spec:
# ...
logging:
type: external
valueFrom:
configMapKeyRef:
name: my-config-map
key: my-config-map-key
KafkaRebalance
resourceKafka
schema referenceKafkaSpec
schema referenceKafkaClusterSpec
schema referenceGenericKafkaListener
schema referenceKafkaListenerAuthenticationTls
schema referenceKafkaListenerAuthenticationScramSha512
schema referenceKafkaListenerAuthenticationOAuth
schema referenceGenericSecretSource
schema referenceCertSecretSource
schema referenceKafkaListenerAuthenticationCustom
schema referenceGenericKafkaListenerConfiguration
schema referenceCertAndKeySecretSource
schema referenceGenericKafkaListenerConfigurationBootstrap
schema referenceGenericKafkaListenerConfigurationBroker
schema referenceEphemeralStorage
schema referencePersistentClaimStorage
schema referencePersistentClaimStorageOverride
schema referenceJbodStorage
schema referenceKafkaAuthorizationSimple
schema referenceKafkaAuthorizationOpa
schema referenceKafkaAuthorizationKeycloak
schema referenceKafkaAuthorizationCustom
schema referenceRack
schema referenceProbe
schema referenceJvmOptions
schema referenceSystemProperty
schema referenceKafkaJmxOptions
schema referenceKafkaJmxAuthenticationPassword
schema referenceJmxPrometheusExporterMetrics
schema referenceExternalConfigurationReference
schema referenceInlineLogging
schema referenceExternalLogging
schema referenceKafkaClusterTemplate
schema referenceStatefulSetTemplate
schema referenceMetadataTemplate
schema referencePodTemplate
schema referenceInternalServiceTemplate
schema referenceResourceTemplate
schema referencePodDisruptionBudgetTemplate
schema referenceContainerTemplate
schema referenceContainerEnvVar
schema referenceZookeeperClusterSpec
schema referenceZookeeperClusterTemplate
schema referenceEntityOperatorSpec
schema referenceEntityTopicOperatorSpec
schema referenceEntityUserOperatorSpec
schema referenceTlsSidecar
schema referenceEntityOperatorTemplate
schema referenceCertificateAuthority
schema referenceCruiseControlSpec
schema referenceCruiseControlTemplate
schema referenceBrokerCapacity
schema referenceBrokerCapacityOverride
schema referenceJmxTransSpec
schema referenceJmxTransOutputDefinitionTemplate
schema referenceJmxTransQueryTemplate
schema referenceJmxTransTemplate
schema referenceKafkaExporterSpec
schema referenceKafkaExporterTemplate
schema referenceKafkaStatus
schema referenceCondition
schema referenceListenerStatus
schema referenceListenerAddress
schema referenceKafkaConnect
schema referenceKafkaConnectSpec
schema referenceClientTls
schema referenceKafkaClientAuthenticationTls
schema referenceKafkaClientAuthenticationScramSha256
schema referencePasswordSecretSource
schema referenceKafkaClientAuthenticationScramSha512
schema referenceKafkaClientAuthenticationPlain
schema referenceKafkaClientAuthenticationOAuth
schema referenceJaegerTracing
schema referenceKafkaConnectTemplate
schema referenceDeploymentTemplate
schema referenceBuildConfigTemplate
schema referenceExternalConfiguration
schema referenceExternalConfigurationEnv
schema referenceExternalConfigurationEnvVarSource
schema referenceExternalConfigurationVolumeSource
schema referenceBuild
schema referenceDockerOutput
schema referenceImageStreamOutput
schema referencePlugin
schema referenceJarArtifact
schema referenceTgzArtifact
schema referenceZipArtifact
schema referenceMavenArtifact
schema referenceOtherArtifact
schema referenceKafkaConnectStatus
schema referenceConnectorPlugin
schema referenceKafkaTopic
schema referenceKafkaTopicSpec
schema referenceKafkaTopicStatus
schema referenceKafkaUser
schema referenceKafkaUserSpec
schema referenceKafkaUserTlsClientAuthentication
schema referenceKafkaUserTlsExternalClientAuthentication
schema referenceKafkaUserScramSha512ClientAuthentication
schema referencePassword
schema referencePasswordSource
schema referenceKafkaUserAuthorizationSimple
schema referenceAclRule
schema referenceAclRuleTopicResource
schema referenceAclRuleGroupResource
schema referenceAclRuleClusterResource
schema referenceAclRuleTransactionalIdResource
schema referenceKafkaUserQuotas
schema referenceKafkaUserTemplate
schema referenceKafkaUserStatus
schema referenceKafkaMirrorMaker
schema referenceKafkaMirrorMakerSpec
schema referenceKafkaMirrorMakerConsumerSpec
schema referenceKafkaMirrorMakerProducerSpec
schema referenceKafkaMirrorMakerTemplate
schema referenceKafkaMirrorMakerStatus
schema referenceKafkaBridge
schema referenceKafkaBridgeSpec
schema referenceKafkaBridgeHttpConfig
schema referenceKafkaBridgeHttpCors
schema referenceKafkaBridgeAdminClientSpec
schema referenceKafkaBridgeConsumerSpec
schema referenceKafkaBridgeProducerSpec
schema referenceKafkaBridgeTemplate
schema referenceKafkaBridgeStatus
schema referenceKafkaConnector
schema referenceKafkaConnectorSpec
schema referenceKafkaConnectorStatus
schema referenceKafkaMirrorMaker2
schema referenceKafkaMirrorMaker2Spec
schema referenceKafkaMirrorMaker2ClusterSpec
schema referenceKafkaMirrorMaker2MirrorSpec
schema referenceKafkaMirrorMaker2ConnectorSpec
schema referenceKafkaMirrorMaker2Status
schema referenceKafkaRebalance
schema referenceKafkaRebalanceSpec
schema referenceKafkaRebalanceStatus
schema referenceStrimzi simplifies the process of running Apache Kafka in a Kubernetes cluster.
This guide describes how to configure and manage a Strimzi deployment.
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.
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.
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.
Listeners are used to connect to Kafka brokers.
Strimzi provides a generic GenericKafkaListener
schema with properties to configure listeners through the Kafka
resource.
The GenericKafkaListener
provides a flexible approach to listener configuration.
You can specify properties to configure internal listeners for connecting within the Kubernetes cluster, or external listeners for connecting outside the Kubernetes cluster.
Each listener is defined as an array in the Kafka
resource.
You can configure as many listeners as required, as long as their names and ports are unique.
You might want to configure multiple external listeners, for example, to handle access from networks that require different authentication mechanisms.
Or you might need to join your Kubernetes network to an outside network.
In which case, you can configure internal listeners (using the useServiceDnsDomain
property) so that the Kubernetes service DNS domain (typically .cluster.local
) is not used.
For more information on the configuration options available for listeners,
see the GenericKafkaListener
schema reference.
You can configure listeners for secure connection using authentication. For more information, see Securing access to Kafka brokers.
You can configure external listeners for client access outside a Kubernetes environment using a specified connection mechanism, such as a loadbalancer. For more information on the configuration options for connecting an external client, see Accessing Kafka from external clients outside of the Kubernetes cluster.
You can provide your own server certificates, called Kafka listener certificates, for TLS listeners or external listeners which have TLS encryption enabled. For more information, see Kafka listener certificates.
Note
|
If you scale your Kafka cluster while using external listeners, it might trigger a rolling update of all Kafka brokers. This depends on the configuration. |
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
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.
This section describes how to configure a Kafka deployment in your Strimzi cluster. A Kafka cluster is deployed with a ZooKeeper cluster. The deployment can also include the Topic Operator and User Operator, which manage Kafka topics and users.
You configure Kafka using the Kafka
resource.
Configuration options are also available for ZooKeeper and the Entity Operator within the Kafka
resource.
The Entity Operator comprises the Topic Operator and User Operator.
The full schema of the Kafka
resource is described in the Kafka
schema reference.
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 for connecting brokers, see Listener configuration.
You can configure your Kafka cluster to allow or decline actions executed by users. For more information, see Securing access to Kafka brokers.
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 client CA certificates before their renewal period ends. You can also replace the keys used by the cluster and client CA certificates. For more information, see Renewing CA certificates manually and Replacing private keys.
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 the Kafka
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: 3.2.1 (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: "3.2"
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 the log4j.properties
key. For the Kafka kafka.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
and memory
, 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
, or for external listeners, as route
, loadbalancer
, nodeport
or ingress
.
Enables TLS encryption for each listener. Default is false
. TLS encryption is not required for route
listeners.
Defines whether the fully-qualified DNS names including the cluster service suffix (usually .cluster.local
) are assigned.
Listener authentication mechanism specified as mutual TLS, 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
or nodeport
.
Optional configuration for a Kafka listener certificate managed by an external Certificate Authority. The brokerCertChainAndKey
specifies a Secret
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
or jbod
.
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
and class
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 standard topology.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>
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. |
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 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, see image
.
resources
The resources
property configures the amount of resources allocated to the Topic Operator.
For more details about resource request and limit configuration, see resources
.
logging
The logging
property configures the logging of the Topic Operator.
For more details, see logging
.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
zookeeper:
# ...
entityOperator:
# ...
topicOperator:
watchedNamespace: my-topic-namespace
reconciliationIntervalSeconds: 60
# ...
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, see image
.
resources
The resources
property configures the amount of resources allocated to the User Operator.
For more details about resource request and limit configuration, see resources
.
logging
The logging
property configures the logging of the User Operator.
For more details, see logging
.
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 with kafka-
. So a KafkaUser named my-user
would create a Secret named kafka-my-user
.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
zookeeper:
# ...
entityOperator:
# ...
userOperator:
watchedNamespace: my-user-namespace
reconciliationIntervalSeconds: 60
# ...
As stateful applications, Kafka and ZooKeeper need to store data on disk. Strimzi supports three storage types for this data:
Ephemeral
Persistent
JBOD storage
Note
|
JBOD storage is supported only for Kafka, not for ZooKeeper. |
When configuring a Kafka
resource, you can specify the type of storage used by the Kafka broker and its corresponding ZooKeeper node. You configure the storage type using the storage
property in the following resources:
Kafka.spec.kafka
Kafka.spec.zookeeper
The storage type is configured in the type
field.
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. |
An efficient data storage infrastructure is essential to the optimal performance of Strimzi.
Block storage is required. File storage, such as NFS, does not work with Kafka.
Choose from one of the following options for your block storage:
Cloud-based block storage solutions, such as Amazon Elastic Block Store (EBS)
Storage Area Network (SAN) volumes accessed by a protocol such as Fibre Channel or iSCSI
Note
|
Strimzi does not require Kubernetes raw block volumes. |
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.
Use separate disks for Apache Kafka and ZooKeeper.
Three types of data storage are supported:
Ephemeral (Recommended for development only)
Persistent
JBOD (Just a Bunch of Disks, suitable for Kafka only)
For more information, see Kafka and ZooKeeper storage.
Solid-state drives (SSDs), though not essential, can improve the performance of Kafka in large clusters where data is sent to and received from multiple topics asynchronously. SSDs are particularly effective with ZooKeeper, which requires fast, low latency data access.
Note
|
You do not need to provision replicated storage because Kafka and ZooKeeper both have built-in data replication. |
Ephemeral storage uses emptyDir
volumes to store data.
To use ephemeral storage, set the type
field to ephemeral
.
Important
|
emptyDir volumes are not persistent and the data stored in them is lost when the pod is restarted.
After the new pod is started, it must recover all data from the other nodes of the cluster.
Ephemeral storage is not suitable for use with single-node ZooKeeper clusters or for Kafka topics with a replication factor of 1. This configuration will cause data loss.
|
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
storage:
type: ephemeral
# ...
zookeeper:
# ...
storage:
type: ephemeral
# ...
The ephemeral 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
.
Persistent storage uses Persistent Volume Claims to provision persistent volumes for storing data. Persistent Volume Claims can be used to provision volumes of many different types, depending on the Storage Class which will provision the volume. The data types which can be used with persistent volume claims include many types of SAN storage as well as Local persistent volumes.
To use persistent storage, the type
has to be set to persistent-claim
.
Persistent storage supports additional configuration options:
id
(optional)Storage identification number. This option is mandatory for storage volumes defined in a JBOD storage declaration.
Default is 0
.
size
(required)Defines the size of the persistent volume claim, for example, "1000Gi".
class
(optional)The Kubernetes Storage Class to use for dynamic volume provisioning.
selector
(optional)Allows selecting a specific persistent volume to use. It contains key:value pairs representing labels for selecting such a volume.
deleteClaim
(optional)Boolean value which specifies if the Persistent Volume Claim has to be deleted when the cluster is undeployed.
Default is false
.
Warning
|
Increasing the size of persistent volumes in an existing Strimzi cluster is only supported in Kubernetes versions that support persistent volume resizing. The persistent volume to be resized must use a storage class that supports volume expansion. For other versions of Kubernetes and storage classes which do not support volume expansion, you must decide the necessary storage size before deploying the cluster. Decreasing the size of existing persistent volumes is not possible. |
size
# ...
storage:
type: persistent-claim
size: 1000Gi
# ...
The following example demonstrates the use of a storage class.
# ...
storage:
type: persistent-claim
size: 1Gi
class: my-storage-class
# ...
Finally, a selector
can be used to select a specific labeled persistent volume to provide needed features such as an SSD.
# ...
storage:
type: persistent-claim
size: 1Gi
selector:
hdd-type: ssd
deleteClaim: true
# ...
You can specify a different storage class for one or more Kafka brokers or ZooKeeper nodes, instead of using the default storage class.
This is useful if, for example, storage classes are restricted to different availability zones or data centers.
You can use the overrides
field for this purpose.
In this example, the default storage class is named my-storage-class
:
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
labels:
app: my-cluster
name: my-cluster
namespace: myproject
spec:
# ...
kafka:
replicas: 3
storage:
deleteClaim: true
size: 100Gi
type: persistent-claim
class: my-storage-class
overrides:
- broker: 0
class: my-storage-class-zone-1a
- broker: 1
class: my-storage-class-zone-1b
- broker: 2
class: my-storage-class-zone-1c
# ...
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 will use my-storage-class-zone-1a
.
The persistent volumes of ZooKeeper node 1 will use my-storage-class-zone-1b
.
The persistent volumes of ZooKeeepr node 2 will use my-storage-class-zone-1c
.
The persistent volumes of Kafka broker 0 will use my-storage-class-zone-1a
.
The persistent volumes of Kafka broker 1 will use my-storage-class-zone-1b
.
The persistent volumes of Kafka broker 2 will use my-storage-class-zone-1c
.
The overrides
property is currently used only to override storage class configurations. Overriding other storage configuration fields is not currently supported.
Other fields from the storage configuration are currently not supported.
When persistent storage is used, it creates Persistent Volume Claims with the following names:
data-cluster-name-kafka-idx
Persistent Volume Claim for the volume used for storing data for the Kafka broker pod idx
.
data-cluster-name-zookeeper-idx
Persistent Volume Claim for the volume used for storing data for the ZooKeeper node pod idx
.
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
.
You can provision increased storage capacity by increasing the size of the persistent volumes used by an existing Strimzi cluster. Resizing persistent volumes is supported in clusters that use either a single persistent volume or multiple persistent volumes in a JBOD storage configuration.
Note
|
You can increase but not decrease the size of persistent volumes. Decreasing the size of persistent volumes is not currently supported in Kubernetes. |
A Kubernetes cluster with support for volume resizing.
The Cluster Operator is running.
A Kafka cluster using persistent volumes created using a storage class that supports volume expansion.
In a Kafka
resource, increase the size of the persistent volume allocated to the Kafka cluster, the ZooKeeper cluster, or both.
To increase the volume size allocated to the Kafka cluster, edit the spec.kafka.storage
property.
To increase the volume size allocated to the ZooKeeper cluster, edit the spec.zookeeper.storage
property.
For example, to increase the volume size from 1000Gi
to 2000Gi
:
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.
For more information about resizing persistent volumes in Kubernetes, see Resizing Persistent Volumes using Kubernetes.
You can configure Strimzi to use JBOD, a data storage configuration of multiple disks or volumes. JBOD is one approach to providing increased data storage for Kafka brokers. It can also improve performance.
A JBOD configuration is described by one or more volumes, each of which can be either ephemeral or persistent. The rules and constraints for JBOD volume declarations are the same as those for ephemeral and persistent storage. For example, you cannot decrease the size of a persistent storage volume after it has been provisioned, or you cannot change the value of sizeLimit
when type=ephemeral.
To use JBOD with Strimzi, the storage type
must be set to jbod
. The volumes
property allows you to describe the disks that make up your JBOD storage array or configuration. The following fragment shows an example JBOD configuration:
# ...
storage:
type: jbod
volumes:
- id: 0
type: persistent-claim
size: 100Gi
deleteClaim: false
- id: 1
type: persistent-claim
size: 100Gi
deleteClaim: false
# ...
The ids cannot be changed once the JBOD volumes are created.
Users can add or remove volumes from the JBOD configuration.
When persistent storage is used to declare JBOD volumes, the naming scheme of the resulting Persistent Volume Claims is as follows:
data-id-cluster-name-kafka-idx
Where id
is the ID of the volume used for storing data for Kafka broker pod idx
.
The JBOD volumes will be used by the Kafka brokers as log directories mounted into the following path:
/var/lib/kafka/data-id/kafka-log_idx_
Where id
is the ID of the volume used for storing data for Kafka broker pod idx
. For example /var/lib/kafka/data-0/kafka-log0
.
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 the Kafka
resource.
Add the new volumes to the volumes
array.
For example, add the new volume with id 2
:
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.
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 the Kafka
resource.
Remove one or more volumes from the volumes
array.
For example, remove the volumes with ids 1
and 2
:
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.
Scale Kafka clusters by adding or removing brokers. If a cluster already has topics defined, you also have to reassign partitions.
You 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.
You configure the Kafka.spec.kafka.replicas
configuration to add or reduce the number of brokers.
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.
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.
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.
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.
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 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.
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.
The steps describe a secure reassignment process that uses TLS. You’ll need a Kafka cluster that uses TLS encryption and authentication.
You have a running Cluster Operator.
You have a running Kafka cluster based on a Kafka
resource configured with internal TLS authentication and encryption.
apiVersion: 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.
my-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.
my-topic
and my-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:
- resource:
type: topic
name: my-topic
patternType: literal
operation: Write
host: "*"
- resource:
type: topic
name: my-topic
patternType: literal
operation: Create
host: "*"
- resource:
type: topic
name: my-topic
patternType: literal
operation: Describe
host: "*"
- resource:
type: cluster
name: my-cluster
patternType: literal
operation: Alter
host: "*"
# ...
# ...
User authentication mechanism defined as mutual tls
.
Simple authorization and accompanying list of ACL rules.
Note
|
Permission for a Describe operation is required as a minimum for TLS access to a topic.
|
Extract the cluster CA certificate and password from the <cluster_name>-cluster-ca-cert
Secret of the Kafka cluster.
kubectl get secret <cluster_name>-cluster-ca-cert -o jsonpath='{.data.ca\.p12}' | base64 -d > ca.p12
kubectl get secret <cluster_name>-cluster-ca-cert -o jsonpath='{.data.ca\.password}' | base64 -d > ca.password
Replace <cluster_name> with the name of the Kafka cluster.
When you deploy Kafka using the Kafka
resource, a Secret with the cluster CA certificate is created with the Kafka cluster name (<cluster_name>-cluster-ca-cert
).
For example, my-cluster-cluster-ca-cert
.
Run a new interactive pod container using the Strimzi Kafka image to connect to a running Kafka broker.
kubectl run --restart=Never --image=quay.io/strimzi/kafka:0.31.0-kafka-3.2.1 <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.
topic-a
and topic-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.
topic-a
and topic-b
to brokers 0
, 1
and 2
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
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 authentication.
You have a running Kafka cluster based on a Kafka
resource configured with internal TLS authentication and encryption.
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.
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 authentication.
You have a running Kafka cluster based on a Kafka
resource configured with internal TLS authentication and encryption.
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 your Kafka
resource to reduce the number of brokers.
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.
Strimzi supports using JmxTrans to read JMX metrics from Kafka brokers.
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.
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.
|
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:
# ...
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:
# ...
Maintenance time windows allow you to schedule certain rolling updates of your Kafka and ZooKeeper clusters to start at a convenient time.
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.
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.
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 the Kafka
resource.
For example to allow maintenance between 0800 and 1059 and between 1400 and 1559 you would set the maintenanceTimeWindows
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:
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.
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.
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.
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 is 9094
.
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 is 9094
.
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 is external
and port is 9094
.
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 is external
and port is 9094
.
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 pod idx
. 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.
This section describes how to configure a Kafka Connect deployment in your Strimzi cluster.
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.
The full schema of the KafkaConnect
resource is described in KafkaConnect
schema reference.
For more information on deploying connector plugins, see Extending Kafka Connect with connector plugins.
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 the Kafka Connect REST API, or use KafkaConnector
custom resources.
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 Creating and managing 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 the KafkaConnect
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/1.3.1.Final/debezium-connector-postgres-1.3.1.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/0.7.0/camel-telegram-kafka-connector-0.7.0-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, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-256/SCRAM-SHA-512 or PLAIN mechanism. 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
and memory
, 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 the log4j.properties
or log4j2.properties
key. For the Kafka Connect log4j.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 standard topology.kubernetes.io/zone
label. To consume from the closest replica, enable the RackAwareReplicaSelector
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 also set for distributed tracing using Jaeger.
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.
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.
