and is mounted as a volume by the Kafka broker pods.
cluster-name-kafka-brokers
-
Secret with Kafka broker keys.
cluster-name-network-policy-kafka
-
Network policy managing access to the Kafka services.
strimzi-namespace-name-cluster-name-kafka-init
-
Cluster role binding used by the Kafka brokers.
cluster-name-jmx
-
Secret with JMX username and password used to secure the Kafka broker port. This resource is created only when JMX is enabled in Kafka.
data-cluster-name-kafka-idx
-
Persistent Volume Claim for the volume used for storing data for the Kafka broker pod
idx
. This resource is created only if persistent storage is selected for provisioning persistent volumes to store data. data-id-cluster-name-kafka-idx
-
Persistent Volume Claim for the volume
id
used for storing data for the Kafka broker podidx
. This resource is created only if persistent storage is selected for JBOD volumes when provisioning persistent volumes to store data.
These resources are only created if the Entity Operator is deployed using the Cluster Operator.
cluster-name-entity-operator
-
Name given to the following Entity Operator resources:
-
Deployment with Topic and User Operators.
-
Service account used by the Entity Operator.
-
cluster-name-entity-operator-random-string
-
Pod created by the Entity Operator deployment.
cluster-name-entity-topic-operator-config
-
ConfigMap with ancillary configuration for Topic Operators.
cluster-name-entity-user-operator-config
-
ConfigMap with ancillary configuration for User Operators.
cluster-name-entity-topic-operator-certs
-
Secret with Topic Operator keys for communication with Kafka and ZooKeeper.
cluster-name-entity-user-operator-certs
-
Secret with User Operator keys for communication with Kafka and ZooKeeper.
strimzi-cluster-name-entity-topic-operator
-
Role binding used by the Entity Topic Operator.
strimzi-cluster-name-entity-user-operator
-
Role binding used by the Entity User Operator.
These resources are only created if the Kafka Exporter is deployed using the Cluster Operator.
cluster-name-kafka-exporter
-
Name given to the following Kafka Exporter resources:
-
Deployment with Kafka Exporter.
-
Service used to collect consumer lag metrics.
-
Service account used by the Kafka Exporter.
-
cluster-name-kafka-exporter-random-string
-
Pod created by the Kafka Exporter deployment.
These resources are only created if Cruise Control was deployed using the Cluster Operator.
cluster-name-cruise-control
-
Name given to the following Cruise Control resources:
-
Deployment with Cruise Control.
-
Service used to communicate with Cruise Control.
-
Service account used by the Cruise Control.
-
cluster-name-cruise-control-random-string
-
Pod created by the Cruise Control deployment.
cluster-name-cruise-control-config
-
ConfigMap that contains the Cruise Control ancillary configuration, and is mounted as a volume by the Cruise Control pods.
cluster-name-cruise-control-certs
-
Secret with Cruise Control keys for communication with Kafka and ZooKeeper.
cluster-name-network-policy-cruise-control
-
Network policy managing access to the Cruise Control service.
These resources are only created if JMXTrans is deployed using the Cluster Operator.
cluster-name-jmxtrans
-
Name given to the following JMXTrans resources:
-
Deployment with JMXTrans.
-
Service account used by the JMXTrans.
-
cluster-name-jmxtrans-random-string
-
Pod created by the JMXTrans deployment.
cluster-name-jmxtrans-config
-
ConfigMap that contains the JMXTrans ancillary configuration, and is mounted as a volume by the JMXTrans pods.
2.2. Kafka Connect cluster configuration
Configure a Kafka Connect deployment using the KafkaConnect
resource.
Kafka Connect is an integration toolkit for streaming data between Kafka brokers and other systems using connector plugins.
Kafka Connect provides a framework for integrating Kafka with an external data source or target, such as a database, for import or export of data using connectors.
Connectors are plugins that provide the connection configuration needed.
KafkaConnect
schema reference describes the full schema of the KafkaConnect
resource.
For more information on deploying connector plugins, see Extending Kafka Connect with connector plugins.
2.2.1. Configuring Kafka Connect
Use Kafka Connect to set up external data connections to your Kafka cluster.
Use the properties of the KafkaConnect
resource to configure your Kafka Connect deployment.
KafkaConnector
resources allow you to create and manage connector instances for Kafka Connect in a Kubernetes-native way.
In your Kafka Connect configuration, you enable KafkaConnectors for a Kafka Connect cluster by adding the strimzi.io/use-connector-resources
annotation.
You can also add a build
configuration so that Strimzi automatically builds a container image with the connector plugins you require for your data connections.
External configuration for Kafka Connect connectors is specified through the externalConfiguration
property.
To manage connectors, you can use use KafkaConnector
custom resources or the Kafka Connect REST API.
KafkaConnector
resources must be deployed to the same namespace as the Kafka Connect cluster they link to.
For more information on using these methods to create, reconfigure, or delete connectors, see Adding connectors.
Connector configuration is passed to Kafka Connect as part of an HTTP request and stored within Kafka itself. ConfigMaps and Secrets are standard Kubernetes resources used for storing configurations and confidential data. You can use ConfigMaps and Secrets to configure certain elements of a connector. You can then reference the configuration values in HTTP REST commands, which keeps the configuration separate and more secure, if needed. This method applies especially to confidential data, such as usernames, passwords, or certificates.
You can tune the configuration to handle high volumes of messages. For more information, see Handling high volumes of messages.
-
A Kubernetes cluster
-
A running Cluster Operator
See the Deploying and Upgrading Strimzi guide for instructions on running a:
-
Edit the
spec
properties of theKafkaConnect
resource.The properties you can configure are shown in this example configuration:
apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnect (1) metadata: name: my-connect-cluster annotations: strimzi.io/use-connector-resources: "true" (2) spec: replicas: 3 (3) authentication: (4) type: tls certificateAndKey: certificate: source.crt key: source.key secretName: my-user-source bootstrapServers: my-cluster-kafka-bootstrap:9092 (5) tls: (6) trustedCertificates: - secretName: my-cluster-cluster-cert certificate: ca.crt - secretName: my-cluster-cluster-cert certificate: ca2.crt config: (7) group.id: my-connect-cluster offset.storage.topic: my-connect-cluster-offsets config.storage.topic: my-connect-cluster-configs status.storage.topic: my-connect-cluster-status key.converter: org.apache.kafka.connect.json.JsonConverter value.converter: org.apache.kafka.connect.json.JsonConverter key.converter.schemas.enable: true value.converter.schemas.enable: true config.storage.replication.factor: 3 offset.storage.replication.factor: 3 status.storage.replication.factor: 3 build: (8) output: (9) type: docker image: my-registry.io/my-org/my-connect-cluster:latest pushSecret: my-registry-credentials plugins: (10) - name: debezium-postgres-connector artifacts: - type: tgz url: https://repo1.maven.org/maven2/io/debezium/debezium-connector-postgres/2.1.1.Final/debezium-connector-postgres-2.1.1.Final-plugin.tar.gz sha512sum: 962a12151bdf9a5a30627eebac739955a4fd95a08d373b86bdcea2b4d0c27dd6e1edd5cb548045e115e33a9e69b1b2a352bee24df035a0447cb820077af00c03 - name: camel-telegram artifacts: - type: tgz url: https://repo.maven.apache.org/maven2/org/apache/camel/kafkaconnector/camel-telegram-kafka-connector/0.9.0/camel-telegram-kafka-connector-0.9.0-package.tar.gz sha512sum: a9b1ac63e3284bea7836d7d24d84208c49cdf5600070e6bd1535de654f6920b74ad950d51733e8020bf4187870699819f54ef5859c7846ee4081507f48873479 externalConfiguration: (11) env: - name: AWS_ACCESS_KEY_ID valueFrom: secretKeyRef: name: aws-creds key: awsAccessKey - name: AWS_SECRET_ACCESS_KEY valueFrom: secretKeyRef: name: aws-creds key: awsSecretAccessKey resources: (12) requests: cpu: "1" memory: 2Gi limits: cpu: "2" memory: 2Gi logging: (13) type: inline loggers: log4j.rootLogger: "INFO" readinessProbe: (14) initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 metricsConfig: (15) type: jmxPrometheusExporter valueFrom: configMapKeyRef: name: my-config-map key: my-key jvmOptions: (16) "-Xmx": "1g" "-Xms": "1g" image: my-org/my-image:latest (17) rack: topologyKey: topology.kubernetes.io/zone (18) template: (19) pod: affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: application operator: In values: - postgresql - mongodb topologyKey: "kubernetes.io/hostname" connectContainer: (20) env: - name: JAEGER_SERVICE_NAME value: my-jaeger-service - name: JAEGER_AGENT_HOST value: jaeger-agent-name - name: JAEGER_AGENT_PORT value: "6831"
-
Use
KafkaConnect
. -
Enables KafkaConnectors for the Kafka Connect cluster.
-
The number of replica nodes for the workers that run tasks.
-
Authentication for the Kafka Connect cluster, specified as mTLS, token-based OAuth, SASL-based SCRAM-SHA-256/SCRAM-SHA-512, or PLAIN. By default, Kafka Connect connects to Kafka brokers using a plain text connection.
-
Bootstrap server for connection to the Kafka Connect cluster.
-
TLS encryption with key names under which TLS certificates are stored in X.509 format for the cluster. If certificates are stored in the same secret, it can be listed multiple times.
-
Kafka Connect configuration of workers (not connectors). Standard Apache Kafka configuration may be provided, restricted to those properties not managed directly by Strimzi.
-
Build configuration properties for building a container image with connector plugins automatically.
-
(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
. -
External configuration for Kafka connectors using environment variables, as shown here, or volumes. You can also use configuration provider plugins to load configuration values from external sources.
-
Requests for reservation of supported resources, currently
cpu
andmemory
, and limits to specify the maximum resources that can be consumed. -
Specified Kafka Connect loggers and log levels added directly (
inline
) or indirectly (external
) through a ConfigMap. A custom ConfigMap must be placed under thelog4j.properties
orlog4j2.properties
key. For the Kafka Connectlog4j.rootLogger
logger, you can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. -
Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).
-
Prometheus metrics, which are enabled by referencing a ConfigMap containing configuration for the Prometheus JMX exporter in this example. You can enable metrics without further configuration using a reference to a ConfigMap containing an empty file under
metricsConfig.valueFrom.configMapKeyRef.key
. -
JVM configuration options to optimize performance for the Virtual Machine (VM) running Kafka Connect.
-
ADVANCED OPTION: Container image configuration, which is recommended only in special situations.
-
SPECIALIZED OPTION: Rack awareness configuration for the deployment. This is a specialized option intended for a deployment within the same location, not across regions. Use this option if you want connectors to consume from the closest replica rather than the leader replica. In certain cases, consuming from the closest replica can improve network utilization or reduce costs . The
topologyKey
must match a node label containing the rack ID. The example used in this configuration specifies a zone using the standardtopology.kubernetes.io/zone
label. To consume from the closest replica, enable theRackAwareReplicaSelector
in the Kafka broker configuration. -
Template customization. Here a pod is scheduled with anti-affinity, so the pod is not scheduled on nodes with the same hostname.
-
Environment variables are set for distributed tracing.
-
-
Create or update the resource:
kubectl apply -f KAFKA-CONNECT-CONFIG-FILE
-
If authorization is enabled for Kafka Connect, configure Kafka Connect users to enable access to the Kafka Connect consumer group and topics.
2.2.2. 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.
2.2.3. Configuring Kafka Connect user authorization
This procedure describes how to authorize user access to Kafka Connect.
When any type of authorization is being used in Kafka, a Kafka Connect user requires read/write access rights to the consumer group and the internal topics of Kafka Connect.
The properties for the consumer group and internal topics are automatically configured by Strimzi,
or they can be specified explicitly in the spec
of the KafkaConnect
resource.
KafkaConnect
resourceapiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
name: my-connect
spec:
# ...
config:
group.id: my-connect-cluster (1)
offset.storage.topic: my-connect-cluster-offsets (2)
config.storage.topic: my-connect-cluster-configs (3)
status.storage.topic: my-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.
This procedure shows how access is provided when simple
authorization is being used.
Simple authorization uses ACL rules, handled by the Kafka AclAuthorizer
plugin, to provide the right level of access.
For more information on configuring a KafkaUser
resource to use simple authorization, see the AclRule
schema reference.
Note
|
The default values for the consumer group and topics will differ when running multiple instances. |
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
authorization
property in theKafkaUser
resource to provide access rights to the user.In the following example, access rights are configured for the Kafka Connect topics and consumer group using
literal
name values:Property Name offset.storage.topic
connect-cluster-offsets
status.storage.topic
connect-cluster-status
config.storage.topic
connect-cluster-configs
group
connect-cluster
apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaUser metadata: name: my-user labels: strimzi.io/cluster: my-cluster spec: # ... authorization: type: simple acls: # access to offset.storage.topic - resource: type: topic name: connect-cluster-offsets patternType: literal operations: - Create - Describe - Read - Write host: "*" # access to status.storage.topic - resource: type: topic name: connect-cluster-status patternType: literal operations: - Create - Describe - Read - Write host: "*" # access to config.storage.topic - resource: type: topic name: connect-cluster-configs patternType: literal operations: - Create - Describe - Read - Write host: "*" # consumer group - resource: type: group name: connect-cluster patternType: literal operations: - Read host: "*"
-
Create or update the resource.
kubectl apply -f KAFKA-USER-CONFIG-FILE
2.2.4. List of Kafka Connect cluster resources
The following resources are created by the Cluster Operator in the Kubernetes cluster:
- connect-cluster-name-connect
-
Deployment which is in charge to create the Kafka Connect worker node pods.
- connect-cluster-name-connect-api
-
Service which exposes the REST interface for managing the Kafka Connect cluster.
- connect-cluster-name-config
-
ConfigMap which contains the Kafka Connect ancillary configuration and is mounted as a volume by the Kafka broker pods.
- connect-cluster-name-connect
-
Pod Disruption Budget configured for the Kafka Connect worker nodes.
2.3. Kafka MirrorMaker 2.0 cluster configuration
Configure a Kafka MirrorMaker 2.0 deployment using the KafkaMirrorMaker2
resource.
MirrorMaker 2.0 replicates data between two or more Kafka clusters, within or across data centers.
KafkaMirrorMaker2
schema reference describes the full schema of the KafkaMirrorMaker2
resource.
MirrorMaker 2.0 resource configuration differs from the previous version of MirrorMaker. If you choose to use MirrorMaker 2.0, there is currently no legacy support, so any resources must be manually converted into the new format.
2.3.1. MirrorMaker 2.0 data replication
Data replication across clusters supports scenarios that require:
-
Recovery of data in the event of a system failure
-
Aggregation of data for analysis
-
Restriction of data access to a specific cluster
-
Provision of data at a specific location to improve latency
MirrorMaker 2.0 configuration
MirrorMaker 2.0 consumes messages from a source Kafka cluster and writes them to a target Kafka cluster.
MirrorMaker 2.0 uses:
-
Source cluster configuration to consume data from the source cluster
-
Target cluster configuration to output data to the target cluster
MirrorMaker 2.0 is based on the Kafka Connect framework, connectors managing the transfer of data between clusters.
You configure MirrorMaker 2.0 to define the Kafka Connect deployment, including the connection details of the source and target clusters, and then run a set of MirrorMaker 2.0 connectors to make the connection.
MirrorMaker 2.0 consists of the following connectors:
MirrorSourceConnector
-
The source connector replicates topics from a source cluster to a target cluster. It also replicates ACLs and is necessary for the
MirrorCheckpointConnector
to run. MirrorCheckpointConnector
-
The checkpoint connector periodically tracks offsets. If enabled, it also synchronizes consumer group offsets between the source and target cluster.
MirrorHeartbeatConnector
-
The heartbeat connector periodically checks connectivity between the source and target cluster.
Note
|
If you are using the User Operator to manage ACLs, ACL replication through the connector is not possible. |
The process of mirroring data from a source cluster to a target cluster is asynchronous. Each MirrorMaker 2.0 instance mirrors data from one source cluster to one target cluster. You can use more than one MirrorMaker 2.0 instance to mirror data between any number of clusters.

By default, a check for new topics in the source cluster is made every 10 minutes.
You can change the frequency by adding refresh.topics.interval.seconds
to the source connector configuration.
Cluster configuration
You can use MirrorMaker 2.0 in active/passive or active/active cluster configurations.
- active/active cluster configuration
-
An active/active configuration has two active clusters replicating data bidirectionally. Applications can use either cluster. Each cluster can provide the same data. In this way, you can make the same data available in different geographical locations. As consumer groups are active in both clusters, consumer offsets for replicated topics are not synchronized back to the source cluster.
- active/passive cluster configuration
-
An active/passive configuration has an active cluster replicating data to a passive cluster. The passive cluster remains on standby. You might use the passive cluster for data recovery in the event of system failure.
The expectation is that producers and consumers connect to active clusters only. A MirrorMaker 2.0 cluster is required at each target destination.
Bidirectional replication (active/active)
The MirrorMaker 2.0 architecture supports bidirectional replication in an active/active cluster configuration.
Each cluster replicates the data of the other cluster using the concept of source and remote topics. As the same topics are stored in each cluster, remote topics are automatically renamed by MirrorMaker 2.0 to represent the source cluster. The name of the originating cluster is prepended to the name of the topic.

By flagging the originating cluster, topics are not replicated back to that cluster.
The concept of replication through remote topics is useful when configuring an architecture that requires data aggregation. Consumers can subscribe to source and remote topics within the same cluster, without the need for a separate aggregation cluster.
Unidirectional replication (active/passive)
The MirrorMaker 2.0 architecture supports unidirectional replication in an active/passive cluster configuration.
You can use an active/passive cluster configuration to make backups or migrate data to another cluster. In this situation, you might not want automatic renaming of remote topics.
You can override automatic renaming by adding IdentityReplicationPolicy
to the source connector configuration.
With this configuration applied, topics retain their original names.
Topic configuration synchronization
MirrorMaker 2.0 supports topic configuration synchronization between source and target clusters. You specify source topics in the MirrorMaker 2.0 configuration. MirrorMaker 2.0 monitors the source topics. MirrorMaker 2.0 detects and propagates changes to the source topics to the remote topics. Changes might include automatically creating missing topics and partitions.
Note
|
In most cases you write to local topics and read from remote topics. Though write operations are not prevented on remote topics, they should be avoided. |
Offset tracking
MirrorMaker 2.0 tracks offsets for consumer groups using internal topics.
offset-syncs
topic-
The
offset-syncs
topic maps the source and target offsets for replicated topic partitions from record metadata. checkpoints
topic-
The
checkpoints
topic maps the last committed offset in the source and target cluster for replicated topic partitions in each consumer group.
As they used internally by MirrorMaker 2.0, you do not interact directly with these topics.
MirrorCheckpointConnector
emits checkpoints for offset tracking.
Offsets for the checkpoints
topic are tracked at predetermined intervals through configuration.
Both topics enable replication to be fully restored from the correct offset position on failover.
The location of the offset-syncs
topic is the source
cluster by default.
You can use the offset-syncs.topic.location
connector configuration to change this to the target
cluster.
You need read/write access to the cluster that contains the topic.
Using the target cluster as the location of the offset-syncs
topic allows you to use MirrorMaker 2.0 even if you have only read access to the source cluster.
Synchronizing consumer group offsets
The __consumer_offsets
topic stores information on committed offsets for each consumer group.
Offset synchronization periodically transfers the consumer offsets for the consumer groups of a source cluster into the consumer offsets topic of a target cluster.
Offset synchronization is particularly useful in an active/passive configuration. If the active cluster goes down, consumer applications can switch to the passive (standby) cluster and pick up from the last transferred offset position.
To use topic offset synchronization, enable the synchronization by adding sync.group.offsets.enabled
to the checkpoint connector configuration, and setting the property to true
.
Synchronization is disabled by default.
When using the IdentityReplicationPolicy
in the source connector, it also has to be configured in the checkpoint connector configuration.
This ensures that the mirrored consumer offsets will be applied for the correct topics.
Consumer offsets are only synchronized for consumer groups that are not active in the target cluster.
If the consumer groups are in the target cluster, the synchronization cannot be performed and an UNKNOWN_MEMBER_ID
error is returned.
If enabled, the synchronization of offsets from the source cluster is made periodically.
You can change the frequency by adding sync.group.offsets.interval.seconds
and emit.checkpoints.interval.seconds
to the checkpoint connector configuration.
The properties specify the frequency in seconds that the consumer group offsets are synchronized, and the frequency of checkpoints emitted for offset tracking.
The default for both properties is 60 seconds.
You can also change the frequency of checks for new consumer groups using the refresh.groups.interval.seconds
property, which is performed every 10 minutes by default.
Because the synchronization is time-based, any switchover by consumers to a passive cluster will likely result in some duplication of messages.
Note
|
If you have an application written in Java, you can use the RemoteClusterUtils.java utility to synchronize offsets through the application. The utility fetches remote offsets for a consumer group from the checkpoints topic.
|
Connectivity checks
MirrorHeartbeatConnector
emits heartbeats to check connectivity between clusters.
An internal heartbeat
topic is replicated from the source cluster.
Target clusters use the heartbeat
topic to check the following:
-
The connector managing connectivity between clusters is running
-
The source cluster is available
2.3.2. Connector configuration
Use Mirrormaker 2.0 connector configuration for the internal connectors that orchestrate the synchronization of data between Kafka clusters.
The following table describes connector properties and the connectors you configure to use them.
Property | sourceConnector | checkpointConnector | heartbeatConnector |
---|---|---|---|
|
✓ |
✓ |
✓ |
|
✓ |
✓ |
✓ |
|
✓ |
✓ |
✓ |
|
✓ |
✓ |
|
|
✓ |
✓ |
|
|
✓ |
✓ |
|
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
2.3.3. Connector producer and consumer configuration
MirrorMaker 2.0 connectors use internal producers and consumers. If needed, you can configure these producers and consumers to override the default settings.
For example, you can increase the batch.size
for the source producer that sends topics to the target Kafka cluster to better accommodate large volumes of messages.
Important
|
Producer and consumer configuration options depend on the MirrorMaker 2.0 implementation, and may be subject to change. |
The following tables describe the producers and consumers for each of the connectors and where you can add configuration.
Type | Description | Configuration |
---|---|---|
Producer |
Sends topic messages to the target Kafka cluster. Consider tuning the configuration of this producer when it is handling large volumes of data. |
|
Producer |
Writes to the |
|
Consumer |
Retrieves topic messages from the source Kafka cluster. |
|
Type | Description | Configuration |
---|---|---|
Producer |
Emits consumer offset checkpoints. |
|
Consumer |
Loads the |
|
Note
|
You can set offset-syncs.topic.location to target to use the target Kafka cluster as the location of the offset-syncs topic.
|
Type | Description | Configuration |
---|---|---|
Producer |
Emits heartbeats. |
|
The following example shows how you configure the producers and consumers.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
name: my-mirror-maker2
spec:
version: 3.3.2
# ...
mirrors:
- sourceCluster: "my-cluster-source"
targetCluster: "my-cluster-target"
sourceConnector:
tasksMax: 5
config:
producer.override.batch.size: 327680
producer.override.linger.ms: 100
producer.request.timeout.ms: 30000
consumer.fetch.max.bytes: 52428800
# ...
checkpointConnector:
config:
producer.override.request.timeout.ms: 30000
consumer.max.poll.interval.ms: 300000
# ...
heartbeatConnector:
config:
producer.override.request.timeout.ms: 30000
# ...
2.3.4. Specifying a maximum number of tasks
Connectors create the tasks that are responsible for moving data in and out of Kafka. Each connector comprises one or more tasks that are distributed across a group of worker pods that run the tasks. Increasing the number of tasks can help with performance issues when replicating a large number of partitions or synchronizing the offsets of a large number of consumer groups.
Tasks run in parallel. Workers are assigned one or more tasks. A single task is handled by one worker pod, so you don’t need more worker pods than tasks. If there are more tasks than workers, workers handle multiple tasks.
You can specify the maximum number of connector tasks in your MirrorMaker configuration using the tasksMax
property.
Without specifying a maximum number of tasks, the default setting is a single task.
The heartbeat connector always uses a single task.
The number of tasks that are started for the source and checkpoint connectors is the lower value between the maximum number of possible tasks and the value for tasksMax
.
For the source connector, the maximum number of tasks possible is one for each partition being replicated from the source cluster.
For the checkpoint connector, the maximum number of tasks possible is one for each consumer group being replicated from the source cluster.
When setting a maximum number of tasks, consider the number of partitions and the hardware resources that support the process.
If the infrastructure supports the processing overhead, increasing the number of tasks can improve throughput and latency. For example, adding more tasks reduces the time taken to poll the source cluster when there is a high number of partitions or consumer groups.
Increasing the number of tasks for the checkpoint connector is useful when you have a large number of partitions.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
name: my-mirror-maker2
spec:
# ...
mirrors:
- sourceCluster: "my-cluster-source"
targetCluster: "my-cluster-target"
sourceConnector:
tasksMax: 10
# ...
Increasing the number of tasks for the checkpoint connector is useful when you have a large number of consumer groups.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
name: my-mirror-maker2
spec:
# ...
mirrors:
- sourceCluster: "my-cluster-source"
targetCluster: "my-cluster-target"
checkpointConnector:
tasksMax: 10
# ...
By default, MirrorMaker 2.0 checks for new consumer groups every 10 minutes.
You can adjust the refresh.groups.interval.seconds
configuration to change the frequency.
Take care when adjusting lower.
More frequent checks can have a negative impact on performance.
Checking connector task operations
If you are using Prometheus and Grafana to monitor your deployment, you can check MirrorMaker 2.0 performance. The example MirrorMaker 2.0 Grafana dashboard provided with Strimzi shows the following metrics related to tasks and latency.
-
The number of tasks
-
Replication latency
-
Offset synchronization latency
2.3.5. ACL rules synchronization
ACL access to remote topics is possible if you are not using the User Operator.
If AclAuthorizer
is being used, without the User Operator, ACL rules that manage access to brokers also apply to remote topics.
Users that can read a source topic can read its remote equivalent.
Note
|
OAuth 2.0 authorization does not support access to remote topics in this way. |
2.3.6. Configuring Kafka MirrorMaker 2.0
Use the properties of the KafkaMirrorMaker2
resource to configure your Kafka MirrorMaker 2.0 deployment.
Use MirrorMaker 2.0 to synchronize data between Kafka clusters.
The configuration must specify:
-
Each Kafka cluster
-
Connection information for each cluster, including authentication
-
The replication flow and direction
-
Cluster to cluster
-
Topic to topic
-
Note
|
The previous version of MirrorMaker continues to be supported. If you wish to use the resources configured for the previous version, they must be updated to the format supported by MirrorMaker 2.0. |
MirrorMaker 2.0 provides default configuration values for properties such as replication factors. A minimal configuration, with defaults left unchanged, would be something like this example:
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
name: my-mirror-maker2
spec:
version: 3.3.2
connectCluster: "my-cluster-target"
clusters:
- alias: "my-cluster-source"
bootstrapServers: my-cluster-source-kafka-bootstrap:9092
- alias: "my-cluster-target"
bootstrapServers: my-cluster-target-kafka-bootstrap:9092
mirrors:
- sourceCluster: "my-cluster-source"
targetCluster: "my-cluster-target"
sourceConnector: {}
You can configure access control for source and target clusters using mTLS or SASL authentication. This procedure shows a configuration that uses TLS encryption and mTLS authentication for the source and target cluster.
You can specify the topics and consumer groups you wish to replicate from a source cluster in the KafkaMirrorMaker2
resource.
You use the topicsPattern
and groupsPattern
properties to do this.
You can provide a list of names or use a regular expression.
By default, all topics and consumer groups are replicated if you do not set the topicsPattern
and groupsPattern
properties.
You can also replicate all topics and consumer groups by using ".*"
as a regular expression.
However, try to specify only the topics and consumer groups you need to avoid causing any unnecessary extra load on the cluster.
You can tune the configuration to handle high volumes of messages. For more information, see Handling high volumes of messages.
-
Strimzi is running
-
Source and target Kafka clusters are available
-
Edit the
spec
properties for theKafkaMirrorMaker2
resource.The properties you can configure are shown in this example configuration:
apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaMirrorMaker2 metadata: name: my-mirror-maker2 spec: version: 3.3.2 # (1) replicas: 3 # (2) connectCluster: "my-cluster-target" # (3) clusters: # (4) - alias: "my-cluster-source" # (5) authentication: # (6) certificateAndKey: certificate: source.crt key: source.key secretName: my-user-source type: tls bootstrapServers: my-cluster-source-kafka-bootstrap:9092 # (7) tls: # (8) trustedCertificates: - certificate: ca.crt secretName: my-cluster-source-cluster-ca-cert - alias: "my-cluster-target" # (9) authentication: # (10) certificateAndKey: certificate: target.crt key: target.key secretName: my-user-target type: tls bootstrapServers: my-cluster-target-kafka-bootstrap:9092 # (11) config: # (12) config.storage.replication.factor: 1 offset.storage.replication.factor: 1 status.storage.replication.factor: 1 ssl.cipher.suites: "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384" # (13) ssl.enabled.protocols: "TLSv1.2" ssl.protocol: "TLSv1.2" ssl.endpoint.identification.algorithm: HTTPS # (14) tls: # (15) trustedCertificates: - certificate: ca.crt secretName: my-cluster-target-cluster-ca-cert mirrors: # (16) - sourceCluster: "my-cluster-source" # (17) targetCluster: "my-cluster-target" # (18) sourceConnector: # (19) tasksMax: 10 # (20) autoRestart: # (21) enabled: true config: replication.factor: 1 # (22) offset-syncs.topic.replication.factor: 1 # (23) sync.topic.acls.enabled: "false" # (24) refresh.topics.interval.seconds: 60 # (25) replication.policy.separator: "" # (26) replication.policy.class: "org.apache.kafka.connect.mirror.IdentityReplicationPolicy" # (27) heartbeatConnector: # (28) autoRestart: enabled: true config: heartbeats.topic.replication.factor: 1 # (29) checkpointConnector: # (30) autoRestart: enabled: true config: checkpoints.topic.replication.factor: 1 # (31) refresh.groups.interval.seconds: 600 # (32) sync.group.offsets.enabled: true # (33) sync.group.offsets.interval.seconds: 60 # (34) emit.checkpoints.interval.seconds: 60 # (35) replication.policy.class: "org.apache.kafka.connect.mirror.IdentityReplicationPolicy" topicsPattern: "topic1|topic2|topic3" # (36) groupsPattern: "group1|group2|group3" # (37) resources: # (38) requests: cpu: "1" memory: 2Gi limits: cpu: "2" memory: 2Gi logging: # (39) type: inline loggers: connect.root.logger.level: "INFO" readinessProbe: # (40) initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 jvmOptions: # (41) "-Xmx": "1g" "-Xms": "1g" image: my-org/my-image:latest # (42) rack: topologyKey: topology.kubernetes.io/zone # (43) template: # (44) pod: affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: application operator: In values: - postgresql - mongodb topologyKey: "kubernetes.io/hostname" connectContainer: # (45) 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 # (46) externalConfiguration: # (47) env: - name: AWS_ACCESS_KEY_ID valueFrom: secretKeyRef: name: aws-creds key: awsAccessKey - name: AWS_SECRET_ACCESS_KEY valueFrom: secretKeyRef: name: aws-creds key: awsSecretAccessKey
-
The Kafka Connect and Mirror Maker 2.0 version, which will always be the same.
-
The number of replica nodes for the workers that run tasks.
-
Kafka cluster alias for Kafka Connect, which must specify the target Kafka cluster. The Kafka cluster is used by Kafka Connect for its internal topics.
-
Specification for the Kafka clusters being synchronized.
-
Cluster alias for the source Kafka cluster.
-
Authentication for the source cluster, specified as mTLS, token-based OAuth, SASL-based SCRAM-SHA-256/SCRAM-SHA-512, or PLAIN.
-
Bootstrap server for connection to the source Kafka cluster.
-
TLS encryption with key names under which TLS certificates are stored in X.509 format for the source Kafka cluster. If certificates are stored in the same secret, it can be listed multiple times.
-
Cluster alias for the target Kafka cluster.
-
Authentication for the target Kafka cluster is configured in the same way as for the source Kafka cluster.
-
Bootstrap server for connection to the target Kafka cluster.
-
Kafka Connect configuration. Standard Apache Kafka configuration may be provided, restricted to those properties not managed directly by Strimzi.
-
SSL properties for external listeners to run with a specific cipher suite for a TLS version.
-
Hostname verification is enabled by setting to
HTTPS
. An empty string disables the verification. -
TLS encryption for the target Kafka cluster is configured in the same way as for the source Kafka cluster.
-
Cluster alias for the source cluster used by the MirrorMaker 2.0 connectors.
-
Cluster alias for the target cluster used by the MirrorMaker 2.0 connectors.
-
Configuration for the
MirrorSourceConnector
that creates remote topics. Theconfig
overrides the default configuration options. -
The maximum number of tasks that the connector may create. Tasks handle the data replication and run in parallel. If the infrastructure supports the processing overhead, increasing this value can improve throughput. Kafka Connect distributes the tasks between members of the cluster. If there are more tasks than workers, workers are assigned multiple tasks. For sink connectors, aim to have one task for each topic partition consumed. For source connectors, the number of tasks that can run in parallel may also depend on the external system. The connector creates fewer than the maximum number of tasks if it cannot achieve the parallelism.
-
Enables automatic restarts of failed connectors and tasks. Up to seven restart attempts are made, after which restarts must be made manually.
-
Replication factor for mirrored topics created at the target cluster.
-
Replication factor for the
MirrorSourceConnector
offset-syncs
internal topic that maps the offsets of the source and target clusters. -
When ACL rules synchronization is enabled, ACLs are applied to synchronized topics. The default is
true
. This feature is not compatible with the User Operator. If you are using the User Operator, set this property tofalse
. -
Optional setting to change the frequency of checks for new topics. The default is for a check every 10 minutes.
-
Defines the separator used for the renaming of remote topics.
-
Adds a policy that overrides the automatic renaming of remote topics. Instead of prepending the name with the name of the source cluster, the topic retains its original name. This optional setting is useful for active/passive backups and data migration. To configure topic offset synchronization, this property must also be set for the
checkpointConnector.config
. -
Configuration for the
MirrorHeartbeatConnector
that performs connectivity checks. Theconfig
overrides the default configuration options. -
Replication factor for the heartbeat topic created at the target cluster.
-
Configuration for the
MirrorCheckpointConnector
that tracks offsets. Theconfig
overrides the default configuration options. -
Replication factor for the checkpoints topic created at the target cluster.
-
Optional setting to change the frequency of checks for new consumer groups. The default is for a check every 10 minutes.
-
Optional setting to synchronize consumer group offsets, which is useful for recovery in an active/passive configuration. Synchronization is not enabled by default.
-
If the synchronization of consumer group offsets is enabled, you can adjust the frequency of the synchronization.
-
Adjusts the frequency of checks for offset tracking. If you change the frequency of offset synchronization, you might also need to adjust the frequency of these checks.
-
Topic replication from the source cluster defined as a comma-separated list or regular expression pattern. The source connector replicates the specified topics. The checkpoint connector tracks offsets for the specified topics. Here we request three topics by name.
-
Consumer group replication from the source cluster defined as a comma-separated list or regular expression pattern. The checkpoint connector replicates the specified consumer groups. Here we request three consumer groups by name.
-
Requests for reservation of supported resources, currently
cpu
andmemory
, and limits to specify the maximum resources that can be consumed. -
Specified Kafka Connect loggers and log levels added directly (
inline
) or indirectly (external
) through a ConfigMap. A custom ConfigMap must be placed under thelog4j.properties
orlog4j2.properties
key. For the Kafka Connectlog4j.rootLogger
logger, you can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. -
Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).
-
JVM configuration options to optimize performance for the Virtual Machine (VM) running Kafka MirrorMaker.
-
ADVANCED OPTION: Container image configuration, which is recommended only in special situations.
-
SPECIALIZED OPTION: Rack awareness configuration for the deployment. This is a specialized option intended for a deployment within the same location, not across regions. Use this option if you want connectors to consume from the closest replica rather than the leader replica. In certain cases, consuming from the closest replica can improve network utilization or reduce costs . The
topologyKey
must match a node label containing the rack ID. The example used in this configuration specifies a zone using the standardtopology.kubernetes.io/zone
label. To consume from the closest replica, enable theRackAwareReplicaSelector
in the Kafka broker configuration. -
Template customization. Here a pod is scheduled with anti-affinity, so the pod is not scheduled on nodes with the same hostname.
-
Environment variables are set for distributed tracing.
-
Distributed tracing is enabled for Jaeger.
-
External configuration for a Kubernetes Secret mounted to Kafka MirrorMaker as an environment variable. You can also use configuration provider plugins to load configuration values from external sources.
-
-
Create or update the resource:
kubectl apply -f MIRRORMAKER-CONFIGURATION-FILE
2.3.7. Securing a Kafka MirrorMaker 2.0 deployment
This procedure describes in outline the configuration required to secure a MirrorMaker 2.0 deployment.
You need separate configuration for the source Kafka cluster and the target Kafka cluster. You also need separate user configuration to provide the credentials required for MirrorMaker to connect to the source and target Kafka clusters.
For the Kafka clusters, you specify internal listeners for secure connections within a Kubernetes cluster and external listeners for connections outside the Kubernetes cluster.
You can configure authentication and authorization mechanisms. The security options implemented for the source and target Kafka clusters must be compatible with the security options implemented for MirrorMaker 2.0.
After you have created the cluster and user authentication credentials, you specify them in your MirrorMaker configuration for secure connections.
Note
|
In this procedure, the 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). |
Before starting this procedure, take a look at the example configuration files provided by Strimzi. They include examples for securing a deployment of MirrorMaker 2.0 using mTLS or SCRAM-SHA-512 authentication. The examples specify internal listeners for connecting within a Kubernetes cluster.
The examples provide the configuration for full authorization, including all the ACLs needed by MirrorMaker 2.0 to allow operations on the source and target Kafka clusters.
-
Strimzi is running
-
Separate namespaces for source and target clusters
The procedure assumes that the source and target Kafka clusters are installed to separate namespaces If you want to use the Topic Operator, you’ll need to do this. The Topic Operator only watches a single cluster in a specified namespace.
By separating the clusters into namespaces, you will need to copy the cluster secrets so they can be accessed outside the namespace. You need to reference the secrets in the MirrorMaker configuration.
-
Configure two
Kafka
resources, one to secure the source Kafka cluster and one to secure the target Kafka cluster.You can add listener configuration for authentication and enable authorization.
In this example, an internal listener is configured for a Kafka cluster with TLS encryption and mTLS authentication. Kafka
simple
authorization is enabled.Example source Kafka cluster configuration with TLS encryption and mTLS authenticationapiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-source-cluster spec: kafka: version: 3.3.2 replicas: 1 listeners: - name: tls port: 9093 type: internal tls: true authentication: type: tls authorization: type: simple config: offsets.topic.replication.factor: 1 transaction.state.log.replication.factor: 1 transaction.state.log.min.isr: 1 default.replication.factor: 1 min.insync.replicas: 1 inter.broker.protocol.version: "3.3" storage: type: jbod volumes: - id: 0 type: persistent-claim size: 100Gi deleteClaim: false zookeeper: replicas: 1 storage: type: persistent-claim size: 100Gi deleteClaim: false entityOperator: topicOperator: {} userOperator: {}
Example target Kafka cluster configuration with TLS encryption and mTLS authenticationapiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-target-cluster spec: kafka: version: 3.3.2 replicas: 1 listeners: - name: tls port: 9093 type: internal tls: true authentication: type: tls authorization: type: simple config: offsets.topic.replication.factor: 1 transaction.state.log.replication.factor: 1 transaction.state.log.min.isr: 1 default.replication.factor: 1 min.insync.replicas: 1 inter.broker.protocol.version: "3.3" storage: type: jbod volumes: - id: 0 type: persistent-claim size: 100Gi deleteClaim: false zookeeper: replicas: 1 storage: type: persistent-claim size: 100Gi deleteClaim: false entityOperator: topicOperator: {} userOperator: {}
-
Create or update the
Kafka
resources in separate namespaces.kubectl apply -f <kafka_configuration_file> -n <namespace>
The Cluster Operator creates the listeners and sets up the cluster and client certificate authority (CA) certificates to enable authentication within the Kafka cluster.
The certificates are created in the secret
<cluster_name>-cluster-ca-cert
. -
Configure two
KafkaUser
resources, one for a user of the source Kafka cluster and one for a user of the target Kafka cluster.-
Configure the same authentication and authorization types as the corresponding source and target Kafka cluster. For example, if you used
tls
authentication and thesimple
authorization type in theKafka
configuration for the source Kafka cluster, use the same in theKafkaUser
configuration. -
Configure the ACLs needed by MirrorMaker 2.0 to allow operations on the source and target Kafka clusters.
The ACLs are used by the internal MirrorMaker connectors, and by the underlying Kafka Connect framework.
Example source user configuration for mTLS authenticationapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaUser metadata: name: my-source-user labels: strimzi.io/cluster: my-source-cluster spec: authentication: type: tls authorization: type: simple acls: # MirrorSourceConnector - resource: # Not needed if offset-syncs.topic.location=target type: topic name: mm2-offset-syncs.my-target-cluster.internal operations: - Create - DescribeConfigs - Read - Write - resource: # Needed for every topic which is mirrored type: topic name: "*" operations: - DescribeConfigs - Read # MirrorCheckpointConnector - resource: type: cluster operations: - Describe - resource: # Needed for every group for which offsets are synced type: group name: "*" operations: - Describe - resource: # Not needed if offset-syncs.topic.location=target type: topic name: mm2-offset-syncs.my-target-cluster.internal operations: - Read
Example target user configuration for mTLS authenticationapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaUser metadata: name: my-target-user labels: strimzi.io/cluster: my-target-cluster spec: authentication: type: tls authorization: type: simple acls: # Underlying Kafka Connect internal topics to store configuration, offsets, or status - resource: type: group name: mirrormaker2-cluster operations: - Read - resource: type: topic name: mirrormaker2-cluster-configs operations: - Create - Describe - DescribeConfigs - Read - Write - resource: type: topic name: mirrormaker2-cluster-status operations: - Create - Describe - DescribeConfigs - Read - Write - resource: type: topic name: mirrormaker2-cluster-offsets operations: - Create - Describe - DescribeConfigs - Read - Write # MirrorSourceConnector - resource: # Needed for every topic which is mirrored type: topic name: "*" operations: - Create - Alter - AlterConfigs - Write # MirrorCheckpointConnector - resource: type: cluster operations: - Describe - resource: type: topic name: my-source-cluster.checkpoints.internal operations: - Create - Describe - Read - Write - resource: # Needed for every group for which the offset is synced type: group name: "*" operations: - Read - Describe # MirrorHeartbeatConnector - resource: type: topic name: heartbeats operations: - Create - Describe - Write
NoteYou can use a certificate issued outside the User Operator by setting type
totls-external
. For more information, seeKafkaUserSpec
schema reference. -
-
Create or update a
KafkaUser
resource in each of the namespaces you created for the source and target Kafka clusters.kubectl apply -f <kafka_user_configuration_file> -n <namespace>
The User Operator creates the users representing the client (MirrorMaker), and the security credentials used for client authentication, based on the chosen authentication type.
The User Operator creates a new secret with the same name as the
KafkaUser
resource. The secret contains a private and public key for mTLS authentication. The public key is contained in a user certificate, which is signed by the clients CA. -
Configure a
KafkaMirrorMaker2
resource with the authentication details to connect to the source and target Kafka clusters.Example MirrorMaker 2.0 configuration with TLS encryption and mTLS authenticationapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaMirrorMaker2 metadata: name: my-mirror-maker-2 spec: version: 3.3.2 replicas: 1 connectCluster: "my-target-cluster" clusters: - alias: "my-source-cluster" bootstrapServers: my-source-cluster-kafka-bootstrap:9093 tls: # (1) trustedCertificates: - secretName: my-source-cluster-cluster-ca-cert certificate: ca.crt authentication: # (2) type: tls certificateAndKey: secretName: my-source-user certificate: user.crt key: user.key - alias: "my-target-cluster" bootstrapServers: my-target-cluster-kafka-bootstrap:9093 tls: # (3) trustedCertificates: - secretName: my-target-cluster-cluster-ca-cert certificate: ca.crt authentication: # (4) type: tls certificateAndKey: secretName: my-target-user certificate: user.crt key: user.key config: # -1 means it will use the default replication factor configured in the broker config.storage.replication.factor: -1 offset.storage.replication.factor: -1 status.storage.replication.factor: -1 mirrors: - sourceCluster: "my-source-cluster" targetCluster: "my-target-cluster" sourceConnector: config: replication.factor: 1 offset-syncs.topic.replication.factor: 1 sync.topic.acls.enabled: "false" heartbeatConnector: config: heartbeats.topic.replication.factor: 1 checkpointConnector: config: checkpoints.topic.replication.factor: 1 sync.group.offsets.enabled: "true" topicsPattern: "topic1|topic2|topic3" groupsPattern: "group1|group2|group3"
-
The TLS certificates for the source Kafka cluster. If they are in a separate namespace, copy the cluster secrets from the namespace of the Kafka cluster.
-
The user authentication for accessing the source Kafka cluster using the TLS mechanism.
-
The TLS certificates for the target Kafka cluster.
-
The user authentication for accessing the target Kafka cluster.
-
-
Create or update the
KafkaMirrorMaker2
resource in the same namespace as the target Kafka cluster.kubectl apply -f <mirrormaker2_configuration_file> -n <namespace_of_target_cluster>
-
type-KafkaMirrorMaker2ClusterSpec-reference[]
2.3.8. Performing a restart of a Kafka MirrorMaker 2.0 connector
This procedure describes how to manually trigger a restart of a Kafka MirrorMaker 2.0 connector by using a Kubernetes annotation.
-
The Cluster Operator is running.
-
Find the name of the
KafkaMirrorMaker2
custom resource that controls the Kafka MirrorMaker 2.0 connector you want to restart:kubectl get KafkaMirrorMaker2
-
Find the name of the Kafka MirrorMaker 2.0 connector to be restarted from the
KafkaMirrorMaker2
custom resource.kubectl describe KafkaMirrorMaker2 KAFKAMIRRORMAKER-2-NAME
-
To restart the connector, annotate the
KafkaMirrorMaker2
resource in Kubernetes. In this example,kubectl annotate
restarts a connector namedmy-source->my-target.MirrorSourceConnector
:kubectl annotate KafkaMirrorMaker2 KAFKAMIRRORMAKER-2-NAME "strimzi.io/restart-connector=my-source->my-target.MirrorSourceConnector"
-
Wait for the next reconciliation to occur (every two minutes by default).
The Kafka MirrorMaker 2.0 connector is restarted, as long as the annotation was detected by the reconciliation process. When the restart request is accepted, the annotation is removed from the
KafkaMirrorMaker2
custom resource.
2.3.9. Performing a restart of a Kafka MirrorMaker 2.0 connector task
This procedure describes how to manually trigger a restart of a Kafka MirrorMaker 2.0 connector task by using a Kubernetes annotation.
-
The Cluster Operator is running.
-
Find the name of the
KafkaMirrorMaker2
custom resource that controls the Kafka MirrorMaker 2.0 connector you want to restart:kubectl get KafkaMirrorMaker2
-
Find the name of the Kafka MirrorMaker 2.0 connector and the ID of the task to be restarted from the
KafkaMirrorMaker2
custom resource. Task IDs are non-negative integers, starting from 0.kubectl describe KafkaMirrorMaker2 KAFKAMIRRORMAKER-2-NAME
-
To restart the connector task, annotate the
KafkaMirrorMaker2
resource in Kubernetes. In this example,kubectl annotate
restarts task 0 of a connector namedmy-source->my-target.MirrorSourceConnector
:kubectl annotate KafkaMirrorMaker2 KAFKAMIRRORMAKER-2-NAME "strimzi.io/restart-connector-task=my-source->my-target.MirrorSourceConnector:0"
-
Wait for the next reconciliation to occur (every two minutes by default).
The Kafka MirrorMaker 2.0 connector task is restarted, as long as the annotation was detected by the reconciliation process. When the restart task request is accepted, the annotation is removed from the
KafkaMirrorMaker2
custom resource.
2.4. Kafka MirrorMaker cluster configuration
Configure a Kafka MirrorMaker deployment using the KafkaMirrorMaker
resource.
KafkaMirrorMaker replicates data between Kafka clusters.
KafkaMirrorMaker
schema reference describes the full schema of the KafkaMirrorMaker
resource.
You can use Strimzi with MirrorMaker or MirrorMaker 2.0. MirrorMaker 2.0 is the latest version, and offers a more efficient way to mirror data between Kafka clusters.
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 .
|
2.4.1. Configuring Kafka MirrorMaker
Use the properties of the KafkaMirrorMaker
resource to configure your Kafka MirrorMaker deployment.
You can configure access control for producers and consumers using TLS or SASL authentication. This procedure shows a configuration that uses TLS encryption and mTLS authentication on the consumer and producer side.
-
See the Deploying and Upgrading Strimzi guide for instructions on running a:
-
Source and target Kafka clusters must be available
-
Edit the
spec
properties for theKafkaMirrorMaker
resource.The properties you can configure are shown in this example configuration:
apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaMirrorMaker metadata: name: my-mirror-maker spec: replicas: 3 (1) consumer: bootstrapServers: my-source-cluster-kafka-bootstrap:9092 (2) groupId: "my-group" (3) numStreams: 2 (4) offsetCommitInterval: 120000 (5) tls: (6) trustedCertificates: - secretName: my-source-cluster-ca-cert certificate: ca.crt authentication: (7) type: tls certificateAndKey: secretName: my-source-secret certificate: public.crt key: private.key config: (8) max.poll.records: 100 receive.buffer.bytes: 32768 ssl.cipher.suites: "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384" (9) ssl.enabled.protocols: "TLSv1.2" ssl.protocol: "TLSv1.2" ssl.endpoint.identification.algorithm: HTTPS (10) producer: bootstrapServers: my-target-cluster-kafka-bootstrap:9092 abortOnSendFailure: false (11) tls: trustedCertificates: - secretName: my-target-cluster-ca-cert certificate: ca.crt authentication: type: tls certificateAndKey: secretName: my-target-secret certificate: public.crt key: private.key config: compression.type: gzip batch.size: 8192 ssl.cipher.suites: "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384" (12) ssl.enabled.protocols: "TLSv1.2" ssl.protocol: "TLSv1.2" ssl.endpoint.identification.algorithm: HTTPS (13) include: "my-topic|other-topic" (14) resources: (15) requests: cpu: "1" memory: 2Gi limits: cpu: "2" memory: 2Gi logging: (16) type: inline loggers: mirrormaker.root.logger: "INFO" readinessProbe: (17) initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 metricsConfig: (18) type: jmxPrometheusExporter valueFrom: configMapKeyRef: name: my-config-map key: my-key jvmOptions: (19) "-Xmx": "1g" "-Xms": "1g" image: my-org/my-image:latest (20) template: (21) pod: affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: application operator: In values: - postgresql - mongodb topologyKey: "kubernetes.io/hostname" connectContainer: (22) env: - name: JAEGER_SERVICE_NAME value: my-jaeger-service - name: JAEGER_AGENT_HOST value: jaeger-agent-name - name: JAEGER_AGENT_PORT value: "6831" tracing: (23) type: jaeger
-
Bootstrap servers for consumer and producer.
-
TLS encryption with key names under which TLS certificates are stored in X.509 format for consumer or producer. If certificates are stored in the same secret, it can be listed multiple times.
-
Authentication for consumer or producer, specified as mTLS, token-based OAuth, SASL-based SCRAM-SHA-256/SCRAM-SHA-512, or PLAIN.
-
SSL properties for external listeners to run with a specific cipher suite for a TLS version.
-
Hostname verification is enabled by setting to
HTTPS
. An empty string disables the verification. -
If the
abortOnSendFailure
property is set totrue
, Kafka MirrorMaker will exit and the container will restart following a send failure for a message. -
SSL properties for external listeners to run with a specific cipher suite for a TLS version.
-
Hostname verification is enabled by setting to
HTTPS
. An empty string disables the verification. -
A included topics mirrored from source to target Kafka cluster.
-
Requests for reservation of supported resources, currently
cpu
andmemory
, and limits to specify the maximum resources that can be consumed. -
Specified loggers and log levels added directly (
inline
) or indirectly (external
) through a ConfigMap. A custom ConfigMap must be placed under thelog4j.properties
orlog4j2.properties
key. MirrorMaker has a single logger calledmirrormaker.root.logger
. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. -
Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).
-
Prometheus metrics, which are enabled by referencing a ConfigMap containing configuration for the Prometheus JMX exporter in this example. You can enable metrics without further configuration using a reference to a ConfigMap containing an empty file under
metricsConfig.valueFrom.configMapKeyRef.key
. -
JVM configuration options to optimize performance for the Virtual Machine (VM) running Kafka MirrorMaker.
-
ADVANCED OPTION: Container image configuration, which is recommended only in special situations.
-
Template customization. Here a pod is scheduled with anti-affinity, so the pod is not scheduled on nodes with the same hostname.
-
Environment variables are set for distributed tracing.
-
Distributed tracing is enabled for Jaeger.
WarningWith the abortOnSendFailure
property set tofalse
, the producer attempts to send the next message in a topic. The original message might be lost, as there is no attempt to resend a failed message. -
Create or update the resource:
kubectl apply -f <your-file>
2.4.2. List of Kafka MirrorMaker cluster resources
The following resources are created by the Cluster Operator in the Kubernetes cluster:
- <mirror-maker-name>-mirror-maker
-
Deployment which is responsible for creating the Kafka MirrorMaker pods.
- <mirror-maker-name>-config
-
ConfigMap which contains ancillary configuration for the Kafka MirrorMaker, and is mounted as a volume by the Kafka broker pods.
- <mirror-maker-name>-mirror-maker
-
Pod Disruption Budget configured for the Kafka MirrorMaker worker nodes.
2.5. Kafka Bridge cluster configuration
Configure a Kafka Bridge deployment using the KafkaBridge
resource.
Kafka Bridge provides an API for integrating HTTP-based clients with a Kafka cluster.
KafkaBridge
schema reference describes the full schema of the KafkaBridge
resource.
2.5.1. Configuring the Kafka Bridge
Use the Kafka Bridge to make HTTP-based requests to the Kafka cluster.
Use the properties of the KafkaBridge
resource to configure your Kafka Bridge deployment.
In order to prevent issues arising when client consumer requests are processed by different Kafka Bridge instances, address-based routing must be employed to ensure that requests are routed to the right Kafka Bridge instance. Additionally, each independent Kafka Bridge instance must have a replica. A Kafka Bridge instance has its own state which is not shared with another instances.