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 the KafkaUser
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 |
---|---|
|
|
|
|
|
|
|
|
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
operation: Write
host: "*"
- resource:
type: topic
name: connect-cluster-offsets
patternType: literal
operation: Create
host: "*"
- resource:
type: topic
name: connect-cluster-offsets
patternType: literal
operation: Describe
host: "*"
- resource:
type: topic
name: connect-cluster-offsets
patternType: literal
operation: Read
host: "*"
# access to status.storage.topic
- resource:
type: topic
name: connect-cluster-status
patternType: literal
operation: Write
host: "*"
- resource:
type: topic
name: connect-cluster-status
patternType: literal
operation: Create
host: "*"
- resource:
type: topic
name: connect-cluster-status
patternType: literal
operation: Describe
host: "*"
- resource:
type: topic
name: connect-cluster-status
patternType: literal
operation: Read
host: "*"
# access to config.storage.topic
- resource:
type: topic
name: connect-cluster-configs
patternType: literal
operation: Write
host: "*"
- resource:
type: topic
name: connect-cluster-configs
patternType: literal
operation: Create
host: "*"
- resource:
type: topic
name: connect-cluster-configs
patternType: literal
operation: Describe
host: "*"
- resource:
type: topic
name: connect-cluster-configs
patternType: literal
operation: Read
host: "*"
# consumer group
- resource:
type: group
name: connect-cluster
patternType: literal
operation: Read
host: "*"
Create or update the resource.
kubectl apply -f KAFKA-USER-CONFIG-FILE
The following resources are created by the Cluster Operator in the Kubernetes cluster:
Deployment which is in charge to create the Kafka Connect worker node pods.
Service which exposes the REST interface for managing the Kafka Connect cluster.
ConfigMap which contains the Kafka Connect ancillary configuration and is mounted as a volume by the Kafka broker pods.
Pod Disruption Budget configured for the Kafka Connect worker nodes.
This chapter describes how to configure a Kafka MirrorMaker deployment in your Strimzi cluster to replicate data between Kafka clusters.
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.
If you are using MirrorMaker, you configure the KafkaMirrorMaker
resource.
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 .
|
The following procedure shows how the resource is configured:
The full schema of the KafkaMirrorMaker
resource is described in the KafkaMirrorMaker schema reference.
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 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 the KafkaMirrorMaker
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, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-256/SCRAM-SHA-512 or PLAIN mechanism.
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 to true
, 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
and memory
, 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 the log4j.properties
or log4j2.properties
key. MirrorMaker has a single logger called mirrormaker.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 also set for distributed tracing using Jaeger.
Warning
|
With the abortOnSendFailure property set to false , 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>
The following resources are created by the Cluster Operator in the Kubernetes cluster:
Deployment which is responsible for creating the Kafka MirrorMaker pods.
ConfigMap which contains ancillary configuration for the Kafka MirrorMaker, and is mounted as a volume by the Kafka broker pods.
Pod Disruption Budget configured for the Kafka MirrorMaker worker nodes.
This section describes how to configure a Kafka MirrorMaker 2.0 deployment in your Strimzi cluster.
MirrorMaker 2.0 is used to replicate data between two or more active Kafka clusters, within or across data centers.
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
If you are using MirrorMaker 2.0, you configure the KafkaMirrorMaker2
resource.
The full schema of the KafkaMirrorMaker2
resource is described in the KafkaMirrorMaker2 schema reference.
MirrorMaker 2.0 introduces an entirely new way of replicating data between clusters.
As a result, the 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.
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.
MirrorMaker 2.0 uses the following connectors:
MirrorSourceConnector
The source connector replicates topics from a source cluster to a target cluster.
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.
The process of mirroring data from one cluster to another cluster is asynchronous. The recommended pattern is for messages to be produced locally alongside the source Kafka cluster, then consumed remotely close to the target Kafka cluster.
MirrorMaker 2.0 can be used with more than one source cluster.
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.
However, increasing the frequency of the operation might affect overall performance.
You can use MirrorMaker 2.0 in active/passive or active/active cluster configurations.
In an active/active configuration, both clusters are active and provide the same data simultaneously, which is useful if you want to make the same data available locally in different geographical locations.
In an active/passive configuration, the data from an active cluster is replicated in a passive cluster, which remains on standby, for example, 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.
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.
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 is automatically synchronized between source and target clusters. By synchronizing configuration properties, the need for rebalancing is reduced.
MirrorMaker 2.0 monitors source topics and propagates any configuration changes to remote topics, checking for and creating missing partitions. Only MirrorMaker 2.0 can write to remote topics.
MirrorMaker 2.0 tracks offsets for consumer groups using internal topics.
The offset-syncs
topic maps the source and target offsets for replicated topic partitions from record metadata
The checkpoints
topic maps the last committed offset in the source and target cluster for replicated topic partitions in each consumer group
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.
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.
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
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 |
---|---|---|---|
|
✓ |
✓ |
✓ |
|
✓ |
✓ |
✓ |
|
✓ |
✓ |
✓ |
|
✓ |
✓ |
|
|
✓ |
✓ |
|
|
✓ |
✓ |
|
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
||
|
✓ |
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: 3.2.1
# ...
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
# ...
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.
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
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. |
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 TLS 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: 3.2.1
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 TLS or SASL authentication. This procedure shows a configuration that uses TLS encryption and 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 the KafkaMirrorMaker2
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: 3.2.1 (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)
config:
replication.factor: 1 (21)
offset-syncs.topic.replication.factor: 1 (22)
sync.topic.acls.enabled: "false" (23)
refresh.topics.interval.seconds: 60 (24)
replication.policy.separator: "" (25)
replication.policy.class: "org.apache.kafka.connect.mirror.IdentityReplicationPolicy" (26)
heartbeatConnector: (27)
config:
heartbeats.topic.replication.factor: 1 (28)
checkpointConnector: (29)
config:
checkpoints.topic.replication.factor: 1 (30)
refresh.groups.interval.seconds: 600 (31)
sync.group.offsets.enabled: true (32)
sync.group.offsets.interval.seconds: 60 (33)
emit.checkpoints.interval.seconds: 60 (34)
replication.policy.class: "org.apache.kafka.connect.mirror.IdentityReplicationPolicy"
topicsPattern: "topic1|topic2|topic3" (35)
groupsPattern: "group1|group2|group3" (36)
resources: (37)
requests:
cpu: "1"
memory: 2Gi
limits:
cpu: "2"
memory: 2Gi
logging: (38)
type: inline
loggers:
connect.root.logger.level: "INFO"
readinessProbe: (39)
initialDelaySeconds: 15
timeoutSeconds: 5
livenessProbe:
initialDelaySeconds: 15
timeoutSeconds: 5
jvmOptions: (40)
"-Xmx": "1g"
"-Xms": "1g"
image: my-org/my-image:latest (41)
rack:
topologyKey: topology.kubernetes.io/zone (42)
template: (43)
pod:
affinity:
podAntiAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
- labelSelector:
matchExpressions:
- key: application
operator: In
values:
- postgresql
- mongodb
topologyKey: "kubernetes.io/hostname"
connectContainer: (44)
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 (45)
externalConfiguration: (46)
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, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-256/SCRAM-SHA-512 or PLAIN mechanism.
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. The config
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.
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 to false
.
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. The config
overrides the default configuration options.
Replication factor for the heartbeat topic created at the target cluster.
Configuration for the MirrorCheckpointConnector
that tracks offsets. The config
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
and memory
, 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 the log4j.properties
or log4j2.properties
key. For the Kafka Connect log4j.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 standard topology.kubernetes.io/zone
label. To consume from the closest replica, enable the RackAwareReplicaSelector
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 also set for distributed tracing using 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
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 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 TLS 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 authentication.
Kafka simple
authorization is enabled.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-source-cluster
spec:
kafka:
version: 3.2.1
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: "3.2"
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: {}
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-target-cluster
spec:
kafka:
version: 3.2.1
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: "3.2"
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 simple
authorization type in the Kafka
configuration for the source Kafka cluster,
use the same in the KafkaUser
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.
apiVersion: 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
operation: Create
- resource: # Not needed if offset-syncs.topic.location=target
type: topic
name: mm2-offset-syncs.my-target-cluster.internal
operation: DescribeConfigs
- resource: # Not needed if offset-syncs.topic.location=target
type: topic
name: mm2-offset-syncs.my-target-cluster.internal
operation: Write
- resource: # Needed for every topic which is mirrored
type: topic
name: "*"
operation: Read
- resource: # Needed for every topic which is mirrored
type: topic
name: "*"
operation: DescribeConfigs
# MirrorCheckpointConnector
- resource:
type: cluster
operation: Describe
- resource: # Needed for every group for which offsets are synced
type: group
name: "*"
operation: Describe
- resource: # Not needed if offset-syncs.topic.location=target
type: topic
name: mm2-offset-syncs.my-target-cluster.internal
operation: Read
apiVersion: 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
operation: Read
- resource:
type: topic
name: mirrormaker2-cluster-configs
operation: Read
- resource:
type: topic
name: mirrormaker2-cluster-configs
operation: Describe
- resource:
type: topic
name: mirrormaker2-cluster-configs
operation: DescribeConfigs
- resource:
type: topic
name: mirrormaker2-cluster-configs
operation: Write
- resource:
type: topic
name: mirrormaker2-cluster-configs
operation: Create
- resource:
type: topic
name: mirrormaker2-cluster-status
operation: Read
- resource:
type: topic
name: mirrormaker2-cluster-status
operation: Describe
- resource:
type: topic
name: mirrormaker2-cluster-status
operation: DescribeConfigs
- resource:
type: topic
name: mirrormaker2-cluster-status
operation: Write
- resource:
type: topic
name: mirrormaker2-cluster-status
operation: Create
- resource:
type: topic
name: mirrormaker2-cluster-offsets
operation: Read
- resource:
type: topic
name: mirrormaker2-cluster-offsets
operation: Write
- resource:
type: topic
name: mirrormaker2-cluster-offsets
operation: Describe
- resource:
type: topic
name: mirrormaker2-cluster-offsets
operation: DescribeConfigs
- resource:
type: topic
name: mirrormaker2-cluster-offsets
operation: Create
# MirrorSourceConnector
- resource: # Needed for every topic which is mirrored
type: topic
name: "*"
operation: Create
- resource: # Needed for every topic which is mirrored
type: topic
name: "*"
operation: Alter
- resource: # Needed for every topic which is mirrored
type: topic
name: "*"
operation: AlterConfigs
- resource: # Needed for every topic which is mirrored
type: topic
name: "*"
operation: Write
# MirrorCheckpointConnector
- resource:
type: cluster
operation: Describe
- resource:
type: topic
name: my-source-cluster.checkpoints.internal
operation: Create
- resource:
type: topic
name: my-source-cluster.checkpoints.internal
operation: Describe
- resource:
type: topic
name: my-source-cluster.checkpoints.internal
operation: Write
- resource: # Needed for every group for which the offset is synced
type: group
name: "*"
operation: Read
- resource: # Needed for every group for which the offset is synced
type: group
name: "*"
operation: Describe
- resource: # Needed for every topic which is mirrored
type: topic
name: "*"
operation: Read
# MirrorHeartbeatConnector
- resource:
type: topic
name: heartbeats
operation: Create
- resource:
type: topic
name: heartbeats
operation: Describe
- resource:
type: topic
name: heartbeats
operation: Write
Note
|
You can use a certificate issued outside the User Operator by setting type to tls-external .
For more information, see User authentication.
|
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 TLS client authentication.
The public key is contained in a user certificate, which is signed by the client Certificate Authority (CA).
Configure a KafkaMirrorMaker2
resource with the authentication details to connect to the source and target Kafka clusters.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
name: my-mirror-maker-2
spec:
version: 3.2.1
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>
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 named my-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.
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 named my-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.
This section describes how to configure a Kafka Bridge deployment in your Strimzi cluster.
Kafka Bridge provides an API for integrating HTTP-based clients with a Kafka cluster.
If you are using the Kafka Bridge, you configure the KafkaBridge
resource.
The full schema of the KafkaBridge
resource is described in KafkaBridge
schema reference.
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 the KafkaBridge
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, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-256/SCRAM-SHA-512 or PLAIN mechanism. 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
and memory
, 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 the log4j.properties
or log4j2.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 also set for distributed tracing using Jaeger.
Create or update the resource:
kubectl apply -f KAFKA-BRIDGE-CONFIG-FILE
The following resources are created by the Cluster Operator in the Kubernetes cluster:
Deployment which is in charge to create the Kafka Bridge worker node pods.
Service which exposes the REST interface of the Kafka Bridge cluster.
ConfigMap which contains the Kafka Bridge ancillary configuration and is mounted as a volume by the Kafka broker pods.
Pod Disruption Budget configured for the Kafka Bridge worker nodes.
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
# ...
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.
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
# ...
# ...
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.
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
Note
|
On Kubernetes 1.16 and 1.17, the support for topologySpreadConstraint is disabled by default.
In order to use topologySpreadConstraint , you have to enable the EvenPodsSpread feature gate in Kubernetes API server and scheduler.
|
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.
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 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.
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 the strimzi.io/name
label.
Set the topologyKey
to kubernetes.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>
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.
The topologyKey
should be set to kubernetes.io/hostname
to specify that the selected pods should not be scheduled on nodes with the same hostname.
For example:
apiVersion: kafka.strimzi.io/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>
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>
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
and tolerations
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>
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.
For more information on configuring logging for specific Kafka components or operators, see the following sections.
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 the logging.valueFrom.configMapKeyRef.name
to the name of the ConfigMap and logging.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>
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 the logging.valueFrom.configMapKeyRef.name
to the name of the ConfigMap and logging.valueFrom.configMapKeyRef.key
to the key in this ConfigMap.
For the Topic Operator, logging is specified in the topicOperator
configuration of the Kafka
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
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.
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 new Secret
or ConfigMap
object.
This capability avoids disruption when a Kafka Connect instance hosts multiple connectors.
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.
|
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
or Secret
that contains the configuration properties.
In this example, a ConfigMap
object named my-connector-configuration
contains connector properties:
ConfigMap
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.
apiVersion: 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 form config.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.
apiVersion: 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.
apiVersion: 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.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnector
metadata:
name: my-connector
labels:
strimzi.io/cluster: my-connect
spec:
# ...
config:
option: ${configmaps:my-project/my-connector-configuration:option1}
# ...
# ...
Placeholders for the property values in the config map are referenced in the connector configuration.
The placeholder structure is configmaps:<path_and_file_name>:<property>
.
KubernetesConfigMapConfigProvider
reads and extracts the option1 property value from the external config map.
This procedure shows how to use the Environment Variables Configuration Provider plugin.
In the procedure, environment variables provide configuration properties for a connector. A database password is specified as an environment variable.
A Kubernetes cluster is available.
A Kafka cluster is running.
The Cluster Operator is running.
Specify the Environment Variables Configuration Provider in the Kafka Connect configuration.
Define environment variables using the externalConfiguration
property.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
name: my-connect
annotations:
strimzi.io/use-connector-resources: "true"
spec:
# ...
config:
# ...
config.providers: env # (1)
config.providers.env.class: io.strimzi.kafka.EnvVarConfigProvider # (2)
# ...
externalConfiguration:
env:
- name: DB_PASSWORD # (3)
valueFrom:
secretKeyRef:
name: db-creds # (4)
key: dbPassword # (5)
# ...
The alias for the configuration provider is used to define other configuration parameters.
The provider parameters use the alias from config.providers
, taking the form config.providers.${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.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnector
metadata:
name: my-connector
labels:
strimzi.io/cluster: my-connect
spec:
# ...
config:
option: ${env:DB_PASSWORD}
# ...
# ...
Use an external listener to expose your Strimzi Kafka cluster to a client outside a Kubernetes environment.
Specify the connection type
to expose Kafka in the external listener configuration.
nodeport
uses NodePort
type Services
loadbalancer
uses Loadbalancer
type Services
ingress
uses Kubernetes Ingress
and the NGINX Ingress Controller for Kubernetes
route
uses OpenShift Routes
and the HAProxy router
For more information on listener configuration, see GenericKafkaListener
schema reference.
If you want to know more about the pros and cons of each connection type, refer to Accessing Apache Kafka in Strimzi.
Note
|
route is only supported on OpenShift
|
This procedure describes how to access a Strimzi Kafka cluster from an external client using node ports.
To connect to a broker, you need a hostname and port number for the Kafka bootstrap address, as well as the certificate used for authentication.
A Kubernetes cluster
A running Cluster Operator
Configure a Kafka
resource with an external listener set to the nodeport
type.
For example:
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
kafka:
# ...
listeners:
- name: external
port: 9094
type: nodeport
tls: true
authentication:
type: tls
# ...
# ...
zookeeper:
# ...
Create or update the resource.
kubectl apply -f <kafka_configuration_file>
NodePort
type services are created for each Kafka broker, as well as an external bootstrap service.
The bootstrap service routes external traffic to the Kafka brokers.
Node addresses used for connection are propagated to the status
of the Kafka custom resource.
The cluster CA certificate to verify the identity of the kafka brokers is also created in the secret <cluster_name>-cluster-ca-cert
.
Retrieve the bootstrap address you can use to access the Kafka cluster from the status of the Kafka
resource.
kubectl get kafka <kafka_cluster_name> -o=jsonpath='{.status.listeners[?(@.name=="<listener_name>")].bootstrapServers}{"\n"}'
For example:
kubectl get kafka my-cluster -o=jsonpath='{.status.listeners[?(@.name=="external")].bootstrapServers}{"\n"}'
If TLS encryption is enabled, extract the public certificate of the broker certification authority.
kubectl get secret KAFKA-CLUSTER-NAME-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
Use the extracted certificate in your Kafka client to configure TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication.
This procedure describes how to access a Strimzi Kafka cluster from an external client using loadbalancers.
To connect to a broker, you need the address of the bootstrap loadbalancer, as well as the certificate used for TLS encryption.
A Kubernetes cluster
A running Cluster Operator
Configure a Kafka
resource with an external listener set to the loadbalancer
type.
For example:
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
kafka:
# ...
listeners:
- name: external
port: 9094
type: loadbalancer
tls: true
# ...
# ...
zookeeper:
# ...
Create or update the resource.
kubectl apply -f <kafka_configuration_file>
loadbalancer
type services and loadbalancers are created for each Kafka broker, as well as an external bootstrap service.
The bootstrap service routes external traffic to all Kafka brokers.
DNS names and IP addresses used for connection are propagated to the status
of each service.
The cluster CA certificate to verify the identity of the kafka brokers is also created in the secret <cluster_name>-cluster-ca-cert
.
Retrieve the address of the bootstrap service you can use to access the Kafka cluster from the status of the Kafka
resource.
kubectl get kafka <kafka_cluster_name> -o=jsonpath='{.status.listeners[?(@.name=="<listener_name>")].bootstrapServers}{"\n"}'
For example:
kubectl get kafka my-cluster -o=jsonpath='{.status.listeners[?(@.name=="external")].bootstrapServers}{"\n"}'
If TLS encryption is enabled, extract the public certificate of the broker certification authority.
kubectl get secret KAFKA-CLUSTER-NAME-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
Use the extracted certificate in your Kafka client to configure TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication.
This procedure shows how to access a Strimzi Kafka cluster from an external client outside of Kubernetes using Nginx Ingress.
To connect to a broker, you need a hostname (advertised address) for the Ingress bootstrap address, as well as the certificate used for authentication.
For access using Ingress, the port is always 443.
Kafka uses a binary protocol over TCP, but the NGINX Ingress Controller for Kubernetes is designed to work with the HTTP protocol. To be able to pass the Kafka connections through the Ingress, Strimzi uses the TLS passthrough feature of the NGINX Ingress Controller for Kubernetes. Ensure TLS passthrough is enabled in your NGINX Ingress Controller for Kubernetes deployment.
Because it is using the TLS passthrough functionality, TLS encryption cannot be disabled when exposing Kafka using Ingress
.
For more information about enabling TLS passthrough, see TLS passthrough documentation.
Kubernetes cluster
Deployed NGINX Ingress Controller for Kubernetes with TLS passthrough enabled
A running Cluster Operator
Configure a Kafka
resource with an external listener set to the ingress
type.
Specify the Ingress hosts for the bootstrap service and Kafka brokers.
For example:
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
kafka:
# ...
listeners:
- name: external
port: 9094
type: ingress
tls: true
authentication:
type: tls
configuration: (1)
bootstrap:
host: bootstrap.myingress.com
brokers:
- broker: 0
host: broker-0.myingress.com
- broker: 1
host: broker-1.myingress.com
- broker: 2
host: broker-2.myingress.com
# ...
zookeeper:
# ...
Ingress hosts for the bootstrap service and Kafka brokers.
Create or update the resource.
kubectl apply -f <kafka_configuration_file>
ClusterIP
type services are created for each Kafka broker, as well as an additional bootstrap service.
These services are used by the Ingress controller to route traffic to the Kafka brokers.
An Ingress
resource is also created for each service to expose them using the Ingress controller.
The Ingress hosts are propagated to the status
of each service.
The cluster CA certificate to verify the identity of the kafka brokers is also created in the secret <cluster_name>-cluster-ca-cert
.
Use the address for the bootstrap host you specified in the configuration
and port 443 (BOOTSTRAP-HOST:443) in your Kafka client as the bootstrap address to connect to the Kafka cluster.
Extract the public certificate of the broker certificate authority.
kubectl get secret KAFKA-CLUSTER-NAME-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
Use the extracted certificate in your Kafka client to configure the TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication.
This procedure describes how to access a Strimzi Kafka cluster from an external client outside of OpenShift using routes.
To connect to a broker, you need a hostname for the route bootstrap address, as well as the certificate used for TLS encryption.
For access using routes, the port is always 443.
An OpenShift cluster
A running Cluster Operator
Configure a Kafka
resource with an external listener set to the route
type.
For example:
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
labels:
app: my-cluster
name: my-cluster
namespace: myproject
spec:
kafka:
# ...
listeners:
- name: listener1
port: 9094
type: route
tls: true
# ...
# ...
zookeeper:
# ...
Warning
|
An OpenShift Route address comprises the name of the Kafka cluster, the name of the listener, and the name of the namespace it is created in.
For example, my-cluster-kafka-listener1-bootstrap-myproject (CLUSTER-NAME-kafka-LISTENER-NAME-bootstrap-NAMESPACE). Be careful that the whole length of the address does not exceed a maximum limit of 63 characters.
|
Create or update the resource.
kubectl apply -f <kafka_configuration_file>
ClusterIP
type services are created for each Kafka broker, as well as an external bootstrap service.