-
A Kubernetes cluster
-
A running Cluster Operator
See the Deploying and Upgrading Strimzi guide for instructions on running a:
-
Edit the
spec
properties for theKafkaBridge
resource.The properties you can configure are shown in this example configuration:
apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaBridge metadata: name: my-bridge spec: replicas: 3 (1) bootstrapServers: <cluster_name>-cluster-kafka-bootstrap:9092 (2) tls: (3) trustedCertificates: - secretName: my-cluster-cluster-cert certificate: ca.crt - secretName: my-cluster-cluster-cert certificate: ca2.crt authentication: (4) type: tls certificateAndKey: secretName: my-secret certificate: public.crt key: private.key http: (5) port: 8080 cors: (6) allowedOrigins: "https://strimzi.io" allowedMethods: "GET,POST,PUT,DELETE,OPTIONS,PATCH" consumer: (7) config: auto.offset.reset: earliest producer: (8) config: delivery.timeout.ms: 300000 resources: (9) requests: cpu: "1" memory: 2Gi limits: cpu: "2" memory: 2Gi logging: (10) type: inline loggers: logger.bridge.level: "INFO" # enabling DEBUG just for send operation logger.send.name: "http.openapi.operation.send" logger.send.level: "DEBUG" jvmOptions: (11) "-Xmx": "1g" "-Xms": "1g" readinessProbe: (12) initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 image: my-org/my-image:latest (13) template: (14) pod: affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: application operator: In values: - postgresql - mongodb topologyKey: "kubernetes.io/hostname" bridgeContainer: (15) env: - name: JAEGER_SERVICE_NAME value: my-jaeger-service - name: JAEGER_AGENT_HOST value: jaeger-agent-name - name: JAEGER_AGENT_PORT value: "6831"
-
Bootstrap server for connection to the target Kafka cluster. Use the name of the Kafka cluster as the <cluster_name>.
-
TLS encryption with key names under which TLS certificates are stored in X.509 format for the source Kafka cluster. If certificates are stored in the same secret, it can be listed multiple times.
-
Authentication for the Kafka Bridge cluster, specified as mTLS, token-based OAuth, SASL-based SCRAM-SHA-256/SCRAM-SHA-512, or PLAIN. By default, the Kafka Bridge connects to Kafka brokers without authentication.
-
HTTP access to Kafka brokers.
-
CORS access specifying selected resources and access methods. Additional HTTP headers in requests describe the origins that are permitted access to the Kafka cluster.
-
Consumer configuration options.
-
Producer configuration options.
-
Requests for reservation of supported resources, currently
cpu
andmemory
, and limits to specify the maximum resources that can be consumed. -
Specified Kafka Bridge loggers and log levels added directly (
inline
) or indirectly (external
) through a ConfigMap. A custom ConfigMap must be placed under thelog4j.properties
orlog4j2.properties
key. For the Kafka Bridge loggers, you can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. -
JVM configuration options to optimize performance for the Virtual Machine (VM) running the Kafka Bridge.
-
Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).
-
Optional: Container image configuration, which is recommended only in special situations.
-
Template customization. Here a pod is scheduled with anti-affinity, so the pod is not scheduled on nodes with the same hostname.
-
Environment variables are set for distributed tracing.
-
Create or update the resource:
kubectl apply -f KAFKA-BRIDGE-CONFIG-FILE
2.5.2. List of Kafka Bridge cluster resources
The following resources are created by the Cluster Operator in the Kubernetes cluster:
- bridge-cluster-name-bridge
-
Deployment which is in charge to create the Kafka Bridge worker node pods.
- bridge-cluster-name-bridge-service
-
Service which exposes the REST interface of the Kafka Bridge cluster.
- bridge-cluster-name-bridge-config
-
ConfigMap which contains the Kafka Bridge ancillary configuration and is mounted as a volume by the Kafka broker pods.
- bridge-cluster-name-bridge
-
Pod Disruption Budget configured for the Kafka Bridge worker nodes.
2.6. Customizing Kubernetes resources
A Strimzi deployment creates Kubernetes resources, such as Deployments
, StatefulSets
, Pods
, and Services
.
These resources are managed by Strimzi operators.
Only the operator that is responsible for managing a particular Kubernetes resource can change that resource.
If you try to manually change an operator-managed Kubernetes resource, the operator will revert your changes back.
Changing an operator-managed Kubernetes resource can be useful if you want to perform certain tasks, such as:
-
Adding custom labels or annotations that control how
Pods
are treated by Istio or other services -
Managing how
Loadbalancer
-type Services are created by the cluster
You can make the changes using the template
property in the Strimzi custom resources.
The template
property is supported in the following resources.
The API reference provides more details about the customizable fields.
Kafka.spec.kafka
Kafka.spec.zookeeper
Kafka.spec.entityOperator
Kafka.spec.kafkaExporter
Kafka.spec.cruiseControl
Kafka.spec.jmxTrans
KafkaConnect.spec
KafkaMirrorMaker.spec
KafkaMirrorMaker2.spec
KafkaBridge.spec
KafkaUser.spec
In the following example, the template
property is used to modify the labels in a Kafka broker’s pod.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
labels:
app: my-cluster
spec:
kafka:
# ...
template:
pod:
metadata:
labels:
mylabel: myvalue
# ...
2.6.1. Customizing the image pull policy
Strimzi allows you to customize the image pull policy for containers in all pods deployed by the Cluster Operator.
The image pull policy is configured using the environment variable STRIMZI_IMAGE_PULL_POLICY
in the Cluster Operator deployment.
The STRIMZI_IMAGE_PULL_POLICY
environment variable can be set to three different values:
Always
-
Container images are pulled from the registry every time the pod is started or restarted.
IfNotPresent
-
Container images are pulled from the registry only when they were not pulled before.
Never
-
Container images are never pulled from the registry.
The image pull policy can be currently customized only for all Kafka, Kafka Connect, and Kafka MirrorMaker clusters at once. Changing the policy will result in a rolling update of all your Kafka, Kafka Connect, and Kafka MirrorMaker clusters.
-
For more information about Cluster Operator configuration, see Using the Cluster Operator.
-
For more information about Image Pull Policies, see Disruptions.
2.6.2. Applying a termination grace period
Apply a termination grace period to give a Kafka cluster enough time to shut down cleanly.
Specify the time using the terminationGracePeriodSeconds
property.
Add the property to the template.pod
configuration of the Kafka
custom resource.
The time you add will depend on the size of your Kafka cluster. The Kubernetes default for the termination grace period is 30 seconds. If you observe that your clusters are not shutting down cleanly, you can increase the termination grace period.
A termination grace period is applied every time a pod is restarted. The period begins when Kubernetes sends a term (termination) signal to the processes running in the pod. The period should reflect the amount of time required to transfer the processes of the terminating pod to another pod before they are stopped. After the period ends, a kill signal stops any processes still running in the pod.
The following example adds a termination grace period of 120 seconds to the Kafka
custom resource.
You can also specify the configuration in the custom resources of other Kafka components.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
template:
pod:
terminationGracePeriodSeconds: 120
# ...
# ...
2.7. Configuring pod scheduling
When two applications are scheduled to the same Kubernetes node, both applications might use the same resources like disk I/O and impact performance. That can lead to performance degradation. Scheduling Kafka pods in a way that avoids sharing nodes with other critical workloads, using the right nodes or dedicated a set of nodes only for Kafka are the best ways how to avoid such problems.
2.7.1. Specifying affinity, tolerations, and topology spread constraints
Use affinity, tolerations and topology spread constraints to schedule the pods of kafka resources onto nodes.
Affinity, tolerations and topology spread constraints are configured using the affinity
, tolerations
, and topologySpreadConstraint
properties in following resources:
-
Kafka.spec.kafka.template.pod
-
Kafka.spec.zookeeper.template.pod
-
Kafka.spec.entityOperator.template.pod
-
KafkaConnect.spec.template.pod
-
KafkaBridge.spec.template.pod
-
KafkaMirrorMaker.spec.template.pod
-
KafkaMirrorMaker2.spec.template.pod
The format of the affinity
, tolerations
, and topologySpreadConstraint
properties follows the Kubernetes specification.
The affinity configuration can include different types of affinity:
-
Pod affinity and anti-affinity
-
Node affinity
Use pod anti-affinity to avoid critical applications sharing nodes
Use pod anti-affinity to ensure that critical applications are never scheduled on the same disk. When running a Kafka cluster, it is recommended to use pod anti-affinity to ensure that the Kafka brokers do not share nodes with other workloads, such as databases.
Use node affinity to schedule workloads onto specific nodes
The Kubernetes cluster usually consists of many different types of worker nodes. Some are optimized for CPU heavy workloads, some for memory, while other might be optimized for storage (fast local SSDs) or network. Using different nodes helps to optimize both costs and performance. To achieve the best possible performance, it is important to allow scheduling of Strimzi components to use the right nodes.
Kubernetes uses node affinity to schedule workloads onto specific nodes.
Node affinity allows you to create a scheduling constraint for the node on which the pod will be scheduled.
The constraint is specified as a label selector.
You can specify the label using either the built-in node label like beta.kubernetes.io/instance-type
or custom labels to select the right node.
Use node affinity and tolerations for dedicated nodes
Use taints to create dedicated nodes, then schedule Kafka pods on the dedicated nodes by configuring node affinity and tolerations.
Cluster administrators can mark selected Kubernetes nodes as tainted. Nodes with taints are excluded from regular scheduling and normal pods will not be scheduled to run on them. Only services which can tolerate the taint set on the node can be scheduled on it. The only other services running on such nodes will be system services such as log collectors or software defined networks.
Running Kafka and its components on dedicated nodes can have many advantages. There will be no other applications running on the same nodes which could cause disturbance or consume the resources needed for Kafka. That can lead to improved performance and stability.
2.7.2. Configuring pod anti-affinity to schedule each Kafka broker on a different worker node
Many Kafka brokers or ZooKeeper nodes can run on the same Kubernetes worker node.
If the worker node fails, they will all become unavailable at the same time.
To improve reliability, you can use podAntiAffinity
configuration to schedule each Kafka broker or ZooKeeper node on a different Kubernetes worker node.
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
affinity
property in the resource specifying the cluster deployment. To make sure that no worker nodes are shared by Kafka brokers or ZooKeeper nodes, use thestrimzi.io/name
label. Set thetopologyKey
tokubernetes.io/hostname
to specify that the selected pods are not scheduled on nodes with the same hostname. This will still allow the same worker node to be shared by a single Kafka broker and a single ZooKeeper node. For example:apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka spec: kafka: # ... template: pod: affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: strimzi.io/name operator: In values: - CLUSTER-NAME-kafka topologyKey: "kubernetes.io/hostname" # ... zookeeper: # ... template: pod: affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: strimzi.io/name operator: In values: - CLUSTER-NAME-zookeeper topologyKey: "kubernetes.io/hostname" # ...
Where
CLUSTER-NAME
is the name of your Kafka custom resource. -
If you even want to make sure that a Kafka broker and ZooKeeper node do not share the same worker node, use the
strimzi.io/cluster
label. For example:apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka spec: kafka: # ... template: pod: affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: strimzi.io/cluster operator: In values: - CLUSTER-NAME topologyKey: "kubernetes.io/hostname" # ... zookeeper: # ... template: pod: affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: strimzi.io/cluster operator: In values: - CLUSTER-NAME topologyKey: "kubernetes.io/hostname" # ...
Where
CLUSTER-NAME
is the name of your Kafka custom resource. -
Create or update the resource.
kubectl apply -f <kafka_configuration_file>
2.7.3. Configuring pod anti-affinity in Kafka components
Pod anti-affinity configuration helps with the stability and performance of Kafka brokers. By using podAntiAffinity
, Kubernetes will not schedule Kafka brokers on the same nodes as other workloads.
Typically, you want to avoid Kafka running on the same worker node as other network or storage intensive applications such as databases, storage or other messaging platforms.
-
A Kubernetes cluster
-
A running Cluster Operator
-
Edit the
affinity
property in the resource specifying the cluster deployment. Use labels to specify the pods which should not be scheduled on the same nodes. ThetopologyKey
should be set tokubernetes.io/hostname
to specify that the selected pods should not be scheduled on nodes with the same hostname. For example:apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka spec: kafka: # ... template: pod: affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: application operator: In values: - postgresql - mongodb topologyKey: "kubernetes.io/hostname" # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f <kafka_configuration_file>
2.7.4. Configuring node affinity in Kafka components
-
A Kubernetes cluster
-
A running Cluster Operator
-
Label the nodes where Strimzi components should be scheduled.
This can be done using
kubectl label
:kubectl label node NAME-OF-NODE node-type=fast-network
Alternatively, some of the existing labels might be reused.
-
Edit the
affinity
property in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka spec: kafka: # ... template: pod: affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: node-type operator: In values: - fast-network # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f <kafka_configuration_file>
2.7.5. Setting up dedicated nodes and scheduling pods on them
-
A Kubernetes cluster
-
A running Cluster Operator
-
Select the nodes which should be used as dedicated.
-
Make sure there are no workloads scheduled on these nodes.
-
Set the taints on the selected nodes:
This can be done using
kubectl taint
:kubectl taint node NAME-OF-NODE dedicated=Kafka:NoSchedule
-
Additionally, add a label to the selected nodes as well.
This can be done using
kubectl label
:kubectl label node NAME-OF-NODE dedicated=Kafka
-
Edit the
affinity
andtolerations
properties in the resource specifying the cluster deployment.For example:
apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka spec: kafka: # ... template: pod: tolerations: - key: "dedicated" operator: "Equal" value: "Kafka" effect: "NoSchedule" affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: dedicated operator: In values: - Kafka # ... zookeeper: # ...
-
Create or update the resource.
This can be done using
kubectl apply
:kubectl apply -f <kafka_configuration_file>
2.8. Logging configuration
Configure logging levels in the custom resources of Kafka components and Strimzi Operators.
You can specify the logging levels directly in the spec.logging
property of the custom resource.
Or you can define the logging properties in a ConfigMap that’s referenced in the custom resource using the configMapKeyRef
property.
The advantages of using a ConfigMap are that the logging properties are maintained in one place and are accessible to more than one resource. You can also reuse the ConfigMap for more than one resource. If you are using a ConfigMap to specify loggers for Strimzi Operators, you can also append the logging specification to add filters.
You specify a logging type
in your logging specification:
-
inline
when specifying logging levels directly -
external
when referencing a ConfigMap
inline
logging configurationspec:
# ...
logging:
type: inline
loggers:
kafka.root.logger.level: "INFO"
external
logging configurationspec:
# ...
logging:
type: external
valueFrom:
configMapKeyRef:
name: my-config-map
key: my-config-map-key
Values for the name
and key
of the ConfigMap are mandatory.
Default logging is used if the name
or key
is not set.
2.8.1. Logging options for Kafka components and operators
For more information on configuring logging for specific Kafka components or operators, see the following sections.
2.8.2. Creating a ConfigMap for logging
To use a ConfigMap to define logging properties, you create the ConfigMap and then reference it as part of the logging definition in the spec
of a resource.
The ConfigMap must contain the appropriate logging configuration.
-
log4j.properties
for Kafka components, ZooKeeper, and the Kafka Bridge -
log4j2.properties
for the Topic Operator and User Operator
The configuration must be placed under these properties.
In this procedure a ConfigMap defines a root logger for a Kafka resource.
-
Create the ConfigMap.
You can create the ConfigMap as a YAML file or from a properties file.
ConfigMap example with a root logger definition for Kafka:
kind: ConfigMap apiVersion: v1 metadata: name: logging-configmap data: log4j.properties: kafka.root.logger.level="INFO"
If you are using a properties file, specify the file at the command line:
kubectl create configmap logging-configmap --from-file=log4j.properties
The properties file defines the logging configuration:
# Define the logger kafka.root.logger.level="INFO" # ...
-
Define external logging in the
spec
of the resource, setting thelogging.valueFrom.configMapKeyRef.name
to the name of the ConfigMap andlogging.valueFrom.configMapKeyRef.key
to the key in this ConfigMap.spec: # ... logging: type: external valueFrom: configMapKeyRef: name: logging-configmap key: log4j.properties
-
Create or update the resource.
kubectl apply -f <kafka_configuration_file>
2.8.3. Adding logging filters to Operators
If you are using a ConfigMap to configure the (log4j2) logging levels for Strimzi Operators, you can also define logging filters to limit what’s returned in the log.
Logging filters are useful when you have a large number of logging messages.
Suppose you set the log level for the logger as DEBUG (rootLogger.level="DEBUG"
).
Logging filters reduce the number of logs returned for the logger at that level, so you can focus on a specific resource.
When the filter is set, only log messages matching the filter are logged.
Filters use markers to specify what to include in the log.
You specify a kind, namespace and name for the marker.
For example, if a Kafka cluster is failing, you can isolate the logs by specifying the kind as Kafka
, and use the namespace and name of the failing cluster.
This example shows a marker filter for a Kafka cluster named my-kafka-cluster
.
rootLogger.level="INFO"
appender.console.filter.filter1.type=MarkerFilter (1)
appender.console.filter.filter1.onMatch=ACCEPT (2)
appender.console.filter.filter1.onMismatch=DENY (3)
appender.console.filter.filter1.marker=Kafka(my-namespace/my-kafka-cluster) (4)
-
The
MarkerFilter
type compares a specified marker for filtering. -
The
onMatch
property accepts the log if the marker matches. -
The
onMismatch
property rejects the log if the marker does not match. -
The marker used for filtering is in the format KIND(NAMESPACE/NAME-OF-RESOURCE).
You can create one or more filters. Here, the log is filtered for two Kafka clusters.
appender.console.filter.filter1.type=MarkerFilter
appender.console.filter.filter1.onMatch=ACCEPT
appender.console.filter.filter1.onMismatch=DENY
appender.console.filter.filter1.marker=Kafka(my-namespace/my-kafka-cluster-1)
appender.console.filter.filter2.type=MarkerFilter
appender.console.filter.filter2.onMatch=ACCEPT
appender.console.filter.filter2.onMismatch=DENY
appender.console.filter.filter2.marker=Kafka(my-namespace/my-kafka-cluster-2)
To add filters to the Cluster Operator, update its logging ConfigMap YAML file (install/cluster-operator/050-ConfigMap-strimzi-cluster-operator.yaml
).
-
Update the
050-ConfigMap-strimzi-cluster-operator.yaml
file to add the filter properties to the ConfigMap.In this example, the filter properties return logs only for the
my-kafka-cluster
Kafka cluster:kind: ConfigMap apiVersion: v1 metadata: name: strimzi-cluster-operator data: log4j2.properties: #... appender.console.filter.filter1.type=MarkerFilter appender.console.filter.filter1.onMatch=ACCEPT appender.console.filter.filter1.onMismatch=DENY appender.console.filter.filter1.marker=Kafka(my-namespace/my-kafka-cluster)
Alternatively, edit the
ConfigMap
directly:kubectl edit configmap strimzi-cluster-operator
-
If you updated the YAML file instead of editing the
ConfigMap
directly, apply the changes by deploying the ConfigMap:kubectl create -f install/cluster-operator/050-ConfigMap-strimzi-cluster-operator.yaml
To add filters to the Topic Operator or User Operator, create or edit a logging ConfigMap.
In this procedure a logging ConfigMap is created with filters for the Topic Operator. The same approach is used for the User Operator.
-
Create the ConfigMap.
You can create the ConfigMap as a YAML file or from a properties file.
In this example, the filter properties return logs only for the
my-topic
topic:kind: ConfigMap apiVersion: v1 metadata: name: logging-configmap data: log4j2.properties: rootLogger.level="INFO" appender.console.filter.filter1.type=MarkerFilter appender.console.filter.filter1.onMatch=ACCEPT appender.console.filter.filter1.onMismatch=DENY appender.console.filter.filter1.marker=KafkaTopic(my-namespace/my-topic)
If you are using a properties file, specify the file at the command line:
kubectl create configmap logging-configmap --from-file=log4j2.properties
The properties file defines the logging configuration:
# Define the logger rootLogger.level="INFO" # Set the filters appender.console.filter.filter1.type=MarkerFilter appender.console.filter.filter1.onMatch=ACCEPT appender.console.filter.filter1.onMismatch=DENY appender.console.filter.filter1.marker=KafkaTopic(my-namespace/my-topic) # ...
-
Define external logging in the
spec
of the resource, setting thelogging.valueFrom.configMapKeyRef.name
to the name of the ConfigMap andlogging.valueFrom.configMapKeyRef.key
to the key in this ConfigMap.For the Topic Operator, logging is specified in the
topicOperator
configuration of theKafka
resource.spec: # ... entityOperator: topicOperator: logging: type: external valueFrom: configMapKeyRef: name: logging-configmap key: log4j2.properties
-
Apply the changes by deploying the Cluster Operator:
create -f install/cluster-operator -n my-cluster-operator-namespace
3. Loading configuration values from external sources
Use configuration provider plugins 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. Use them, for example, to provide the credentials for Kafka Connect connector configuration.
- Kubernetes Configuration Provider
-
The Kubernetes Configuration Provider plugin loads configuration data from Kubernetes secrets or ConfigMaps.
Suppose you have a
Secret
object that’s managed outside the Kafka namespace, or outside the Kafka cluster. The Kubernetes Configuration Provider allows you to reference the values of the secret in your configuration without extracting the files. You just need to tell the provider what secret to use and provide access rights. The provider loads the data without needing to restart the Kafka component, even when using a newSecret
orConfigMap
object. This capability avoids disruption when a Kafka Connect instance hosts multiple connectors. - Environment Variables Configuration Provider
-
The Environment Variables Configuration Provider plugin loads configuration data from environment variables.
The values for the environment variables can be mapped from secrets or ConfigMaps. You can use the Environment Variables Configuration Provider, for example, to load certificates or JAAS configuration from environment variables mapped from Kubernetes secrets.
Note
|
Kubernetes Configuration Provider can’t use mounted files.
For example, it can’t load values that need the location of a truststore or keystore.
Instead, you can mount ConfigMaps or secrets into a Kafka Connect pod as environment variables or volumes.
You can use the Environment Variables Configuration Provider to load values for environment variables.
You add configuration using the externalConfiguration property in KafkaConnect.spec .
You don’t need to set up access rights with this approach.
However, Kafka Connect will need a restart when using a new Secret or ConfigMap for a connector.
This will cause disruption to all the Kafka Connect instance’s connectors.
|
3.1. Loading configuration values from a ConfigMap
This procedure shows how to use the Kubernetes Configuration Provider plugin.
In the procedure, an external ConfigMap
object provides configuration properties for a connector.
-
A Kubernetes cluster is available.
-
A Kafka cluster is running.
-
The Cluster Operator is running.
-
Create a
ConfigMap
orSecret
that contains the configuration properties.In this example, a
ConfigMap
object namedmy-connector-configuration
contains connector properties:ExampleConfigMap
with connector propertiesapiVersion: v1 kind: ConfigMap metadata: name: my-connector-configuration data: option1: value1 option2: value2
-
Specify the Kubernetes Configuration Provider in the Kafka Connect configuration.
The specification shown here can support loading values from secrets and ConfigMaps.
Example Kafka Connect configuration to enable the Kubernetes Configuration ProviderapiVersion: 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.secrets.class: io.strimzi.kafka.KubernetesSecretConfigProvider # (2) config.providers.configmaps.class: io.strimzi.kafka.KubernetesConfigMapConfigProvider # (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
. -
KubernetesSecretConfigProvider
provides values from secrets. -
KubernetesConfigMapConfigProvider
provides values from config maps.
-
-
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 the
KafkaConnect
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: ${configmaps:my-project/my-connector-configuration:option1} # ... # ...
Placeholders for the property values in the config map are referenced in the connector configuration. The placeholder structure is
configmaps:<path_and_file_name>:<property>
.KubernetesConfigMapConfigProvider
reads and extracts the option1 property value from the external config map.
3.2. Loading configuration values from environment variables
This procedure shows how to use the Environment Variables Configuration Provider plugin.
In the procedure, environment variables provide configuration properties for a connector. A database password is specified as an environment variable.
-
A Kubernetes cluster is available.
-
A Kafka cluster is running.
-
The Cluster Operator is running.
-
Specify the Environment Variables Configuration Provider in the Kafka Connect configuration.
Define environment variables using the
externalConfiguration
property.Example Kafka Connect configuration to enable the Environment Variables Configuration ProviderapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnect metadata: name: my-connect annotations: strimzi.io/use-connector-resources: "true" spec: # ... config: # ... config.providers: env # (1) config.providers.env.class: io.strimzi.kafka.EnvVarConfigProvider # (2) # ... externalConfiguration: env: - name: DB_PASSWORD # (3) valueFrom: secretKeyRef: name: db-creds # (4) key: dbPassword # (5) # ...
-
The alias for the configuration provider is used to define other configuration parameters. The provider parameters use the alias from
config.providers
, taking the formconfig.providers.${alias}.class
. -
EnvVarConfigProvider
provides values from environment variables. -
The
DB_PASSWORD
environment variable takes a password value from a secret. -
The name of the secret containing the predefined password.
-
The key for the password stored inside the secret.
-
-
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:DB_PASSWORD} # ... # ...
4. Applying security context to Strimzi pods and containers
Security context defines constraints on pods and containers. By specifying a security context, pods and containers only have the permissions they need. For example, permissions can control runtime operations or access to resources.
4.1. How to configure security context
Use security provider plugins or template configuration to apply security context to Strimzi pods and containers.
Apply security context at the pod or container level:
- Pod-level security context
-
Pod-level security context is applied to all containers in a specific pod.
- Container-level security context
-
Container-level security context is applied to a specific container.
With Strimzi, security context is applied through one or both of the following methods:
- Template configuration
-
Use
template
configuration of Strimzi custom resources to specify security context at the pod or container level. - Pod security provider plugins
-
Use pod security provider plugins to automatically set security context across all pods and containers using preconfigured settings.
Pod security providers offer a simpler alternative to specifying security context through template
configuration.
You can use both approaches.
The template
approach has a higher priority.
Security context configured through template
properties overrides the configuration set by pod security providers.
So you might use pod security providers to automatically configure the security context for most containers.
And also use template
configuration to set container-specific security context where needed.
The template
approach provides flexibility, but it also means you have to configure security context in numerous places to capture the security you want for all pods and containers.
For example, you’ll need to apply the configuration to each pod in a Kafka cluster, as well as the pods for deployments of other Kafka components.
To avoid repeating the same configuration, you can use the following pod security provider plugins so that the security configuration is in one place.
- Baseline Provider
-
The Baseline Provider is based on the Kubernetes baseline security profile. The baseline profile prevents privilege escalations and defines other standard access controls and limitations.
- Restricted Provider
-
The Restricted Provider is based on the Kubernetes restricted security profile. The restricted profile is more restrictive than the baseline profile, and is used where security needs to be tighter.
For more information on the Kubernetes security profiles, see Pod security standards.
4.1.1. Template configuration for security context
In the following example, security context is configured for Kafka brokers in the template
configuration of the Kafka
resource.
Security context is specified at the pod and container level.
template
configuration for security contextapiVersion: {KafkaApiVersion}
kind: Kafka
metadata:
name: my-cluster
spec:
# ...
kafka:
template:
pod: # (1)
securityContext:
runAsUser: 1000001
fsGroup: 0
kafkaContainer: # (2)
securityContext:
runAsUser: 2000
# ...
-
Pod security context
-
Container security context of the Kafka broker container
4.1.2. Baseline Provider for pod security
The Baseline Provider is the default pod security provider. It configures the pods managed by Strimzi with a baseline security profile. The baseline profile is compatible with previous versions of Strimzi.
The Baseline Provider is enabled by default if you don’t specify a provider.
Though you can enable it explicitly by setting the STRIMZI_POD_SECURITY_PROVIDER_CLASS
environment variable to baseline
when configuring the Cluster Operator.
# ...
env:
# ...
- name: STRIMZI_POD_SECURITY_PROVIDER_CLASS
value: baseline
# ...
Instead of specifying baseline
as the value, you can specify the io.strimzi.plugin.security.profiles.impl.BaselinePodSecurityProvider
fully-qualified domain name.
4.1.3. Restricted Provider for pod security
The Restricted Provider provides a higher level of security than the Baseline Provider. It configures the pods managed by Strimzi with a restricted security profile.
You enable the Restricted Provider by setting the STRIMZI_POD_SECURITY_PROVIDER_CLASS
environment variable to restricted
when configuring the Cluster Operator.
# ...
env:
# ...
- name: STRIMZI_POD_SECURITY_PROVIDER_CLASS
value: restricted
# ...
Instead of specifying restricted
as the value, you can specify the io.strimzi.plugin.security.profiles.impl.RestrictedPodSecurityProvider
fully-qualified domain name.
If you change to the Restricted Provider from the default Baseline Provider, the following restrictions are implemented in addition to the constraints defined in the baseline security profile:
-
Limits allowed volume types
-
Disallows privilege escalation
-
Requires applications to run under a non-root user
-
Requires
seccomp
(secure computing mode) profiles to be set asRuntimeDefault
orLocalhost
-
Limits container capabilities to use only the
NET_BIND_SERVICE
capability
With the Restricted Provider enabled, containers created by the Cluster Operator are set with the following security context.
# ...
securityContext:
allowPrivilegeEscalation: false
capabilities:
drop:
- ALL
runAsNonRoot: true
seccompProfile:
type: RuntimeDefault
# ...
Note
|
Container capabilities and
|
-
Security context on Kubernetes
-
Pod security standards on Kubernetes (including profile descriptions)
4.2. Enabling the Restricted Provider for the Cluster Operator
Security pod providers configure the security context constraints of the pods and containers created by the Cluster Operator.
The Baseline Provider is the default pod security provider used by Strimzi.
You can switch to the Restricted Provider by changing the STRIMZI_POD_SECURITY_PROVIDER_CLASS
environment variable in the Cluster Operator configuration.
To make the required changes, configure the 060-Deployment-strimzi-cluster-operator.yaml
Cluster Operator installation file located in install/cluster-operator/
.
By enabling a new pod security provider, any pods or containers created by the Cluster Operator are subject to the limitations it imposes. Pods and containers that are already running are restarted for the changes to take affect.
-
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.
-
Add or amend the
STRIMZI_POD_SECURITY_PROVIDER_CLASS
environment variable with a value ofrestricted
.Cluster Operator configuration for the Restricted Provider# ... env: # ... - name: STRIMZI_POD_SECURITY_PROVIDER_CLASS value: restricted # ...
Or you can specify the
io.strimzi.plugin.security.profiles.impl.RestrictedPodSecurityProvider
fully-qualified domain name. -
Deploy the Cluster Operator:
kubectl create -f install/cluster-operator -n myproject
-
(Optional) Use
template
configuration to set security context for specific components at the pod or container level.Adding security context throughtemplate
configurationtemplate: pod: securityContext: runAsUser: 1000001 fsGroup: 0 kafkaContainer: securityContext: runAsUser: 2000 # ...
If you apply specific security context for a component using
template
configuration, it takes priority over the general configuration provided by the pod security provider.
4.3. Implementing a custom pod security provider
If Strimzi’s Baseline Provider and Restricted Provider don’t quite match your needs, you can develop a custom pod security provider to deliver all-encompassing pod and container security context constraints.
Implement a custom pod security provider to apply your own security context profile. You can decide what applications and privileges to include in the profile.
Your custom pod security provider can implement the PodSecurityProvider.java
interface that gets the security context for pods and containers; or it can extend the Baseline Provider or Restricted Provider classes.
The pod security provider plugins use the Java Service Provider Interface, so your custom pod security provider also requires a provider configuration file for service discovery.
To implement your own provider, the general steps include the following:
-
Build the JAR file for the provider.
-
Add the JAR file to the Cluster Operator image.
-
Specify the custom pod security provider when setting the Cluster Operator environment variable
STRIMZI_POD_SECURITY_PROVIDER_CLASS
.
4.4. Handling of security context by Kubernetes platform
Handling of security context depends on the tooling of the Kubernetes platform you are using.
For example, OpenShift uses built-in security context constraints (SCCs) to control permissions. SCCs are the settings and strategies that control the security features a pod has access to.
By default, OpenShift injects security context configuration automatically. In most cases, this means you don’t need to configure security context for the pods and containers created by the Cluster Operator. Although you can still create and manage your own SCCs.
For more information, see the OpenShift documentation.
5. Using Strimzi Operators
Use the Strimzi operators to manage your Kafka cluster, and Kafka topics and users.
5.1. 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.
5.2. Using the Cluster Operator
The Cluster Operator is used to deploy a Kafka cluster and other Kafka components.
For information on deploying the Cluster Operator, see Deploying the Cluster Operator.
5.2.1. 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
, StatefulSet
, 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 2.0 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 StatefulSet
, 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
5.2.2. 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 .
|
5.2.3. 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 relate to the container configuration for the deployment of the Cluster Operator image.
For more information on image configuration, see, image .
|
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
-
JMX_TRANS
-
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 example3.2.3=quay.io/strimzi/kafka:latest-kafka-3.2.3, 3.3.2=quay.io/strimzi/kafka:latest-kafka-3.3.2
. 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:latest
. 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 example3.2.3=quay.io/strimzi/kafka:latest-kafka-3.2.3, 3.3.2=quay.io/strimzi/kafka:latest-kafka-3.3.2
. 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 example3.2.3=quay.io/strimzi/kafka:latest-kafka-3.2.3, 3.3.2=quay.io/strimzi/kafka:latest-kafka-3.3.2
. 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:latest
. The image name to use as the default when deploying the Topic Operator if no image is specified as theKafka.spec.entityOperator.topicOperator.image
in theKafka
resource. STRIMZI_DEFAULT_USER_OPERATOR_IMAGE
-
Optional, default
quay.io/strimzi/operator:latest
. The image name to use as the default when deploying the User Operator if no image is specified as theKafka.spec.entityOperator.userOperator.image
in theKafka
resource. STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGE
-
Optional, default
quay.io/strimzi/kafka:latest-kafka-3.3.2
. 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.
5.2.4. 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
5.2.5. 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.
5.2.6. 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.
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
5.3. 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. |
5.3.1. 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
|
5.3.2. 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 latest, 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:latest-kafka-3.3.2 --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.
|
5.3.3. 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
5.3.4. 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>
5.4. 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. |
5.4.1. 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
5.4.2. 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.
5.5. 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
5.5.1. 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 upgrade or downgrade through one of the Strimzi versions in between.
|
5.5.2. 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.
5.5.3. UseStrimziPodSets feature gate
The UseStrimziPodSets
feature gate has a default state of enabled.
The UseStrimziPodSets
feature gate introduces a resource for managing pods called StrimziPodSet
.
When the feature gate is enabled, this resource is used instead of the StatefulSets.
Strimzi handles the creation and management of pods instead of Kubernetes.
Using StrimziPodSets instead of StatefulSets provides more control over the functionality.
When this feature gate is disabled, Strimzi relies on StatefulSets to create and manage pods for the ZooKeeper and Kafka clusters. Strimzi creates the StatefulSet and Kubernetes creates the pods according to the StatefulSet definition. When a pod is deleted, Kubernetes is responsible for recreating it. The use of StatefulSets has the following limitations:
-
Pods are always created or removed based on their index numbers
-
All pods in the StatefulSet need to have a similar configuration
-
Changing storage configuration for the Pods in the StatefulSet is complicated
To disable the UseStrimziPodSets
feature gate, specify -UseStrimziPodSets
in the STRIMZI_FEATURE_GATES
environment variable in the Cluster Operator configuration.
Important
|
The UseStrimziPodSets feature gate must be disabled when downgrading to Strimzi 0.27 and earlier versions.
|
5.5.4. (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.
Important
|
The UseKRaft feature gate depends on the UseStrimziPodSets feature gate.
When enabling the UseKRaft feature gate, make sure that the USeStrimziPodSets feature gate is enabled as well.
|
5.5.5. 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 beta stage in Strimzi 0.30. It moves to GA in Strimzi 0.35 when the support for StatefulSets is completely removed. -
The
UseKRaft
feature gate is available for development only and does not currently have a planned release for moving to the beta phase.
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 (planned) |
|
0.29 |
- |
- |
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 |
5.6. Monitoring operators using Prometheus metrics
Strimzi operators expose Prometheus metrics. The metrics are automatically enabled and contain information about:
-
Number of reconciliations
-
Number of Custom Resources the operator is processing
-
Duration of reconciliations
-
JVM metrics from the operators
Additionally, we provide an example Grafana dashboard.
For more information about Prometheus, see the Introducing Metrics to Kafka in the Deploying and Upgrading Strimzi guide.
6. Cruise Control for cluster rebalancing
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. |
6.1. 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.
6.2. 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.
6.2.1. 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. |
6.2.2. 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. |
6.2.3. 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:
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.
6.2.4. 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
|
If you change the inherited main optimization goals, you must ensure that the hard goals, if configured in the hard.goals property in Kafka.spec.cruiseControl.config , are a subset of the main optimization goals that you configured. Otherwise, errors will occur when generating optimization proposals.
|
6.2.5. 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:
default.goals: >
com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal,
com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal,
com.linkedin.kafka.cruisecontrol.analyzer.goals.DiskCapacityGoal
# ...
If no default optimization goals are specified, the cached proposal is generated using the main optimization goals.
6.2.6. 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.
6.3. 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
6.3.1. 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.
6.3.2. 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.
6.3.3. 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 use the
strimzi.io/refresh
annotation to generate a new optimization proposal for aKafkaRebalance
resource.
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.
6.3.4. 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.
6.3.5. 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. |
6.3.6. 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. |
6.3.7. 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.
6.4. 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.
6.4.1. 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.
6.4.2. 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.
6.4.3. 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.
6.4.4. 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.
6.5. 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.
-
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) 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.NetworkInboundCapacityGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkOutboundCapacityGoal # ... 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 # ...
-
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.
6.6. 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.
-
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 |
6.7. 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.
6.8. 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
.
6.9. 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
.
7. 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. |

7.1. 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.
7.2. 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.
7.2.1. 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. |
7.2.2. 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. |
7.2.3. 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. |
7.2.4. 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. |
7.2.5. 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 |
7.2.6. 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
# ...
For more information on configuring template properties, see Customizing Kubernetes resources.
7.2.7. 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
# ...
7.3. 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.
7.3.1. 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.
7.3.2. 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. |
7.3.3. 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 26. 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.
7.3.4. 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 27. 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.
7.4. TLS connections
7.4.1. 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.
7.4.2. 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.
7.4.3. 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.
7.4.4. Cruise Control
Cruise Control uses encryption for communication with both Kafka and ZooKeeper. A TLS sidecar is used when communicating with ZooKeeper.
7.4.5. Kafka Client connections
Encrypted or unencrypted communication between Kafka brokers and clients is configured using the tls
property for spec.kafka.listeners
.
7.5. 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.
7.6. 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.
7.7. 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)
7.7.1. 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
7.7.2. 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.
7.7.3. 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.
8. Managing Strimzi
This chapter covers tasks to maintain a deployment of Strimzi.
8.1. Working with custom resources
You can use kubectl
commands to retrieve information and perform other operations on Strimzi custom resources.
Using kubectl
with the status
subresource of a custom resource allows you to get the information about the resource.
8.1.1. Performing kubectl
operations on custom resources
Use kubectl
commands, such as get
, describe
, edit
, or delete
, to perform operations on resource types.
For example, kubectl get kafkatopics
retrieves a list of all Kafka topics and kubectl get kafkas
retrieves all deployed Kafka clusters.
When referencing resource types, you can use both singular and plural names:
kubectl get kafkas
gets the same results as kubectl get kafka
.
You can also use the short name of the resource.
Learning short names can save you time when managing Strimzi.
The short name for Kafka
is k
, so you can also run kubectl get k
to list all Kafka clusters.
kubectl get k
NAME DESIRED KAFKA REPLICAS DESIRED ZK REPLICAS
my-cluster 3 3
Strimzi resource | Long name | Short name |
---|---|---|
Kafka |
kafka |
k |
Kafka Topic |
kafkatopic |
kt |
Kafka User |
kafkauser |
ku |
Kafka Connect |
kafkaconnect |
kc |
Kafka Connector |
kafkaconnector |
kctr |
Kafka Mirror Maker |
kafkamirrormaker |
kmm |
Kafka Mirror Maker 2 |
kafkamirrormaker2 |
kmm2 |
Kafka Bridge |
kafkabridge |
kb |
Kafka Rebalance |
kafkarebalance |
kr |
Resource categories
Categories of custom resources can also be used in kubectl
commands.
All Strimzi custom resources belong to the category strimzi
, so you can use strimzi
to get all the Strimzi resources with one command.
For example, running kubectl get strimzi
lists all Strimzi custom resources in a given namespace.
kubectl get strimzi
NAME DESIRED KAFKA REPLICAS DESIRED ZK REPLICAS
kafka.kafka.strimzi.io/my-cluster 3 3
NAME PARTITIONS REPLICATION FACTOR
kafkatopic.kafka.strimzi.io/kafka-apps 3 3
NAME AUTHENTICATION AUTHORIZATION
kafkauser.kafka.strimzi.io/my-user tls simple
The kubectl get strimzi -o name
command returns all resource types and resource names.
The -o name
option fetches the output in the type/name format
kubectl get strimzi -o name
kafka.kafka.strimzi.io/my-cluster
kafkatopic.kafka.strimzi.io/kafka-apps
kafkauser.kafka.strimzi.io/my-user
You can combine this strimzi
command with other commands.
For example, you can pass it into a kubectl delete
command to delete all resources in a single command.
kubectl delete $(kubectl get strimzi -o name)
kafka.kafka.strimzi.io "my-cluster" deleted
kafkatopic.kafka.strimzi.io "kafka-apps" deleted
kafkauser.kafka.strimzi.io "my-user" deleted
Deleting all resources in a single operation might be useful, for example, when you are testing new Strimzi features.
Querying the status of sub-resources
There are other values you can pass to the -o
option.
For example, by using -o yaml
you get the output in YAML format.
Using -o json
will return it as JSON.
You can see all the options in kubectl get --help
.
One of the most useful options is the JSONPath support, which allows you to pass JSONPath expressions to query the Kubernetes API. A JSONPath expression can extract or navigate specific parts of any resource.
For example, you can use the JSONPath expression {.status.listeners[?(@.name=="tls")].bootstrapServers}
to get the bootstrap address from the status of the Kafka custom resource and use it in your Kafka clients.
Here, the command finds the bootstrapServers
value of the listener named tls
:
kubectl get kafka my-cluster -o=jsonpath='{.status.listeners[?(@.name=="tls")].bootstrapServers}{"\n"}'
my-cluster-kafka-bootstrap.myproject.svc:9093
By changing the name condition you can also get the address of the other Kafka listeners.
You can use jsonpath
to extract any other property or group of properties from any custom resource.
8.1.2. Strimzi custom resource status information
Status properties provide status information for certain custom resources.
The following table lists the custom resources that provide status information (when deployed) and the schemas that define the status properties.
Strimzi resource | Schema reference | Publishes status information on… |
---|---|---|
|
The Kafka cluster |
|
|
Kafka topics in the Kafka cluster |
|
|
Kafka users in the Kafka cluster |
|
|
The Kafka Connect cluster |
|
|
|
|
|
The Kafka MirrorMaker 2.0 cluster |
|
|
The Kafka MirrorMaker cluster |
|
|
The Strimzi Kafka Bridge |
|
|
The status and results of a rebalance |
The status
property of a resource provides information on the state of the resource.
The status.conditions
and status.observedGeneration
properties are common to all resources.
status.conditions
-
Status conditions describe the current state of a resource. Status condition properties are useful for tracking progress related to the resource achieving its desired state, as defined by the configuration specified in its
spec
. Status condition properties provide the time and reason the state of the resource changed, and details of events preventing or delaying the operator from realizing the desired state. status.observedGeneration
-
Last observed generation denotes the latest reconciliation of the resource by the Cluster Operator. If the value of
observedGeneration
is different from the value ofmetadata.generation
((the current version of the deployment), the operator has not yet processed the latest update to the resource. If these values are the same, the status information reflects the most recent changes to the resource.
The status
properties also provide resource-specific information.
For example, KafkaStatus
provides information on listener addresses, and the ID of the Kafka cluster.
Strimzi creates and maintains the status of custom resources, periodically evaluating the current state of the custom resource and updating its status accordingly.
When performing an update on a custom resource using kubectl edit
, for example, its status
is not editable. Moreover, changing the status
would not affect the configuration of the Kafka cluster.
Here we see the status
properties for a Kafka
custom resource.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
spec:
# ...
status:
clusterId: XP9FP2P-RByvEy0W4cOEUA # (1)
conditions: # (2)
- lastTransitionTime: '2023-01-20T17:56:29.396588Z'
status: 'True'
type: Ready # (3)
listeners: # (4)
- addresses:
- host: my-cluster-kafka-bootstrap.prm-project.svc
port: 9092
bootstrapServers: 'my-cluster-kafka-bootstrap.prm-project.svc:9092'
name: plain
type: plain
- addresses:
- host: my-cluster-kafka-bootstrap.prm-project.svc
port: 9093
bootstrapServers: 'my-cluster-kafka-bootstrap.prm-project.svc:9093'
certificates:
- |
-----BEGIN CERTIFICATE-----
-----END CERTIFICATE-----
name: tls
type: tls
- addresses:
- host: >-
2054284155.us-east-2.elb.amazonaws.com
port: 9095
bootstrapServers: >-
2054284155.us-east-2.elb.amazonaws.com:9095
certificates:
- |
-----BEGIN CERTIFICATE-----
-----END CERTIFICATE-----
name: external2
type: external2
- addresses:
- host: ip-10-0-172-202.us-east-2.compute.internal
port: 31644
bootstrapServers: 'ip-10-0-172-202.us-east-2.compute.internal:31644'
certificates:
- |
-----BEGIN CERTIFICATE-----
-----END CERTIFICATE-----
name: external1
type: external1
observedGeneration: 3 # (5)
-
The Kafka cluster ID.
-
Status
conditions
describe the current state of the Kafka cluster. -
The
Ready
condition indicates that the Cluster Operator considers the Kafka cluster able to handle traffic. -
The
listeners
describe Kafka bootstrap addresses by type. -
The
observedGeneration
value indicates the last reconciliation of theKafka
custom resource by the Cluster Operator.
Note
|
The Kafka bootstrap addresses listed in the status do not signify that those endpoints or the Kafka cluster is in a Ready state.
|
You can access status information for a resource from the command line. For more information, see Finding the status of a custom resource.
8.1.3. Finding the status of a custom resource
This procedure describes how to find the status of a custom resource.
-
A Kubernetes cluster.
-
The Cluster Operator is running.
-
Specify the custom resource and use the
-o jsonpath
option to apply a standard JSONPath expression to select thestatus
property:kubectl get kafka <kafka_resource_name> -o jsonpath='{.status}'
This expression returns all the status information for the specified custom resource. You can use dot notation, such as
status.listeners
orstatus.observedGeneration
, to fine-tune the status information you wish to see.
-
For more information about using JSONPath, see JSONPath support.
8.2. Pausing reconciliation of custom resources
Sometimes it is useful to pause the reconciliation of custom resources managed by Strimzi Operators, so that you can perform fixes or make updates. If reconciliations are paused, any changes made to custom resources are ignored by the Operators until the pause ends.
If you want to pause reconciliation of a custom resource, set the strimzi.io/pause-reconciliation
annotation to true
in its configuration.
This instructs the appropriate Operator to pause reconciliation of the custom resource.
For example, you can apply the annotation to the KafkaConnect
resource so that reconciliation by the Cluster Operator is paused.
You can also create a custom resource with the pause annotation enabled. The custom resource is created, but it is ignored.
-
The Strimzi Operator that manages the custom resource is running.
-
Annotate the custom resource in Kubernetes, setting
pause-reconciliation
totrue
:kubectl annotate <kind_of_custom_resource> <name_of_custom_resource> strimzi.io/pause-reconciliation="true"
For example, for the
KafkaConnect
custom resource:kubectl annotate KafkaConnect my-connect strimzi.io/pause-reconciliation="true"
-
Check that the status conditions of the custom resource show a change to
ReconciliationPaused
:kubectl describe <kind_of_custom_resource> <name_of_custom_resource>
The
type
condition changes toReconciliationPaused
at thelastTransitionTime
.Example custom resource with a paused reconciliation condition typeapiVersion: kafka.strimzi.io/v1beta2 kind: KafkaConnect metadata: annotations: strimzi.io/pause-reconciliation: "true" strimzi.io/use-connector-resources: "true" creationTimestamp: 2021-03-12T10:47:11Z #... spec: # ... status: conditions: - lastTransitionTime: 2021-03-12T10:47:41.689249Z status: "True" type: ReconciliationPaused
-
To resume reconciliation, you can set the annotation to
false
, or remove the annotation.
8.3. Evicting pods with the Strimzi Drain Cleaner
Kafka and ZooKeeper pods might be evicted during Kubernetes upgrades, maintenance, or pod rescheduling.
If your Kafka broker and ZooKeeper pods were deployed by Strimzi, you can use the Strimzi Drain Cleaner tool to handle the pod evictions.
The Strimzi Drain Cleaner handles the eviction instead of Kubernetes.
You must set the podDisruptionBudget
for your Kafka deployment to 0
(zero).
Kubernetes will then no longer be allowed to evict the pod automatically.
By deploying the Strimzi Drain Cleaner, you can use the Cluster Operator to move Kafka pods instead of Kubernetes. The Cluster Operator ensures that topics are never under-replicated. Kafka can remain operational during the eviction process. The Cluster Operator waits for topics to synchronize, as the Kubernetes worker nodes drain consecutively.
An admission webhook notifies the Strimzi Drain Cleaner of pod eviction requests to the Kubernetes API. The Strimzi Drain Cleaner then adds a rolling update annotation to the pods to be drained. This informs the Cluster Operator to perform a rolling update of an evicted pod.
Note
|
If you are not using the Strimzi Drain Cleaner, you can add pod annotations to perform rolling updates manually. |
The Strimzi Drain Cleaner deployment files include a ValidatingWebhookConfiguration
resource file.
The resource provides the configuration for registering the webhook with the Kubernetes API.
The configuration defines the rules
for the Kubernetes API to follow in the event of a pod eviction request.
The rules specify that only CREATE
operations related to pods/eviction
sub-resources are intercepted.
If these rules are met, the API forwards the notification.
The clientConfig
points to the Strimzi Drain Cleaner service and /drainer
endpoint that exposes the webhook.
The webhook uses a secure TLS connection, which requires authentication.
The caBundle
property specifies the certificate chain to validate HTTPS communication.
Certificates are encoded in Base64.
apiVersion: admissionregistration.k8s.io/v1
kind: ValidatingWebhookConfiguration
# ...
webhooks:
- name: strimzi-drain-cleaner.strimzi.io
rules:
- apiGroups: [""]
apiVersions: ["v1"]
operations: ["CREATE"]
resources: ["pods/eviction"]
scope: "Namespaced"
clientConfig:
service:
namespace: "strimzi-drain-cleaner"
name: "strimzi-drain-cleaner"
path: /drainer
port: 443
caBundle: Cg==
# ...
8.3.1. Downloading the Strimzi Drain Cleaner deployment files
To deploy and use the Strimzi Drain Cleaner, you need to download the deployment files.
The Strimzi Drain Cleaner deployment files are available from the GitHub releases page.
8.3.2. Deploying the Strimzi Drain Cleaner using installation files
Deploy the Strimzi Drain Cleaner to the Kubernetes cluster where the Cluster Operator and Kafka cluster are running.
-
You have downloaded the Strimzi Drain Cleaner deployment files.
-
You have a highly available Kafka cluster deployment running with Kubernetes worker nodes that you would like to update.
-
Topics are replicated for high availability.
Topic configuration specifies a replication factor of at least 3 and a minimum number of in-sync replicas to 1 less than the replication factor.
Kafka topic replicated for high availabilityapiVersion: 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 # ...
If you don’t want to include Kafka or ZooKeeper pods in Drain Cleaner operations, change the default environment variables in the Drain Cleaner Deployment
configuration file.
-
Set
STRIMZI_DRAIN_KAFKA
tofalse
to exclude Kafka pods -
Set
STRIMZI_DRAIN_ZOOKEEPER
tofalse
to exclude ZooKeeper pods
apiVersion: apps/v1
kind: Deployment
spec:
# ...
template:
spec:
serviceAccountName: strimzi-drain-cleaner
containers:
- name: strimzi-drain-cleaner
# ...
env:
- name: STRIMZI_DRAIN_KAFKA
value: "true"
- name: STRIMZI_DRAIN_ZOOKEEPER
value: "false"
# ...
-
Configure a pod disruption budget of
0
(zero) for your Kafka deployment usingtemplate
settings in theKafka
resource.Specifying a pod disruption budgetapiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster namespace: myproject spec: kafka: template: podDisruptionBudget: maxUnavailable: 0 # ... zookeeper: template: podDisruptionBudget: maxUnavailable: 0 # ...
Reducing the maximum pod disruption budget to zero prevents Kubernetes from automatically evicting the pods in case of voluntary disruptions, leaving the Strimzi Drain Cleaner and Strimzi Cluster Operator to roll the pod which will be scheduled by Kubernetes on a different worker node.
Add the same configuration for ZooKeeper if you want to use Strimzi Drain Cleaner to drain ZooKeeper nodes.
-
Update the
Kafka
resource:kubectl apply -f <kafka_configuration_file>
-
Deploy the Strimzi Drain Cleaner.
-
If you are using
cert-manager
with Kubernetes, apply the resources in the/install/drain-cleaner/certmanager
directory.kubectl apply -f ./install/drain-cleaner/certmanager
The TLS certificates for the webhook are generated automatically and injected into the webhook configuration.
-
If you are not using
cert-manager
with Kubernetes, do the following:-
Add TLS certificates to use in the deployment.
Any certificates you add must be renewed before they expire.
-
Apply the resources in the
/install/drain-cleaner/kubernetes
directory.kubectl apply -f ./install/drain-cleaner/kubernetes
-
-
To run the Drain Cleaner on OpenShift, apply the resources in the
/install/drain-cleaner/openshift
directory.kubectl apply -f ./install/drain-cleaner/openshift
-
8.3.3. Deploying the Strimzi Drain Cleaner using Helm
Helm charts are used to package, configure, and deploy Kubernetes resources. Strimzi provides a Helm chart to deploy the Strimzi Drain Cleaner.
The Drain Cleaner is deployed on the Kubernetes cluster with the default chart configuration, which assumes that cert-manager
issues the TLS certificates required by the Drain Cleaner.
You can install the Drain Cleaner with cert-manager
support or provide your own TLS certificates.
-
You have downloaded the Strimzi Drain Cleaner deployment files.
-
The Helm client must be installed on a local machine.
-
Helm must be installed to the Kubernetes cluster.
Default configuration values are passed into the chart using parameters defined in a values.yaml
file.
If you don’t want to use the default configuration, you can override the defaults when you install the chart using the --set
argument.
You specify values in the format --set key=value[,key=value]
.
The values.yaml
file supplied with the Helm deployment files describes the available configuration parameters, including those shown in the following table.
You can override the default image settings.
You can also set secret.create
as true
and add your own TLS certificates instead of using cert-manager
to generate the certificates.
For information on using OpenSSL to generate certificates, see Adding or renewing the TLS certificates used by the Strimzi Drain Cleaner.
Any certificates you add must be renewed before they expire. You can use the configuration to control how certificates are watched for updates using environment variables. For more information on how the environment variables work, see Watching the TLS certificates used by the Strimzi Drain Cleaner.
Parameter | Description | Default |
---|---|---|
|
Number of replicas of the Drain Cleaner webhook |
|
|
Drain Cleaner image registry |
|
|
Drain Cleaner image repository |
|
|
Drain Cleaner image name |
|
|
Drain Cleaner image tag |
|
|
Image pull policy for all pods deployed by the Drain Cleaner |
|
|
Set to |
|
|
Default namespace for the Drain Cleaner deployment. |
|
|
Configures resources for the Drain Cleaner pod |
|
|
Add a node selector to the Drain Cleaner pod |
|
|
Add tolerations to the Drain Cleaner pod |
|
|
Add topology spread constraints to the Drain Cleaner pod |
|
|
Add affinities to the Drain Cleaner pod |
|
-
Use the Helm command line tool to add the Strimzi Helm chart repository:
helm repo add strimzi https://strimzi.io/charts/
-
Deploy the Drain Cleaner:
helm install drain-cleaner strimzi/strimzi-drain-cleaner
Specify any changes to the default configuration as parameter values.