The services route the traffic from the OpenShift Routes to the Kafka brokers.
An OpenShift Route
resource is also created for each service to expose them using the HAProxy load balancer.
DNS addresses used for connection are propagated to the status
of each service.
The cluster CA certificate to verify the identity of the kafka brokers is also created in the secret <cluster_name>-cluster-ca-cert
.
Retrieve the address of the bootstrap service you can use to access the Kafka cluster from the status of the Kafka
resource.
kubectl get kafka <kafka_cluster_name> -o=jsonpath='{.status.listeners[?(@.name=="<listener_name>")].bootstrapServers}{"\n"}'
For example:
kubectl get kafka my-cluster -o=jsonpath='{.status.listeners[?(@.name=="listener1")].bootstrapServers}{"\n"}'
Extract the public certificate of the broker certification authority.
kubectl get secret KAFKA-CLUSTER-NAME-cluster-ca-cert -o jsonpath='{.data.ca\.crt}' | base64 -d > ca.crt
Use the extracted certificate in your Kafka client to configure TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication.
You can secure your Kafka cluster by managing the access each client has to the Kafka brokers.
A secure connection between Kafka brokers and clients can encompass:
Encryption for data exchange
Authentication to prove identity
Authorization to allow or decline actions executed by users
This chapter explains how to set up secure connections between Kafka brokers and clients, with sections describing:
Security options for Kafka clusters and clients
How to secure Kafka brokers
How to use an authorization server for OAuth 2.0 token-based authentication and authorization
Use the Kafka
resource to configure the mechanisms used for Kafka authentication and authorization.
For clients inside the Kubernetes cluster, you can create plain
(without encryption) or tls
internal listeners.
For clients outside the Kubernetes cluster, you create external listeners and specify a connection mechanism,
which can be nodeport
, loadbalancer
, ingress
or route
(on OpenShift).
For more information on the configuration options for connecting an external client, see Accessing Kafka outside of the Kubernetes cluster.
Supported authentication options:
Mutual TLS authentication (only on the listeners with TLS enabled encryption)
SCRAM-SHA-512 authentication
The authentication option you choose depends on how you wish to authenticate client access to Kafka brokers.
Note
|
Try exploring the standard authentication options before using custom authentication. Custom authentication allows for any type of kafka-supported authentication. It can provide more flexibility, but also adds complexity. |
The listener authentication
property is used to specify an authentication mechanism specific to that listener.
If no authentication
property is specified then the listener does not authenticate clients which connect through that listener.
The listener will accept all connections without authentication.
Authentication must be configured when using the User Operator to manage KafkaUsers
.
The following example shows:
A plain
listener configured for SCRAM-SHA-512 authentication
A tls
listener with mutual TLS authentication
An external
listener with mutual TLS authentication
Each listener is configured with a unique name and port within a Kafka cluster.
Note
|
Listeners cannot be configured to use the ports reserved for inter-broker communication (9091 or 9090) and metrics (9404). |
# ...
listeners:
- name: plain
port: 9092
type: internal
tls: true
authentication:
type: scram-sha-512
- name: tls
port: 9093
type: internal
tls: true
authentication:
type: tls
- name: external
port: 9094
type: loadbalancer
tls: true
authentication:
type: tls
# ...
Mutual TLS authentication is always used for the communication between Kafka brokers and ZooKeeper pods.
Strimzi can configure Kafka to use TLS (Transport Layer Security) to provide encrypted communication between Kafka brokers and clients either with or without mutual authentication. For mutual, or two-way, authentication, both the server and the client present certificates. When you configure mutual authentication, the broker authenticates the client (client authentication) and the client authenticates the broker (server authentication).
Note
|
TLS authentication is more commonly one-way, with one party authenticating the identity of another. For example, when HTTPS is used between a web browser and a web server, the browser obtains proof of the identity of the web server. |
SCRAM (Salted Challenge Response Authentication Mechanism) is an authentication protocol that can establish mutual authentication using passwords. Strimzi can configure Kafka to use SASL (Simple Authentication and Security Layer) SCRAM-SHA-512 to provide authentication on both unencrypted and encrypted client connections.
When SCRAM-SHA-512 authentication is used with a TLS client connection, the TLS protocol provides the encryption, but is not used for authentication.
The following properties of SCRAM make it safe to use SCRAM-SHA-512 even on unencrypted connections:
The passwords are not sent in the clear over the communication channel. Instead the client and the server are each challenged by the other to offer proof that they know the password of the authenticating user.
The server and client each generate a new challenge for each authentication exchange. This means that the exchange is resilient against replay attacks.
When a KafkaUser.spec.authentication.type
is configured with scram-sha-512
the User Operator will generate a random 12-character password consisting of upper and lowercase ASCII letters and numbers.
By default, Strimzi automatically creates a NetworkPolicy
resource for every listener that is enabled on a Kafka broker.
This NetworkPolicy
allows applications to connect to listeners in all namespaces.
Use network policies as part of the listener configuration.
If you want to restrict access to a listener at the network level to only selected applications or namespaces, use the networkPolicyPeers
property.
Each listener can have a different networkPolicyPeers
configuration.
For more information on network policy peers, refer to the NetworkPolicyPeer API reference.
If you want to use custom network policies, you can set the STRIMZI_NETWORK_POLICY_GENERATION
environment variable to false
in the Cluster Operator configuration.
For more information, see Cluster Operator configuration.
Note
|
Your configuration of Kubernetes must support ingress NetworkPolicies in order to use network policies in Strimzi.
|
You can use the properties of the GenericKafkaListenerConfiguration schema to add further configuration to listeners.
You can configure authorization for Kafka brokers using the authorization
property in the Kafka.spec.kafka
resource.
If the authorization
property is missing, no authorization is enabled and clients have no restrictions.
When enabled, authorization is applied to all enabled listeners.
The authorization method is defined in the type
field.
Supported authorization options:
OAuth 2.0 authorization (if you are using OAuth 2.0 token based authentication)
Super users can access all resources in your Kafka cluster regardless of any access restrictions, and are supported by all authorization mechanisms.
To designate super users for a Kafka cluster, add a list of user principals to the superUsers
property.
If a user uses TLS client authentication, their username is the common name from their certificate subject prefixed with CN=
.
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
# ...
Use the KafkaUser
resource to configure the authentication mechanism, authorization mechanism, and access rights for Kafka clients.
In terms of configuring security, clients are represented as users.
You can authenticate and authorize user access to Kafka brokers. Authentication permits access, and authorization constrains the access to permissible actions.
You can also create super users that have unconstrained access to Kafka brokers.
The authentication and authorization mechanisms must match the specification for the listener used to access the Kafka brokers.
For more information on configuring a KafkaUser
resource to access Kafka brokers securely, see the following sections:
A KafkaUser
resource includes a label that defines the appropriate name of the Kafka cluster (derived from the name of the Kafka
resource) to which it belongs.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
name: my-user
labels:
strimzi.io/cluster: my-cluster
The label is used by the User Operator to identify the KafkaUser
resource and create a new user, and also in subsequent handling of the user.
If the label does not match the Kafka cluster, the User Operator cannot identify the KafkaUser
and the user is not created.
If the status of the KafkaUser
resource remains empty, check your label.
User authentication is configured using the authentication
property in KafkaUser.spec
.
The authentication mechanism enabled for the user is specified using the type
field.
Supported authentication types:
tls
for TLS client authentication
tls-external
for TLS client authentication using external certificates
scram-sha-512
for SCRAM-SHA-512 authentication
If tls
or scram-sha-512
is specified, the User Operator creates authentication credentials when it creates the user.
If tls-external
is specified, the user still uses TLS client authentication, but no authentication credentials are created.
Use this option when you’re providing your own certificates.
When no authentication type is specified, the User Operator does not create the user or its credentials.
You can use tls-external
to authenticate with TLS client authentication using a certificate issued outside the User Operator.
The User Operator does not generate a TLS certificate or a secret.
You can still manage ACL rules and quotas through the User Operator in the same way as when you’re using the tls
mechanism.
This means that you use the CN=USER-NAME
format when specifying ACL rules and quotas.
USER-NAME is the common name given in a TLS certificate.
To use TLS client authentication, you set the type
field in the KafkaUser
resource to tls
.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
name: my-user
labels:
strimzi.io/cluster: my-cluster
spec:
authentication:
type: tls
# ...
When the user is created by the User Operator, it creates a new secret with the same name as the KafkaUser
resource.
The secret contains a private and public key for TLS client authentication.
The public key is contained in a user certificate, which is signed by the client Certificate Authority (CA).
If you are using the clients CA generated by the Cluster Operator, the user certificates generated by the User Operator are also renewed when the client CA is renewed by the Cluster Operator.
All keys are in X.509 format.
Secrets provide private keys and certificates in PEM and PKCS #12 formats.
For more information on securing Kafka communication with secrets, see Managing TLS certificates.
apiVersion: v1
kind: Secret
metadata:
name: my-user
labels:
strimzi.io/kind: KafkaUser
strimzi.io/cluster: my-cluster
type: Opaque
data:
ca.crt: # Public key of the client CA
user.crt: # User certificate that contains the public key of the user
user.key: # Private key of the user
user.p12: # PKCS #12 archive file for storing certificates and keys
user.password: # Password for protecting the PKCS #12 archive file
To use TLS client authentication using a certificate issued outside the User Operator, you set the type
field in the KafkaUser
resource to tls-external
.
A secret and credentials are not created for the user.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
name: my-user
labels:
strimzi.io/cluster: my-cluster
spec:
authentication:
type: tls-external
# ...
To use the SCRAM-SHA-512 authentication mechanism, you set the type
field in the KafkaUser
resource to scram-sha-512
.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
name: my-user
labels:
strimzi.io/cluster: my-cluster
spec:
authentication:
type: scram-sha-512
# ...
When the user is created by the User Operator, it creates a new secret with the same name as the KafkaUser
resource.
The secret contains the generated password in the password
key, which is encoded with base64.
In order to use the password, it must be decoded.
apiVersion: v1
kind: Secret
metadata:
name: my-user
labels:
strimzi.io/kind: KafkaUser
strimzi.io/cluster: my-cluster
type: Opaque
data:
password: Z2VuZXJhdGVkcGFzc3dvcmQ= (1)
sasl.jaas.config: b3JnLmFwYWNoZS5rYWZrYS5jb21tb24uc2VjdXJpdHkuc2NyYW0uU2NyYW1Mb2dpbk1vZHVsZSByZXF1aXJlZCB1c2VybmFtZT0ibXktdXNlciIgcGFzc3dvcmQ9ImdlbmVyYXRlZHBhc3N3b3JkIjsK (2)
The generated password, base64 encoded.
The JAAS configuration string for SASL SCRAM-SHA-512 authentication, base64 encoded.
Decoding the generated password:
echo "Z2VuZXJhdGVkcGFzc3dvcmQ=" | base64 --decode
When a user is created, Strimzi generates a random password.
You can use your own password instead of the one generated by Strimzi. To do so, create a secret with the password and reference it in the KafkaUser
resource.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
name: my-user
labels:
strimzi.io/cluster: my-cluster
spec:
authentication:
type: scram-sha-512
password:
valueFrom:
secretKeyRef:
name: my-secret (1)
key: my-password (2)
# ...
The name of the secret containing the predefined password.
The key for the password stored inside the secret.
User authorization is configured using the authorization
property in KafkaUser.spec
.
The authorization type enabled for a user is specified using the type
field.
To use simple authorization, you set the type
property to simple
in KafkaUser.spec.authorization
.
The simple authorization will use the Kafka Admin API to manage the ACL rules inside your Kafka cluster.
Whether ACL management in the User Operator is enabled or not depends on your authorization configuration in the Kafka cluster.
For simple authorization, ACL management is always enabled.
For OPA authorization, ACL management is always disabled. Authorization rules are configured in the OPA server.
For Keycloak authorization, you can manage the ACL rules directly in Keycloak. You can also delegate authorization to the simple authorizer as a fallback option in the configuration. When delegation to the simple authorizer is enabled, the User Operator will enable management of ACL rules as well.
For custom authorization using a custom authorization plugin, use the supportsAdminApi
property in the .spec.kafka.authorization
configuration of the Kafka
custom resource to enable or disable the support.
If ACL managment is not enabled, Strimzi rejects a resource if it contains any ACL rules.
If you’re using a standalone deployment of the User Operator, ACL management is enabled by default.
You can disable it using the STRIMZI_ACLS_ADMIN_API_SUPPORTED
environment variable.
If no authorization is specified, the User Operator does not provision any access rights for the user.
Whether such a KafkaUser
can still access resources depends on the authorizer being used.
For example, for the AclAuthorizer
this is determined by its allow.everyone.if.no.acl.found
configuration.
AclAuthorizer
uses ACL rules to manage access to Kafka brokers.
ACL rules grant access rights to the user, which you specify in the acls
property.
For more information about the AclRule
object, see the AclRule
schema reference.
If a user is added to a list of super users in a Kafka broker configuration,
the user is allowed unlimited access to the cluster regardless of any authorization constraints defined in ACLs in KafkaUser
.
For more information on configuring super user access to brokers, see Kafka authorization.
You can configure the spec
for the KafkaUser
resource to enforce quotas so that a user does not exceed a configured level of access to Kafka brokers.
You can set size-based network usage and time-based CPU utilization thresholds.
You can also add a partition mutation quota to control the rate at which requests to change partitions are accepted for user requests.
KafkaUser
with user quotasapiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
name: my-user
labels:
strimzi.io/cluster: my-cluster
spec:
# ...
quotas:
producerByteRate: 1048576 (1)
consumerByteRate: 2097152 (2)
requestPercentage: 55 (3)
controllerMutationRate: 10 (4)
Byte-per-second quota on the amount of data the user can push to a Kafka broker
Byte-per-second quota on the amount of data the user can fetch from a Kafka broker
CPU utilization limit as a percentage of time for a client group
Number of concurrent partition creation and deletion operations (mutations) allowed per second
For more information on these properties, see the KafkaUserQuotas
schema reference.
To establish secure access to Kafka brokers, you configure and apply:
A Kafka
resource to:
Create listeners with a specified authentication type
Configure authorization for the whole Kafka cluster
A KafkaUser
resource to access the Kafka brokers securely through the listeners
Configure the Kafka
resource to set up:
Listener authentication
Network policies that restrict access to Kafka listeners
Kafka authorization
Super users for unconstrained access to brokers
Authentication is configured independently for each listener. Authorization is always configured for the whole Kafka cluster.
The Cluster Operator creates the listeners and sets up the cluster and client certificate authority (CA) certificates to enable authentication within the Kafka cluster.
You can replace the certificates generated by the Cluster Operator by installing your own certificates. You can also configure your listener to use a Kafka listener certificate managed by an external Certificate Authority. Certificates are available in PKCS #12 format (.p12) and PEM (.crt) formats.
Use KafkaUser
to enable the authentication and authorization mechanisms that a specific client uses to access Kafka.
Configure the KafkaUser
resource to set up:
Authentication to match the enabled listener authentication
Authorization to match the enabled Kafka authorization
Quotas to control the use of resources by clients
The User Operator creates the user representing the client and the security credentials used for client authentication, based on the chosen authentication type.
Refer to the schema reference for more information on access configuration properties:
This procedure shows the steps involved in securing Kafka brokers when running Strimzi.
The security implemented for Kafka brokers must be compatible with the security implemented for the clients requiring access.
Kafka.spec.kafka.listeners[*].authentication
matches KafkaUser.spec.authentication
Kafka.spec.kafka.authorization
matches KafkaUser.spec.authorization
The steps show the configuration for simple authorization and a listener using TLS authentication.
For more information on listener configuration, see GenericKafkaListener
schema reference.
Alternatively, you can use SCRAM-SHA or OAuth 2.0 for listener authentication, and OAuth 2.0 or OPA for Kafka authorization.
Configure the Kafka
resource.
Configure the authorization
property for authorization.
Configure the listeners
property to create a listener with authentication.
For example:
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
kafka:
# ...
authorization: (1)
type: simple
superUsers: (2)
- CN=client_1
- user_2
- CN=client_3
listeners:
- name: tls
port: 9093
type: internal
tls: true
authentication:
type: tls (3)
# ...
zookeeper:
# ...
Authorization enables simple
authorization on the Kafka broker using the AclAuthorizer
Kafka plugin.
List of user principals with unlimited access to Kafka. CN is the common name from the client certificate when TLS authentication is used.
Listener authentication mechanisms may be configured for each listener, and specified as mutual TLS, SCRAM-SHA-512 or token-based OAuth 2.0.
If you are configuring an external listener, the configuration is dependent on the chosen connection mechanism.
Create or update the Kafka
resource.
kubectl apply -f <kafka_configuration_file>
The Kafka cluster is configured with a Kafka broker listener using TLS authentication.
A service is created for each Kafka broker pod.
A service is created to serve as the bootstrap address for connection to the Kafka cluster.
The cluster CA certificate to verify the identity of the kafka brokers is also created in the secret <cluster_name>-cluster-ca-cert
.
Create or modify a KafkaUser
to represent a client that requires secure access to the Kafka cluster.
When you configure the KafkaUser
authentication and authorization mechanisms, ensure they match the equivalent Kafka
configuration:
KafkaUser.spec.authentication
matches Kafka.spec.kafka.listeners[*].authentication
KafkaUser.spec.authorization
matches Kafka.spec.kafka.authorization
This procedure shows how a user is created with TLS authentication. You can also create a user with SCRAM-SHA authentication.
The authentication required depends on the type of authentication configured for the Kafka broker listener.
Note
|
Authentication between Kafka users and Kafka brokers depends on the authentication settings for each. For example, it is not possible to authenticate a user with TLS if it is not also enabled in the Kafka configuration. |
A running Kafka cluster configured with a Kafka broker listener using TLS authentication and encryption.
A running User Operator (typically deployed with the Entity Operator).
The authentication type in KafkaUser
should match the authentication configured in Kafka
brokers.
Configure the KafkaUser
resource.
For example:
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaUser
metadata:
name: my-user
labels:
strimzi.io/cluster: my-cluster
spec:
authentication: (1)
type: tls
authorization:
type: simple (2)
acls:
- resource:
type: topic
name: my-topic
patternType: literal
operation: Read
- resource:
type: topic
name: my-topic
patternType: literal
operation: Describe
- resource:
type: group
name: my-group
patternType: literal
operation: Read
User authentication mechanism, defined as mutual tls
or scram-sha-512
.
Simple authorization, which requires an accompanying list of ACL rules.
Create or update the KafkaUser
resource.
kubectl apply -f <user_config_file>
The user is created, as well as a Secret with the same name as the KafkaUser
resource.
The Secret contains a private and public key for TLS client authentication.
For information on configuring a Kafka client with properties for secure connection to Kafka brokers, see Setting up access for clients outside of Kubernetes.
You can restrict access to a listener to only selected applications by using the networkPolicyPeers
property.
A Kubernetes cluster with support for Ingress NetworkPolicies.
The Cluster Operator is running.
Open the Kafka
resource.
In the networkPolicyPeers
property, define the application pods or namespaces that will be allowed to access the Kafka cluster.
For example, to configure a tls
listener to allow connections only from application pods with the label app
set to kafka-client
:
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
kafka:
# ...
listeners:
- name: tls
port: 9093
type: internal
tls: true
authentication:
type: tls
networkPolicyPeers:
- podSelector:
matchLabels:
app: kafka-client
# ...
zookeeper:
# ...
Create or update the resource.
Use kubectl apply
:
kubectl apply -f your-file
Strimzi supports the use of OAuth 2.0 authentication using the OAUTHBEARER and PLAIN mechanisms.
OAuth 2.0 enables standardized token-based authentication and authorization between applications, using a central authorization server to issue tokens that grant limited access to resources.
Kafka brokers and clients both need to be configured to use OAuth 2.0. You can configure OAuth 2.0 authentication, then OAuth 2.0 authorization.
Note
|
OAuth 2.0 authentication can be used in conjunction with Kafka authorization. |
Using OAuth 2.0 authentication, application clients can access resources on application servers (called resource servers) without exposing account credentials.
The application client passes an access token as a means of authenticating, which application servers can also use to determine the level of access to grant. The authorization server handles the granting of access and inquiries about access.
In the context of Strimzi:
Kafka brokers act as OAuth 2.0 resource servers
Kafka clients act as OAuth 2.0 application clients
Kafka clients authenticate to Kafka brokers. The brokers and clients communicate with the OAuth 2.0 authorization server, as necessary, to obtain or validate access tokens.
For a deployment of Strimzi, OAuth 2.0 integration provides:
Server-side OAuth 2.0 support for Kafka brokers
Client-side OAuth 2.0 support for Kafka MirrorMaker, Kafka Connect, and the Kafka Bridge
Strimzi supports the OAUTHBEARER and PLAIN mechanisms for OAuth 2.0 authentication. Both mechanisms allow Kafka clients to establish authenticated sessions with Kafka brokers. The authentication flow between clients, the authorization server, and Kafka brokers is different for each mechanism.
We recommend that you configure clients to use OAUTHBEARER whenever possible. OAUTHBEARER provides a higher level of security than PLAIN because client credentials are never shared with Kafka brokers. Consider using PLAIN only with Kafka clients that do not support OAUTHBEARER.
You configure Kafka broker listeners to use OAuth 2.0 authentication for connecting clients.
If necessary, you can use the OAUTHBEARER and PLAIN mechanisms on the same oauth
listener.
The properties to support each mechanism must be explicitly specified in the oauth
listener configuration.
OAUTHBEARER is automatically enabled in the oauth
listener configuration for the Kafka broker.
You can set the enableOauthBearer
property to true
, though this is not required.
# ...
authentication:
type: oauth
# ...
enableOauthBearer: true
Many Kafka client tools use libraries that provide basic support for OAUTHBEARER at the protocol level. To support application development, Strimzi provides an OAuth callback handler for the upstream Kafka Client Java libraries (but not for other libraries). Therefore, you do not need to write your own callback handlers. An application client can use the callback handler to provide the access token. Clients written in other languages, such as Go, must use custom code to connect to the authorization server and obtain the access token.