Example configuration that changes the number of webhook replicashelm install drain-cleaner --set replicaCount=2 strimzi/strimzi-drain-cleaner
-
Verify that the Cluster Operator has been deployed successfully:
helm ls
8.3.4. Using the Strimzi Drain Cleaner
Use the Strimzi Drain Cleaner in combination with the Cluster Operator to move Kafka broker or ZooKeeper pods from nodes that are being drained. When you run the Strimzi Drain Cleaner, it annotates pods with a rolling update pod annotation. The Cluster Operator performs rolling updates based on the annotation.
-
You have deployed the Strimzi Drain Cleaner.
-
Drain a specified Kubernetes node hosting the Kafka broker or ZooKeeper pods.
kubectl get nodes kubectl drain <name-of-node> --delete-emptydir-data --ignore-daemonsets --timeout=6000s --force
-
Check the eviction events in the Strimzi Drain Cleaner log to verify that the pods have been annotated for restart.
Strimzi Drain Cleaner log show annotations of podsINFO ... Received eviction webhook for Pod my-cluster-zookeeper-2 in namespace my-project INFO ... Pod my-cluster-zookeeper-2 in namespace my-project will be annotated for restart INFO ... Pod my-cluster-zookeeper-2 in namespace my-project found and annotated for restart INFO ... Received eviction webhook for Pod my-cluster-kafka-0 in namespace my-project INFO ... Pod my-cluster-kafka-0 in namespace my-project will be annotated for restart INFO ... Pod my-cluster-kafka-0 in namespace my-project found and annotated for restart
-
Check the reconciliation events in the Cluster Operator log to verify the rolling updates.
Cluster Operator log shows rolling updatesINFO PodOperator:68 - Reconciliation #13(timer) Kafka(my-project/my-cluster): Rolling Pod my-cluster-zookeeper-2 INFO PodOperator:68 - Reconciliation #13(timer) Kafka(my-project/my-cluster): Rolling Pod my-cluster-kafka-0 INFO AbstractOperator:500 - Reconciliation #13(timer) Kafka(my-project/my-cluster): reconciled
8.3.5. Adding or renewing the TLS certificates used by the Strimzi Drain Cleaner
The Drain Cleaner uses a webhook to receive eviction notifications from the Kubernetes API.
The webhook uses a secure TLS connection and authenticates using TLS certificates.
If you are not deploying the Drain Cleaner using the cert-manager
or on Openshift, you must create and renew the TLS certificates.
You must then add them to the files used to deploy the Drain Cleaner.
The certificates must also be renewed before they expire.
To renew the certificates, you repeat the steps used to generate and add the certificates to the initial deployment of the Drain Cleaner.
Generate and add certificates to the standard installation files or your Helm configuration when deploying the Drain Cleaner on Kubernetes without cert-manager
.
Note
|
If you are using cert-manager to deploy the Drain Cleaner, you don’t need to add or renew TLS certificates. The same applies when deploying the Drain Cleaner on OpenShift, as OpenShift injects the certificates. In both cases, TLS certificates are added and renewed automatically.
|
-
The OpenSSL TLS management tool for generating certificates.
Use
openssl help
for command-line descriptions of the options used.
-
From the command line, create a directory called
tls-certificate
:mkdir tls-certificate cd tls-certificate
Now use OpenSSL to create the certificates in the
tls-certificate
directory. -
Generate a CA (Certificate Authority) public certificate and private key:
openssl req -nodes -new -x509 -keyout ca.key -out ca.crt -subj "/CN=Strimzi Drain Cleaner CA"
A
ca.crt
andca.key
file are created. -
Generate a private key for the Drain Cleaner:
openssl genrsa -out tls.key 2048
A
tls.key
file is created. -
Generate a CSR (Certificate Signing Request) and sign it by adding the CA public certificate (
ca.crt
) you generated:openssl req -new -key tls.key -subj "/CN=strimzi-drain-cleaner.strimzi-drain-cleaner.svc" \ | openssl x509 -req -CA ca.crt -CAkey ca.key -CAcreateserial -extfile <(printf "subjectAltName=DNS:strimzi-drain-cleaner.strimzi-drain-cleaner.svc") -out tls.crt
A
tls.crt
file is created.NoteIf you change the name of the Strimzi Drain Cleaner service or install it into a different namespace, you must change the SAN (Subject Alternative Name) of the certificate, following the format <service_name>.<namespace_name>.svc
. -
Encode the CA public certificate into base64.
base64 tls-certificate/ca.crt
With the certificates generated, add them to the installation files or to your Helm configuration depending on your deployment method.
-
Copy the base64-encoded CA public certificate as the value for the
caBundle
property of the070-ValidatingWebhookConfiguration.yaml
installation file:# ... clientConfig: service: namespace: "strimzi-drain-cleaner" name: "strimzi-drain-cleaner" path: /drainer port: 443 caBundle: LS0tLS1CRUdJTiBDRVJUSUZJQ0FURS0tLS0... # ...
-
Create a namespace called
strimzi-drain-cleaner
in your Kubernetes cluster:kubectl create ns strimzi-drain-cleaner
-
Create a secret named
strimzi-drain-cleaner
with thetls.crt
andtls.key
files you generated:kubectl create secret tls strimzi-drain-cleaner \ -n strimzi-drain-cleaner \ --cert=tls-certificate/tls.crt \ --key=tls-certificate/tls.key
The secret is used in the Drain Cleaner deployment.
Example secret for the Drain Cleaner deploymentapiVersion: v1 kind: Secret metadata: # ... name: strimzi-drain-cleaner namespace: strimzi-drain-cleaner # ... data: tls.crt: LS0tLS1CRUdJTiBDRVJUSUZJQ0FURS0tLS... tls.key: LS0tLS1CRUdJTiBSU0EgUFJJVkFURSBLR...
You can now use the certificates and updated installation files to deploy the Drain Cleaner using installation files.
-
Edit the
values.yaml
configuration file used in the Helm deployment. -
Set the
certManager.create
parameter tofalse
. -
Set the
secret.create
parameter totrue
. -
Copy the certificates as
secret
parameters.Example secret configuration for the Drain Cleaner deployment# ... certManager: create: false secret: create: true tls_crt: "Cg==" # (1) tls_key: "Cg==" # (2) ca_bundle: "Cg==" # (3)
-
The public key (
tls.crt
) signed by the CA public certificate. -
The private key (
tls.key
). -
The base-64 encoded CA public certificate (
ca.crt
).
-
You can now use the certificates and updated configuration file to deploy the Drain Cleaner using Helm.
8.3.6. Watching the TLS certificates used by the Strimzi Drain Cleaner
By default, the Drain Cleaner deployment watches the secret containing the TLS certificates its uses for authentication.
The Drain Cleaner watches for changes, such as certificate renewals.
If it detects a change, it restarts to reload the TLS certificates.
The Drain Cleaner installation files enable this behavior by default.
But you can disable the watching of certificates by setting the STRIMZI_CERTIFICATE_WATCH_ENABLED
environment variable to false
in the Deployment
configuration (060-Deployment.yaml
) of the Drain Cleaner installation files.
With STRIMZI_CERTIFICATE_WATCH_ENABLED
enabled, you can also use the following environment variables for watching TLS certificates.
Environment Variable | Description | Default |
---|---|---|
|
Enables or disables the certificate watch |
|
|
The namespace where the Drain Cleaner is deployed and where the certificate secret exists |
|
|
The Drain Cleaner pod name |
- |
|
The name of the secret containing TLS certificates |
|
|
The list of fields inside the secret that contain the TLS certificates |
|
apiVersion: apps/v1
kind: Deployment
metadata:
name: strimzi-drain-cleaner
labels:
app: strimzi-drain-cleaner
namespace: strimzi-drain-cleaner
spec:
# ...
spec:
serviceAccountName: strimzi-drain-cleaner
containers:
- name: strimzi-drain-cleaner
# ...
env:
- name: STRIMZI_DRAIN_KAFKA
value: "true"
- name: STRIMZI_DRAIN_ZOOKEEPER
value: "true"
- name: STRIMZI_CERTIFICATE_WATCH_ENABLED
value: "true"
- name: STRIMZI_CERTIFICATE_WATCH_NAMESPACE
valueFrom:
fieldRef:
fieldPath: metadata.namespace
- name: STRIMZI_CERTIFICATE_WATCH_POD_NAME
valueFrom:
fieldRef:
fieldPath: metadata.name
# ...
Tip
|
Use the Downward API mechanism to configure STRIMZI_CERTIFICATE_WATCH_NAMESPACE and STRIMZI_CERTIFICATE_WATCH_POD_NAME .
|
8.4. Manually starting rolling updates of Kafka and ZooKeeper clusters
Strimzi supports the use of annotations on resources to manually trigger a rolling update of Kafka and ZooKeeper clusters through the Cluster Operator. Rolling updates restart the pods of the resource with new ones.
Manually performing a rolling update on a specific pod or set of pods is usually only required in exceptional circumstances. However, rather than deleting the pods directly, if you perform the rolling update through the Cluster Operator you ensure the following:
-
The manual deletion of the pod does not conflict with simultaneous Cluster Operator operations, such as deleting other pods in parallel.
-
The Cluster Operator logic handles the Kafka configuration specifications, such as the number of in-sync replicas.
8.4.1. Prerequisites
To perform a manual rolling update, you need a running Cluster Operator and Kafka cluster.
See the Deploying and Upgrading Strimzi guide for instructions on running a:
8.4.2. Performing a rolling update using a pod management annotation
This procedure describes how to trigger a rolling update of a Kafka cluster or ZooKeeper cluster.
To trigger the update, you add an annotation to the resource you are using to manage the pods running on the cluster.
You annotate the StatefulSet
or StrimziPodSet
resource (if you enabled the UseStrimziPodSets feature gate).
-
Find the name of the resource that controls the Kafka or ZooKeeper pods you want to manually update.
For example, if your Kafka cluster is named my-cluster, the corresponding names are my-cluster-kafka and my-cluster-zookeeper.
-
Use
kubectl annotate
to annotate the appropriate resource in Kubernetes.Annotating a StatefulSetkubectl annotate statefulset <cluster_name>-kafka strimzi.io/manual-rolling-update=true kubectl annotate statefulset <cluster_name>-zookeeper strimzi.io/manual-rolling-update=true
Annotating a StrimziPodSetkubectl annotate strimzipodset <cluster_name>-kafka strimzi.io/manual-rolling-update=true kubectl annotate strimzipodset <cluster_name>-zookeeper strimzi.io/manual-rolling-update=true
-
Wait for the next reconciliation to occur (every two minutes by default). A rolling update of all pods within the annotated resource is triggered, as long as the annotation was detected by the reconciliation process. When the rolling update of all the pods is complete, the annotation is removed from the resource.
8.4.3. Performing a rolling update using a Pod annotation
This procedure describes how to manually trigger a rolling update of an existing Kafka cluster or ZooKeeper cluster using a Kubernetes Pod
annotation.
When multiple pods are annotated, consecutive rolling updates are performed within the same reconciliation run.
You can perform a rolling update on a Kafka cluster regardless of the topic replication factor used. But for Kafka to stay operational during the update, you’ll need the following:
-
A highly available Kafka cluster deployment running with nodes that you wish to update.
-
Topics replicated for high availability.
Topic configuration specifies a replication factor of at least 3 and a minimum number of in-sync replicas to 1 less than the replication factor.
Kafka topic replicated for high availabilityapiVersion: 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 # ...
-
Find the name of the Kafka or ZooKeeper
Pod
you want to manually update.For example, if your Kafka cluster is named my-cluster, the corresponding
Pod
names are my-cluster-kafka-index and my-cluster-zookeeper-index. The index starts at zero and ends at the total number of replicas minus one. -
Annotate the
Pod
resource in Kubernetes.Use
kubectl annotate
:kubectl annotate pod cluster-name-kafka-index strimzi.io/manual-rolling-update=true kubectl annotate pod cluster-name-zookeeper-index strimzi.io/manual-rolling-update=true
-
Wait for the next reconciliation to occur (every two minutes by default). A rolling update of the annotated
Pod
is triggered, as long as the annotation was detected by the reconciliation process. When the rolling update of a pod is complete, the annotation is removed from thePod
.
8.5. Discovering services using labels and annotations
Service discovery makes it easier for client applications running in the same Kubernetes cluster as Strimzi to interact with a Kafka cluster.
A service discovery label and annotation is generated for services used to access the Kafka cluster:
-
Internal Kafka bootstrap service
-
HTTP Bridge service
The label helps to make the service discoverable, and the annotation provides connection details that a client application can use to make the connection.
The service discovery label, strimzi.io/discovery
, is set as true
for the Service
resources.
The service discovery annotation has the same key, providing connection details in JSON format for each service.
Example internal Kafka bootstrap service
apiVersion: v1
kind: Service
metadata:
annotations:
strimzi.io/discovery: |-
[ {
"port" : 9092,
"tls" : false,
"protocol" : "kafka",
"auth" : "scram-sha-512"
}, {
"port" : 9093,
"tls" : true,
"protocol" : "kafka",
"auth" : "tls"
} ]
labels:
strimzi.io/cluster: my-cluster
strimzi.io/discovery: "true"
strimzi.io/kind: Kafka
strimzi.io/name: my-cluster-kafka-bootstrap
name: my-cluster-kafka-bootstrap
spec:
#...
Example HTTP Bridge service
apiVersion: v1
kind: Service
metadata:
annotations:
strimzi.io/discovery: |-
[ {
"port" : 8080,
"tls" : false,
"auth" : "none",
"protocol" : "http"
} ]
labels:
strimzi.io/cluster: my-bridge
strimzi.io/discovery: "true"
strimzi.io/kind: KafkaBridge
strimzi.io/name: my-bridge-bridge-service
8.5.1. Returning connection details on services
You can find the services by specifying the discovery label when fetching services from the command line or a corresponding API call.
kubectl get service -l strimzi.io/discovery=true
The connection details are returned when retrieving the service discovery label.
8.6. Recovering a cluster from persistent volumes
You can recover a Kafka cluster from persistent volumes (PVs) if they are still present.
You might want to do this, for example, after:
-
A namespace was deleted unintentionally
-
A whole Kubernetes cluster is lost, but the PVs remain in the infrastructure
8.6.1. Recovery from namespace deletion
Recovery from namespace deletion is possible because of the relationship between persistent volumes and namespaces.
A PersistentVolume
(PV) is a storage resource that lives outside of a namespace.
A PV is mounted into a Kafka pod using a PersistentVolumeClaim
(PVC), which lives inside a namespace.
The reclaim policy for a PV tells a cluster how to act when a namespace is deleted. If the reclaim policy is set as:
-
Delete (default), PVs are deleted when PVCs are deleted within a namespace
-
Retain, PVs are not deleted when a namespace is deleted
To ensure that you can recover from a PV if a namespace is deleted unintentionally, the policy must be reset from Delete to Retain in the PV specification using the persistentVolumeReclaimPolicy
property:
apiVersion: v1
kind: PersistentVolume
# ...
spec:
# ...
persistentVolumeReclaimPolicy: Retain
Alternatively, PVs can inherit the reclaim policy of an associated storage class. Storage classes are used for dynamic volume allocation.
By configuring the reclaimPolicy
property for the storage class, PVs that use the storage class are created with the appropriate reclaim policy.
The storage class is configured for the PV using the storageClassName
property.
apiVersion: v1
kind: StorageClass
metadata:
name: gp2-retain
parameters:
# ...
# ...
reclaimPolicy: Retain
apiVersion: v1
kind: PersistentVolume
# ...
spec:
# ...
storageClassName: gp2-retain
Note
|
If you are using Retain as the reclaim policy, but you want to delete an entire cluster, you need to delete the PVs manually. Otherwise they will not be deleted, and may cause unnecessary expenditure on resources. |
8.6.2. Recovery from loss of a Kubernetes cluster
When a cluster is lost, you can use the data from disks/volumes to recover the cluster if they were preserved within the infrastructure. The recovery procedure is the same as with namespace deletion, assuming PVs can be recovered and they were created manually.
8.6.3. Recovering a deleted cluster from persistent volumes
This procedure describes how to recover a deleted cluster from persistent volumes (PVs).
In this situation, the Topic Operator identifies that topics exist in Kafka, but the KafkaTopic
resources do not exist.
When you get to the step to recreate your cluster, you have two options:
-
Use Option 1 when you can recover all
KafkaTopic
resources.The
KafkaTopic
resources must therefore be recovered before the cluster is started so that the corresponding topics are not deleted by the Topic Operator. -
Use Option 2 when you are unable to recover all
KafkaTopic
resources.In this case, you deploy your cluster without the Topic Operator, delete the Topic Operator topic store metadata, and then redeploy the Kafka cluster with the Topic Operator so it can recreate the
KafkaTopic
resources from the corresponding topics.
Note
|
If the Topic Operator is not deployed, you only need to recover the PersistentVolumeClaim (PVC) resources.
|
In this procedure, it is essential that PVs are mounted into the correct PVC to avoid data corruption.
A volumeName
is specified for the PVC and this must match the name of the PV.
For more information, see:
Note
|
The procedure does not include recovery of KafkaUser resources, which must be recreated manually.
If passwords and certificates need to be retained, secrets must be recreated before creating the KafkaUser resources.
|
-
Check information on the PVs in the cluster:
kubectl get pv
Information is presented for PVs with data.
Example output showing columns important to this procedure:
NAME RECLAIMPOLICY CLAIM pvc-5e9c5c7f-3317-11ea-a650-06e1eadd9a4c ... Retain ... myproject/data-my-cluster-zookeeper-1 pvc-5e9cc72d-3317-11ea-97b0-0aef8816c7ea ... Retain ... myproject/data-my-cluster-zookeeper-0 pvc-5ead43d1-3317-11ea-97b0-0aef8816c7ea ... Retain ... myproject/data-my-cluster-zookeeper-2 pvc-7e1f67f9-3317-11ea-a650-06e1eadd9a4c ... Retain ... myproject/data-0-my-cluster-kafka-0 pvc-7e21042e-3317-11ea-9786-02deaf9aa87e ... Retain ... myproject/data-0-my-cluster-kafka-1 pvc-7e226978-3317-11ea-97b0-0aef8816c7ea ... Retain ... myproject/data-0-my-cluster-kafka-2
-
NAME shows the name of each PV.
-
RECLAIM POLICY shows that PVs are retained.
-
CLAIM shows the link to the original PVCs.
-
-
Recreate the original namespace:
kubectl create namespace myproject
-
Recreate the original PVC resource specifications, linking the PVCs to the appropriate PV:
For example:
apiVersion: v1 kind: PersistentVolumeClaim metadata: name: data-0-my-cluster-kafka-0 spec: accessModes: - ReadWriteOnce resources: requests: storage: 100Gi storageClassName: gp2-retain volumeMode: Filesystem volumeName: pvc-7e1f67f9-3317-11ea-a650-06e1eadd9a4c
-
Edit the PV specifications to delete the
claimRef
properties that bound the original PVC.For example:
apiVersion: v1 kind: PersistentVolume metadata: annotations: kubernetes.io/createdby: aws-ebs-dynamic-provisioner pv.kubernetes.io/bound-by-controller: "yes" pv.kubernetes.io/provisioned-by: kubernetes.io/aws-ebs creationTimestamp: "<date>" finalizers: - kubernetes.io/pv-protection labels: failure-domain.beta.kubernetes.io/region: eu-west-1 failure-domain.beta.kubernetes.io/zone: eu-west-1c name: pvc-7e226978-3317-11ea-97b0-0aef8816c7ea resourceVersion: "39431" selfLink: /api/v1/persistentvolumes/pvc-7e226978-3317-11ea-97b0-0aef8816c7ea uid: 7efe6b0d-3317-11ea-a650-06e1eadd9a4c spec: accessModes: - ReadWriteOnce awsElasticBlockStore: fsType: xfs volumeID: aws://eu-west-1c/vol-09db3141656d1c258 capacity: storage: 100Gi claimRef: apiVersion: v1 kind: PersistentVolumeClaim name: data-0-my-cluster-kafka-2 namespace: myproject resourceVersion: "39113" uid: 54be1c60-3319-11ea-97b0-0aef8816c7ea nodeAffinity: required: nodeSelectorTerms: - matchExpressions: - key: failure-domain.beta.kubernetes.io/zone operator: In values: - eu-west-1c - key: failure-domain.beta.kubernetes.io/region operator: In values: - eu-west-1 persistentVolumeReclaimPolicy: Retain storageClassName: gp2-retain volumeMode: Filesystem
In the example, the following properties are deleted:
claimRef: apiVersion: v1 kind: PersistentVolumeClaim name: data-0-my-cluster-kafka-2 namespace: myproject resourceVersion: "39113" uid: 54be1c60-3319-11ea-97b0-0aef8816c7ea
-
Deploy the Cluster Operator.
kubectl create -f install/cluster-operator -n my-project
-
Recreate your cluster.
Follow the steps depending on whether or not you have all the
KafkaTopic
resources needed to recreate your cluster.Option 1: If you have all the
KafkaTopic
resources that existed before you lost your cluster, including internal topics such as committed offsets from__consumer_offsets
:-
Recreate all
KafkaTopic
resources.It is essential that you recreate the resources before deploying the cluster, or the Topic Operator will delete the topics.
-
Deploy the Kafka cluster.
For example:
kubectl apply -f kafka.yaml
Option 2: If you do not have all the
KafkaTopic
resources that existed before you lost your cluster:-
Deploy the Kafka cluster, as with the first option, but without the Topic Operator by removing the
topicOperator
property from the Kafka resource before deploying.If you include the Topic Operator in the deployment, the Topic Operator will delete all the topics.
-
Delete the internal topic store topics from the Kafka cluster:
kubectl run kafka-admin -ti --image=quay.io/strimzi/kafka:latest-kafka-3.3.2 --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.
-
Enable the Topic Operator by redeploying the Kafka cluster with the
topicOperator
property to recreate theKafkaTopic
resources.For example:
apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: #... entityOperator: topicOperator: {} (1) #...
-
Here we show the default configuration, which has no additional properties. You specify the required configuration using the properties described in
EntityTopicOperatorSpec
schema reference.
-
-
Verify the recovery by listing the
KafkaTopic
resources:kubectl get KafkaTopic
8.7. Setting limits on brokers using the Kafka Static Quota plugin
Use the Kafka Static Quota plugin to set throughput and storage limits on brokers in your Kafka cluster.
You enable the plugin and set limits by configuring the Kafka
resource.
You can set a byte-rate threshold and storage quotas to put limits on the clients interacting with your brokers.
You can set byte-rate thresholds for producer and consumer bandwidth. The total limit is distributed across all clients accessing the broker. For example, you can set a byte-rate threshold of 40 MBps for producers. If two producers are running, they are each limited to a throughput of 20 MBps.
Storage quotas throttle Kafka disk storage limits between a soft limit and hard limit. The limits apply to all available disk space. Producers are slowed gradually between the soft and hard limit. The limits prevent disks filling up too quickly and exceeding their capacity. Full disks can lead to issues that are hard to rectify. The hard limit is the maximum storage limit.
Note
|
For JBOD storage, the limit applies across all disks. If a broker is using two 1 TB disks and the quota is 1.1 TB, one disk might fill and the other disk will be almost empty. |
-
The Cluster Operator that manages the Kafka cluster is running.
-
Add the plugin properties to the
config
of theKafka
resource.The plugin properties are shown in this example configuration.
Example Kafka Static Quota plugin configurationapiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: kafka: # ... config: client.quota.callback.class: io.strimzi.kafka.quotas.StaticQuotaCallback (1) client.quota.callback.static.produce: 1000000 (2) client.quota.callback.static.fetch: 1000000 (3) client.quota.callback.static.storage.soft: 400000000000 (4) client.quota.callback.static.storage.hard: 500000000000 (5) client.quota.callback.static.storage.check-interval: 5 (6)
-
Loads the Kafka Static Quota plugin.
-
Sets the producer byte-rate threshold. 1 MBps in this example.
-
Sets the consumer byte-rate threshold. 1 MBps in this example.
-
Sets the lower soft limit for storage. 400 GB in this example.
-
Sets the higher hard limit for storage. 500 GB in this example.
-
Sets the interval in seconds between checks on storage. 5 seconds in this example. You can set this to 0 to disable the check.
-
-
Update the resource.
kubectl apply -f <kafka_configuration_file>
8.8. Frequently asked questions
8.8.1. Questions related to the Cluster Operator
Why do I need cluster administrator privileges to install Strimzi?
To install Strimzi, you need to be able to create the following cluster-scoped resources:
-
Custom Resource Definitions (CRDs) to instruct Kubernetes about resources that are specific to Strimzi, such as
Kafka
andKafkaConnect
-
ClusterRoles
andClusterRoleBindings
Cluster-scoped resources, which are not scoped to a particular Kubernetes namespace, typically require cluster administrator privileges to install.
As a cluster administrator, you can inspect all the resources being installed (in the /install/
directory) to ensure that the ClusterRoles
do not grant unnecessary privileges.
After installation, the Cluster Operator runs as a regular Deployment
, so any standard (non-admin) Kubernetes user with privileges to access the Deployment
can configure it.
The cluster administrator can grant standard users the privileges necessary to manage Kafka
custom resources.
See also:
Why does the Cluster Operator need to create ClusterRoleBindings
?
Kubernetes has built-in privilege escalation prevention, which means that the Cluster Operator cannot grant privileges it does not have itself, specifically, it cannot grant such privileges in a namespace it cannot access. Therefore, the Cluster Operator must have the privileges necessary for all the components it orchestrates.
The Cluster Operator needs to be able to grant access so that:
-
The Topic Operator can manage
KafkaTopics
, by creatingRoles
andRoleBindings
in the namespace that the operator runs in -
The User Operator can manage
KafkaUsers
, by creatingRoles
andRoleBindings
in the namespace that the operator runs in -
The failure domain of a
Node
is discovered by Strimzi, by creating aClusterRoleBinding
When using rack-aware partition assignment, the broker pod needs to be able to get information about the Node
it is running on,
for example, the Availability Zone in Amazon AWS.
A Node
is a cluster-scoped resource, so access to it can only be granted through a ClusterRoleBinding
, not a namespace-scoped RoleBinding
.
Can standard Kubernetes users create Kafka custom resources?
By default, standard Kubernetes users will not have the privileges necessary to manage the custom resources handled by the Cluster Operator. The cluster administrator can grant a user the necessary privileges using Kubernetes RBAC resources.
For more information, see Designating Strimzi administrators in the Deploying and Upgrading Strimzi guide.
What do the failed to acquire lock warnings in the log mean?
For each cluster, the Cluster Operator executes only one operation at a time. The Cluster Operator uses locks to make sure that there are never two parallel operations running for the same cluster. Other operations must wait until the current operation completes before the lock is released.
- INFO
-
Examples of cluster operations include cluster creation, rolling update, scale down , and scale up.
If the waiting time for the lock takes too long, the operation times out and the following warning message is printed to the log:
2018-03-04 17:09:24 WARNING AbstractClusterOperations:290 - Failed to acquire lock for kafka cluster lock::kafka::myproject::my-cluster
Depending on the exact configuration of STRIMZI_FULL_RECONCILIATION_INTERVAL_MS
and STRIMZI_OPERATION_TIMEOUT_MS
, this
warning message might appear occasionally without indicating any underlying issues.
Operations that time out are picked up in the next periodic reconciliation, so that the operation can acquire the lock and execute again.
Should this message appear periodically, even in situations when there should be no other operations running for a given cluster, it might indicate that the lock was not properly released due to an error. If this is the case, try restarting the Cluster Operator.
Why is hostname verification failing when connecting to NodePorts using TLS?
Currently, off-cluster access using NodePorts with TLS encryption enabled does not support TLS hostname verification. As a result, the clients that verify the hostname will fail to connect. For example, the Java client will fail with the following exception:
Caused by: java.security.cert.CertificateException: No subject alternative names matching IP address 168.72.15.231 found
at sun.security.util.HostnameChecker.matchIP(HostnameChecker.java:168)
at sun.security.util.HostnameChecker.match(HostnameChecker.java:94)
at sun.security.ssl.X509TrustManagerImpl.checkIdentity(X509TrustManagerImpl.java:455)
at sun.security.ssl.X509TrustManagerImpl.checkIdentity(X509TrustManagerImpl.java:436)
at sun.security.ssl.X509TrustManagerImpl.checkTrusted(X509TrustManagerImpl.java:252)
at sun.security.ssl.X509TrustManagerImpl.checkServerTrusted(X509TrustManagerImpl.java:136)
at sun.security.ssl.ClientHandshaker.serverCertificate(ClientHandshaker.java:1501)
... 17 more
To connect, you must disable hostname verification.
In the Java client, you can do this by setting the configuration option ssl.endpoint.identification.algorithm
to an empty string.
When configuring the client using a properties file, you can do it this way:
ssl.endpoint.identification.algorithm=
When configuring the client directly in Java, set the configuration option to an empty string:
props.put("ssl.endpoint.identification.algorithm", "");
9. Custom resource API reference
9.1. Common configuration properties
Common configuration properties apply to more than one resource.
9.1.1. replicas
Use the replicas
property to configure replicas.
The type of replication depends on the resource.
-
KafkaTopic
uses a replication factor to configure the number of replicas of each partition within a Kafka cluster. -
Kafka components use replicas to configure the number of pods in a deployment to provide better availability and scalability.
Note
|
When running a Kafka component on Kubernetes it may not be necessary to run multiple replicas for high availability. When the node where the component is deployed crashes, Kubernetes will automatically reschedule the Kafka component pod to a different node. However, running Kafka components with multiple replicas can provide faster failover times as the other nodes will be up and running. |
9.1.2. bootstrapServers
Use the bootstrapServers
property to configure a list of bootstrap servers.
The bootstrap server lists can refer to Kafka clusters that are not deployed in the same Kubernetes cluster. They can also refer to a Kafka cluster not deployed by Strimzi.
If on the same Kubernetes cluster, each list must ideally contain the Kafka cluster bootstrap service which is named CLUSTER-NAME-kafka-bootstrap
and a port number.
If deployed by Strimzi but on different Kubernetes clusters, the list content depends on the approach used for exposing the clusters (routes, ingress, nodeports or loadbalancers).
When using Kafka with a Kafka cluster not managed by Strimzi, you can specify the bootstrap servers list according to the configuration of the given cluster.
9.1.3. ssl
Use the three allowed ssl
configuration options for client connection using a specific cipher suite for a TLS version.
A cipher suite combines algorithms for secure connection and data transfer.
You can also configure the ssl.endpoint.identification.algorithm
property to enable or disable hostname verification.
# ...
spec:
config:
ssl.cipher.suites: "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384" (1)
ssl.enabled.protocols: "TLSv1.2" (2)
ssl.protocol: "TLSv1.2" (3)
ssl.endpoint.identification.algorithm: HTTPS (4)
# ...
-
The cipher suite for TLS using a combination of
ECDHE
key exchange mechanism,RSA
authentication algorithm,AES
bulk encyption algorithm andSHA384
MAC algorithm. -
The SSl protocol
TLSv1.2
is enabled. -
Specifies the
TLSv1.2
protocol to generate the SSL context. Allowed values areTLSv1.1
andTLSv1.2
. -
Hostname verification is enabled by setting to
HTTPS
. An empty string disables the verification.
9.1.4. trustedCertificates
Having set tls
to configure TLS encryption, use the trustedCertificates
property to provide a list of secrets with key names under which the certificates are stored in X.509 format.
You can use the secrets created by the Cluster Operator for the Kafka cluster,
or you can create your own TLS certificate file, then create a Secret
from the file:
kubectl create secret generic MY-SECRET \
--from-file=MY-TLS-CERTIFICATE-FILE.crt
tls:
trustedCertificates:
- secretName: my-cluster-cluster-cert
certificate: ca.crt
- secretName: my-cluster-cluster-cert
certificate: ca2.crt
If certificates are stored in the same secret, it can be listed multiple times.
If you want to enable TLS encryption, but use the default set of public certification authorities shipped with Java,
you can specify trustedCertificates
as an empty array:
tls:
trustedCertificates: []
For information on configuring mTLS authentication, see the KafkaClientAuthenticationTls
schema reference.
9.1.5. resources
Configure resource requests and limits to control resources for Strimzi containers.
You can specify requests and limits for memory
and cpu
resources.
The requests should be enough to ensure a stable performance of Kafka.
How you configure resources in a production environment depends on a number of factors. For example, applications are likely to be sharing resources in your Kubernetes cluster.
For Kafka, the following aspects of a deployment can impact the resources you need:
-
Throughput and size of messages
-
The number of network threads handling messages
-
The number of producers and consumers
-
The number of topics and partitions
The values specified for resource requests are reserved and always available to the container. Resource limits specify the maximum resources that can be consumed by a given container. The amount between the request and limit is not reserved and might not be always available. A container can use the resources up to the limit only when they are available. Resource limits are temporary and can be reallocated.
If you set limits without requests or vice versa, Kubernetes uses the same value for both. Setting equal requests and limits for resources guarantees quality of service, as Kubernetes will not kill containers unless they exceed their limits.
You can configure resource requests and limits for one or more supported resources.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
#...
resources:
requests:
memory: 64Gi
cpu: "8"
limits:
memory: 64Gi
cpu: "12"
entityOperator:
#...
topicOperator:
#...
resources:
requests:
memory: 512Mi
cpu: "1"
limits:
memory: 512Mi
cpu: "1"
Resource requests and limits for the Topic Operator and User Operator are set in the Kafka
resource.
If the resource request is for more than the available free resources in the Kubernetes cluster, the pod is not scheduled.
Note
|
Strimzi uses the Kubernetes syntax for specifying memory and cpu resources.
For more information about managing computing resources on Kubernetes, see Managing Compute Resources for Containers.
|
- Memory resources
-
When configuring memory resources, consider the total requirements of the components.
Kafka runs inside a JVM and uses an operating system page cache to store message data before writing to disk. The memory request for Kafka should fit the JVM heap and page cache. You can configure the
jvmOptions
property to control the minimum and maximum heap size.Other components don’t rely on the page cache. You can configure memory resources without configuring the
jvmOptions
to control the heap size.Memory requests and limits are specified in megabytes, gigabytes, mebibytes, and gibibytes. Use the following suffixes in the specification:
-
M
for megabytes -
G
for gigabytes -
Mi
for mebibytes -
Gi
for gibibytes
Example resources using different memory units# ... resources: requests: memory: 512Mi limits: memory: 2Gi # ...
For more details about memory specification and additional supported units, see Meaning of memory.
-
- CPU resources
-
A CPU request should be enough to give a reliable performance at any time. CPU requests and limits are specified as cores or millicpus/millicores.
CPU cores are specified as integers (
5
CPU core) or decimals (2.5
CPU core). 1000 millicores is the same as1
CPU core.Example CPU units# ... resources: requests: cpu: 500m limits: cpu: 2.5 # ...
The computing power of 1 CPU core may differ depending on the platform where Kubernetes is deployed.
For more information on CPU specification, see Meaning of CPU.
9.1.6. image
Use the image
property to configure the container image used by the component.
Overriding container images is recommended only in special situations where you need to use a different container registry or a customized image.
For example, if your network does not allow access to the container repository used by Strimzi, you can copy the Strimzi images or build them from the source. However, if the configured image is not compatible with Strimzi images, it might not work properly.
A copy of the container image might also be customized and used for debugging.
You can specify which container image to use for a component using the image
property in the following resources:
-
Kafka.spec.kafka
-
Kafka.spec.zookeeper
-
Kafka.spec.entityOperator.topicOperator
-
Kafka.spec.entityOperator.userOperator
-
Kafka.spec.entityOperator.tlsSidecar
-
Kafka.spec.jmxTrans
-
KafkaConnect.spec
-
KafkaMirrorMaker.spec
-
KafkaMirrorMaker2.spec
-
KafkaBridge.spec
Configuring the image
property for Kafka, Kafka Connect, and Kafka MirrorMaker
Kafka, Kafka Connect, and Kafka MirrorMaker support multiple versions of Kafka. Each component requires its own image. The default images for the different Kafka versions are configured in the following environment variables:
-
STRIMZI_KAFKA_IMAGES
-
STRIMZI_KAFKA_CONNECT_IMAGES
-
STRIMZI_KAFKA_MIRROR_MAKER_IMAGES
These environment variables contain mappings between the Kafka versions and their corresponding images.
The mappings are used together with the image
and version
properties:
-
If neither
image
norversion
are given in the custom resource then theversion
will default to the Cluster Operator’s default Kafka version, and the image will be the one corresponding to this version in the environment variable. -
If
image
is given butversion
is not, then the given image is used and theversion
is assumed to be the Cluster Operator’s default Kafka version. -
If
version
is given butimage
is not, then the image that corresponds to the given version in the environment variable is used. -
If both
version
andimage
are given, then the given image is used. The image is assumed to contain a Kafka image with the given version.
The image
and version
for the different components can be configured in the following properties:
-
For Kafka in
spec.kafka.image
andspec.kafka.version
. -
For Kafka Connect and Kafka MirrorMaker in
spec.image
andspec.version
.
Warning
|
It is recommended to provide only the version and leave the image property unspecified.
This reduces the chance of making a mistake when configuring the custom resource.
If you need to change the images used for different versions of Kafka, it is preferable to configure the Cluster Operator’s environment variables.
|
Configuring the image
property in other resources
For the image
property in the other custom resources, the given value will be used during deployment.
If the image
property is missing, the image
specified in the Cluster Operator configuration will be used.
If the image
name is not defined in the Cluster Operator configuration, then the default value will be used.
-
For Topic Operator:
-
Container image specified in the
STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE
environment variable from the Cluster Operator configuration. -
quay.io/strimzi/operator:latest
container image.
-
-
For User Operator:
-
Container image specified in the
STRIMZI_DEFAULT_USER_OPERATOR_IMAGE
environment variable from the Cluster Operator configuration. -
quay.io/strimzi/operator:latest
container image.
-
-
For Entity Operator TLS sidecar:
-
Container image specified in the
STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGE
environment variable from the Cluster Operator configuration. -
quay.io/strimzi/kafka:latest-kafka-3.3.2
container image.
-
-
For Kafka Exporter:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_EXPORTER_IMAGE
environment variable from the Cluster Operator configuration. -
quay.io/strimzi/kafka:latest-kafka-3.3.2
container image.
-
-
For Kafka Bridge:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_BRIDGE_IMAGE
environment variable from the Cluster Operator configuration. -
quay.io/strimzi/kafka-bridge:0.24.0
container image.
-
-
For Kafka broker initializer:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_INIT_IMAGE
environment variable from the Cluster Operator configuration. -
quay.io/strimzi/operator:latest
container image.
-
-
For Kafka jmxTrans:
-
Container image specified in the
STRIMZI_DEFAULT_JMXTRANS_IMAGE
environment variable from the Cluster Operator configuration. -
quay.io/strimzi/jmxtrans:latest
container image.
-
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
image: my-org/my-image:latest
# ...
zookeeper:
# ...
9.1.7. livenessProbe
and readinessProbe
healthchecks
Use the livenessProbe
and readinessProbe
properties to configure healthcheck probes supported in Strimzi.
Healthchecks are periodical tests which verify the health of an application. When a Healthcheck probe fails, Kubernetes assumes that the application is not healthy and attempts to fix it.
For more details about the probes, see Configure Liveness and Readiness Probes.
Both livenessProbe
and readinessProbe
support the following options:
-
initialDelaySeconds
-
timeoutSeconds
-
periodSeconds
-
successThreshold
-
failureThreshold
# ...
readinessProbe:
initialDelaySeconds: 15
timeoutSeconds: 5
livenessProbe:
initialDelaySeconds: 15
timeoutSeconds: 5
# ...
For more information about the livenessProbe
and readinessProbe
options, see the Probe schema reference.
9.1.8. metricsConfig
Use the metricsConfig
property to enable and configure Prometheus metrics.
The metricsConfig
property contains a reference to a ConfigMap that has additional configurations for the Prometheus JMX Exporter.
Strimzi supports Prometheus metrics using Prometheus JMX exporter to convert the JMX metrics supported by Apache Kafka and ZooKeeper to Prometheus metrics.
To enable Prometheus metrics export without further configuration, you can reference a ConfigMap containing an empty file under metricsConfig.valueFrom.configMapKeyRef.key
.
When referencing an empty file, all metrics are exposed as long as they have not been renamed.
kind: ConfigMap
apiVersion: v1
metadata:
name: my-configmap
data:
my-key: |
lowercaseOutputName: true
rules:
# Special cases and very specific rules
- pattern: kafka.server<type=(.+), name=(.+), clientId=(.+), topic=(.+), partition=(.*)><>Value
name: kafka_server_$1_$2
type: GAUGE
labels:
clientId: "$3"
topic: "$4"
partition: "$5"
# further configuration
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
metricsConfig:
type: jmxPrometheusExporter
valueFrom:
configMapKeyRef:
name: my-config-map
key: my-key
# ...
zookeeper:
# ...
When metrics are enabled, they are exposed on port 9404.
When the metricsConfig
(or deprecated metrics
) property is not defined in the resource, the Prometheus metrics are disabled.
For more information about setting up and deploying Prometheus and Grafana, see Introducing Metrics to Kafka in the Deploying and Upgrading Strimzi guide.
9.1.9. jvmOptions
The following Strimzi components run inside a Java Virtual Machine (JVM):
-
Apache Kafka
-
Apache ZooKeeper
-
Apache Kafka Connect
-
Apache Kafka MirrorMaker
-
Strimzi Kafka Bridge
To optimize their performance on different platforms and architectures, you configure the jvmOptions
property in the following resources:
-
Kafka.spec.kafka
-
Kafka.spec.zookeeper
-
Kafka.spec.entityOperator.userOperator
-
Kafka.spec.entityOperator.topicOperator
-
Kafka.spec.cruiseControl
-
KafkaConnect.spec
-
KafkaMirrorMaker.spec
-
KafkaMirrorMaker2.spec
-
KafkaBridge.spec
You can specify the following options in your configuration:
-Xms
-
Minimum initial allocation heap size when the JVM starts
-Xmx
-
Maximum heap size
-XX
-
Advanced runtime options for the JVM
javaSystemProperties
-
Additional system properties
gcLoggingEnabled
Note
|
The units accepted by JVM settings, such as -Xmx and -Xms , are the same units accepted by the JDK java binary in the corresponding image.
Therefore, 1g or 1G means 1,073,741,824 bytes, and Gi is not a valid unit suffix.
This is different from the units used for memory requests and limits, which follow the Kubernetes convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes.
|
-Xms
and -Xmx
optionsIn addition to setting memory request and limit values for your containers, you can use the -Xms
and -Xmx
JVM options to set specific heap sizes for your JVM.
Use the -Xms
option to set an initial heap size and the -Xmx
option to set a maximum heap size.
Specify heap size to have more control over the memory allocated to your JVM. Heap sizes should make the best use of a container’s memory limit (and request) without exceeding it. Heap size and any other memory requirements need to fit within a specified memory limit. If you don’t specify heap size in your configuration, but you configure a memory resource limit (and request), the Cluster Operator imposes default heap sizes automatically. The Cluster Operator sets default maximum and minimum heap values based on a percentage of the memory resource configuration.
The following table shows the default heap values.
Component | Percent of available memory allocated to the heap | Maximum limit |
---|---|---|
Kafka |
50% |
5 GB |
ZooKeeper |
75% |
2 GB |
Kafka Connect |
75% |
None |
MirrorMaker 2.0 |
75% |
None |
MirrorMaker |
75% |
None |
Cruise Control |
75% |
None |
Kafka Bridge |
50% |
31 Gi |
If a memory limit (and request) is not specified, a JVM’s minimum heap size is set to 128M
.
The JVM’s maximum heap size is not defined to allow the memory to increase as needed.
This is ideal for single node environments in test and development.
Setting an appropriate memory request can prevent the following:
-
Kubernetes killing a container if there is pressure on memory from other pods running on the node.
-
Kubernetes scheduling a container to a node with insufficient memory. If
-Xms
is set to-Xmx
, the container will crash immediately; if not, the container will crash at a later time.
In this example, the JVM uses 2 GiB (=2,147,483,648 bytes) for its heap. Total JVM memory usage can be a lot more than the maximum heap size.
-Xmx
and -Xms
configuration# ...
jvmOptions:
"-Xmx": "2g"
"-Xms": "2g"
# ...
Setting the same value for initial (-Xms
) and maximum (-Xmx
) heap sizes avoids the JVM having to allocate memory after startup, at the cost of possibly allocating more heap than is really needed.
Important
|
Containers performing lots of disk I/O, such as Kafka broker containers, require available memory for use as an operating system page cache. For such containers, the requested memory should be significantly higher than the memory used by the JVM. |
-XX
options are used to configure the KAFKA_JVM_PERFORMANCE_OPTS
option of Apache Kafka.
-XX
configurationjvmOptions:
"-XX":
"UseG1GC": true
"MaxGCPauseMillis": 20
"InitiatingHeapOccupancyPercent": 35
"ExplicitGCInvokesConcurrent": true
-XX
configuration-XX:+UseG1GC -XX:MaxGCPauseMillis=20 -XX:InitiatingHeapOccupancyPercent=35 -XX:+ExplicitGCInvokesConcurrent -XX:-UseParNewGC
Note
|
When no -XX options are specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS is used.
|
javaSystemProperties
javaSystemProperties
are used to configure additional Java system properties, such as debugging utilities.
javaSystemProperties
configurationjvmOptions:
javaSystemProperties:
- name: javax.net.debug
value: ssl
For more information about the jvmOptions
, see the JvmOptions
schema reference.
9.1.10. Garbage collector logging
The jvmOptions
property also allows you to enable and disable garbage collector (GC) logging.
GC logging is disabled by default.
To enable it, set the gcLoggingEnabled
property as follows:
# ...
jvmOptions:
gcLoggingEnabled: true
# ...
9.2. Schema properties
9.2.1. Kafka
schema reference
Property | Description |
---|---|
spec |
The specification of the Kafka and ZooKeeper clusters, and Topic Operator. |
status |
The status of the Kafka and ZooKeeper clusters, and Topic Operator. |
9.2.2. KafkaSpec
schema reference
Used in: Kafka
Property | Description |
---|---|
kafka |
Configuration of the Kafka cluster. |
zookeeper |
Configuration of the ZooKeeper cluster. |
entityOperator |
Configuration of the Entity Operator. |
clusterCa |
Configuration of the cluster certificate authority. |
clientsCa |
Configuration of the clients certificate authority. |
cruiseControl |
Configuration for Cruise Control deployment. Deploys a Cruise Control instance when specified. |
jmxTrans |
The |
kafkaExporter |
Configuration of the Kafka Exporter. Kafka Exporter can provide additional metrics, for example lag of consumer group at topic/partition. |
maintenanceTimeWindows |
A list of time windows for maintenance tasks (that is, certificates renewal). Each time window is defined by a cron expression. |
string array |
9.2.3. KafkaClusterSpec
schema reference
Used in: KafkaSpec
Configures a Kafka cluster.
listeners
Use the listeners
property to configure listeners to provide access to Kafka brokers.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
kafka:
# ...
listeners:
- name: plain
port: 9092
type: internal
tls: false
# ...
zookeeper:
# ...
config
Use the config
properties to configure Kafka broker options as keys.
Standard Apache Kafka configuration may be provided, restricted to those properties not managed directly by Strimzi.
Configuration options that cannot be configured relate to:
-
Security (Encryption, Authentication, and Authorization)
-
Listener configuration
-
Broker ID configuration
-
Configuration of log data directories
-
Inter-broker communication
-
ZooKeeper connectivity
The values can be one of the following JSON types:
-
String
-
Number
-
Boolean
You can specify and configure the options listed in the Apache Kafka documentation with the exception of those options that are managed directly by Strimzi. Specifically, all configuration options with keys equal to or starting with one of the following strings are forbidden:
-
listeners
-
advertised.
-
broker.
-
listener.
-
host.name
-
port
-
inter.broker.listener.name
-
sasl.
-
ssl.
-
security.
-
password.
-
principal.builder.class
-
log.dir
-
zookeeper.connect
-
zookeeper.set.acl
-
authorizer.
-
super.user
When a forbidden option is present in the config
property, it is ignored and a warning message is printed to the Cluster Operator log file.
All other supported options are passed to Kafka.
There are exceptions to the forbidden options.
For client connection using a specific cipher suite for a TLS version, you can configure allowed ssl
properties.
You can also configure the zookeeper.connection.timeout.ms
property to set the maximum time allowed for establishing a ZooKeeper connection.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
config:
num.partitions: 1
num.recovery.threads.per.data.dir: 1
default.replication.factor: 3
offsets.topic.replication.factor: 3
transaction.state.log.replication.factor: 3
transaction.state.log.min.isr: 1
log.retention.hours: 168
log.segment.bytes: 1073741824
log.retention.check.interval.ms: 300000
num.network.threads: 3
num.io.threads: 8
socket.send.buffer.bytes: 102400
socket.receive.buffer.bytes: 102400
socket.request.max.bytes: 104857600
group.initial.rebalance.delay.ms: 0
ssl.cipher.suites: "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384"
ssl.enabled.protocols: "TLSv1.2"
ssl.protocol: "TLSv1.2"
zookeeper.connection.timeout.ms: 6000
# ...
brokerRackInitImage
When rack awareness is enabled, Kafka broker pods use init container to collect the labels from the Kubernetes cluster nodes.
The container image used for this container can be configured using the brokerRackInitImage
property.
When the brokerRackInitImage
field is missing, the following images are used in order of priority:
-
Container image specified in
STRIMZI_DEFAULT_KAFKA_INIT_IMAGE
environment variable in the Cluster Operator configuration. -
quay.io/strimzi/operator:latest
container image.
brokerRackInitImage
configurationapiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
rack:
topologyKey: topology.kubernetes.io/zone
brokerRackInitImage: my-org/my-image:latest
# ...
Note
|
Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container registry used by Strimzi. In this case, you should either copy the Strimzi images or build them from the source. If the configured image is not compatible with Strimzi images, it might not work properly. |
logging
Kafka has its own configurable loggers:
-
log4j.logger.org.I0Itec.zkclient.ZkClient
-
log4j.logger.org.apache.zookeeper
-
log4j.logger.kafka
-
log4j.logger.org.apache.kafka
-
log4j.logger.kafka.request.logger
-
log4j.logger.kafka.network.Processor
-
log4j.logger.kafka.server.KafkaApis
-
log4j.logger.kafka.network.RequestChannel$
-
log4j.logger.kafka.controller
-
log4j.logger.kafka.log.LogCleaner
-
log4j.logger.state.change.logger
-
log4j.logger.kafka.authorizer.logger
Kafka uses the Apache log4j
logger implementation.
Use the logging
property to configure loggers and logger levels.
You can set the log levels by specifying the logger and level directly (inline) or use a custom (external) ConfigMap.
If a ConfigMap is used, you set logging.valueFrom.configMapKeyRef.name
property to the name of the ConfigMap containing the external logging configuration. Inside the ConfigMap, the logging configuration is described using log4j.properties
. Both logging.valueFrom.configMapKeyRef.name
and logging.valueFrom.configMapKeyRef.key
properties are mandatory. A ConfigMap using the exact logging configuration specified is created with the custom resource when the Cluster Operator is running, then recreated after each reconciliation. If you do not specify a custom ConfigMap, default logging settings are used. If a specific logger value is not set, upper-level logger settings are inherited for that logger.
For more information about log levels, see Apache logging services.
Here we see examples of inline
and external
logging.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
# ...
kafka:
# ...
logging:
type: inline
loggers:
kafka.root.logger.level: "INFO"
# ...
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
# ...
logging:
type: external
valueFrom:
configMapKeyRef:
name: customConfigMap
key: kafka-log4j.properties
# ...
Any available loggers that are not configured have their level set to OFF
.
If Kafka was deployed using the Cluster Operator, changes to Kafka logging levels are applied dynamically.
If you use external logging, a rolling update is triggered when logging appenders are changed.
Garbage collector logging can also be enabled (or disabled) using the jvmOptions
property.
KafkaClusterSpec
schema properties
Property | Description |
---|---|
version |
The kafka broker version. Defaults to 3.3.2. Consult the user documentation to understand the process required to upgrade or downgrade the version. |
string |
|
replicas |
The number of pods in the cluster. |
integer |
|
image |
The docker image for the pods. The default value depends on the configured |
string |
|
listeners |
Configures listeners of Kafka brokers. |
|
|
config |
Kafka broker config properties with the following prefixes cannot be set: listeners, advertised., broker., listener., host.name, port, inter.broker.listener.name, sasl., ssl., security., password., log.dir, zookeeper.connect, zookeeper.set.acl, zookeeper.ssl, zookeeper.clientCnxnSocket, authorizer., super.user, cruise.control.metrics.topic, cruise.control.metrics.reporter.bootstrap.servers,node.id, process.roles, controller. (with the exception of: zookeeper.connection.timeout.ms, sasl.server.max.receive.size,ssl.cipher.suites, ssl.protocol, ssl.enabled.protocols, ssl.secure.random.implementation,cruise.control.metrics.topic.num.partitions, cruise.control.metrics.topic.replication.factor, cruise.control.metrics.topic.retention.ms,cruise.control.metrics.topic.auto.create.retries, cruise.control.metrics.topic.auto.create.timeout.ms,cruise.control.metrics.topic.min.insync.replicas,controller.quorum.election.backoff.max.ms, controller.quorum.election.timeout.ms, controller.quorum.fetch.timeout.ms). |
map |
|
storage |
Storage configuration (disk). Cannot be updated. The type depends on the value of the |
authorization |
Authorization configuration for Kafka brokers. The type depends on the value of the |
|
|
rack |
Configuration of the |
brokerRackInitImage |
The image of the init container used for initializing the |
string |
|
livenessProbe |
Pod liveness checking. |
readinessProbe |
Pod readiness checking. |
jvmOptions |
JVM Options for pods. |
jmxOptions |
JMX Options for Kafka brokers. |
resources |
CPU and memory resources to reserve. For more information, see the external documentation for core/v1 resourcerequirements. |
metricsConfig |
Metrics configuration. The type depends on the value of the |
logging |
Logging configuration for Kafka. The type depends on the value of the |
template |
Template for Kafka cluster resources. The template allows users to specify how the |
9.2.4. GenericKafkaListener
schema reference
Used in: KafkaClusterSpec
Configures listeners to connect to Kafka brokers within and outside Kubernetes.
You configure the listeners in the Kafka
resource.
Kafka
resource showing listener configurationapiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
#...
listeners:
- name: plain
port: 9092
type: internal
tls: false
- name: tls
port: 9093
type: internal
tls: true
authentication:
type: tls
- name: external1
port: 9094
type: route
tls: true
- name: external2
port: 9095
type: ingress
tls: true
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
#...
listeners
You configure Kafka broker listeners using the listeners
property in the Kafka
resource.