With OAUTHBEARER, the client initiates a session with the Kafka broker for credentials exchange, where credentials take the form of a bearer token provided by the callback handler. Using the callbacks, you can configure token provision in one of three ways:
Client ID and Secret (by using the OAuth 2.0 client credentials mechanism)
A long-lived access token, obtained manually at configuration time
A long-lived refresh token, obtained manually at configuration time
Note
|
OAUTHBEARER authentication can only be used by Kafka clients that support the OAUTHBEARER mechanism at the protocol level. |
To use PLAIN, you must enable it in the oauth
listener configuration for the Kafka broker.
In the following example, PLAIN is enabled in addition to OAUTHBEARER, which is enabled by default.
If you want to use PLAIN only, you can disable OAUTHBEARER by setting enableOauthBearer
to false
.
# ...
authentication:
type: oauth
# ...
enablePlain: true
tokenEndpointUri: https://OAUTH-SERVER-ADDRESS/auth/realms/external/protocol/openid-connect/token
PLAIN is a simple authentication mechanism used by all Kafka client tools. To enable PLAIN to be used with OAuth 2.0 authentication, Strimzi provides OAuth 2.0 over PLAIN server-side callbacks.
With the Strimzi implementation of PLAIN, the client credentials are not stored in ZooKeeper. Instead, client credentials are handled centrally behind a compliant authorization server, similar to when OAUTHBEARER authentication is used.
When used with the OAuth 2.0 over PLAIN callbacks, Kafka clients authenticate with Kafka brokers using either of the following methods:
Client ID and secret (by using the OAuth 2.0 client credentials mechanism)
A long-lived access token, obtained manually at configuration time
For both methods, the client must provide the PLAIN username
and password
properties to pass credentials to the Kafka broker.
The client uses these properties to pass a client ID and secret or username and access token.
Client IDs and secrets are used to obtain access tokens.
Access tokens are passed as password
property values.
You pass the access token with or without an $accessToken:
prefix.
If you configure a token endpoint (tokenEndpointUri
) in the listener configuration, you need the prefix.
If you don’t configure a token endpoint (tokenEndpointUri
) in the listener configuration, you don’t need the prefix.
The Kafka broker interprets the password as a raw access token.
If the password
is set as the access token, the username
must be set to the same principal name that the Kafka broker obtains from the access token.
You can specify username extraction options in your listener using the userNameClaim
, fallbackUserNameClaim
, fallbackUsernamePrefix
, and userInfoEndpointUri
properties.
The username extraction process also depends on your authorization server; in particular, how it maps client IDs to account names.
Kafka broker configuration for OAuth 2.0 involves:
Creating the OAuth 2.0 client in the authorization server
Configuring OAuth 2.0 authentication in the Kafka custom resource
Note
|
In relation to the authorization server, Kafka brokers and Kafka clients are both regarded as OAuth 2.0 clients. |
To configure a Kafka broker to validate the token received during session initiation, the recommended approach is to create an OAuth 2.0 client definition in an authorization server, configured as confidential, with the following client credentials enabled:
Client ID of kafka
(for example)
Client ID and Secret as the authentication mechanism
Note
|
You only need to use a client ID and secret when using a non-public introspection endpoint of the authorization server. The credentials are not typically required when using public authorization server endpoints, as with fast local JWT token validation. |
To use OAuth 2.0 authentication in the Kafka cluster, you specify, for example, a TLS listener configuration for your Kafka cluster custom resource with the authentication method oauth
:
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
kafka:
# ...
listeners:
- name: tls
port: 9093
type: internal
tls: true
authentication:
type: oauth
#...
You can configure plain
, tls
and external
listeners,
but it is recommended not to use plain
listeners or external
listeners with disabled TLS encryption with OAuth 2.0 as this creates a vulnerability to network eavesdropping and unauthorized access through token theft.
You configure an external
listener with type: oauth
for a secure transport layer to communicate with the client.
# ...
listeners:
- name: external
port: 9094
type: loadbalancer
tls: true
authentication:
type: oauth
#...
The tls
property is false by default, so it must be enabled.
When you have defined the type of authentication as OAuth 2.0, you add configuration based on the type of validation, either as fast local JWT validation or token validation using an introspection endpoint.
The procedure to configure OAuth 2.0 for listeners, with descriptions and examples, is described in Configuring OAuth 2.0 support for Kafka brokers.
Fast local JWT token validation checks a JWT token signature locally.
The local check ensures that a token:
Conforms to type by containing a (typ) claim value of Bearer
for an access token
Is valid (not expired)
Has an issuer that matches a validIssuerURI
You specify a validIssuerURI
attribute when you configure the listener, so that any tokens not issued by the authorization server are rejected.
The authorization server does not need to be contacted during fast local JWT token validation.
You activate fast local JWT token validation by specifying a jwksEndpointUri
attribute, the endpoint exposed by the OAuth 2.0 authorization server.
The endpoint contains the public keys used to validate signed JWT tokens, which are sent as credentials by Kafka clients.
Note
|
All communication with the authorization server should be performed using TLS encryption. |
You can configure a certificate truststore as a Kubernetes Secret in your Strimzi project namespace, and use a tlsTrustedCertificates
attribute to point to the Kubernetes Secret containing the truststore file.
You might want to configure a userNameClaim
to properly extract a username from the JWT token.
If you want to use Kafka ACL authorization, you need to identify the user by their username during authentication.
(The sub
claim in JWT tokens is typically a unique ID, not a username.)
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
kafka:
#...
listeners:
- name: tls
port: 9093
type: internal
tls: true
authentication:
type: oauth
validIssuerUri: <https://<auth-server-address>/auth/realms/tls>
jwksEndpointUri: <https://<auth-server-address>/auth/realms/tls/protocol/openid-connect/certs>
userNameClaim: preferred_username
maxSecondsWithoutReauthentication: 3600
tlsTrustedCertificates:
- secretName: oauth-server-cert
certificate: ca.crt
#...
Token validation using an OAuth 2.0 introspection endpoint treats a received access token as opaque. The Kafka broker sends an access token to the introspection endpoint, which responds with the token information necessary for validation. Importantly, it returns up-to-date information if the specific access token is valid, and also information about when the token expires.
To configure OAuth 2.0 introspection-based validation, you specify an introspectionEndpointUri
attribute rather than the jwksEndpointUri
attribute specified for fast local JWT token validation.
Depending on the authorization server, you typically have to specify a clientId
and clientSecret
, because the introspection endpoint is usually protected.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
spec:
kafka:
listeners:
- name: tls
port: 9093
type: internal
tls: true
authentication:
type: oauth
clientId: kafka-broker
clientSecret:
secretName: my-cluster-oauth
key: clientSecret
validIssuerUri: <https://<auth-server-address>/auth/realms/tls>
introspectionEndpointUri: <https://<auth-server-address>/auth/realms/tls/protocol/openid-connect/token/introspect>
userNameClaim: preferred_username
maxSecondsWithoutReauthentication: 3600
tlsTrustedCertificates:
- secretName: oauth-server-cert
certificate: ca.crt
You can configure oauth
listeners to use Kafka session re-authentication for OAuth 2.0 sessions between Kafka clients and Kafka brokers.
This mechanism enforces the expiry of an authenticated session between the client and the broker after a defined period of time.
When a session expires, the client immediately starts a new session by reusing the existing connection rather than dropping it.
Session re-authentication is disabled by default.
To enable it, you set a time value for maxSecondsWithoutReauthentication
in the oauth
listener configuration.
The same property is used to configure session re-authentication for OAUTHBEARER and PLAIN authentication.
For an example configuration, see Configuring OAuth 2.0 support for Kafka brokers.
Session re-authentication must be supported by the Kafka client libraries used by the client.
Session re-authentication can be used with fast local JWT or introspection endpoint token validation.
When the broker’s authenticated session expires, the client must re-authenticate to the existing session by sending a new, valid access token to the broker, without dropping the connection.
If token validation is successful, a new client session is started using the existing connection. If the client fails to re-authenticate, the broker will close the connection if further attempts are made to send or receive messages. Java clients that use Kafka client library 2.2 or later automatically re-authenticate if the re-authentication mechanism is enabled on the broker.
Session re-authentication also applies to refresh tokens, if used. When the session expires, the client refreshes the access token by using its refresh token. The client then uses the new access token to re-authenticate to the existing session.
When session re-authentication is configured, session expiry works differently for OAUTHBEARER and PLAIN authentication.
For OAUTHBEARER and PLAIN, using the client ID and secret method:
The broker’s authenticated session will expire at the configured maxSecondsWithoutReauthentication
.
The session will expire earlier if the access token expires before the configured time.
For PLAIN using the long-lived access token method:
The broker’s authenticated session will expire at the configured maxSecondsWithoutReauthentication
.
Re-authentication will fail if the access token expires before the configured time. Although session re-authentication is attempted, PLAIN has no mechanism for refreshing tokens.
If maxSecondsWithoutReauthentication
is not configured, OAUTHBEARER and PLAIN clients can remain connected to brokers indefinitely, without needing to re-authenticate.
Authenticated sessions do not end with access token expiry.
However, this can be considered when configuring authorization, for example, by using keycloak
authorization or installing a custom authorizer.
A Kafka client is configured with either:
The credentials required to obtain a valid access token from an authorization server (client ID and Secret)
A valid long-lived access token or refresh token, obtained using tools provided by an authorization server
The only information ever sent to the Kafka broker is an access token. The credentials used to authenticate with the authorization server to obtain the access token are never sent to the broker.
When a client obtains an access token, no further communication with the authorization server is needed.
The simplest mechanism is authentication with a client ID and Secret. Using a long-lived access token, or a long-lived refresh token, adds more complexity because there is an additional dependency on authorization server tools.
Note
|
If you are using long-lived access tokens, you may need to configure the client in the authorization server to increase the maximum lifetime of the token. |
If the Kafka client is not configured with an access token directly, the client exchanges credentials for an access token during Kafka session initiation by contacting the authorization server. The Kafka client exchanges either:
Client ID and Secret
Client ID, refresh token, and (optionally) a Secret
OAuth 2.0 authentication flows depend on the underlying Kafka client and Kafka broker configuration. The flows must also be supported by the authorization server used.
The Kafka broker listener configuration determines how clients authenticate using an access token. The client can pass a client ID and secret to request an access token.
If a listener is configured to use PLAIN authentication, the client can authenticate with a client ID and secret or username and access token.
These values are passed as the username
and password
properties of the PLAIN mechanism.
Listener configuration supports the following token validation options:
You can use fast local token validation based on JWT signature checking and local token introspection, without contacting an authorization server. The authorization server provides a JWKS endpoint with public certificates that are used to validate signatures on the tokens.
You can use a call to a token introspection endpoint provided by an authorization server. Each time a new Kafka broker connection is established, the broker passes the access token received from the client to the authorization server. The Kafka broker checks the response to confirm whether or not the token is valid.
Note
|
An authorization server might only allow the use of opaque access tokens, which means that local token validation is not possible. |
Kafka client credentials can also be configured for the following types of authentication:
Direct local access using a previously generated long-lived access token
Contact with the authorization server for a new access token to be issued (using a client ID and a secret, or a refresh token)
You can use the following communication flows for Kafka authentication using the SASL OAUTHBEARER mechanism.
Client using client ID and secret, with broker delegating validation to authorization server
Client using client ID and secret, with broker performing fast local token validation
Client using long-lived access token, with broker delegating validation to authorization server
Client using long-lived access token, with broker performing fast local validation
The Kafka client requests an access token from the authorization server using a client ID and secret, and optionally a refresh token.
The authorization server generates a new access token.
The Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the access token.
The Kafka broker validates the access token by calling a token introspection endpoint on the authorization server using its own client ID and secret.
A Kafka client session is established if the token is valid.
The Kafka client authenticates with the authorization server from the token endpoint, using a client ID and secret, and optionally a refresh token.
The authorization server generates a new access token.
The Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the access token.
The Kafka broker validates the access token locally using a JWT token signature check, and local token introspection.
The Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the long-lived access token.
The Kafka broker validates the access token by calling a token introspection endpoint on the authorization server, using its own client ID and secret.
A Kafka client session is established if the token is valid.
The Kafka client authenticates with the Kafka broker using the SASL OAUTHBEARER mechanism to pass the long-lived access token.
The Kafka broker validates the access token locally using a JWT token signature check and local token introspection.
Warning
|
Fast local JWT token signature validation is suitable only for short-lived tokens as there is no check with the authorization server if a token has been revoked. Token expiration is written into the token, but revocation can happen at any time, so cannot be accounted for without contacting the authorization server. Any issued token would be considered valid until it expires. |
You can use the following communication flows for Kafka authentication using the OAuth PLAIN mechanism.
The Kafka client passes a clientId
as a username and a secret
as a password.
The Kafka broker uses a token endpoint to pass the clientId
and secret
to the authorization server.
The authorization server returns a fresh access token or an error if the client credentials are not valid.
The Kafka broker validates the token in one of the following ways:
If a token introspection endpoint is specified, the Kafka broker validates the access token by calling the endpoint on the authorization server. A session is established if the token validation is successful.
If local token introspection is used, a request is not made to the authorization server. The Kafka broker validates the access token locally using a JWT token signature check.
The Kafka client passes a username and password. The password provides the value of an access token that was obtained manually and configured before running the client.
The password is passed with or without an $accessToken:
string prefix depending on whether or not the Kafka broker listener is configured with a token endpoint for authentication.
If the token endpoint is configured, the password should be prefixed by $accessToken:
to let the broker know that the password parameter contains an access token rather than a client secret. The Kafka broker interprets the username as the account username.
If the token endpoint is not configured on the Kafka broker listener (enforcing a no-client-credentials mode
), the password should provide the access token without the prefix. The Kafka broker interprets the username as the account username.
In this mode, the client doesn’t use a client ID and secret, and the password
parameter is always interpreted as a raw access token.
The Kafka broker validates the token in one of the following ways:
If a token introspection endpoint is specified, the Kafka broker validates the access token by calling the endpoint on the authorization server. A session is established if token validation is successful.
If local token introspection is used, there is no request made to the authorization server. Kafka broker validates the access token locally using a JWT token signature check.
OAuth 2.0 is used for interaction between Kafka clients and Strimzi components.
In order to use OAuth 2.0 for Strimzi, you must:
This procedure describes in general what you need to do to configure an authorization server for integration with Strimzi.
These instructions are not product specific.
The steps are dependent on the chosen authorization server. Consult the product documentation for the authorization server for information on how to set up OAuth 2.0 access.
Note
|
If you already have an authorization server deployed, you can skip the deployment step and use your current deployment. |
Deploy the authorization server to your cluster.
Access the CLI or admin console for the authorization server to configure OAuth 2.0 for Strimzi.
Now prepare the authorization server to work with Strimzi.
Configure a kafka-broker
client.
Configure clients for each Kafka client component of your application.
After deploying and configuring the authorization server, configure the Kafka brokers to use OAuth 2.0.
This procedure describes how to configure Kafka brokers so that the broker listeners are enabled to use OAuth 2.0 authentication using an authorization server.
We advise use of OAuth 2.0 over an encrypted interface through configuration of TLS listeners. Plain listeners are not recommended.
If the authorization server is using certificates signed by the trusted CA and matching the OAuth 2.0 server hostname, TLS connection works using the default settings. Otherwise, you may need to configure the truststore with proper certificates or disable the certificate hostname validation.
When configuring the Kafka broker you have two options for the mechanism used to validate the access token during OAuth 2.0 authentication of the newly connected Kafka client:
For more information on the configuration of OAuth 2.0 authentication for Kafka broker listeners, see:
Strimzi and Kafka are running
An OAuth 2.0 authorization server is deployed
Update the Kafka broker configuration (Kafka.spec.kafka
) of your Kafka
resource in an editor.
kubectl edit kafka my-cluster
Configure the Kafka broker listeners
configuration.
The configuration for each type of listener does not have to be the same, as they are independent.
The examples here show the configuration options as configured for external listeners.
#...
- name: external
port: 9094
type: loadbalancer
tls: true
authentication:
type: oauth (1)
validIssuerUri: <https://<auth-server-address>/auth/realms/external> (2)
jwksEndpointUri: <https://<auth-server-address>/auth/realms/external/protocol/openid-connect/certs> (3)
userNameClaim: preferred_username (4)
maxSecondsWithoutReauthentication: 3600 (5)
tlsTrustedCertificates: (6)
- secretName: oauth-server-cert
certificate: ca.crt
disableTlsHostnameVerification: true (7)
jwksExpirySeconds: 360 (8)
jwksRefreshSeconds: 300 (9)
jwksMinRefreshPauseSeconds: 1 (10)
Listener type set to oauth
.
URI of the token issuer used for authentication.
URI of the JWKS certificate endpoint used for local JWT validation.
The token claim (or key) that contains the actual user name in the token. The user name is the principal used to identify the user. The userNameClaim
value will depend on the authentication flow and the authorization server used.
(Optional) Activates the Kafka re-authentication mechanism that enforces session expiry to the same length of time as the access token. If the specified value is less than the time left for the access token to expire, then the client will have to re-authenticate before the actual token expiry. By default, the session does not expire when the access token expires, and the client does not attempt re-authentication.
(Optional) Trusted certificates for TLS connection to the authorization server.
(Optional) Disable TLS hostname verification. Default is false
.
The duration the JWKS certificates are considered valid before they expire. Default is 360
seconds. If you specify a longer time, consider the risk of allowing access to revoked certificates.
The period between refreshes of JWKS certificates. The interval must be at least 60 seconds shorter than the expiry interval. Default is 300
seconds.
The minimum pause in seconds between consecutive attempts to refresh JWKS public keys. When an unknown signing key is encountered, the JWKS keys refresh is scheduled outside the regular periodic schedule with at least the specified pause since the last refresh attempt. The refreshing of keys follows the rule of exponential backoff, retrying on unsuccessful refreshes with ever increasing pause, until it reaches jwksRefreshSeconds
. The default value is 1.
- name: external
port: 9094
type: loadbalancer
tls: true
authentication:
type: oauth
validIssuerUri: <https://<auth-server-address>/auth/realms/external>
introspectionEndpointUri: <https://<auth-server-address>/auth/realms/external/protocol/openid-connect/token/introspect> (1)
clientId: kafka-broker (2)
clientSecret: (3)
secretName: my-cluster-oauth
key: clientSecret
userNameClaim: preferred_username (4)
maxSecondsWithoutReauthentication: 3600 (5)
URI of the token introspection endpoint.
Client ID to identify the client.
Client Secret and client ID is used for authentication.
The token claim (or key) that contains the actual user name in the token. The user name is the principal used to identify the user. The userNameClaim
value will depend on the authorization server used.
(Optional) Activates the Kafka re-authentication mechanism that enforces session expiry to the same length of time as the access token. If the specified value is less than the time left for the access token to expire, then the client will have to re-authenticate before the actual token expiry. By default, the session does not expire when the access token expires, and the client does not attempt re-authentication.
Depending on how you apply OAuth 2.0 authentication, and the type of authorization server, there are additional (optional) configuration settings you can use:
# ...
authentication:
type: oauth
# ...
checkIssuer: false (1)
checkAudience: true (2)
fallbackUserNameClaim: client_id (3)
fallbackUserNamePrefix: client-account- (4)
validTokenType: bearer (5)
userInfoEndpointUri: https://OAUTH-SERVER-ADDRESS/auth/realms/external/protocol/openid-connect/userinfo (6)
enableOauthBearer: false (7)
enablePlain: true (8)
tokenEndpointUri: https://OAUTH-SERVER-ADDRESS/auth/realms/external/protocol/openid-connect/token (9)
customClaimCheck: "@.custom == 'custom-value'" (10)
clientAudience: AUDIENCE (11)
clientScope: SCOPE (12)
connectTimeoutSeconds: 60 (13)
readTimeoutSeconds: 60 (14)
groupsClaim: "$.groups" (15)
groupsClaimDelimiter: "," (16)
If your authorization server does not provide an iss
claim, it is not possible to perform an issuer check. In this situation, set checkIssuer
to false
and do not specify a validIssuerUri
. Default is true
.
If your authorization server provides an aud
(audience) claim, and you want to enforce an audience check, set checkAudience
to true
. Audience checks identify the intended recipients of tokens. As a result, the Kafka broker will reject tokens that do not have its clientId
in their aud
claim. Default is false
.
An authorization server may not provide a single attribute to identify both regular users and clients. When a client authenticates in its own name, the server might provide a client ID. When a user authenticates using a username and password, to obtain a refresh token or an access token, the server might provide a username attribute in addition to a client ID. Use this fallback option to specify the username claim (attribute) to use if a primary user ID attribute is not available.
In situations where fallbackUserNameClaim
is applicable, it may also be necessary to prevent name collisions between the values of the username claim, and those of the fallback username claim. Consider a situation where a client called producer
exists, but also a regular user called producer
exists. In order to differentiate between the two, you can use this property to add a prefix to the user ID of the client.
(Only applicable when using introspectionEndpointUri
) Depending on the authorization server you are using, the introspection endpoint may or may not return the token type attribute, or it may contain different values. You can specify a valid token type value that the response from the introspection endpoint has to contain.
(Only applicable when using introspectionEndpointUri
) The authorization server may be configured or implemented in such a way to not provide any identifiable information in an Introspection Endpoint response. In order to obtain the user ID, you can configure the URI of the userinfo
endpoint as a fallback. The userNameClaim
, fallbackUserNameClaim
, and fallbackUserNamePrefix
settings are applied to the response of userinfo
endpoint.
Set this to false
to disable the OAUTHBEARER mechanism on the listener. At least one of PLAIN or OAUTHBEARER has to be enabled. Default is true
.
Set to true
to enable PLAIN authentication on the listener, which is supported for clients on all platforms.
Additional configuration for the PLAIN mechanism. If specified, clients can authenticate over PLAIN by passing an access token as the password
using an $accessToken:
prefix.
For production, always use https://
urls.
Additional custom rules can be imposed on the JWT access token during validation by setting this to a JsonPath filter query. If the access token does not contain the necessary data, it is rejected. When using the introspectionEndpointUri
, the custom check is applied to the introspection endpoint response JSON.
An audience
parameter passed to the token endpoint. An audience is used when obtaining an access token for inter-broker authentication. It is also used in the name of a client for OAuth 2.0 over PLAIN client authentication using a clientId
and secret
. This only affects the ability to obtain the token, and the content of the token, depending on the authorization server. It does not affect token validation rules by the listener.
A scope
parameter passed to the token endpoint. A scope is used when obtaining an access token for inter-broker authentication. It is also used in the name of a client for OAuth 2.0 over PLAIN client authentication using a clientId
and secret
. This only affects the ability to obtain the token, and the content of the token, depending on the authorization server. It does not affect token validation rules by the listener.
The connect timeout in seconds when connecting to the authorization server. The default value is 60.
The read timeout in seconds when connecting to the authorization server. The default value is 60.
A JsonPath query used to extract groups information from JWT token or introspection endpoint response. Not set by default. This can be used by a custom authorizer to make authorization decisions based on user groups.
A delimiter used to parse groups information when returned as a single delimited string. The default value is ',' (comma).
Save and exit the editor, then wait for rolling updates to complete.
Check the update in the logs or by watching the pod state transitions:
kubectl logs -f ${POD_NAME} -c ${CONTAINER_NAME}
kubectl get pod -w
The rolling update configures the brokers to use OAuth 2.0 authentication.
This procedure describes how to configure Kafka producer and consumer APIs to use OAuth 2.0 for interaction with Kafka brokers.
Add a client callback plugin to your pom.xml file, and configure the system properties.
Strimzi and Kafka are running
An OAuth 2.0 authorization server is deployed and configured for OAuth access to Kafka brokers
Kafka brokers are configured for OAuth 2.0
Add the client library with OAuth 2.0 support to the pom.xml
file for the Kafka client:
<dependency>
<groupId>io.strimzi</groupId>
<artifactId>kafka-oauth-client</artifactId>
<version>0.10.0</version>
</dependency>
Configure the system properties for the callback:
For example:
System.setProperty(ClientConfig.OAUTH_TOKEN_ENDPOINT_URI, “https://<auth-server-address>/auth/realms/master/protocol/openid-connect/token”); (1)
System.setProperty(ClientConfig.OAUTH_CLIENT_ID, "<client_name>"); (2)
System.setProperty(ClientConfig.OAUTH_CLIENT_SECRET, "<client_secret>"); (3)
System.setProperty(ClientConfig.OAUTH_SCOPE, "<scope_value>") (4)
System.setProperty(ClientConfig.OAUTH_AUDIENCE, "<audience_value") (5)
URI of the authorization server token endpoint.
Client ID, which is the name used when creating the client in the authorization server.
Client secret created when creating the client in the authorization server.
(Optional) The scope
for requesting the token from the token endpoint.
An authorization server may require a client to specify the scope.
(Optional) The audience
for requesting the token from the token endpoint.
An authorization server may require a client to specify the audience.
Enable the OAUTHBEARER or PLAIN mechanism on a TLS encrypted connection in the Kafka client configuration.
For example:
props.put("sasl.jaas.config", "org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required;");
props.put("security.protocol", "SASL_SSL");
props.put("sasl.mechanism", "OAUTHBEARER");
props.put("sasl.login.callback.handler.class", "io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler");
props.put("sasl.jaas.config", "org.apache.kafka.common.security.plain.PlainLoginModule required username=\"$CLIENT_ID_OR_ACCOUNT_NAME\" password=\"$SECRET_OR_ACCESS_TOKEN\" ;");
props.put("security.protocol", "SASL_SSL"); (1)
props.put("sasl.mechanism", "PLAIN");
Here we use SASL_SSL
for use over TLS connections. Use SASL_PLAINTEXT
over unencrypted connections for local development only.
Verify that the Kafka client can access the Kafka brokers.
This procedure describes how to configure Kafka components to use OAuth 2.0 authentication using an authorization server.
You can configure authentication for:
Kafka Connect
Kafka MirrorMaker
Kafka Bridge
In this scenario, the Kafka component and the authorization server are running in the same cluster.
For more information on the configuration of OAuth 2.0 authentication for Kafka components, see:
Strimzi and Kafka are running
An OAuth 2.0 authorization server is deployed and configured for OAuth access to Kafka brokers
Kafka brokers are configured for OAuth 2.0
Create a client secret and mount it to the component as an environment variable.
For example, here we are creating a client Secret
for the Kafka Bridge:
apiVersion: kafka.strimzi.io/v1beta2
kind: Secret
metadata:
name: my-bridge-oauth
type: Opaque
data:
clientSecret: MGQ1OTRmMzYtZTllZS00MDY2LWI5OGEtMTM5MzM2NjdlZjQw (1)
The clientSecret
key must be in base64 format.
Create or edit the resource for the Kafka component so that OAuth 2.0 authentication is configured for the authentication property.
For OAuth 2.0 authentication, you can use:
Client ID and secret
Client ID and refresh token
Access token
TLS
For example, here OAuth 2.0 is assigned to the Kafka Bridge client using a client ID and secret, and TLS:
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaBridge
metadata:
name: my-bridge
spec:
# ...
authentication:
type: oauth (1)
tokenEndpointUri: https://<auth-server-address>/auth/realms/master/protocol/openid-connect/token (2)
clientId: kafka-bridge
clientSecret:
secretName: my-bridge-oauth
key: clientSecret
tlsTrustedCertificates: (3)
- secretName: oauth-server-cert
certificate: tls.crt
Authentication type set to oauth
.
URI of the token endpoint for authentication.
Trusted certificates for TLS connection to the authorization server.
Depending on how you apply OAuth 2.0 authentication, and the type of authorization server, there are additional configuration options you can use:
# ...
spec:
# ...
authentication:
# ...
disableTlsHostnameVerification: true (1)
checkAccessTokenType: false (2)
accessTokenIsJwt: false (3)
scope: any (4)
audience: kafka (5)
connectTimeoutSeconds: 60 (6)
readTimeoutSeconds: 60 (7)
(Optional) Disable TLS hostname verification. Default is false
.
If the authorization server does not return a typ
(type) claim inside the JWT token, you can apply checkAccessTokenType: false
to skip the token type check. Default is true
.
If you are using opaque tokens, you can apply accessTokenIsJwt: false
so that access tokens are not treated as JWT tokens.
(Optional) The scope
for requesting the token from the token endpoint.
An authorization server may require a client to specify the scope.
In this case it is any
.
(Optional) The audience
for requesting the token from the token endpoint.
An authorization server may require a client to specify the audience.
In this case it is kafka
.
(Optional) The connect timeout in seconds when connecting to the authorization server. The default value is 60.
(Optional) The read timeout in seconds when connecting to the authorization server. The default value is 60.
Apply the changes to the deployment of your Kafka resource.
kubectl apply -f your-file
Check the update in the logs or by watching the pod state transitions:
kubectl logs -f ${POD_NAME} -c ${CONTAINER_NAME}
kubectl get pod -w
The rolling updates configure the component for interaction with Kafka brokers using OAuth 2.0 authentication.
When choosing an authorization server, consider the features that best support configuration of your chosen authentication flow.
For the purposes of testing OAuth 2.0 with Strimzi, Keycloak and ORY Hydra were implemented as the OAuth 2.0 authorization server.
For more information, see:
Strimzi supports the use of OAuth 2.0 token-based authorization through Keycloak Keycloak Authorization Services, which allows you to manage security policies and permissions centrally.
Security policies and permissions defined in Keycloak are used to grant access to resources on Kafka brokers. Users and clients are matched against policies that permit access to perform specific actions on Kafka brokers.
Kafka allows all users full access to brokers by default,
and also provides the AclAuthorizer
plugin to configure authorization based on Access Control Lists (ACLs).
ZooKeeper stores ACL rules that grant or deny access to resources based on username. However, OAuth 2.0 token-based authorization with Keycloak offers far greater flexibility on how you wish to implement access control to Kafka brokers. In addition, you can configure your Kafka brokers to use OAuth 2.0 authorization and ACLs.
OAuth 2.0 authorization in Strimzi uses Keycloak server Authorization Services REST endpoints to extend token-based authentication with Keycloak by applying defined security policies on a particular user, and providing a list of permissions granted on different resources for that user. Policies use roles and groups to match permissions to users. OAuth 2.0 authorization enforces permissions locally based on the received list of grants for the user from Keycloak Authorization Services.
A Keycloak authorizer (KeycloakRBACAuthorizer
) is provided with Strimzi.
To be able to use the Keycloak REST endpoints for Authorization Services provided by Keycloak,
you configure a custom authorizer on the Kafka broker.
The authorizer fetches a list of granted permissions from the authorization server as needed, and enforces authorization locally on the Kafka Broker, making rapid authorization decisions for each client request.
This procedure describes how to configure Kafka brokers to use OAuth 2.0 authorization using Keycloak Authorization Services.
Consider the access you require or want to limit for certain users. You can use a combination of Keycloak groups, roles, clients, and users to configure access in Keycloak.
Typically, groups are used to match users based on organizational departments or geographical locations. And roles are used to match users based on their function.
With Keycloak, you can store users and groups in LDAP, whereas clients and roles cannot be stored this way. Storage and access to user data may be a factor in how you choose to configure authorization policies.
Note
|
Super users always have unconstrained access to a Kafka broker regardless of the authorization implemented on the Kafka broker. |
Strimzi must be configured to use OAuth 2.0 with Keycloak for token-based authentication. You use the same Keycloak server endpoint when you set up authorization.
OAuth 2.0 authentication must be configured with the maxSecondsWithoutReauthentication
option to enable re-authentication.
Access the Keycloak Admin Console or use the Keycloak Admin CLI to enable Authorization Services for the Kafka broker client you created when setting up OAuth 2.0 authentication.
Use Authorization Services to define resources, authorization scopes, policies, and permissions for the client.
Bind the permissions to users and clients by assigning them roles and groups.
Configure the Kafka brokers to use Keycloak authorization by updating the Kafka broker configuration (Kafka.spec.kafka
) of your Kafka
resource in an editor.
kubectl edit kafka my-cluster
Configure the Kafka broker kafka
configuration to use keycloak
authorization, and to be able to access the authorization server and Authorization Services.
For example:
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
authorization:
type: keycloak (1)
tokenEndpointUri: <https://<auth-server-address>/auth/realms/external/protocol/openid-connect/token> (2)
clientId: kafka (3)
delegateToKafkaAcls: false (4)
disableTlsHostnameVerification: false (5)
superUsers: (6)
- CN=fred
- sam
- CN=edward
tlsTrustedCertificates: (7)
- secretName: oauth-server-cert
certificate: ca.crt
grantsRefreshPeriodSeconds: 60 (8)
grantsRefreshPoolSize: 5 (9)
connectTimeoutSeconds: 60 (10)
readTimeoutSeconds: 60 (11)
#...
Type keycloak
enables Keycloak authorization.
URI of the Keycloak token endpoint. For production, always use https://
urls.
When you configure token-based oauth
authentication, you specify a jwksEndpointUri
as the URI for local JWT validation.
The hostname for the tokenEndpointUri
URI must be the same.
The client ID of the OAuth 2.0 client definition in Keycloak that has Authorization Services enabled. Typically, kafka
is used as the ID.
(Optional) Delegate authorization to Kafka AclAuthorizer
if access is denied by Keycloak Authorization Services policies.
Default is false
.
(Optional) Disable TLS hostname verification. Default is false
.
(Optional) Designated super users.
(Optional) Trusted certificates for TLS connection to the authorization server.
(Optional) The time between two consecutive grants refresh runs. That is the maximum time for active sessions to detect any permissions changes for the user on Keycloak. The default value is 60.
(Optional) The number of threads to use to refresh (in parallel) the grants for the active sessions. The default value is 5.
(Optional) The connect timeout in seconds when connecting to the Keycloak token endpoint. The default value is 60.
(Optional) The read timeout in seconds when connecting to the Keycloak token endpoint. The default value is 60.
Save and exit the editor, then wait for rolling updates to complete.
Check the update in the logs or by watching the pod state transitions:
kubectl logs -f ${POD_NAME} -c kafka
kubectl get pod -w
The rolling update configures the brokers to use OAuth 2.0 authorization.
Verify the configured permissions by accessing Kafka brokers as clients or users with specific roles, making sure they have the necessary access, or do not have the access they are not supposed to have.
This section describes the authorization models used by Keycloak Authorization Services and Kafka, and defines the important concepts in each model.
To grant permissions to access Kafka, you can map Keycloak Authorization Services objects to Kafka resources by creating an OAuth client specification in Keycloak. Kafka permissions are granted to user accounts or service accounts using Keycloak Authorization Services rules.
Examples are shown of the different user permissions required for common Kafka operations, such as creating and listing topics.
Kafka and Keycloak Authorization Services use different authorization models.
Kafka’s authorization model uses resource types.
When a Kafka client performs an action on a broker, the broker uses the configured KeycloakRBACAuthorizer
to check the client’s permissions, based on the action and resource type.
Kafka uses five resource types to control access: Topic
, Group
, Cluster
, TransactionalId
, and DelegationToken
.
Each resource type has a set of available permissions.
Topic
Create
Write
Read
Delete
Describe
DescribeConfigs
Alter
AlterConfigs
Group
Read
Describe
Delete
Cluster
Create
Describe
Alter
DescribeConfigs
AlterConfigs
IdempotentWrite
ClusterAction
TransactionalId
Describe
Write
DelegationToken
Describe
The Keycloak Authorization Services model has four concepts for defining and granting permissions: resources, authorization scopes, policies, and permissions.
A resource is a set of resource definitions that are used to match resources with permitted actions. A resource might be an individual topic, for example, or all topics with names starting with the same prefix. A resource definition is associated with a set of available authorization scopes, which represent a set of all actions available on the resource. Often, only a subset of these actions is actually permitted.
An authorization scope is a set of all the available actions on a specific resource definition. When you define a new resource, you add scopes from the set of all scopes.
A policy is an authorization rule that uses criteria to match against a list of accounts. Policies can match:
Service accounts based on client ID or roles
User accounts based on username, groups, or roles.
A permission grants a subset of authorization scopes on a specific resource definition to a set of users.
The Kafka authorization model is used as a basis for defining the Keycloak roles and resources that will control access to Kafka.
To grant Kafka permissions to user accounts or service accounts, you first create an OAuth client specification in Keycloak for the Kafka broker.
You then specify Keycloak Authorization Services rules on the client.
Typically, the client id of the OAuth client that represents the broker is kafka
.
The example configuration files provided with Strimzi use kafka
as the OAuth client id.
Note
|
If you have multiple Kafka clusters, you can use a single OAuth client ( |
The kafka
client definition must have the Authorization Enabled option enabled in the Keycloak Admin Console.
All permissions exist within the scope of the kafka
client. If you have different Kafka clusters configured with different OAuth client IDs, they each need a separate set of permissions even though they’re part of the same Keycloak realm.
When the Kafka client uses OAUTHBEARER authentication, the Keycloak authorizer (KeycloakRBACAuthorizer
) uses the access token of the current session to retrieve a list of grants from the Keycloak server.
To retrieve the grants, the authorizer evaluates the Keycloak Authorization Services policies and permissions.
An initial Keycloak configuration usually involves uploading authorization scopes to create a list of all possible actions that can be performed on each Kafka resource type. This step is performed once only, before defining any permissions. You can add authorization scopes manually instead of uploading them.
Authorization scopes must contain all the possible Kafka permissions regardless of the resource type:
Create
Write
Read
Delete
Describe
Alter
DescribeConfig
AlterConfig
ClusterAction
IdempotentWrite
Note
|
If you’re certain you won’t need a permission (for example, |
Resource patterns are used for pattern matching against the targeted resources when performing permission checks.
The general pattern format is RESOURCE-TYPE:PATTERN-NAME
.
The resource types mirror the Kafka authorization model. The pattern allows for two matching options:
Exact matching (when the pattern does not end with *
)
Prefix matching (when the pattern ends with *
)
Topic:my-topic
Topic:orders-*
Group:orders-*
Cluster:*
Additionally, the general pattern format can be prefixed by kafka-cluster:CLUSTER-NAME
followed by a comma, where CLUSTER-NAME refers to the metadata.name
in the Kafka custom resource.
kafka-cluster:my-cluster,Topic:*
kafka-cluster:*,Group:b_*
When the kafka-cluster
prefix is missing, it is assumed to be kafka-cluster:*
.
When defining a resource, you can associate it with a list of possible authorization scopes which are relevant to the resource. Set whatever actions make sense for the targeted resource type.
Though you may add any authorization scope to any resource, only the scopes supported by the resource type are considered for access control.
Policies are used to target permissions to one or more user accounts or service accounts. Targeting can refer to:
Specific user or service accounts
Realm roles or client roles
User groups
JavaScript rules to match a client IP address
A policy is given a unique name and can be reused to target multiple permissions to multiple resources.
Use fine-grained permissions to pull together the policies, resources, and authorization scopes that grant access to users.
The name of each permission should clearly define which permissions it grants to which users.
For example, Dev Team B can read from topics starting with x
.
For more information about how to configure permissions through Keycloak Authorization Services, see Trying Keycloak Authorization Services.
The following examples demonstrate the user permissions required for performing common operations on Kafka.
To create a topic, the Create
permission is required for the specific topic, or for Cluster:kafka-cluster
.
bin/kafka-topics.sh --create --topic my-topic \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties
If a user has the Describe
permission on a specified topic, the topic is listed.
bin/kafka-topics.sh --list \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties
To display a topic’s details, Describe
and DescribeConfigs
permissions are required on the topic.
bin/kafka-topics.sh --describe --topic my-topic \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties
To produce messages to a topic, Describe
and Write
permissions are required on the topic.
If the topic hasn’t been created yet, and topic auto-creation is enabled, the permissions to create a topic are required.
bin/kafka-console-producer.sh --topic my-topic \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --producer.config=/tmp/config.properties
To consume messages from a topic, Describe
and Read
permissions are required on the topic.
Consuming from the topic normally relies on storing the consumer offsets in a consumer group, which requires additional Describe
and Read
permissions on the consumer group.
Two resources
are needed for matching. For example:
Topic:my-topic
Group:my-group-*
bin/kafka-console-consumer.sh --topic my-topic --group my-group-1 --from-beginning \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --consumer.config /tmp/config.properties
As well as the permissions for producing to a topic, an additional IdempotentWrite
permission is required on the
Cluster
resource.
Two resources
are needed for matching. For example:
Topic:my-topic
Cluster:kafka-cluster
bin/kafka-console-producer.sh --topic my-topic \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --producer.config=/tmp/config.properties --producer-property enable.idempotence=true --request-required-acks -1
When listing consumer groups, only the groups on which the user has the Describe
permissions are returned.
Alternatively, if the user has the Describe
permission on the Cluster:kafka-cluster
, all the consumer groups are returned.
bin/kafka-consumer-groups.sh --list \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties
To display a consumer group’s details, the Describe
permission is required on the group and the topics associated with the group.
bin/kafka-consumer-groups.sh --describe --group my-group-1 \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties
To change a topic’s configuration, the Describe
and Alter
permissions are required on the topic.
bin/kafka-topics.sh --alter --topic my-topic --partitions 2 \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties
In order to use kafka-configs.sh
to get a broker’s configuration, the DescribeConfigs
permission is required on the
Cluster:kafka-cluster
.
bin/kafka-configs.sh --entity-type brokers --entity-name 0 --describe --all \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties
To change a Kafka broker’s configuration, DescribeConfigs
and AlterConfigs
permissions are required on Cluster:kafka-cluster
.
bin/kafka-configs --entity-type brokers --entity-name 0 --alter --add-config log.cleaner.threads=2 \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties
To delete a topic, the Describe
and Delete
permissions are required on the topic.
bin/kafka-topics.sh --delete --topic my-topic \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config=/tmp/config.properties
To run leader selection for topic partitions, the Alter
permission is required on the Cluster:kafka-cluster
.
bin/kafka-leader-election.sh --topic my-topic --partition 0 --election-type PREFERRED /
--bootstrap-server my-cluster-kafka-bootstrap:9092 --admin.config /tmp/config.properties
To generate a partition reassignment file, Describe
permissions are required on the topics involved.
bin/kafka-reassign-partitions.sh --topics-to-move-json-file /tmp/topics-to-move.json --broker-list "0,1" --generate \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config /tmp/config.properties > /tmp/partition-reassignment.json
To execute the partition reassignment, Describe
and Alter
permissions are required on Cluster:kafka-cluster
. Also,
Describe
permissions are required on the topics involved.
bin/kafka-reassign-partitions.sh --reassignment-json-file /tmp/partition-reassignment.json --execute \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config /tmp/config.properties
To verify partition reassignment, Describe
, and AlterConfigs
permissions are required on Cluster:kafka-cluster
, and on each
of the topics involved.
bin/kafka-reassign-partitions.sh --reassignment-json-file /tmp/partition-reassignment.json --verify \
--bootstrap-server my-cluster-kafka-bootstrap:9092 --command-config /tmp/config.properties
This example explains how to use Keycloak Authorization Services with keycloak
authorization.
Use Keycloak Authorization Services to enforce access restrictions on Kafka clients.
Keycloak Authorization Services use authorization scopes, policies and permissions to define and apply access control to resources.
Keycloak Authorization Services REST endpoints provide a list of granted permissions on resources for authenticated users. The list of grants (permissions) is fetched from the Keycloak server as the first action after an authenticated session is established by the Kafka client. The list is refreshed in the background so that changes to the grants are detected. Grants are cached and enforced locally on the Kafka broker for each user session to provide fast authorization decisions.
Strimzi provides example configuration files. These include the following example files for setting up Keycloak:
kafka-ephemeral-oauth-single-keycloak-authz.yaml
An example Kafka
custom resource configured for OAuth 2.0 token-based authorization using Keycloak.
You can use the custom resource to deploy a Kafka cluster that uses keycloak
authorization and token-based oauth
authentication.
kafka-authz-realm.json
An example Keycloak realm configured with sample groups, users, roles and clients. You can import the realm into a Keycloak instance to set up fine-grained permissions to access Kafka.