Listeners are defined as an array.
listeners:
- name: plain
port: 9092
type: internal
tls: false
The name and port must be unique within the Kafka cluster. The name can be up to 25 characters long, comprising lower-case letters and numbers. Allowed port numbers are 9092 and higher with the exception of ports 9404 and 9999, which are already used for Prometheus and JMX.
By specifying a unique name and port for each listener, you can configure multiple listeners.
type
The type is set as internal
,
or for external listeners, as route
, loadbalancer
, nodeport
, ingress
or cluster-ip
.
You can also configure a cluster-ip
listener, a type of internal listener you can use to build custom access mechanisms.
- internal
-
You can configure internal listeners with or without encryption using the
tls
property.Exampleinternal
listener configuration#... spec: kafka: #... listeners: #... - name: plain port: 9092 type: internal tls: false - name: tls port: 9093 type: internal tls: true authentication: type: tls #...
- route
-
Configures an external listener to expose Kafka using OpenShift
Routes
and the HAProxy router.A dedicated
Route
is created for every Kafka broker pod. An additionalRoute
is created to serve as a Kafka bootstrap address. Kafka clients can use theseRoutes
to connect to Kafka on port 443. The client connects on port 443, the default router port, but traffic is then routed to the port you configure, which is9094
in this example.Exampleroute
listener configuration#... spec: kafka: #... listeners: #... - name: external1 port: 9094 type: route tls: true #...
- ingress
-
Configures an external listener to expose Kafka using Kubernetes
Ingress
and the Ingress NGINX Controller for Kubernetes.A dedicated
Ingress
resource is created for every Kafka broker pod. An additionalIngress
resource is created to serve as a Kafka bootstrap address. Kafka clients can use theseIngress
resources to connect to Kafka on port 443. The client connects on port 443, the default controller port, but traffic is then routed to the port you configure, which is9095
in the following example.You must specify the hostnames used by the bootstrap and per-broker services using
GenericKafkaListenerConfigurationBootstrap
andGenericKafkaListenerConfigurationBroker
properties.Exampleingress
listener configuration#... spec: kafka: #... listeners: #... - name: external2 port: 9095 type: ingress tls: true 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 #...
NoteExternal listeners using Ingress
are currently only tested with the Ingress NGINX Controller for Kubernetes. - loadbalancer
-
Configures an external listener to expose Kafka using a
Loadbalancer
typeService
.A new loadbalancer service is created for every Kafka broker pod. An additional loadbalancer is created to serve as a Kafka bootstrap address. Loadbalancers listen to the specified port number, which is port
9094
in the following example.You can use the
loadBalancerSourceRanges
property to configure source ranges to restrict access to the specified IP addresses.Exampleloadbalancer
listener configuration#... spec: kafka: #... listeners: - name: external3 port: 9094 type: loadbalancer tls: true configuration: loadBalancerSourceRanges: - 10.0.0.0/8 - 88.208.76.87/32 #...
- nodeport
-
Configures an external listener to expose Kafka using a
NodePort
typeService
.Kafka clients connect directly to the nodes of Kubernetes. An additional
NodePort
type of service is created to serve as a Kafka bootstrap address.When configuring the advertised addresses for the Kafka broker pods, Strimzi uses the address of the node on which the given pod is running. You can use
preferredNodePortAddressType
property to configure the first address type checked as the node address.Examplenodeport
listener configuration#... spec: kafka: #... listeners: #... - name: external4 port: 9095 type: nodeport tls: false configuration: preferredNodePortAddressType: InternalDNS #...
NoteTLS hostname verification is not currently supported when exposing Kafka clusters using node ports. - cluster-ip
-
Configures an internal listener to expose Kafka using a per-broker
ClusterIP
typeService
.The listener does not use a headless service and its DNS names to route traffic to Kafka brokers. You can use this type of listener to expose a Kafka cluster when using the headless service is unsuitable. You might use it with a custom access mechanism, such as one that uses a specific Ingress controller or the Kubernetes Gateway API.
A new
ClusterIP
service is created for each Kafka broker pod. The service is assigned aClusterIP
address to serve as a Kafka bootstrap address with a per-broker port number. For example, you can configure the listener to expose a Kafka cluster over an Nginx Ingress Controller with TCP port configuration.Examplecluster-ip
listener configuration#... spec: kafka: #... listeners: - name: external-cluster-ip type: cluster-ip tls: false port: 9096 #...
port
The port number is the port used in the Kafka cluster, which might not be the same port used for access by a client.
-
loadbalancer
listeners use the specified port number, as dointernal
andcluster-ip
listeners -
ingress
androute
listeners use port 443 for access -
nodeport
listeners use the port number assigned by Kubernetes
For client connection, use the address and port for the bootstrap service of the listener.
You can retrieve this from the status of the Kafka
resource.
kubectl get kafka <kafka_cluster_name> -o=jsonpath='{.status.listeners[?(@.name=="<listener_name>")].bootstrapServers}{"\n"}'
Note
|
Listeners cannot be configured to use the ports set aside for interbroker communication (9090 and 9091) and metrics (9404). |
tls
The TLS property is required.
By default, TLS encryption is not enabled.
To enable it, set the tls
property to true
.
For route
and ingress
type listeners, TLS encryption must be enabled.
authentication
Authentication for the listener can be specified as:
-
mTLS (
tls
) -
SCRAM-SHA-512 (
scram-sha-512
) -
Token-based OAuth 2.0 (
oauth
)
networkPolicyPeers
Use networkPolicyPeers
to configure network policies that restrict access to a listener at the network level.
The following example shows a networkPolicyPeers
configuration for a plain
and a tls
listener.
In the following example:
-
Only application pods matching the labels
app: kafka-sasl-consumer
andapp: kafka-sasl-producer
can connect to theplain
listener. The application pods must be running in the same namespace as the Kafka broker. -
Only application pods running in namespaces matching the labels
project: myproject
andproject: myproject2
can connect to thetls
listener.
The syntax of the networkPolicyPeers
property is the same as the from
property in NetworkPolicy
resources.
listeners:
#...
- name: plain
port: 9092
type: internal
tls: true
authentication:
type: scram-sha-512
networkPolicyPeers:
- podSelector:
matchLabels:
app: kafka-sasl-consumer
- podSelector:
matchLabels:
app: kafka-sasl-producer
- name: tls
port: 9093
type: internal
tls: true
authentication:
type: tls
networkPolicyPeers:
- namespaceSelector:
matchLabels:
project: myproject
- namespaceSelector:
matchLabels:
project: myproject2
# ...
GenericKafkaListener
schema properties
Property | Description |
---|---|
name |
Name of the listener. The name will be used to identify the listener and the related Kubernetes objects. The name has to be unique within given a Kafka cluster. The name can consist of lowercase characters and numbers and be up to 11 characters long. |
string |
|
port |
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. |
integer |
|
type |
Type of the listener. Currently the supported types are
|
string (one of [ingress, internal, route, loadbalancer, cluster-ip, nodeport]) |
|
tls |
Enables TLS encryption on the listener. This is a required property. |
boolean |
|
authentication |
Authentication configuration for this listener. The type depends on the value of the |
|
|
configuration |
Additional listener configuration. |
networkPolicyPeers |
List of peers which should be able to connect to this listener. Peers in this list are combined using a logical OR operation. If this field is empty or missing, all connections will be allowed for this listener. If this field is present and contains at least one item, the listener only allows the traffic which matches at least one item in this list. For more information, see the external documentation for networking.k8s.io/v1 networkpolicypeer. |
NetworkPolicyPeer array |
9.2.5. KafkaListenerAuthenticationTls
schema reference
Used in: GenericKafkaListener
The type
property is a discriminator that distinguishes use of the KafkaListenerAuthenticationTls
type from KafkaListenerAuthenticationScramSha512
, KafkaListenerAuthenticationOAuth
, KafkaListenerAuthenticationCustom
.
It must have the value tls
for the type KafkaListenerAuthenticationTls
.
Property | Description |
---|---|
type |
Must be |
string |
9.2.6. KafkaListenerAuthenticationScramSha512
schema reference
Used in: GenericKafkaListener
The type
property is a discriminator that distinguishes use of the KafkaListenerAuthenticationScramSha512
type from KafkaListenerAuthenticationTls
, KafkaListenerAuthenticationOAuth
, KafkaListenerAuthenticationCustom
.
It must have the value scram-sha-512
for the type KafkaListenerAuthenticationScramSha512
.
Property | Description |
---|---|
type |
Must be |
string |
9.2.7. KafkaListenerAuthenticationOAuth
schema reference
Used in: GenericKafkaListener
The type
property is a discriminator that distinguishes use of the KafkaListenerAuthenticationOAuth
type from KafkaListenerAuthenticationTls
, KafkaListenerAuthenticationScramSha512
, KafkaListenerAuthenticationCustom
.
It must have the value oauth
for the type KafkaListenerAuthenticationOAuth
.
Property | Description |
---|---|
accessTokenIsJwt |
Configure whether the access token is treated as JWT. This must be set to |
boolean |
|
checkAccessTokenType |
Configure whether the access token type check is performed or not. This should be set to |
boolean |
|
checkAudience |
Enable or disable audience checking. Audience checks identify the recipients of tokens. If audience checking is enabled, the OAuth Client ID also has to be configured using the |
boolean |
|
checkIssuer |
Enable or disable issuer checking. By default issuer is checked using the value configured by |
boolean |
|
clientAudience |
The audience to use when making requests to the authorization server’s token endpoint. Used for inter-broker authentication and for configuring OAuth 2.0 over PLAIN using the |
string |
|
clientId |
OAuth Client ID which the Kafka broker can use to authenticate against the authorization server and use the introspect endpoint URI. |
string |
|
clientScope |
The scope to use when making requests to the authorization server’s token endpoint. Used for inter-broker authentication and for configuring OAuth 2.0 over PLAIN using the |
string |
|
clientSecret |
Link to Kubernetes Secret containing the OAuth client secret which the Kafka broker can use to authenticate against the authorization server and use the introspect endpoint URI. |
connectTimeoutSeconds |
The connect timeout in seconds when connecting to authorization server. If not set, the effective connect timeout is 60 seconds. |
integer |
|
customClaimCheck |
JsonPath filter query to be applied to the JWT token or to the response of the introspection endpoint for additional token validation. Not set by default. |
string |
|
disableTlsHostnameVerification |
Enable or disable TLS hostname verification. Default value is |
boolean |
|
enableECDSA |
The |
boolean |
|
enableMetrics |
Enable or disable OAuth metrics. Default value is |
boolean |
|
enableOauthBearer |
Enable or disable OAuth authentication over SASL_OAUTHBEARER. Default value is |
boolean |
|
enablePlain |
Enable or disable OAuth authentication over SASL_PLAIN. There is no re-authentication support when this mechanism is used. Default value is |
boolean |
|
failFast |
Enable or disable termination of Kafka broker processes due to potentially recoverable runtime errors during startup. Default value is |
boolean |
|
fallbackUserNameClaim |
The fallback username claim to be used for the user id if the claim specified by |
string |
|
fallbackUserNamePrefix |
The prefix to use with the value of |
string |
|
groupsClaim |
JsonPath query used to extract groups for the user during authentication. Extracted groups can be used by a custom authorizer. By default no groups are extracted. |
string |
|
groupsClaimDelimiter |
A delimiter used to parse groups when they are extracted as a single String value rather than a JSON array. Default value is ',' (comma). |
string |
|
introspectionEndpointUri |
URI of the token introspection endpoint which can be used to validate opaque non-JWT tokens. |
string |
|
jwksEndpointUri |
URI of the JWKS certificate endpoint, which can be used for local JWT validation. |
string |
|
jwksExpirySeconds |
Configures how often are the JWKS certificates considered valid. The expiry interval has to be at least 60 seconds longer then the refresh interval specified in |
integer |
|
jwksIgnoreKeyUse |
Flag to ignore the 'use' attribute of |
boolean |
|
jwksMinRefreshPauseSeconds |
The minimum pause between two consecutive refreshes. When an unknown signing key is encountered the refresh is scheduled immediately, but will always wait for this minimum pause. Defaults to 1 second. |
integer |
|
jwksRefreshSeconds |
Configures how often are the JWKS certificates refreshed. The refresh interval has to be at least 60 seconds shorter then the expiry interval specified in |
integer |
|
maxSecondsWithoutReauthentication |
Maximum number of seconds the authenticated session remains valid without re-authentication. This enables Apache Kafka re-authentication feature, and causes sessions to expire when the access token expires. If the access token expires before max time or if max time is reached, the client has to re-authenticate, otherwise the server will drop the connection. Not set by default - the authenticated session does not expire when the access token expires. This option only applies to SASL_OAUTHBEARER authentication mechanism (when |
integer |
|
readTimeoutSeconds |
The read timeout in seconds when connecting to authorization server. If not set, the effective read timeout is 60 seconds. |
integer |
|
tlsTrustedCertificates |
Trusted certificates for TLS connection to the OAuth server. |
|
|
tokenEndpointUri |
URI of the Token Endpoint to use with SASL_PLAIN mechanism when the client authenticates with |
string |
|
type |
Must be |
string |
|
userInfoEndpointUri |
URI of the User Info Endpoint to use as a fallback to obtaining the user id when the Introspection Endpoint does not return information that can be used for the user id. |
string |
|
userNameClaim |
Name of the claim from the JWT authentication token, Introspection Endpoint response or User Info Endpoint response which will be used to extract the user id. Defaults to |
string |
|
validIssuerUri |
URI of the token issuer used for authentication. |
string |
|
validTokenType |
Valid value for the |
string |
9.2.8. GenericSecretSource
schema reference
Used in: KafkaClientAuthenticationOAuth
, KafkaListenerAuthenticationCustom
, KafkaListenerAuthenticationOAuth
Property | Description |
---|---|
key |
The key under which the secret value is stored in the Kubernetes Secret. |
string |
|
secretName |
The name of the Kubernetes Secret containing the secret value. |
string |
9.2.9. CertSecretSource
schema reference
Used in: ClientTls
, KafkaAuthorizationKeycloak
, KafkaAuthorizationOpa
, KafkaClientAuthenticationOAuth
, KafkaListenerAuthenticationOAuth
Property | Description |
---|---|
certificate |
The name of the file certificate in the Secret. |
string |
|
secretName |
The name of the Secret containing the certificate. |
string |
9.2.10. KafkaListenerAuthenticationCustom
schema reference
Used in: GenericKafkaListener
To configure custom authentication, set the type
property to custom
.
Custom authentication allows for any type of kafka-supported authentication to be used.
spec:
kafka:
config:
principal.builder.class: SimplePrincipal.class
listeners:
- name: oauth-bespoke
port: 9093
type: internal
tls: true
authentication:
type: custom
sasl: true
listenerConfig:
oauthbearer.sasl.client.callback.handler.class: client.class
oauthbearer.sasl.server.callback.handler.class: server.class
oauthbearer.sasl.login.callback.handler.class: login.class
oauthbearer.connections.max.reauth.ms: 999999999
sasl.enabled.mechanisms: oauthbearer
oauthbearer.sasl.jaas.config: |
org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required ;
secrets:
- name: example
A protocol map is generated that uses the sasl
and tls
values to determine which protocol to map to the listener.
-
SASL = True, TLS = True → SASL_SSL
-
SASL = False, TLS = True → SSL
-
SASL = True, TLS = False → SASL_PLAINTEXT
-
SASL = False, TLS = False → PLAINTEXT
listenerConfig
Listener configuration specified using listenerConfig
is prefixed with listener.name.<listener_name>-<port>
.
For example, sasl.enabled.mechanisms
becomes listener.name.<listener_name>-<port>.sasl.enabled.mechanisms
.
secrets
Secrets are mounted to /opt/kafka/custom-authn-secrets/custom-listener-<listener_name>-<port>/<secret_name>
in the Kafka broker nodes' containers.
For example, the mounted secret (example
) in the example configuration would be located at /opt/kafka/custom-authn-secrets/custom-listener-oauth-bespoke-9093/example
.
Principal builder
You can set a custom principal builder in the Kafka cluster configuration. However, the principal builder is subject to the following requirements:
-
The specified principal builder class must exist on the image. Before building your own, check if one already exists. You’ll need to rebuild the Strimzi images with the required classes.
-
No other listener is using
oauth
type authentication. This is because an OAuth listener appends its own principle builder to the Kafka configuration. -
The specified principal builder is compatible with Strimzi.
Custom principal builders must support peer certificates for authentication, as Strimzi uses these to manage the Kafka cluster.
A custom OAuth principal builder might be identical or very similar to the Strimzi OAuth principal builder.
Note
|
Kafka’s default principal builder class supports the building of principals based on the names of peer certificates.
The custom principal builder should provide a principal of type user using the name of the SSL peer certificate.
|
The following example shows a custom principal builder that satisfies the OAuth requirements of Strimzi.
public final class CustomKafkaPrincipalBuilder implements KafkaPrincipalBuilder {
public KafkaPrincipalBuilder() {}
@Override
public KafkaPrincipal build(AuthenticationContext context) {
if (context instanceof SslAuthenticationContext) {
SSLSession sslSession = ((SslAuthenticationContext) context).session();
try {
return new KafkaPrincipal(
KafkaPrincipal.USER_TYPE, sslSession.getPeerPrincipal().getName());
} catch (SSLPeerUnverifiedException e) {
throw new IllegalArgumentException("Cannot use an unverified peer for authentication", e);
}
}
// Create your own KafkaPrincipal here
...
}
}
KafkaListenerAuthenticationCustom
schema properties
The type
property is a discriminator that distinguishes use of the KafkaListenerAuthenticationCustom
type from KafkaListenerAuthenticationTls
, KafkaListenerAuthenticationScramSha512
, KafkaListenerAuthenticationOAuth
.
It must have the value custom
for the type KafkaListenerAuthenticationCustom
.
Property | Description |
---|---|
listenerConfig |
Configuration to be used for a specific listener. All values are prefixed with listener.name.<listener_name>. |
map |
|
sasl |
Enable or disable SASL on this listener. |
boolean |
|
secrets |
Secrets to be mounted to /opt/kafka/custom-authn-secrets/custom-listener-<listener_name>-<port>/<secret_name>. |
|
|
type |
Must be |
string |
9.2.11. GenericKafkaListenerConfiguration
schema reference
Used in: GenericKafkaListener
Configuration for Kafka listeners.
brokerCertChainAndKey
The brokerCertChainAndKey
property is only used with listeners that have TLS encryption enabled.
You can use the property to provide your own Kafka listener certificates.
loadbalancer
external listener with TLS encryption enabledlisteners:
#...
- name: external
port: 9094
type: loadbalancer
tls: true
authentication:
type: tls
configuration:
brokerCertChainAndKey:
secretName: my-secret
certificate: my-listener-certificate.crt
key: my-listener-key.key
# ...
externalTrafficPolicy
The externalTrafficPolicy
property is used with loadbalancer
and nodeport
listeners.
When exposing Kafka outside of Kubernetes you can choose Local
or Cluster
.
Local
avoids hops to other nodes and preserves the client IP, whereas Cluster
does neither.
The default is Cluster
.
loadBalancerSourceRanges
The loadBalancerSourceRanges
property is only used with loadbalancer
listeners.
When exposing Kafka outside of Kubernetes use source ranges, in addition to labels and annotations, to customize how a service is created.
listeners:
#...
- name: external
port: 9094
type: loadbalancer
tls: false
configuration:
externalTrafficPolicy: Local
loadBalancerSourceRanges:
- 10.0.0.0/8
- 88.208.76.87/32
# ...
# ...
class
The class
property is only used with ingress
listeners.
You can configure the Ingress
class using the class
property.
ingress
using Ingress
class nginx-internal
listeners:
#...
- name: external
port: 9094
type: ingress
tls: true
configuration:
class: nginx-internal
# ...
# ...
preferredNodePortAddressType
The preferredNodePortAddressType
property is only used with nodeport
listeners.
Use the preferredNodePortAddressType
property in your listener configuration to specify the first address type checked as the node address.
This property is useful, for example, if your deployment does not have DNS support, or you only want to expose a broker internally through an internal DNS or IP address.
If an address of this type is found, it is used.
If the preferred address type is not found, Strimzi proceeds through the types in the standard order of priority:
-
ExternalDNS
-
ExternalIP
-
Hostname
-
InternalDNS
-
InternalIP
listeners:
#...
- name: external
port: 9094
type: nodeport
tls: false
configuration:
preferredNodePortAddressType: InternalDNS
# ...
# ...
useServiceDnsDomain
The useServiceDnsDomain
property is only used with internal
and cluster-ip
listeners.
It defines whether the fully-qualified DNS names that include the cluster service suffix (usually .cluster.local
) are used.
With useServiceDnsDomain
set as false
, the advertised addresses are generated without the service suffix; for example, my-cluster-kafka-0.my-cluster-kafka-brokers.myproject.svc
.
With useServiceDnsDomain
set as true
, the advertised addresses are generated with the service suffix; for example, my-cluster-kafka-0.my-cluster-kafka-brokers.myproject.svc.cluster.local
.
Default is false
.
listeners:
#...
- name: plain
port: 9092
type: internal
tls: false
configuration:
useServiceDnsDomain: true
# ...
# ...
If your Kubernetes cluster uses a different service suffix than .cluster.local
, you can configure the suffix using the KUBERNETES_SERVICE_DNS_DOMAIN
environment variable in the Cluster Operator configuration.
See Configuring the Cluster Operator with environment variables for more details.
GenericKafkaListenerConfiguration
schema properties
Property | Description |
---|---|
brokerCertChainAndKey |
Reference to the |
externalTrafficPolicy |
Specifies whether the service routes external traffic to node-local or cluster-wide endpoints. |
string (one of [Local, Cluster]) |
|
loadBalancerSourceRanges |
A list of CIDR ranges (for example |
string array |
|
bootstrap |
Bootstrap configuration. |
brokers |
Per-broker configurations. |
ipFamilyPolicy |
Specifies the IP Family Policy used by the service. Available options are |
string (one of [RequireDualStack, SingleStack, PreferDualStack]) |
|
ipFamilies |
Specifies the IP Families used by the service. Available options are |
string (one or more of [IPv6, IPv4]) array |
|
createBootstrapService |
Whether to create the bootstrap service or not. The bootstrap service is created by default (if not specified differently). This field can be used with the |
boolean |
|
class |
Configures a specific class for |
string |
|
finalizers |
A list of finalizers which will be configured for the |
string array |
|
maxConnectionCreationRate |
The maximum connection creation rate we allow in this listener at any time. New connections will be throttled if the limit is reached. |
integer |
|
maxConnections |
The maximum number of connections we allow for this listener in the broker at any time. New connections are blocked if the limit is reached. |
integer |
|
preferredNodePortAddressType |
Defines which address type should be used as the node address. Available types are:
This field is used to select the preferred address type, which is checked first. If no address is found for this address type, the other types are checked in the default order. This field can only be used with |
string (one of [ExternalDNS, ExternalIP, Hostname, InternalIP, InternalDNS]) |
|
useServiceDnsDomain |
Configures whether the Kubernetes service DNS domain should be used or not. If set to |
boolean |
9.2.12. CertAndKeySecretSource
schema reference
Property | Description |
---|---|
certificate |
The name of the file certificate in the Secret. |
string |
|
key |
The name of the private key in the Secret. |
string |
|
secretName |
The name of the Secret containing the certificate. |
string |
9.2.13. GenericKafkaListenerConfigurationBootstrap
schema reference
Used in: GenericKafkaListenerConfiguration
Broker service equivalents of nodePort
, host
, loadBalancerIP
and annotations
properties are configured in the GenericKafkaListenerConfigurationBroker
schema.
alternativeNames
You can specify alternative names for the bootstrap service.
The names are added to the broker certificates and can be used for TLS hostname verification.
The alternativeNames
property is applicable to all types of listeners.
route
listener configured with an additional bootstrap addresslisteners:
#...
- name: external
port: 9094
type: route
tls: true
authentication:
type: tls
configuration:
bootstrap:
alternativeNames:
- example.hostname1
- example.hostname2
# ...
host
The host
property is used with route
and ingress
listeners to specify the hostnames used by the bootstrap and per-broker services.
A host
property value is mandatory for ingress
listener configuration, as the Ingress controller does not assign any hostnames automatically.
Make sure that the hostnames resolve to the Ingress endpoints.
Strimzi will not perform any validation that the requested hosts are available and properly routed to the Ingress endpoints.
listeners:
#...
- name: external
port: 9094
type: ingress
tls: true
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
# ...
By default, route
listener hosts are automatically assigned by OpenShift.
However, you can override the assigned route hosts by specifying hosts.
Strimzi does not perform any validation that the requested hosts are available. You must ensure that they are free and can be used.
# ...
listeners:
#...
- name: external
port: 9094
type: route
tls: true
authentication:
type: tls
configuration:
bootstrap:
host: bootstrap.myrouter.com
brokers:
- broker: 0
host: broker-0.myrouter.com
- broker: 1
host: broker-1.myrouter.com
- broker: 2
host: broker-2.myrouter.com
# ...
nodePort
By default, the port numbers used for the bootstrap and broker services are automatically assigned by Kubernetes.
You can override the assigned node ports for nodeport
listeners by specifying the requested port numbers.
Strimzi does not perform any validation on the requested ports. You must ensure that they are free and available for use.
# ...
listeners:
#...
- name: external
port: 9094
type: nodeport
tls: true
authentication:
type: tls
configuration:
bootstrap:
nodePort: 32100
brokers:
- broker: 0
nodePort: 32000
- broker: 1
nodePort: 32001
- broker: 2
nodePort: 32002
# ...
loadBalancerIP
Use the loadBalancerIP
property to request a specific IP address when creating a loadbalancer.
Use this property when you need to use a loadbalancer with a specific IP address.
The loadBalancerIP
field is ignored if the cloud provider does not support the feature.
loadbalancer
with specific loadbalancer IP address requests# ...
listeners:
#...
- name: external
port: 9094
type: loadbalancer
tls: true
authentication:
type: tls
configuration:
bootstrap:
loadBalancerIP: 172.29.3.10
brokers:
- broker: 0
loadBalancerIP: 172.29.3.1
- broker: 1
loadBalancerIP: 172.29.3.2
- broker: 2
loadBalancerIP: 172.29.3.3
# ...
annotations
Use the annotations
property to add annotations to Kubernetes resources related to the listeners.
You can use these annotations, for example, to instrument DNS tooling such as External DNS, which automatically assigns DNS names to the loadbalancer services.
loadbalancer
using annotations
# ...
listeners:
#...
- name: external
port: 9094
type: loadbalancer
tls: true
authentication:
type: tls
configuration:
bootstrap:
annotations:
external-dns.alpha.kubernetes.io/hostname: kafka-bootstrap.mydomain.com.
external-dns.alpha.kubernetes.io/ttl: "60"
brokers:
- broker: 0
annotations:
external-dns.alpha.kubernetes.io/hostname: kafka-broker-0.mydomain.com.
external-dns.alpha.kubernetes.io/ttl: "60"
- broker: 1
annotations:
external-dns.alpha.kubernetes.io/hostname: kafka-broker-1.mydomain.com.
external-dns.alpha.kubernetes.io/ttl: "60"
- broker: 2
annotations:
external-dns.alpha.kubernetes.io/hostname: kafka-broker-2.mydomain.com.
external-dns.alpha.kubernetes.io/ttl: "60"
# ...
GenericKafkaListenerConfigurationBootstrap
schema properties
Property | Description |
---|---|
alternativeNames |
Additional alternative names for the bootstrap service. The alternative names will be added to the list of subject alternative names of the TLS certificates. |
string array |
|
host |
The bootstrap host. This field will be used in the Ingress resource or in the Route resource to specify the desired hostname. This field can be used only with |
string |
|
nodePort |
Node port for the bootstrap service. This field can be used only with |
integer |
|
loadBalancerIP |
The loadbalancer is requested with the IP address specified in this field. This feature depends on whether the underlying cloud provider supports specifying the |
string |
|
annotations |
Annotations that will be added to the |
map |
|
labels |
Labels that will be added to the |
map |
9.2.14. GenericKafkaListenerConfigurationBroker
schema reference
Used in: GenericKafkaListenerConfiguration
You can see example configuration for the nodePort
, host
, loadBalancerIP
and annotations
properties in the GenericKafkaListenerConfigurationBootstrap
schema,
which configures bootstrap service overrides.