If you want to try the example with Keycloak, use these files to perform the tasks outlined in this section in the order shown.
When you configure token-based oauth
authentication, you specify a jwksEndpointUri
as the URI for local JWT validation.
When you configure keycloak
authorization, you specify a tokenEndpointUri
as the URI of the Keycloak token endpoint.
The hostname for both URIs must be the same.
In Keycloak, confidential clients with service accounts enabled can authenticate to the server in their own name using a client ID and a secret.
This is convenient for microservices that typically act in their own name, and not as agents of a particular user (like a web site).
Service accounts can have roles assigned like regular users.
They cannot, however, have groups assigned.
As a consequence, if you want to target permissions to microservices using service accounts, you cannot use group policies, and should instead use role policies.
Conversely, if you want to limit certain permissions only to regular user accounts where authentication with a username and password is required, you can achieve that as a side effect of using the group policies rather than the role policies.
This is what is used in this example for permissions that start with ClusterManager
.
Performing cluster management is usually done interactively using CLI tools.
It makes sense to require the user to log in before using the resulting access token to authenticate to the Kafka broker.
In this case, the access token represents the specific user, rather than the client application.
Set up Keycloak, then connect to its Admin Console and add the preconfigured realm.
Use the example kafka-authz-realm.json
file to import the realm.
You can check the authorization rules defined for the realm in the Admin Console.
The rules grant access to the resources on the Kafka cluster configured to use the example Keycloak realm.
A running Kubernetes cluster.
The Strimzi examples/security/keycloak-authorization/kafka-authz-realm.json
file that contains the preconfigured realm.
Install the Keycloak server using the Keycloak Operator as described in Installing the Keycloak Operator in the Keycloak documentation.
Wait until the Keycloak instance is running.
Get the external hostname to be able to access the Admin Console.
NS=sso
kubectl get ingress keycloak -n $NS
In this example, we assume the Keycloak server is running in the sso
namespace.
Get the password for the admin
user.
kubectl get -n $NS pod keycloak-0 -o yaml | less
The password is stored as a secret, so get the configuration YAML file for the Keycloak instance to identify the name of the secret (secretKeyRef.name
).
Use the name of the secret to obtain the clear text password.
SECRET_NAME=credential-keycloak
kubectl get -n $NS secret $SECRET_NAME -o yaml | grep PASSWORD | awk '{print $2}' | base64 -D
In this example, we assume the name of the secret is credential-keycloak
.
Log in to the Admin Console with the username admin
and the password you obtained.
Use https://HOSTNAME
to access the Kubernetes ingress.
You can now upload the example realm to Keycloak using the Admin Console.
Click Add Realm to import the example realm.
Add the examples/security/keycloak-authorization/kafka-authz-realm.json
file, and then click Create.
You now have kafka-authz
as your current realm in the Admin Console.
The default view displays the Master realm.
In the Keycloak Admin Console, go to Clients > kafka > Authorization > Settings and check that Decision Strategy is set to Affirmative.
An affirmative policy means that at least one policy must be satisfied for a client to access the Kafka cluster.
In the Keycloak Admin Console, go to Groups, Users, Roles and Clients to view the realm configuration.
Groups
are used to create user groups and set user permissions. Groups are sets of users with a name assigned. They are used to compartmentalize users into geographical, organizational or departmental units.
Groups can be linked to an LDAP identity provider. You can make a user a member of a group through a custom LDAP server admin user interface, for example, to grant permissions on Kafka resources.
Users
are used to create users. For this example, alice
and bob
are defined. alice
is a member of the ClusterManager
group and bob
is a member of ClusterManager-my-cluster
group.
Users can be stored in an LDAP identity provider.
Roles
mark users or clients as having certain permissions.
Roles are a concept analogous to groups. They are usually used to tag users with organizational roles and have the requisite permissions.
Roles cannot be stored in an LDAP identity provider.
If LDAP is a requirement, you can use groups instead, and add Keycloak roles to the groups so that when users are assigned a group they also get a corresponding role.
Clients
can have specific configurations. For this example, kafka
, kafka-cli
, team-a-client
, and team-b-client
clients are configured.
The kafka
client is used by Kafka brokers to perform the necessary OAuth 2.0 communication for access token validation.
This client also contains the authorization services resource definitions, policies, and authorization scopes used to perform authorization on the Kafka brokers.
The authorization configuration is defined in the kafka
client from the Authorization tab, which becomes visible when Authorization Enabled is switched on from the Settings tab.
The kafka-cli
client is a public client that is used by the Kafka command line tools when authenticating with username and password to obtain an access token or a refresh token.
The team-a-client
and team-b-client
clients are confidential clients representing services with partial access to certain Kafka topics.
In the Keycloak Admin Console, go to Authorization > Permissions to see the granted permissions that use the resources and policies defined for the realm.
For example, the kafka
client has the following permissions:
Dev Team A can write to topics that start with x_ on any cluster Dev Team B can read from topics that start with x_ on any cluster Dev Team B can update consumer group offsets that start with x_ on any cluster ClusterManager of my-cluster Group has full access to cluster config on my-cluster ClusterManager of my-cluster Group has full access to consumer groups on my-cluster ClusterManager of my-cluster Group has full access to topics on my-cluster
The Dev Team A realm role can write to topics that start with x_
on any cluster. This combines a resource called Topic:x_*
, Describe
and Write
scopes, and the Dev Team A
policy. The Dev Team A
policy matches all users that have a realm role called Dev Team A
.
The Dev Team B realm role can read from topics that start with x_
on any cluster. This combines Topic:x_*
, Group:x_*
resources, Describe
and Read
scopes, and the Dev Team B
policy. The Dev Team B
policy matches all users that have a realm role called Dev Team B
. Matching users and clients have the ability to read from topics, and update the consumed offsets for topics and consumer groups that have names starting with x_
.
Deploy a Kafka cluster configured to connect to the Keycloak server.
Use the example kafka-ephemeral-oauth-single-keycloak-authz.yaml
file to deploy the Kafka cluster as a Kafka
custom resource.
The example deploys a single-node Kafka cluster with keycloak
authorization and oauth
authentication.
The Keycloak authorization server is deployed to your Kubernetes cluster and loaded with the example realm.
The Cluster Operator is deployed to your Kubernetes cluster.
The Strimzi examples/security/keycloak-authorization/kafka-ephemeral-oauth-single-keycloak-authz.yaml
custom resource.
Use the hostname of the Keycloak instance you deployed to prepare a truststore certificate for Kafka brokers to communicate with the Keycloak server.
SSO_HOST=SSO-HOSTNAME
SSO_HOST_PORT=$SSO_HOST:443
STOREPASS=storepass
echo "Q" | openssl s_client -showcerts -connect $SSO_HOST_PORT 2>/dev/null | awk ' /BEGIN CERTIFICATE/,/END CERTIFICATE/ { print $0 } ' > /tmp/sso.crt
The certificate is required as Kubernetes ingress is used to make a secure (HTTPS) connection.
Deploy the certificate to Kubernetes as a secret.
kubectl create secret generic oauth-server-cert --from-file=/tmp/sso.crt -n $NS
Set the hostname as an environment variable
SSO_HOST=SSO-HOSTNAME
Create and deploy the example Kafka cluster.
cat examples/security/keycloak-authorization/kafka-ephemeral-oauth-single-keycloak-authz.yaml | sed -E 's#\${SSO_HOST}'"#$SSO_HOST#" | kubectl create -n $NS -f -
Create a new pod for an interactive CLI session. Set up a truststore with a Keycloak certificate for TLS connectivity. The truststore is to connect to Keycloak and the Kafka broker.
The Keycloak authorization server is deployed to your Kubernetes cluster and loaded with the example realm.
In the Keycloak Admin Console, check the roles assigned to the clients are displayed in Clients > Service Account Roles.
The Kafka cluster configured to connect with Keycloak is deployed to your Kubernetes cluster.
Run a new interactive pod container using the Strimzi Kafka image to connect to a running Kafka broker.
NS=sso
kubectl run -ti --restart=Never --image=quay.io/strimzi/kafka:0.31.0-kafka-3.2.1 kafka-cli -n $NS -- /bin/sh
Note
|
If kubectl times out waiting on the image download, subsequent attempts may result in an AlreadyExists error.
|
Attach to the pod container.
kubectl attach -ti kafka-cli -n $NS
Use the hostname of the Keycloak instance to prepare a certificate for client connection using TLS.
SSO_HOST=SSO-HOSTNAME
SSO_HOST_PORT=$SSO_HOST:443
STOREPASS=storepass
echo "Q" | openssl s_client -showcerts -connect $SSO_HOST_PORT 2>/dev/null | awk ' /BEGIN CERTIFICATE/,/END CERTIFICATE/ { print $0 } ' > /tmp/sso.crt
Create a truststore for TLS connection to the Kafka brokers.
keytool -keystore /tmp/truststore.p12 -storetype pkcs12 -alias sso -storepass $STOREPASS -import -file /tmp/sso.crt -noprompt
Use the Kafka bootstrap address as the hostname of the Kafka broker and the tls
listener port (9093) to prepare a certificate for the Kafka broker.
KAFKA_HOST_PORT=my-cluster-kafka-bootstrap:9093
STOREPASS=storepass
echo "Q" | openssl s_client -showcerts -connect $KAFKA_HOST_PORT 2>/dev/null | awk ' /BEGIN CERTIFICATE/,/END CERTIFICATE/ { print $0 } ' > /tmp/my-cluster-kafka.crt
Add the certificate for the Kafka broker to the truststore.
keytool -keystore /tmp/truststore.p12 -storetype pkcs12 -alias my-cluster-kafka -storepass $STOREPASS -import -file /tmp/my-cluster-kafka.crt -noprompt
Keep the session open to check authorized access.
Check the authorization rules applied through the Keycloak realm using an interactive CLI session. Apply the checks using Kafka’s example producer and consumer clients to create topics with user and service accounts that have different levels of access.
Use the team-a-client
and team-b-client
clients to check the authorization rules.
Use the alice
admin user to perform additional administrative tasks on Kafka.
The Strimzi Kafka image used in this example contains Kafka producer and consumer binaries.
ZooKeeper and Kafka are running in the Kubernetes cluster to be able to send and receive messages.
The interactive CLI Kafka client session is started.
Prepare a Kafka configuration file with authentication properties for the team-a-client
client.
SSO_HOST=SSO-HOSTNAME
cat > /tmp/team-a-client.properties << EOF
security.protocol=SASL_SSL
ssl.truststore.location=/tmp/truststore.p12
ssl.truststore.password=$STOREPASS
ssl.truststore.type=PKCS12
sasl.mechanism=OAUTHBEARER
sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
oauth.client.id="team-a-client" \
oauth.client.secret="team-a-client-secret" \
oauth.ssl.truststore.location="/tmp/truststore.p12" \
oauth.ssl.truststore.password="$STOREPASS" \
oauth.ssl.truststore.type="PKCS12" \
oauth.token.endpoint.uri="https://$SSO_HOST/auth/realms/kafka-authz/protocol/openid-connect/token" ;
sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler
EOF
The SASL OAUTHBEARER mechanism is used. This mechanism requires a client ID and client secret, which means the client first connects to the Keycloak server to obtain an access token. The client then connects to the Kafka broker and uses the access token to authenticate.
Prepare a Kafka configuration file with authentication properties for the team-b-client
client.
cat > /tmp/team-b-client.properties << EOF
security.protocol=SASL_SSL
ssl.truststore.location=/tmp/truststore.p12
ssl.truststore.password=$STOREPASS
ssl.truststore.type=PKCS12
sasl.mechanism=OAUTHBEARER
sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
oauth.client.id="team-b-client" \
oauth.client.secret="team-b-client-secret" \
oauth.ssl.truststore.location="/tmp/truststore.p12" \
oauth.ssl.truststore.password="$STOREPASS" \
oauth.ssl.truststore.type="PKCS12" \
oauth.token.endpoint.uri="https://$SSO_HOST/auth/realms/kafka-authz/protocol/openid-connect/token" ;
sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler
EOF
Authenticate admin user alice
by using curl
and performing a password grant authentication to obtain a refresh token.
USERNAME=alice
PASSWORD=alice-password
GRANT_RESPONSE=$(curl -X POST "https://$SSO_HOST/auth/realms/kafka-authz/protocol/openid-connect/token" -H 'Content-Type: application/x-www-form-urlencoded' -d "grant_type=password&username=$USERNAME&password=$PASSWORD&client_id=kafka-cli&scope=offline_access" -s -k)
REFRESH_TOKEN=$(echo $GRANT_RESPONSE | awk -F "refresh_token\":\"" '{printf $2}' | awk -F "\"" '{printf $1}')
The refresh token is an offline token that is long-lived and does not expire.
Prepare a Kafka configuration file with authentication properties for the admin user alice
.
cat > /tmp/alice.properties << EOF
security.protocol=SASL_SSL
ssl.truststore.location=/tmp/truststore.p12
ssl.truststore.password=$STOREPASS
ssl.truststore.type=PKCS12
sasl.mechanism=OAUTHBEARER
sasl.jaas.config=org.apache.kafka.common.security.oauthbearer.OAuthBearerLoginModule required \
oauth.refresh.token="$REFRESH_TOKEN" \
oauth.client.id="kafka-cli" \
oauth.ssl.truststore.location="/tmp/truststore.p12" \
oauth.ssl.truststore.password="$STOREPASS" \
oauth.ssl.truststore.type="PKCS12" \
oauth.token.endpoint.uri="https://$SSO_HOST/auth/realms/kafka-authz/protocol/openid-connect/token" ;
sasl.login.callback.handler.class=io.strimzi.kafka.oauth.client.JaasClientOauthLoginCallbackHandler
EOF
The kafka-cli
public client is used for the oauth.client.id
in the sasl.jaas.config
.
Since it’s a public client it does not require a secret.
The client authenticates with the refresh token that was authenticated in the previous step.
The refresh token requests an access token behind the scenes, which is then sent to the Kafka broker for authentication.
Use the team-a-client
configuration to check that you can produce messages to topics that start with a_
or x_
.
Write to topic my-topic
.
bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic my-topic \
--producer.config=/tmp/team-a-client.properties
First message
This request returns a Not authorized to access topics: [my-topic]
error.
team-a-client
has a Dev Team A
role that gives it permission to perform any supported actions on topics that start with a_
, but can only write to topics that start with x_
.
The topic named my-topic
matches neither of those rules.
Write to topic a_messages
.
bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic a_messages \
--producer.config /tmp/team-a-client.properties
First message
Second message
Messages are produced to Kafka successfully.
Press CTRL+C to exit the CLI application.
Check the Kafka container log for a debug log of Authorization GRANTED
for the request.
kubectl logs my-cluster-kafka-0 -f -n $NS
Use the team-a-client
configuration to consume messages from topic a_messages
.
Fetch messages from topic a_messages
.
bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic a_messages \
--from-beginning --consumer.config /tmp/team-a-client.properties
The request returns an error because the Dev Team A
role for team-a-client
only has access to consumer groups that have names starting with a_
.
Update the team-a-client
properties to specify the custom consumer group it is permitted to use.
bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic a_messages \
--from-beginning --consumer.config /tmp/team-a-client.properties --group a_consumer_group_1
The consumer receives all the messages from the a_messages
topic.
The team-a-client
is an account without any cluster-level access, but it can be used with some administrative operations.
List topics.
bin/kafka-topics.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/team-a-client.properties --list
The a_messages
topic is returned.
List consumer groups.
bin/kafka-consumer-groups.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/team-a-client.properties --list
The a_consumer_group_1
consumer group is returned.
Fetch details on the cluster configuration.
bin/kafka-configs.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/team-a-client.properties \
--entity-type brokers --describe --entity-default
The request returns an error because the operation requires cluster level permissions that team-a-client
does not have.
Use the team-b-client
configuration to produce messages to topics that start with b_
.
Write to topic a_messages
.
bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic a_messages \
--producer.config /tmp/team-b-client.properties
Message 1
This request returns a Not authorized to access topics: [a_messages]
error.
Write to topic b_messages
.
bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic b_messages \
--producer.config /tmp/team-b-client.properties
Message 1
Message 2
Message 3
Messages are produced to Kafka successfully.
Write to topic x_messages
.
bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \
--producer.config /tmp/team-b-client.properties
Message 1
A Not authorized to access topics: [x_messages]
error is returned,
The team-b-client
can only read from topic x_messages
.
Write to topic x_messages
using team-a-client
.
bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \
--producer.config /tmp/team-a-client.properties
Message 1
This request returns a Not authorized to access topics: [x_messages]
error.
The team-a-client
can write to the x_messages
topic, but it does not have a permission to create a topic if it does not yet exist.
Before team-a-client
can write to the x_messages
topic, an admin power user must create it with the correct configuration, such as the number of partitions and replicas.
Use admin user alice
to manage Kafka.
alice
has full access to manage everything on any Kafka cluster.
Create the x_messages
topic as alice
.
bin/kafka-topics.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/alice.properties \
--topic x_messages --create --replication-factor 1 --partitions 1
The topic is created successfully.
List all topics as alice
.
bin/kafka-topics.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/alice.properties --list
bin/kafka-topics.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/team-a-client.properties --list
bin/kafka-topics.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/team-b-client.properties --list
Admin user alice
can list all the topics, whereas team-a-client
and team-b-client
can only list the topics they have access to.
The Dev Team A
and Dev Team B
roles both have Describe
permission on topics that start with x_
, but they cannot see the other team’s topics because they do not have Describe
permissions on them.
Use the team-a-client
to produce messages to the x_messages
topic:
bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \
--producer.config /tmp/team-a-client.properties
Message 1
Message 2
Message 3
As alice
created the x_messages
topic, messages are produced to Kafka successfully.
Use the team-b-client
to produce messages to the x_messages
topic.
bin/kafka-console-producer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \
--producer.config /tmp/team-b-client.properties
Message 4
Message 5
This request returns a Not authorized to access topics: [x_messages]
error.
Use the team-b-client
to consume messages from the x_messages
topic:
bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \
--from-beginning --consumer.config /tmp/team-b-client.properties --group x_consumer_group_b
The consumer receives all the messages from the x_messages
topic.
Use the team-a-client
to consume messages from the x_messages
topic.
bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \
--from-beginning --consumer.config /tmp/team-a-client.properties --group x_consumer_group_a
This request returns a Not authorized to access topics: [x_messages]
error.
Use the team-a-client
to consume messages from a consumer group that begins with a_
.
bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \
--from-beginning --consumer.config /tmp/team-a-client.properties --group a_consumer_group_a
This request returns a Not authorized to access topics: [x_messages]
error.
Dev Team A
has no Read
access on topics that start with a x_
.
Use alice
to produce messages to the x_messages
topic.
bin/kafka-console-consumer.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --topic x_messages \
--from-beginning --consumer.config /tmp/alice.properties
Messages are produced to Kafka successfully.
alice
can read from or write to any topic.
Use alice
to read the cluster configuration.
bin/kafka-configs.sh --bootstrap-server my-cluster-kafka-bootstrap:9093 --command-config /tmp/alice.properties \
--entity-type brokers --describe --entity-default
The cluster configuration for this example is empty.
Use the Strimzi operators to manage your Kafka cluster, and Kafka topics and users.
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.
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.
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
and ClusterRole
RoleBinding
and ClusterRoleBinding
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 |
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
resourcesThe 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:
- 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:
- "kafka.strimzi.io"
resources:
# The cluster operator runs the KafkaAssemblyOperator, which needs to access and manage Kafka resources
- kafkas
- kafkas/status
# The cluster operator runs the KafkaConnectAssemblyOperator, which needs to access and manage KafkaConnect resources
- kafkaconnects
- kafkaconnects/status
# The cluster operator runs the KafkaConnectorAssemblyOperator, which needs to access and manage KafkaConnector resources
- kafkaconnectors
- kafkaconnectors/status
# The cluster operator runs the KafkaMirrorMakerAssemblyOperator, which needs to access and manage KafkaMirrorMaker resources
- kafkamirrormakers
- kafkamirrormakers/status
# The cluster operator runs the KafkaBridgeAssemblyOperator, which needs to access and manage BridgeMaker resources
- kafkabridges
- kafkabridges/status
# The cluster operator runs the KafkaMirrorMaker2AssemblyOperator, which needs to access and manage KafkaMirrorMaker2 resources
- kafkamirrormaker2s
- kafkamirrormaker2s/status
# The cluster operator runs the KafkaRebalanceAssemblyOperator, which needs to access and manage KafkaRebalance resources
- kafkarebalances
- kafkarebalances/status
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
- apiGroups:
- "core.strimzi.io"
resources:
# The cluster operator uses StrimziPodSets to manage the Kafka and ZooKeeper pods
- strimzipodsets
- strimzipodsets/status
verbs:
- get
- list
- watch
- create
- delete
- patch
- update
- apiGroups:
# The cluster operator needs the extensions api as the operator supports Kubernetes version 1.11+
# apps/v1 was introduced in Kubernetes 1.14
- "extensions"
resources:
# The cluster operator needs to access and manage deployments to run deployment based Strimzi components
- deployments
- deployments/scale
# The cluster operator needs to access replica sets to manage Strimzi components and to determine error states
- replicasets
# The cluster operator needs to access and manage replication controllers to manage replicasets
- replicationcontrollers
# 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:
- "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:
- 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
resourcesThe 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
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 .
|
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.
env:
- name: STRIMZI_NAMESPACE
valueFrom:
fieldRef:
fieldPath: metadata.namespace
STRIMZI_FULL_RECONCILIATION_INTERVAL_MS
Optional, default is 120000 ms. The interval between periodic reconciliations, in milliseconds.
STRIMZI_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 default tickTime
(whose default is 2000) at 40000 ms.
If you require a higher timeout, change the maxSessionTimeout
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 as spec.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
, and KafkaMirrorMaker2
resources.
KafkaRebalance
and KafkaConnector
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 example 3.1.0=quay.io/strimzi/kafka:0.31.0-kafka-3.1.0, 3.2.1=quay.io/strimzi/kafka:0.31.0-kafka-3.2.1
.
This is used when a Kafka.spec.kafka.version
property is specified but not the Kafka.spec.kafka.image
in the Kafka
resource.
STRIMZI_DEFAULT_KAFKA_INIT_IMAGE
Optional, default quay.io/strimzi/operator:0.31.0
.
The image name to use as default for the init container if no image is specified as the kafka-init-image
in the Kafka
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 example 3.1.0=quay.io/strimzi/kafka:0.31.0-kafka-3.1.0, 3.2.1=quay.io/strimzi/kafka:0.31.0-kafka-3.2.1
.
This is used when a KafkaConnect.spec.version
property is specified but not the KafkaConnect.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 example 3.1.0=quay.io/strimzi/kafka:0.31.0-kafka-3.1.0, 3.2.1=quay.io/strimzi/kafka:0.31.0-kafka-3.2.1
.
This is used when a KafkaMirrorMaker.spec.version
property is specified but not the KafkaMirrorMaker.spec.image
.
STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGE
Optional, default quay.io/strimzi/operator:0.31.0
.
The image name to use as the default when deploying the Topic Operator
if no image is specified as the Kafka.spec.entityOperator.topicOperator.image
in the Kafka
resource.
STRIMZI_DEFAULT_USER_OPERATOR_IMAGE
Optional, default quay.io/strimzi/operator:0.31.0
.
The image name to use as the default when deploying the User Operator
if no image is specified as the Kafka.spec.entityOperator.userOperator.image
in the Kafka
resource.
STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGE
Optional, default quay.io/strimzi/kafka:0.31.0-kafka-3.2.1
.
The image name to use as the default when deploying the sidecar container for the Entity Operator if
no image is specified as the Kafka.spec.entityOperator.tlsSidecar.image
in the Kafka
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 are Always
, IfNotPresent
, and Never
.
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 the imagePullSecrets
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.
env:
- name: STRIMZI_KUBERNETES_VERSION
value: |
major=1
minor=16
gitVersion=v1.16.2
gitCommit=c97fe5036ef3df2967d086711e6c0c405941e14b
gitTreeState=clean
buildDate=2019-10-15T19:09:08Z
goVersion=go1.12.10
compiler=gc
platform=linux/amd64
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 to true
, the Cluster Operator reconciles only the StrimziPodSet
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.
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.
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
#...
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.
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
, and RoleBinding
) resources.
To add proxy environment variables to the Cluster Operator, update its Deployment
configuration (install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml
).
apiVersion: 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
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.
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
, and RoleBinding
) 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.
apiVersion: 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 to true
(default).
In this example, the name of the lease is changed to my-strimzi-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 the 022-ClusterRole-strimzi-cluster-operator-role.yaml
file.
Update resourceNames
with the name of the Lease
resource.
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
name: strimzi-cluster-operator-leader-election
labels:
app: strimzi
rules:
resourceNames:
- my-strimzi-cluster-operator
# ...
(optional) Edit the the RoleBinding
resource in the 022-RoleBinding-strimzi-cluster-operator.yaml
file.
Update subjects.name
and subjects.namespace
with the name of the Lease
resource and the namespace where it was created.
apiVersion: 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
NAME 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 the AVAILABLE
output shows the correct number of replicas.
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 will automatically switch to FIPS mode. This prevents Strimzi from running on the cluster. When you deploy Strimzi to the cluster, you will see errors similar to the following:
Exception in thread "main" io.fabric8.kubernetes.client.KubernetesClientException: An error has occurred.
...
Caused by: java.security.KeyStoreException: sun.security.pkcs11.wrapper.PKCS11Exception: CKR_SESSION_READ_ONLY
...
Caused by: sun.security.pkcs11.wrapper.PKCS11Exception: CKR_SESSION_READ_ONLY
...
If you want to run Strimzi on your FIPS-enabled cluster, you can disable the OpenJDK FIPS mode by setting the FIPS_MODE
environment variable to disabled
in the deployment configuration for the Cluster Operator.
The Strimzi deployment won’t be FIPS compliant, but the Strimzi operators as well as all of its operands will be able to run on the FIPS-enabled Kubernetes cluster.
To disable the FIPS mode in the Cluster Operator, update its Deployment
configuration (install/cluster-operator/060-Deployment-strimzi-cluster-operator.yaml
) and add the FIPS_MODE
environment variable.
apiVersion: 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
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. |
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.
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.
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 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 as exampleTopic.
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
|
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 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. |
In previous releases of Strimzi, topic metadata was stored in ZooKeeper. The new process removes this requirement, bringing the metadata into the Kafka cluster, and under the control of the Topic Operator.
When upgrading to Strimzi 0.31.0, 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.
If you are reverting back to a version of Strimzi earlier than 0.22, which uses ZooKeeper for the storage of topic metadata, you still downgrade your Cluster Operator to the previous version, then downgrade Kafka brokers and client applications to the previous Kafka version as standard.
However, you must also delete the topics that were created for the topic store using a kafka-admin
command, specifying the bootstrap address of the Kafka cluster.
For example:
kubectl run kafka-admin -ti --image=quay.io/strimzi/kafka:0.31.0-kafka-3.2.1 --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.
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 the kafka-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.
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.
|
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 TLS authentication and encryption.
A running Topic Operator (typically deployed with the Entity Operator).
For deleting a topic, delete.topic.enable=true
(default) in the spec.kafka.config
of the Kafka
resource.
Configure the KafkaTopic
resource.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
name: orders
labels:
strimzi.io/cluster: my-cluster
spec:
partitions: 10
replicas: 2
Tip
|
When 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>
NAME 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 shows True
.
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
NotReady
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>
NAME 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
READY
status# ...
status:
conditions:
- lastTransitionTime: '2022-06-13T10:15:03.761084Z'
status: 'True'
type: Ready
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 the Kafka
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>
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. |
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 TLS authentication and encryption.
A running User Operator (typically deployed with the Entity Operator).
Configure the KafkaUser
resource.
This example specifies TLS authentication and simple authorization using ACLs.
apiVersion: 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
operation: Read
host: "*"
- resource:
type: topic
name: my-topic
patternType: literal
operation: Describe
host: "*"
- resource:
type: group
name: my-group
patternType: literal
operation: Read
host: "*"
# Example Producer Acls for topic my-topic
- resource:
type: topic
name: my-topic
patternType: literal
operation: Write
host: "*"
- resource:
type: topic
name: my-topic
patternType: literal
operation: Create
host: "*"
- resource:
type: topic
name: my-topic
patternType: literal
operation: Describe
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>
NAME 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 shows True
.
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
NotReady
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.
apiVersion: 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>
NAME 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
READY
status# ...
status:
conditions:
- lastTransitionTime: "2022-06-10T10:33:40.166846Z"
status: "True"
type: Ready
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 the Kafka
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.
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
The ControlPlaneListener
feature gate has a default state of enabled.
Use the ControlPlaneListener
feature gate to change the communication paths used for inter-broker communications within your Kafka cluster.
In Strimzi, control plane traffic consists of controller connections that maintain the desired state of the Kafka cluster.
Data plane traffic mainly consists of data replication between the leader broker and the follower brokers.
When ControlPlaneListener
is enabled, control plane traffic goes through a dedicated control plane listener on port 9090.
Data plane traffic continues to use the internal listener on port 9091.
Using control plane listeners might improve performance because important controller connections, such as partition leadership changes, are not delayed by data replication across brokers.
To disable the ControlPlaneListener
feature gate, specify -ControlPlaneListener
in the STRIMZI_FEATURE_GATES
environment variable in the Cluster Operator configuration.
When the ControlPlaneListener
feature gate is disabled, control plane and data plane traffic go through the same internal listener on port 9091.
This was the default behavior before the feature gate was introduced.
Important
|
The ControlPlaneListener feature gate must be disabled when upgrading from or downgrading to Strimzi 0.22 and earlier versions.
|
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.
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.
|
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 the Kafka
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.
Liveness and readiness probes are disabled.
All Kafka nodes have both the controller
and broker
KRaft roles.
Kafka clusters with separate controller
and broker
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.
|
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 beta stage in Strimzi 0.27 and is expected to remain in the beta stage until Strimzi 0.31.
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 and is expected to remain in the beta stage until Strimzi 0.32.
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 |
- |
|
0.24 |
0.27 |
0.30 |
|
0.28 |
0.30 |
- |
|
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 |
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.
Cruise Control is an open source system for automating Kafka operations, such as monitoring cluster workload, rebalancing a cluster based on predefined constraints, and detecting and fixing anomalies. It consists of four main components—the Load Monitor, the Analyzer, the Anomaly Detector, and the Executor—and a REST API for client interactions.
You can deploy Cruise Control to your Strimzi cluster and use it to rebalance a Kafka cluster.
You deploy Cruise Control through configuration of a Kafka
resource.
You perform rebalances through the KafkaRebalance
resource, which generates and applies optimization proposals.
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.
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 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.
Strimzi provides example configuration files.
Example YAML configuration files for Cruise Control are provided in examples/cruise-control/
.
Cruise Control reduces the time and effort involved in running an efficient and balanced Kafka cluster.
A typical cluster can become unevenly loaded over time. Partitions that handle large amounts of message traffic might be unevenly 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.
When you approve an optimization proposal, Cruise Control applies it to your Kafka cluster. When the cluster rebalancing operation is complete, the broker pods are used more effectively and the Kafka cluster is more evenly balanced.
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.
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. |
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. |
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 in Kafka.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.
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 in Kafka.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.
|
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 in Kafka.spec.cruiseControl.config
.
To modify the default optimization goals, edit the default.goals
property in Kafka.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.
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.
You use the KafkaRebalance
resource to generate and apply the changes suggested in optimization proposals.
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
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 the spec.mode
property is not defined in the KafkaRebalance
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 the add-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 the spec.brokers
property of the KafkaRebalance
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, the remove-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 the spec.brokers
property in the KafkaRebalance
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.
When an optimization proposal is generated, a summary and broker load is returned.
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 the Status.OptimizationResult
property of the KafkaRebalance
resource.
The information provided is a summary of the full optimization proposal.
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.
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.
You approve the optimization proposal by setting the strimzi.io/rebalance
annotation of the KafkaRebalance
resource to approve
.
Cruise Control applies the proposal to the Kafka cluster and starts a cluster rebalance operation.
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 a KafkaRebalance
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.
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. |
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. |
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.
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.
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.
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 if cruiseControl.config.concurrency.adjuster.min.isr.check.enabled
is set to true
in the Kafka
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.
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.
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.
Through Kafka
resource configuration, the Cluster Operator can deploy Cruise Control when deploying 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 the Kafka
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
and memory
, 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 the log4j.properties
key. Cruise Control has a single logger named rootLogger.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>
NAME 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 the AVAILABLE
output shows 1
.
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.
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 the spec
property empty.
Cruise Control rebalances a Kafka cluster in full
mode by default.
apiVersion: 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 the spec.mode
property.
full
modeapiVersion: kafka.strimzi.io/v1beta2
kind: KafkaRebalance
metadata:
name: my-rebalance
labels:
strimzi.io/cluster: my-cluster
spec:
mode: full
add-brokers
modeIf 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.
add-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
modeIf 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.
remove-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.
Note
|
The 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
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.
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.
Review the optimization proposal.
The optimization proposal is contained in the Status.Optimization Result
property of the KafkaRebalance
resource.
kubectl describe kafkarebalance <kafka_rebalance_resource_name>
Status:
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 |
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 is ProposalReady
.
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 with strimzi.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 with strimzi.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 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 a KafkaRebalance
resource.
When the status changes to Ready
, the rebalance is complete.
To use the same KafkaRebalance
custom resource to generate another optimization proposal, apply the refresh
annotation to the custom resource.
This moves the custom resource to the PendingProposal
or ProposalReady
state. You can then review the optimization proposal and approve it, if desired.
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 with approve
.
The status of the KafkaRebalance
custom resource is Rebalancing
.
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
.
KafkaRebalance
resourceIf 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 the KafkaRebalance
resource is missing.
The Cruise Control server is not deployed as the cruiseControl
property in the Kafka
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 is NotReady
.
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 to ProposalReady
.
Distributed tracing allows you to track the progress of transactions between applications in a distributed system. In a microservices architecture, tracing tracks the progress of transactions between services. Trace data is useful for monitoring application performance and investigating issues with target systems and end-user applications.
In Strimzi, tracing facilitates the end-to-end tracking of messages: from source systems to Kafka, and then from Kafka to target systems and applications. It complements the metrics that are available to view in Grafana dashboards, as well as the component loggers.
Support for tracing is built in to the following components:
Kafka Connect
MirrorMaker
MirrorMaker 2.0
Strimzi Kafka Bridge
Strimzi uses the OpenTracing and Jaeger Tracing projects to implement tracing.
Tracing is enabled by setting the .spec.tracing.type
property to jaeger
in the component configuration.
Note
|
Support for |
You enable and configure tracing for these components using template configuration properties in their custom resources.
To enable tracing in Kafka producers, consumers, and Kafka Streams API applications, you instrument application code using the OpenTracing Apache Kafka Client Instrumentation library (included with Strimzi). When instrumented, clients generate trace data; for example, when producing messages or writing offsets to the log.
Traces are sampled according to a sampling strategy and then visualized in the Jaeger user interface.
Note
|
Tracing is not supported for Kafka brokers. Setting up tracing for applications and systems beyond Strimzi is outside the scope of this chapter. To learn more about this subject, search for "inject and extract" in the OpenTracing documentation. |
To set up tracing for Strimzi, follow these procedures in order:
Set up tracing for clients:
Instrument clients with tracers:
Set up tracing for MirrorMaker, Kafka Connect, and the Kafka Bridge
The Jaeger backend components are deployed to your Kubernetes cluster. For deployment instructions, see the Jaeger documentation.
Strimzi uses the OpenTracing and Jaeger projects.
OpenTracing is an API specification that is independent from the tracing or monitoring system.
The OpenTracing APIs are used to instrument application code
Instrumented applications generate traces for individual transactions across the distributed system
Traces are composed of spans that define specific units of work over time
Jaeger is a tracing system for microservices-based distributed systems.
Jaeger implements the OpenTracing APIs and provides client libraries for instrumentation
The Jaeger user interface allows you to query, filter, and analyze trace data
Initialize a Jaeger tracer to instrument your client applications for distributed tracing.
Configure and initialize a Jaeger tracer using a set of tracing environment variables.
In each client application:
Add Maven dependencies for Jaeger to the pom.xml
file for the client application:
<dependency>
<groupId>io.jaegertracing</groupId>
<artifactId>jaeger-client</artifactId>
<version>1.3.2</version>
</dependency>
Define the configuration of the Jaeger tracer using the tracing environment variables.
Create the Jaeger tracer from the environment variables that you defined in step two:
Tracer tracer = Configuration.fromEnv().getTracer();
Note
|
For alternative ways to initialize a Jaeger tracer, see the Java OpenTracing library documentation. |
Register the Jaeger tracer as a global tracer:
GlobalTracer.register(tracer);
A Jaeger tracer is now initialized for the client application to use.
Use these environment variables when configuring a Jaeger tracer for Kafka clients.
Note
|
The tracing environment variables are part of the Jaeger project and are subject to change. For the latest environment variables, see the Jaeger documentation. |
Property | Required | Description |
---|---|---|
|
Yes |
The name of the Jaeger tracer service. |
|
No |
The hostname for communicating with the |
|
No |
The port used for communicating with the |
|
No |
The |
|
No |
The authentication token to send to the endpoint as a bearer token. |
|
No |
The username to send to the endpoint if using basic authentication. |
|
No |
The password to send to the endpoint if using basic authentication. |
|
No |
A comma-separated list of formats to use for propagating the trace context. Defaults to the standard Jaeger format. Valid values are |
|
No |
Indicates whether the reporter should also log the spans. |
|
No |
The reporter’s maximum queue size. |
|
No |
The reporter’s flush interval, in ms. Defines how frequently the Jaeger reporter flushes span batches. |
|
No |
The sampling strategy to use for client traces:
To sample all traces, use the Constant sampling strategy with a parameter of 1. For an overview of the Jaeger architecture and client sampling configuration parameters, see the Jaeger documentation. |
|
No |
The sampler parameter (number). |
|
No |
The hostname and port to use if a Remote sampling strategy is selected. |
|
No |
A comma-separated list of tracer-level tags that are added to all reported spans. The value can also refer to an environment variable using the format |
Instrument Kafka producer and consumer clients, and Kafka Streams API applications for distributed tracing.
Use a Decorator pattern or Interceptors to instrument your Java producer and consumer application code for tracing.
In the application code of each producer and consumer application:
Add the Maven dependency for OpenTracing to the producer or consumer’s pom.xml
file.
<dependency>
<groupId>io.opentracing.contrib</groupId>
<artifactId>opentracing-kafka-client</artifactId>
<version>0.1.15</version>
</dependency>
Instrument your client application code using either a Decorator pattern or Interceptors.
To use a Decorator pattern:
// Create an instance of the KafkaProducer:
KafkaProducer<Integer, String> producer = new KafkaProducer<>(senderProps);
// Create an instance of the TracingKafkaProducer:
TracingKafkaProducer<Integer, String> tracingProducer = new TracingKafkaProducer<>(producer,
tracer);
// Send:
tracingProducer.send(...);
// Create an instance of the KafkaConsumer:
KafkaConsumer<Integer, String> consumer = new KafkaConsumer<>(consumerProps);
// Create an instance of the TracingKafkaConsumer:
TracingKafkaConsumer<Integer, String> tracingConsumer = new TracingKafkaConsumer<>(consumer,
tracer);
// Subscribe:
tracingConsumer.subscribe(Collections.singletonList("messages"));
// Get messages:
ConsumerRecords<Integer, String> records = tracingConsumer.poll(1000);
// Retrieve SpanContext from polled record (consumer side):
ConsumerRecord<Integer, String> record = ...
SpanContext spanContext = TracingKafkaUtils.extractSpanContext(record.headers(), tracer);
To use Interceptors:
// Register the tracer with GlobalTracer:
GlobalTracer.register(tracer);
// Add the TracingProducerInterceptor to the sender properties:
senderProps.put(ProducerConfig.INTERCEPTOR_CLASSES_CONFIG,
TracingProducerInterceptor.class.getName());
// Create an instance of the KafkaProducer:
KafkaProducer<Integer, String> producer = new KafkaProducer<>(senderProps);
// Send:
producer.send(...);
// Add the TracingConsumerInterceptor to the consumer properties:
consumerProps.put(ConsumerConfig.INTERCEPTOR_CLASSES_CONFIG,
TracingConsumerInterceptor.class.getName());
// Create an instance of the KafkaConsumer:
KafkaConsumer<Integer, String> consumer = new KafkaConsumer<>(consumerProps);
// Subscribe:
consumer.subscribe(Collections.singletonList("messages"));
// Get messages:
ConsumerRecords<Integer, String> records = consumer.poll(1000);
// Retrieve the SpanContext from a polled message (consumer side):
ConsumerRecord<Integer, String> record = ...
SpanContext spanContext = TracingKafkaUtils.extractSpanContext(record.headers(), tracer);
A span is a logical unit of work in Jaeger, with an operation name, start time, and duration.
To use a Decorator pattern to instrument your producer and consumer applications, define custom span names by passing a BiFunction
object as an additional argument when creating the TracingKafkaProducer
and TracingKafkaConsumer
objects. The OpenTracing Apache Kafka Client Instrumentation library includes several built-in span names.
// Create a BiFunction for the KafkaProducer that operates on (String operationName, ProducerRecord consumerRecord) and returns a String to be used as the name:
BiFunction<String, ProducerRecord, String> producerSpanNameProvider =
(operationName, producerRecord) -> "CUSTOM_PRODUCER_NAME";
// Create an instance of the KafkaProducer:
KafkaProducer<Integer, String> producer = new KafkaProducer<>(senderProps);
// Create an instance of the TracingKafkaProducer
TracingKafkaProducer<Integer, String> tracingProducer = new TracingKafkaProducer<>(producer,
tracer,
producerSpanNameProvider);
// Spans created by the tracingProducer will now have "CUSTOM_PRODUCER_NAME" as the span name.
// Create a BiFunction for the KafkaConsumer that operates on (String operationName, ConsumerRecord consumerRecord) and returns a String to be used as the name:
BiFunction<String, ConsumerRecord, String> consumerSpanNameProvider =
(operationName, consumerRecord) -> operationName.toUpperCase();
// Create an instance of the KafkaConsumer:
KafkaConsumer<Integer, String> consumer = new KafkaConsumer<>(consumerProps);
// Create an instance of the TracingKafkaConsumer, passing in the consumerSpanNameProvider BiFunction:
TracingKafkaConsumer<Integer, String> tracingConsumer = new TracingKafkaConsumer<>(consumer,
tracer,
consumerSpanNameProvider);
// Spans created by the tracingConsumer will have the operation name as the span name, in upper-case.
// "receive" -> "RECEIVE"
When defining custom span names, you can use the following BiFunctions
in the ClientSpanNameProvider
class. If no spanNameProvider
is specified, CONSUMER_OPERATION_NAME
and PRODUCER_OPERATION_NAME
are used.
BiFunction | Description |
---|---|
|
Returns the |
|
Returns a String concatenation of |
|
Returns the name of the topic that the message was sent to or retrieved from in the format |
|
Returns a String concatenation of |
|
Returns the operation name and the topic name: |
|
Returns a String concatenation of |
This section describes how to instrument Kafka Streams API applications for distributed tracing.
In each Kafka Streams API application:
Add the opentracing-kafka-streams
dependency to the pom.xml file for your Kafka Streams API application:
<dependency>
<groupId>io.opentracing.contrib</groupId>
<artifactId>opentracing-kafka-streams</artifactId>
<version>0.1.15</version>
</dependency>
Create an instance of the TracingKafkaClientSupplier
supplier interface:
KafkaClientSupplier supplier = new TracingKafkaClientSupplier(tracer);
Provide the supplier interface to KafkaStreams
:
KafkaStreams streams = new KafkaStreams(builder.build(), new StreamsConfig(config), supplier);
streams.start();
Distributed tracing is supported for MirrorMaker, MirrorMaker 2.0, Kafka Connect, and the Strimzi Kafka Bridge.