By default, Strimzi tries to automatically determine the hostnames and ports that your Kafka cluster advertises to its clients. This is not sufficient in all situations, because the infrastructure on which Strimzi is running might not provide the right hostname or port through which Kafka can be accessed.
You can specify a broker ID and customize the advertised hostname and port in the configuration
property of the listener.
Strimzi will then automatically configure the advertised address in the Kafka brokers and add it to the broker certificates so it can be used for TLS hostname verification.
Overriding the advertised host and ports is available for all types of listeners.
route
listener configured with overrides for advertised addresseslisteners:
#...
- name: external
port: 9094
type: route
tls: true
authentication:
type: tls
configuration:
brokers:
- broker: 0
advertisedHost: example.hostname.0
advertisedPort: 12340
- broker: 1
advertisedHost: example.hostname.1
advertisedPort: 12341
- broker: 2
advertisedHost: example.hostname.2
advertisedPort: 12342
# ...
GenericKafkaListenerConfigurationBroker
schema properties
Property | Description |
---|---|
broker |
ID of the kafka broker (broker identifier). Broker IDs start from 0 and correspond to the number of broker replicas. |
integer |
|
advertisedHost |
The host name which will be used in the brokers' |
string |
|
advertisedPort |
The port number which will be used in the brokers' |
integer |
|
host |
The broker host. This field will be used in the Ingress resource or in the Route resource to specify the desired hostname. This field can be used only with |
string |
|
nodePort |
Node port for the per-broker service. This field can be used only with |
integer |
|
loadBalancerIP |
The loadbalancer is requested with the IP address specified in this field. This feature depends on whether the underlying cloud provider supports specifying the |
string |
|
annotations |
Annotations that will be added to the |
map |
|
labels |
Labels that will be added to the |
map |
9.2.15. EphemeralStorage
schema reference
Used in: JbodStorage
, KafkaClusterSpec
, ZookeeperClusterSpec
The type
property is a discriminator that distinguishes use of the EphemeralStorage
type from PersistentClaimStorage
.
It must have the value ephemeral
for the type EphemeralStorage
.
Property | Description |
---|---|
id |
Storage identification number. It is mandatory only for storage volumes defined in a storage of type 'jbod'. |
integer |
|
sizeLimit |
When type=ephemeral, defines the total amount of local storage required for this EmptyDir volume (for example 1Gi). |
string |
|
type |
Must be |
string |
9.2.16. PersistentClaimStorage
schema reference
Used in: JbodStorage
, KafkaClusterSpec
, ZookeeperClusterSpec
The type
property is a discriminator that distinguishes use of the PersistentClaimStorage
type from EphemeralStorage
.
It must have the value persistent-claim
for the type PersistentClaimStorage
.
Property | Description |
---|---|
type |
Must be |
string |
|
size |
When type=persistent-claim, defines the size of the persistent volume claim (i.e 1Gi). Mandatory when type=persistent-claim. |
string |
|
selector |
Specifies a specific persistent volume to use. It contains key:value pairs representing labels for selecting such a volume. |
map |
|
deleteClaim |
Specifies if the persistent volume claim has to be deleted when the cluster is un-deployed. |
boolean |
|
class |
The storage class to use for dynamic volume allocation. |
string |
|
id |
Storage identification number. It is mandatory only for storage volumes defined in a storage of type 'jbod'. |
integer |
|
overrides |
Overrides for individual brokers. The |
9.2.17. PersistentClaimStorageOverride
schema reference
Used in: PersistentClaimStorage
Property | Description |
---|---|
class |
The storage class to use for dynamic volume allocation for this broker. |
string |
|
broker |
Id of the kafka broker (broker identifier). |
integer |
9.2.18. JbodStorage
schema reference
Used in: KafkaClusterSpec
The type
property is a discriminator that distinguishes use of the JbodStorage
type from EphemeralStorage
, PersistentClaimStorage
.
It must have the value jbod
for the type JbodStorage
.
Property | Description |
---|---|
type |
Must be |
string |
|
volumes |
List of volumes as Storage objects representing the JBOD disks array. |
9.2.19. KafkaAuthorizationSimple
schema reference
Used in: KafkaClusterSpec
Simple authorization in Strimzi uses the AclAuthorizer
plugin, the default Access Control Lists (ACLs) authorization plugin provided with Apache Kafka.
ACLs allow you to define which users have access to which resources at a granular level.
Configure the Kafka
custom resource to use simple authorization.
Set the type
property in the authorization
section to the value simple
,
and configure a list of super users.
Access rules are configured for the KafkaUser
, as described in the ACLRule schema reference.
superUsers
A list of user principals treated as super users, so that they are always allowed without querying ACL rules.
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
# ...
Note
|
The super.user configuration option in the config property in Kafka.spec.kafka is ignored.
Designate super users in the authorization property instead.
For more information, see Kafka broker configuration.
|
KafkaAuthorizationSimple
schema properties
The type
property is a discriminator that distinguishes use of the KafkaAuthorizationSimple
type from KafkaAuthorizationOpa
, KafkaAuthorizationKeycloak
, KafkaAuthorizationCustom
.
It must have the value simple
for the type KafkaAuthorizationSimple
.
Property | Description |
---|---|
type |
Must be |
string |
|
superUsers |
List of super users. Should contain list of user principals which should get unlimited access rights. |
string array |
9.2.20. KafkaAuthorizationOpa
schema reference
Used in: KafkaClusterSpec
To use Open Policy Agent authorization, set the type
property in the authorization
section to the value opa
,
and configure OPA properties as required.
Strimzi uses Open Policy Agent plugin for Kafka authorization as the authorizer.
For more information about the format of the input data and policy examples, see Open Policy Agent plugin for Kafka authorization.
url
The URL used to connect to the Open Policy Agent server. The URL has to include the policy which will be queried by the authorizer. Required.
allowOnError
Defines whether a Kafka client should be allowed or denied by default when the authorizer fails to query the Open Policy Agent, for example, when it is temporarily unavailable.
Defaults to false
- all actions will be denied.
initialCacheCapacity
Initial capacity of the local cache used by the authorizer to avoid querying the Open Policy Agent for every request.
Defaults to 5000
.
maximumCacheSize
Maximum capacity of the local cache used by the authorizer to avoid querying the Open Policy Agent for every request.
Defaults to 50000
.
expireAfterMs
The expiration of the records kept in the local cache to avoid querying the Open Policy Agent for every request.
Defines how often the cached authorization decisions are reloaded from the Open Policy Agent server.
In milliseconds.
Defaults to 3600000
milliseconds (1 hour).
tlsTrustedCertificates
Trusted certificates for TLS connection to the OPA server.
superUsers
A list of user principals treated as super users, so that they are always allowed without querying the open Policy Agent policy.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
namespace: myproject
spec:
kafka:
# ...
authorization:
type: opa
url: http://opa:8181/v1/data/kafka/allow
allowOnError: false
initialCacheCapacity: 1000
maximumCacheSize: 10000
expireAfterMs: 60000
superUsers:
- CN=fred
- sam
- CN=edward
# ...
KafkaAuthorizationOpa
schema properties
The type
property is a discriminator that distinguishes use of the KafkaAuthorizationOpa
type from KafkaAuthorizationSimple
, KafkaAuthorizationKeycloak
, KafkaAuthorizationCustom
.
It must have the value opa
for the type KafkaAuthorizationOpa
.
Property | Description |
---|---|
type |
Must be |
string |
|
url |
The URL used to connect to the Open Policy Agent server. The URL has to include the policy which will be queried by the authorizer. This option is required. |
string |
|
allowOnError |
Defines whether a Kafka client should be allowed or denied by default when the authorizer fails to query the Open Policy Agent, for example, when it is temporarily unavailable). Defaults to |
boolean |
|
initialCacheCapacity |
Initial capacity of the local cache used by the authorizer to avoid querying the Open Policy Agent for every request Defaults to |
integer |
|
maximumCacheSize |
Maximum capacity of the local cache used by the authorizer to avoid querying the Open Policy Agent for every request. Defaults to |
integer |
|
expireAfterMs |
The expiration of the records kept in the local cache to avoid querying the Open Policy Agent for every request. Defines how often the cached authorization decisions are reloaded from the Open Policy Agent server. In milliseconds. Defaults to |
integer |
|
tlsTrustedCertificates |
Trusted certificates for TLS connection to the OPA server. |
|
|
superUsers |
List of super users, which is specifically a list of user principals that have unlimited access rights. |
string array |
|
enableMetrics |
Defines whether the Open Policy Agent authorizer plugin should provide metrics. Defaults to |
boolean |
9.2.21. KafkaAuthorizationKeycloak
schema reference
Used in: KafkaClusterSpec
The type
property is a discriminator that distinguishes use of the KafkaAuthorizationKeycloak
type from KafkaAuthorizationSimple
, KafkaAuthorizationOpa
, KafkaAuthorizationCustom
.
It must have the value keycloak
for the type KafkaAuthorizationKeycloak
.
Property | Description |
---|---|
type |
Must be |
string |
|
clientId |
OAuth Client ID which the Kafka client can use to authenticate against the OAuth server and use the token endpoint URI. |
string |
|
tokenEndpointUri |
Authorization server token endpoint URI. |
string |
|
tlsTrustedCertificates |
Trusted certificates for TLS connection to the OAuth server. |
|
|
disableTlsHostnameVerification |
Enable or disable TLS hostname verification. Default value is |
boolean |
|
delegateToKafkaAcls |
Whether authorization decision should be delegated to the 'Simple' authorizer if DENIED by Keycloak Authorization Services policies. Default value is |
boolean |
|
grantsRefreshPeriodSeconds |
The time between two consecutive grants refresh runs in seconds. The default value is 60. |
integer |
|
grantsRefreshPoolSize |
The number of threads to use to refresh grants for active sessions. The more threads, the more parallelism, so the sooner the job completes. However, using more threads places a heavier load on the authorization server. The default value is 5. |
integer |
|
superUsers |
List of super users. Should contain list of user principals which should get unlimited access rights. |
string array |
|
connectTimeoutSeconds |
The connect timeout in seconds when connecting to authorization server. If not set, the effective connect timeout is 60 seconds. |
integer |
|
readTimeoutSeconds |
The read timeout in seconds when connecting to authorization server. If not set, the effective read timeout is 60 seconds. |
integer |
|
enableMetrics |
Enable or disable OAuth metrics. Default value is |
boolean |
9.2.22. KafkaAuthorizationCustom
schema reference
Used in: KafkaClusterSpec
To use custom authorization in Strimzi, you can configure your own Authorizer
plugin to define Access Control Lists (ACLs).
ACLs allow you to define which users have access to which resources at a granular level.
Configure the Kafka
custom resource to use custom authorization.
Set the type
property in the authorization
section to the value custom
,
and the set following properties.
Important
|
The custom authorizer must implement the org.apache.kafka.server.authorizer.Authorizer interface, and support configuration of super.users using the super.users configuration property.
|
authorizerClass
(Required) Java class that implements the org.apache.kafka.server.authorizer.Authorizer
interface to support custom ACLs.
superUsers
A list of user principals treated as super users, so that they are always allowed without querying ACL rules.
You can add configuration for initializing the custom authorizer using Kafka.spec.kafka.config
.
Kafka.spec
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
namespace: myproject
spec:
kafka:
# ...
authorization:
type: custom
authorizerClass: io.mycompany.CustomAuthorizer
superUsers:
- CN=client_1
- user_2
- CN=client_3
# ...
config:
authorization.custom.property1=value1
authorization.custom.property2=value2
# ...
In addition to the Kafka
custom resource configuration, the JAR file containing the custom authorizer class along with its dependencies must be available on the classpath of the Kafka broker.
The Strimzi Maven build process provides a mechanism to add custom third-party libraries to the generated Kafka broker container image by adding them as dependencies in the pom.xml
file under the docker-images/kafka/kafka-thirdparty-libs
directory. The directory contains different folders for different Kafka versions. Choose the appropriate folder. Before modifying the pom.xml
file, the third-party library must be available in a Maven repository, and that Maven repository must be accessible to the Strimzi build process.
Note
|
The super.user configuration option in the config property in Kafka.spec.kafka is ignored.
Designate super users in the authorization property instead.
For more information, see Kafka broker configuration.
|
Custom authorization can make use of group membership information extracted from the JWT token during authentication when using oauth
authentication and configuring groupsClaim
configuration attribute. Groups are available on the OAuthKafkaPrincipal
object during authorize() call as follows:
public List<AuthorizationResult> authorize(AuthorizableRequestContext requestContext, List<Action> actions) {
KafkaPrincipal principal = requestContext.principal();
if (principal instanceof OAuthKafkaPrincipal) {
OAuthKafkaPrincipal p = (OAuthKafkaPrincipal) principal;
for (String group: p.getGroups()) {
System.out.println("Group: " + group);
}
}
}
KafkaAuthorizationCustom
schema properties
The type
property is a discriminator that distinguishes use of the KafkaAuthorizationCustom
type from KafkaAuthorizationSimple
, KafkaAuthorizationOpa
, KafkaAuthorizationKeycloak
.
It must have the value custom
for the type KafkaAuthorizationCustom
.
Property | Description |
---|---|
type |
Must be |
string |
|
authorizerClass |
Authorization implementation class, which must be available in classpath. |
string |
|
superUsers |
List of super users, which are user principals with unlimited access rights. |
string array |
|
supportsAdminApi |
Indicates whether the custom authorizer supports the APIs for managing ACLs using the Kafka Admin API. Defaults to |
boolean |
9.2.23. Rack
schema reference
The rack
option configures rack awareness.
A rack can represent an availability zone, data center, or an actual rack in your data center.
The rack is configured through a topologyKey
.
topologyKey
identifies a label on Kubernetes nodes that contains the name of the topology in its value.
An example of such a label is topology.kubernetes.io/zone
(or failure-domain.beta.kubernetes.io/zone
on older Kubernetes versions), which contains the name of the availability zone in which the Kubernetes node runs.
You can configure your Kafka cluster to be aware of the rack in which it runs, and enable additional features such as spreading partition replicas across different racks or consuming messages from the closest replicas.
For more information about Kubernetes node labels, see Well-Known Labels, Annotations and Taints. Consult your Kubernetes administrator regarding the node label that represents the zone or rack into which the node is deployed.
Spreading partition replicas across racks
When rack awareness is configured, Strimzi will set broker.rack
configuration for each Kafka broker.
The broker.rack
configuration assigns a rack ID to each broker.
When broker.rack
is configured, Kafka brokers will spread partition replicas across as many different racks as possible.
When replicas are spread across multiple racks, the probability that multiple replicas will fail at the same time is lower than if they would be in the same rack.
Spreading replicas improves resiliency, and is important for availability and reliability.
To enable rack awareness in Kafka, add the rack
option to the .spec.kafka
section of the Kafka
custom resource as shown in the example below.
rack
configuration for KafkaapiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
rack:
topologyKey: topology.kubernetes.io/zone
# ...
Note
|
The rack in which brokers are running can change in some cases when the pods are deleted or restarted.
As a result, the replicas running in different racks might then share the same rack.
Use Cruise Control and the KafkaRebalance resource with the RackAwareGoal to make sure that replicas remain distributed across different racks.
|
When rack awareness is enabled in the Kafka
custom resource, Strimzi will automatically add the Kubernetes preferredDuringSchedulingIgnoredDuringExecution
affinity rule to distribute the Kafka brokers across the different racks.
However, the preferred rule does not guarantee that the brokers will be spread.
Depending on your exact Kubernetes and Kafka configurations, you should add additional affinity
rules or configure topologySpreadConstraints
for both ZooKeeper and Kafka to make sure the nodes are properly distributed accross as many racks as possible.
For more information see Configuring pod scheduling.
Consuming messages from the closest replicas
Rack awareness can also be used in consumers to fetch data from the closest replica. This is useful for reducing the load on your network when a Kafka cluster spans multiple datacenters and can also reduce costs when running Kafka in public clouds. However, it can lead to increased latency.
In order to be able to consume from the closest replica, rack awareness has to be configured in the Kafka cluster, and the RackAwareReplicaSelector
has to be enabled.
The replica selector plugin provides the logic that enables clients to consume from the nearest replica.
The default implementation uses LeaderSelector
to always select the leader replica for the client.
Specify RackAwareReplicaSelector
for the replica.selector.class
to switch from the default implementation.
rack
configuration with enabled replica-aware selectorapiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
rack:
topologyKey: topology.kubernetes.io/zone
config:
# ...
replica.selector.class: org.apache.kafka.common.replica.RackAwareReplicaSelector
# ...
In addition to the Kafka broker configuration, you also need to specify the client.rack
option in your consumers.
The client.rack
option should specify the rack ID in which the consumer is running.
RackAwareReplicaSelector
associates matching broker.rack
and client.rack
IDs, to find the nearest replica and consume from it.
If there are multiple replicas in the same rack, RackAwareReplicaSelector
always selects the most up-to-date replica.
If the rack ID is not specified, or if it cannot find a replica with the same rack ID, it will fall back to the leader replica.

You can also configure Kafka Connect, MirrorMaker 2.0 and Kafka Bridge so that connectors consume messages from the closest replicas.
You enable rack awareness in the KafkaConnect
, KafkaMirrorMaker2
, and KafkaBridge
custom resources.
The configuration does does not set affinity rules, but you can also configure affinity
or topologySpreadConstraints
.
For more information see Configuring pod scheduling.
When deploying Kafka Connect using Strimzi, you can use the rack
section in the KafkaConnect
custom resource to automatically configure the client.rack
option.
rack
configuration for Kafka ConnectapiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
# ...
spec:
# ...
rack:
topologyKey: topology.kubernetes.io/zone
# ...
When deploying MirrorMaker 2 using Strimzi, you can use the rack
section in the KafkaMirrorMaker2
custom resource to automatically configure the client.rack
option.
rack
configuration for MirrorMaker 2.0apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
# ...
spec:
# ...
rack:
topologyKey: topology.kubernetes.io/zone
# ...
When deploying Kafka Bridge using Strimzi, you can use the rack
section in the KafkaBridge
custom resource to automatically configure the client.rack
option.
rack
configuration for Kafka BridgeapiVersion: kafka.strimzi.io/v1beta2
kind: KafkaBridge
# ...
spec:
# ...
rack:
topologyKey: topology.kubernetes.io/zone
# ...
Rack
schema properties
Property | Description |
---|---|
topologyKey |
A key that matches labels assigned to the Kubernetes cluster nodes. The value of the label is used to set a broker’s |
string |
9.2.24. Probe
schema reference
Used in: CruiseControlSpec
, EntityTopicOperatorSpec
, EntityUserOperatorSpec
, KafkaBridgeSpec
, KafkaClusterSpec
, KafkaConnectSpec
, KafkaExporterSpec
, KafkaMirrorMaker2Spec
, KafkaMirrorMakerSpec
, TlsSidecar
, ZookeeperClusterSpec
Property | Description |
---|---|
failureThreshold |
Minimum consecutive failures for the probe to be considered failed after having succeeded. Defaults to 3. Minimum value is 1. |
integer |
|
initialDelaySeconds |
The initial delay before first the health is first checked. Default to 15 seconds. Minimum value is 0. |
integer |
|
periodSeconds |
How often (in seconds) to perform the probe. Default to 10 seconds. Minimum value is 1. |
integer |
|
successThreshold |
Minimum consecutive successes for the probe to be considered successful after having failed. Defaults to 1. Must be 1 for liveness. Minimum value is 1. |
integer |
|
timeoutSeconds |
The timeout for each attempted health check. Default to 5 seconds. Minimum value is 1. |
integer |
9.2.25. JvmOptions
schema reference
Used in: CruiseControlSpec
, EntityTopicOperatorSpec
, EntityUserOperatorSpec
, KafkaBridgeSpec
, KafkaClusterSpec
, KafkaConnectSpec
, KafkaMirrorMaker2Spec
, KafkaMirrorMakerSpec
, ZookeeperClusterSpec
Property | Description |
---|---|
-XX |
A map of -XX options to the JVM. |
map |
|
-Xms |
-Xms option to to the JVM. |
string |
|
-Xmx |
-Xmx option to to the JVM. |
string |
|
gcLoggingEnabled |
Specifies whether the Garbage Collection logging is enabled. The default is false. |
boolean |
|
javaSystemProperties |
A map of additional system properties which will be passed using the |
|
9.2.26. SystemProperty
schema reference
Used in: JvmOptions
Property | Description |
---|---|
name |
The system property name. |
string |
|
value |
The system property value. |
string |
9.2.27. KafkaJmxOptions
schema reference
Configures JMX connection options.
Get JMX metrics from Kafka brokers, ZooKeeper nodes, Kafka Connect, and MirrorMaker 2.0. by connecting to port 9999.
Use the jmxOptions
property to configure a password-protected or an unprotected JMX port.
Using password protection prevents unauthorized pods from accessing the port.
You can then obtain metrics about the component.
For example, for each Kafka broker you can obtain bytes-per-second usage data from clients, or the request rate of the network of the broker.
To enable security for the JMX port, set the type
parameter in the authentication
field to password
.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
jmxOptions:
authentication:
type: "password"
# ...
zookeeper:
# ...
jmxOptions:
authentication:
type: "password"
#...
You can then deploy a pod into a cluster and obtain JMX metrics using the headless service by specifying which broker you want to address.
For example, to get JMX metrics from broker 0 you specify:
"CLUSTER-NAME-kafka-0.CLUSTER-NAME-kafka-brokers"
CLUSTER-NAME-kafka-0
is name of the broker pod, and CLUSTER-NAME-kafka-brokers
is the name of the headless service to return the IPs of the broker pods.
If the JMX port is secured, you can get the username and password by referencing them from the JMX Secret in the deployment of your pod.
For an unprotected JMX port, use an empty object {}
to open the JMX port on the headless service.
You deploy a pod and obtain metrics in the same way as for the protected port, but in this case any pod can read from the JMX port.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
jmxOptions: {}
# ...
zookeeper:
# ...
jmxOptions: {}
# ...
-
For more information on the Kafka component metrics exposed using JMX, see the Apache Kafka documentation.
KafkaJmxOptions
schema properties
Property | Description |
---|---|
authentication |
Authentication configuration for connecting to the JMX port. The type depends on the value of the |
9.2.28. KafkaJmxAuthenticationPassword
schema reference
Used in: KafkaJmxOptions
The type
property is a discriminator that distinguishes use of the KafkaJmxAuthenticationPassword
type from other subtypes which may be added in the future.
It must have the value password
for the type KafkaJmxAuthenticationPassword
.
Property | Description |
---|---|
type |
Must be |
string |
9.2.29. JmxPrometheusExporterMetrics
schema reference
Used in: CruiseControlSpec
, KafkaClusterSpec
, KafkaConnectSpec
, KafkaMirrorMaker2Spec
, KafkaMirrorMakerSpec
, ZookeeperClusterSpec
The type
property is a discriminator that distinguishes use of the JmxPrometheusExporterMetrics
type from other subtypes which may be added in the future.
It must have the value jmxPrometheusExporter
for the type JmxPrometheusExporterMetrics
.
Property | Description |
---|---|
type |
Must be |
string |
|
valueFrom |
ConfigMap entry where the Prometheus JMX Exporter configuration is stored. For details of the structure of this configuration, see the Prometheus JMX Exporter. |
9.2.30. ExternalConfigurationReference
schema reference
Used in: ExternalLogging
, JmxPrometheusExporterMetrics
Property | Description |
---|---|
configMapKeyRef |
Reference to the key in the ConfigMap containing the configuration. For more information, see the external documentation for core/v1 configmapkeyselector. |
9.2.31. InlineLogging
schema reference
Used in: CruiseControlSpec
, EntityTopicOperatorSpec
, EntityUserOperatorSpec
, KafkaBridgeSpec
, KafkaClusterSpec
, KafkaConnectSpec
, KafkaMirrorMaker2Spec
, KafkaMirrorMakerSpec
, ZookeeperClusterSpec
The type
property is a discriminator that distinguishes use of the InlineLogging
type from ExternalLogging
.
It must have the value inline
for the type InlineLogging
.
Property | Description |
---|---|
type |
Must be |
string |
|
loggers |
A Map from logger name to logger level. |
map |
9.2.32. ExternalLogging
schema reference
Used in: CruiseControlSpec
, EntityTopicOperatorSpec
, EntityUserOperatorSpec
, KafkaBridgeSpec
, KafkaClusterSpec
, KafkaConnectSpec
, KafkaMirrorMaker2Spec
, KafkaMirrorMakerSpec
, ZookeeperClusterSpec
The type
property is a discriminator that distinguishes use of the ExternalLogging
type from InlineLogging
.
It must have the value external
for the type ExternalLogging
.
Property | Description |
---|---|
type |
Must be |
string |
|
valueFrom |
|
9.2.33. KafkaClusterTemplate
schema reference
Used in: KafkaClusterSpec
Property | Description |
---|---|
statefulset |
Template for Kafka |
pod |
Template for Kafka |
bootstrapService |
Template for Kafka bootstrap |
brokersService |
Template for Kafka broker |
externalBootstrapService |
Template for Kafka external bootstrap |
perPodService |
Template for Kafka per-pod |
externalBootstrapRoute |
Template for Kafka external bootstrap |
perPodRoute |
Template for Kafka per-pod |
externalBootstrapIngress |
Template for Kafka external bootstrap |
perPodIngress |
Template for Kafka per-pod |
persistentVolumeClaim |
Template for all Kafka |
podDisruptionBudget |
Template for Kafka |
kafkaContainer |
Template for the Kafka broker container. |
initContainer |
Template for the Kafka init container. |
clusterCaCert |
Template for Secret with Kafka Cluster certificate public key. |
serviceAccount |
Template for the Kafka service account. |
jmxSecret |
Template for Secret of the Kafka Cluster JMX authentication. |
clusterRoleBinding |
Template for the Kafka ClusterRoleBinding. |
podSet |
Template for Kafka |
9.2.34. StatefulSetTemplate
schema reference
Used in: KafkaClusterTemplate
, ZookeeperClusterTemplate
Property | Description |
---|---|
metadata |
Metadata applied to the resource. |
podManagementPolicy |
PodManagementPolicy which will be used for this StatefulSet. Valid values are |
string (one of [OrderedReady, Parallel]) |
9.2.35. MetadataTemplate
schema reference
Used in: BuildConfigTemplate
, DeploymentTemplate
, InternalServiceTemplate
, PodDisruptionBudgetTemplate
, PodTemplate
, ResourceTemplate
, StatefulSetTemplate
Labels
and Annotations
are used to identify and organize resources, and are configured in the metadata
property.
For example:
# ...
template:
pod:
metadata:
labels:
label1: value1
label2: value2
annotations:
annotation1: value1
annotation2: value2
# ...
The labels
and annotations
fields can contain any labels or annotations that do not contain the reserved string strimzi.io
.
Labels and annotations containing strimzi.io
are used internally by Strimzi and cannot be configured.
MetadataTemplate
schema properties
Property | Description |
---|---|
labels |
Labels added to the resource template. Can be applied to different resources such as |
map |
|
annotations |
Annotations added to the resource template. Can be applied to different resources such as |
map |
9.2.36. PodTemplate
schema reference
Used in: CruiseControlTemplate
, EntityOperatorTemplate
, JmxTransTemplate
, KafkaBridgeTemplate
, KafkaClusterTemplate
, KafkaConnectTemplate
, KafkaExporterTemplate
, KafkaMirrorMakerTemplate
, ZookeeperClusterTemplate
Configures the template for Kafka pods.
PodTemplate
configuration