For MirrorMaker and MirrorMaker 2.0, messages are traced from the source cluster to the target cluster. The trace data records messages entering and leaving the MirrorMaker or MirrorMaker 2.0 component.
Only messages produced and consumed by Kafka Connect itself are traced. To trace messages sent between Kafka Connect and external systems, you must configure tracing in the connectors for those systems. For more information, see Configuring Kafka Connect.
Messages produced and consumed by the Kafka Bridge are traced. Incoming HTTP requests from client applications to send and receive messages through the Kafka Bridge are also traced. To have end-to-end tracing, you must configure tracing in your HTTP clients.
Update the configuration of KafkaMirrorMaker
, KafkaMirrorMaker2
, KafkaConnect
, and KafkaBridge
custom resources to specify and configure a Jaeger tracer service for each resource. Updating a tracing-enabled resource in your Kubernetes cluster triggers two events:
Interceptor classes are updated in the integrated consumers and producers in MirrorMaker, MirrorMaker 2.0, Kafka Connect, or the Strimzi Kafka Bridge.
For MirrorMaker, MirrorMaker 2.0, and Kafka Connect, the tracing agent initializes a Jaeger tracer based on the tracing configuration defined in the resource.
For the Kafka Bridge, a Jaeger tracer based on the tracing configuration defined in the resource is initialized by the Kafka Bridge itself.
Perform these steps for each KafkaMirrorMaker
, KafkaMirrorMaker2
, KafkaConnect
, and KafkaBridge
resource.
In the spec.template
property, configure the Jaeger tracer service. For example:
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaConnect
metadata:
name: my-connect-cluster
spec:
#...
template:
connectContainer: (1)
env:
- name: JAEGER_SERVICE_NAME
value: my-jaeger-service
- name: JAEGER_AGENT_HOST
value: jaeger-agent-name
- name: JAEGER_AGENT_PORT
value: "6831"
tracing: (2)
type: jaeger
#...
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker
metadata:
name: my-mirror-maker
spec:
#...
template:
mirrorMakerContainer:
env:
- name: JAEGER_SERVICE_NAME
value: my-jaeger-service
- name: JAEGER_AGENT_HOST
value: jaeger-agent-name
- name: JAEGER_AGENT_PORT
value: "6831"
tracing:
type: jaeger
#...
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaMirrorMaker2
metadata:
name: my-mm2-cluster
spec:
#...
template:
connectContainer:
env:
- name: JAEGER_SERVICE_NAME
value: my-jaeger-service
- name: JAEGER_AGENT_HOST
value: jaeger-agent-name
- name: JAEGER_AGENT_PORT
value: "6831"
tracing:
type: jaeger
#...
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaBridge
metadata:
name: my-bridge
spec:
#...
template:
bridgeContainer:
env:
- name: JAEGER_SERVICE_NAME
value: my-jaeger-service
- name: JAEGER_AGENT_HOST
value: jaeger-agent-name
- name: JAEGER_AGENT_PORT
value: "6831"
tracing:
type: jaeger
#...
Use the tracing environment variables as template configuration properties.
Set the spec.tracing.type
property to jaeger
.
Create or update the resource:
kubectl apply -f your-file
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 TLS authentication between Kafka brokers and clients.
Certificate Authority (CA) 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 client 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. |
To support encryption, each Strimzi component needs its own private keys and public key certificates. All component certificates are signed by an internal Certificate Authority (CA) called the cluster CA.
Similarly, each Kafka client application connecting to Strimzi using TLS client authentication needs to provide 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 mutual TLS 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.
Certificates provided by users are not renewed.
You can provide your own CA certificates for the cluster CA or clients CA. For more information, see Installing your own CA certificates. If you provide your own certificates, you must manually renew them when needed.
This procedure describes how to install your own CA certificates and keys instead of using the CA certificates and private keys generated by the Cluster Operator.
The Cluster Operator automatically generates and renews the following secrets:
CLUSTER-NAME-cluster-ca
The cluster secret that contains the private key for the cluster CA.
CLUSTER-NAME-cluster-ca-cert
The cluster secret that contains a cluster CA certificate. The certificate contains a public key to validate the identity of Kafka brokers.
CLUSTER-NAME-clients-ca
The client secret that contains the private key for the client CA.
CLUSTER-NAME-clients-ca-cert
The client secret that contains a client CA certificate. The certificate contains a public key to validate the identity of clients accessing the Kafka brokers.
Strimzi uses these secrets by default.
This procedure describes the steps to replace the secrets to use your own cluster or client CA certificates.
The Cluster Operator is running.
A Kafka cluster is not yet deployed.
Your own X.509 certificates and keys in PEM format for the cluster CA or clients CA.
If you want to use a cluster or clients CA which is not a Root CA, you have to include the whole chain in the certificate file. The chain should be in the following order:
The cluster or clients CA
One or more intermediate CAs
The root CA
All CAs in the chain should be configured 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 the following files for the CLUSTER-NAME-cluster-ca-cert
secret:
ca.crt
cluster certificate in PEM format
ca.p12
cluster certificate in PKCS #12 format
ca.password
to access the PKCS #12 file
Some applications cannot use PEM certificates and support only PKCS #12 certificates. You can also add your own cluster certificate in PKCS #12 format.
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.
You can do the same for the CLUSTER-NAME-clients-ca-cert
secret, which also contains certificates in PEM and PKCS #12 format by default.
Replace the CA certificate generated by the Cluster Operator.
Delete the existing secret.
kubectl delete secret CA-CERTIFICATE-SECRET
CA-CERTIFICATE-SECRET is the name of the Secret
:
CLUSTER-NAME-cluster-ca-cert
for the cluster CA certificate
CLUSTER-NAME-clients-ca-cert
for the clients CA certificate
Replace CLUSTER-NAME with the name of your Kafka cluster.
Ignore any "Not Exists" errors.
Create the new secret.
kubectl create secret generic CLUSTER-NAME-clients-ca-cert --from-file=ca.crt=ca.crt
kubectl 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 the private key generated by the Cluster Operator.
Delete the existing secret.
kubectl delete secret CA-KEY-SECRET
CA-KEY-SECRET is the name of CA key:
CLUSTER-NAME-cluster-ca
for the cluster CA key
CLUSTER-NAME-clients-ca
for the clients CA key
Create the new secret.
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.
If you are replacing CA certificates automatically generated by the Cluster Operator, use the next higher incremental value from the existing annotation and follow the replacing CA keys procedure.
If there are no CA certificates automatically generated by the Cluster Operator, 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 the Kafka.spec.clusterCa
or the Kafka.spec.clientsCa
object to not use generated CAs.
Kafka
resource configuring the cluster CA to use certificates you supply for yourselfkind: Kafka
version: kafka.strimzi.io/v1beta2
spec:
# ...
clusterCa:
generateCertificateAuthority: false
To renew CA certificates you have previously installed, see Renewing your own CA certificates.
To replace the private keys of CA certificates you have previously installed, see Replacing private keys used by your own CA certificates.
Strimzi uses secrets to store private and public key certificates for Kafka clusters, clients, and users. Secrets are used for establishing TLS encrypted connections between Kafka brokers, and between brokers and clients. They are also used for mutual TLS authentication.
Cluster and clients secrets are always pairs: one contains the public key and one contains the private key.
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.
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.
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.
Secrets provide private keys and certificates in PEM and PKCS #12 formats. Use the format that’s suitable for your client. Using private keys and certificates in PEM format means that users have to get them from the secrets, and generate a corresponding truststore or keystore to use in their applications. PKCS #12 storage provides a truststore or keystore that can be used directly.
PKCS #12 defines an archive file format (.p12
) for storing cryptography objects into a single file with password protection.
You can use PKCS #12 to manage certificates and keys in one place.
Each secret contains fields specific to PKCS #12.
The .p12
field contains the certificates and keys.
The .password
field is the password that protects the archive.
All keys are 2048 bits in size and are valid by default for 365 days from initial generation. You can change the validity period.
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 TLS client authentication when connecting to Kafka brokers.
<cluster_name>-clients-ca-cert
Contains the public key of the client CA. Kafka brokers use the public key to verify the identity of clients accessing the Kafka brokers when TLS client 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 to Kafka brokers using Kafka listener certificates rather than certificates signed by the cluster CA or clients CA. |
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 archive file for storing certificates and keys. |
|
Password for protecting the PKCS #12 archive file. |
|
The current certificate for the cluster CA. |
Field | Description |
---|---|
|
PKCS #12 archive file for storing certificates and keys. |
|
Password for protecting the PKCS #12 archive file. |
|
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 archive file for storing certificates and keys. |
|
Password for protecting the PKCS #12 archive file. |
|
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 archive file for storing certificates and keys. |
|
Password for protecting the PKCS #12 archive file. |
|
Certificate for TLS communication between the Cluster Operator and Kafka or ZooKeeper.
Signed by a current or former cluster CA private key in |
|
Private key for TLS communication between the Cluster Operator and Kafka or ZooKeeper. |
Field | Description |
---|---|
|
PKCS #12 archive file for storing certificates and keys. |
|
Password for protecting the PKCS #12 archive file. |
|
Certificate for TLS communication between the Topic Operator and Kafka or ZooKeeper.
Signed by a current or former cluster CA private key in |
|
Private key for TLS communication between the Topic Operator and Kafka or ZooKeeper. |
Field | Description |
---|---|
|
PKCS #12 archive file for storing certificates and keys. |
|
Password for protecting the PKCS #12 archive file. |
|
Certificate for TLS communication between the User Operator and Kafka or ZooKeeper.
Signed by a current or former cluster CA private key in |
|
Private key for TLS communication between the User Operator and Kafka or ZooKeeper. |
Field | Description |
---|---|
|
PKCS #12 archive file for storing certificates and keys. |
|
Password for protecting the PKCS #12 archive file. |
|
Certificate for TLS communication between Cruise Control and Kafka or ZooKeeper.
Signed by a current or former cluster CA private key in |
|
Private key for TLS communication between the Cruise Control and Kafka or ZooKeeper. |
Field | Description |
---|---|
|
PKCS #12 archive file for storing certificates and keys. |
|
Password for protecting the PKCS #12 archive file. |
|
Certificate for TLS communication between Kafka Exporter and Kafka or ZooKeeper.
Signed by a current or former cluster CA private key in |
|
Private key for TLS communication between the Kafka Exporter and Kafka or ZooKeeper. |
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 archive file for storing certificates and keys. |
|
Password for protecting the PKCS #12 archive file. |
|
The current certificate for the clients CA. |
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 archive file for storing certificates and keys. |
|
Password for protecting the PKCS #12 archive file. |
|
|
Certificate for the user, signed by the clients CA |
|
|
Private key for the user |
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.
ownerReference
in the CA secretsBy default, the Cluster and Client 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 Client 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 Client CAsapiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
# ...
spec:
# ...
clusterCa:
generateSecretOwnerReference: false
clientsCa:
generateSecretOwnerReference: false
# ...
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
Client 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 TLS 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
Client 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.
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 corresponding Secret
.
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.
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. |
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 the Secret
that contains the CA certificate that you want to renew.
Certificate | Secret | Annotate command |
---|---|---|
Cluster CA |
KAFKA-CLUSTER-NAME-cluster-ca-cert |
|
Clients CA |
KAFKA-CLUSTER-NAME-clients-ca-cert |
|
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 is KAFKA-CLUSTER-NAME-cluster-ca-cert
for the cluster CA certificate and KAFKA-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
and notAfter
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.
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 the Secret
that contains the private key that you want to renew.
Private key for | Secret | Annotate command |
---|---|---|
Cluster CA |
CLUSTER-NAME-cluster-ca |
|
Clients CA |
CLUSTER-NAME-clients-ca |
|
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.
This procedure describes how to renew CA certificates that you are using instead of the certificates generated by the Cluster Operator.
If you are not changing the corresponding CA keys, perform the steps in this procedure. Otherwise, perform the steps to replace private keys used by your own CA certificates.
If you are using your own certificates, the Cluster Operator will not renew them automatically. Therefore, it is important that you follow this procedure during the renewal period of the certificate in order to replace CA certificates that will soon expire.
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
.
apiVersion: 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 under data
.
Increase the value of the CA certificate generation annotation.
Update the strimzi.io/ca-cert-generation
annotation with a higher incremental value.
For example, change strimzi.io/ca-cert-generation=0
to strimzi.io/ca-cert-generation=1
.
If the Secret
is missing the annotation, the value is treated as 0
, so add the annotation with a value of 1
.
When Strimzi generates certificates, the certificate generation annotation is automatically incremented by the Cluster Operator.
For manual renewal of 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.
apiVersion: 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.
This procedure describes how to renew CA certificates and private keys that you are using instead of the certificates and keys generated by the Cluster Operator.
Perform the steps in this procedure when you are also changing the corresponding CA keys. Otherwise, perform the steps to renew your own CA certificates.
If you are using your own certificates, the Cluster Operator will not renew them automatically. Therefore, it is important that you follow this procedure during the renewal period of the certificate in order to replace CA certificates that will soon expire.
The procedure describes the renewal of CA certificates 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 to true
:
kubectl annotate Kafka <name_of_custom_resource> strimzi.io/pause-reconciliation="true"
For example, for a Kafka
custom resource named my-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 to ReconciliationPaused
at the lastTransitionTime
.
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
.
apiVersion: 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 under data
as ca-<date>.crt
, where <date> is the certificate expiry date in the format YEAR-MONTH-DAYTHOUR-MINUTE-SECONDZ.
For example ca-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 under data
and copy the base64-encoded CA certificate from the previous step as the value for ca.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, change strimzi.io/ca-cert-generation=0
to strimzi.io/ca-cert-generation=1
.
If the Secret
is missing the annotation, the value is treated as 0
, so add the annotation with a value of 1
.
When Strimzi generates certificates, the certificate generation annotation is automatically incremented by the Cluster Operator.
For manual renewal of 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.
apiVersion: 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
.
apiVersion: 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 under data
.
Increase the value of the CA key generation annotation.
Update the strimzi.io/ca-key-generation
annotation with a higher incremental value.
For example, change strimzi.io/ca-key-generation=0
to strimzi.io/ca-key-generation=1
.
If the Secret
is missing the annotation, it is treated as 0
, so add the annotation with a value of 1
.
When Strimzi generates certificates, the key generation annotation is automatically incremented by the Cluster Operator.
For manual renewal of 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.
apiVersion: 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 the pause-reconciliation
annotation to false
.
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.
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.
Communication between Kafka brokers is always encrypted using TLS.
Unless the ControlPlaneListener
feature gate is enabled, all inter-broker communication goes through an internal listener on port 9091.
If you enable the feature gate, traffic from the control plane goes through an internal control plane listener on port 9090.
Traffic from the data plane continues to use the existing internal listener on port 9091.
These internal listeners are not available to Kafka clients.
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.
Cruise Control uses encryption for communication with both Kafka and ZooKeeper. A TLS sidecar is used when communicating with ZooKeeper.
Encrypted or unencrypted communication between Kafka brokers and clients is configured using the tls
property for spec.kafka.listeners
.
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 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 TLS 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 certificate with clients that use certificates in X.509 format.
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 will contain 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 for encryption (with or without TLS 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. 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 certificate with clients that use certificates in X.509 format.
You can provide your own server certificates and private keys for any listener with TLS encryption enabled. These user-provided certificates are called Kafka listener certificates.
Providing Kafka listener certificates allows you to leverage existing security infrastructure, such as your organization’s private CA or a public CA. Kafka clients will need to trust the CA which was used to sign the listener certificate.
You must manually renew Kafka listener certificates when needed.
This procedure shows how to configure a listener to use your own private key and server certificate, called a Kafka listener certificate.
Your client applications should use the CA public key as a trusted certificate in order to verify the identity of the Kafka broker.
A Kubernetes cluster.
The Cluster Operator is running.
For each listener, a compatible server certificate signed by an external CA.
Provide an X.509 certificate in PEM format.
Specify the correct Subject Alternative Names (SANs) for each listener. For more information, see Alternative subjects in server certificates for Kafka listeners.
You can provide a certificate that includes the whole CA chain in the certificate file.
Create a Secret
containing your private key and server certificate:
kubectl create secret generic my-secret --from-file=my-listener-key.key --from-file=my-listener-certificate.crt
Edit the Kafka
resource for your cluster. Configure the listener to use your Secret
, certificate file, and private key file in the configuration.brokerCertChainAndKey
property.
loadbalancer
external listener with TLS encryption enabled# ...
listeners:
- name: plain
port: 9092
type: internal
tls: false
- 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
# ...
# ...
listeners:
- name: plain
port: 9092
type: internal
tls: false
- name: tls
port: 9093
type: internal
tls: true
authentication:
type: tls
configuration:
brokerCertChainAndKey:
secretName: my-secret
certificate: my-listener-certificate.crt
key: my-listener-key.key
# ...
Apply the new configuration to create or update the resource:
kubectl apply -f kafka.yaml
The Cluster Operator starts a rolling update of the Kafka cluster, which updates the configuration of the listeners.
Note
|
A rolling update is also started if you update a Kafka listener certificate in a Secret that is already used by a TLS or external listener.
|
In order to use TLS hostname verification with your own Kafka listener certificates, you must use the correct Subject Alternative Names (SANs) for each listener. The certificate SANs must specify hostnames for:
All of the Kafka brokers in your cluster
The Kafka cluster bootstrap service
You can use wildcard certificates if they are supported by your CA.
Use the following examples to help you specify hostnames of the SANs in your certificates for TLS listeners.
//Kafka brokers
*.<cluster-name>-kafka-brokers
*.<cluster-name>-kafka-brokers.<namespace>.svc
// Bootstrap service
<cluster-name>-kafka-bootstrap
<cluster-name>-kafka-bootstrap.<namespace>.svc
// Kafka brokers
<cluster-name>-kafka-0.<cluster-name>-kafka-brokers
<cluster-name>-kafka-0.<cluster-name>-kafka-brokers.<namespace>.svc
<cluster-name>-kafka-1.<cluster-name>-kafka-brokers
<cluster-name>-kafka-1.<cluster-name>-kafka-brokers.<namespace>.svc
# ...
// Bootstrap service
<cluster-name>-kafka-bootstrap
<cluster-name>-kafka-bootstrap.<namespace>.svc
For external listeners which have TLS encryption enabled, the hostnames you need to specify in certificates depends on the external listener type
.
External listener type | In the SANs, specify… |
---|---|
|
Addresses of all Kafka broker You can use a matching wildcard name. |
|
Addresses of all Kafka broker You can use a matching wildcard name. |
|
Addresses of all Kubernetes worker nodes that the Kafka broker pods might be scheduled to. You can use a matching wildcard name. |
This chapter covers tasks to maintain a deployment of Strimzi.
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.
kubectl
operations on custom resourcesUse 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 |
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.
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.
Several resources have a status
property, as described in the following table.
Strimzi resource | Schema reference | Publishes status information on… |
---|---|---|
|
The Kafka cluster. |
|
|
The Kafka Connect cluster, if deployed. |
|
|
KafkaConnector resources, if deployed. |
|
|
The Kafka MirrorMaker tool, if deployed. |
|
|
Kafka topics in your Kafka cluster. |
|
|
Kafka users in your Kafka cluster. |
|
|
The Strimzi Kafka Bridge, if deployed. |
The status
property of a resource provides information on the resource’s:
Current state, in the status.conditions
property
Last observed generation, in the status.observedGeneration
property
The status
property also provides resource-specific information. For example:
KafkaStatus
provides information on listener addresses, and the id of the Kafka cluster.
KafkaConnectStatus
provides the REST API endpoint for Kafka Connect connectors.
KafkaUserStatus
provides the user name of the Kafka user and the Secret
in which their credentials are stored.
KafkaBridgeStatus
provides the HTTP address at which external client applications can access the Bridge service.
A resource’s current state is useful for tracking progress related to the resource achieving its desired state, as defined by the spec
property. The status conditions provide the time and reason the state of the resource changed and details of events preventing or delaying the operator from realizing the resource’s desired state.
The last observed generation is the generation of the resource that was last reconciled by the Cluster Operator. If the value of observedGeneration
is different from the value of metadata.generation
, the operator has not yet processed the latest update to the resource. If these values are the same, the status information reflects the most recent changes to the resource.
Strimzi creates and maintains the status of custom resources, periodically evaluating the current state of the custom resource and updating its status accordingly.
When performing an update on a custom resource using kubectl edit
, for example, its status
is not editable. Moreover, changing the status
would not affect the configuration of the Kafka cluster.
Here we see the status
property specified for a Kafka custom resource.
apiVersion: kafka.strimzi.io/v1beta2
kind: Kafka
metadata:
spec:
# ...
status:
conditions: (1)
- lastTransitionTime: 2021-07-23T23:46:57+0000
status: "True"
type: Ready (2)
observedGeneration: 4 (3)
listeners: (4)
- addresses:
- host: my-cluster-kafka-bootstrap.myproject.svc
port: 9092
type: plain
- addresses:
- host: my-cluster-kafka-bootstrap.myproject.svc
port: 9093
certificates:
- |
-----BEGIN CERTIFICATE-----
...
-----END CERTIFICATE-----
type: tls
- addresses:
- host: 172.29.49.180
port: 9094
certificates:
- |
-----BEGIN CERTIFICATE-----
...
-----END CERTIFICATE-----
type: external
clusterId: CLUSTER-ID (5)
# ...
Status conditions
describe criteria related to the status that cannot be deduced from the existing resource information, or are specific to the instance of a resource.
The Ready
condition indicates whether the Cluster Operator currently considers the Kafka cluster able to handle traffic.
The observedGeneration
indicates the generation of the Kafka
custom resource that was last reconciled by the Cluster Operator.
The listeners
describe the current Kafka bootstrap addresses by type.
The Kafka cluster id.
Important
|
The address in the custom resource status for external listeners with type nodeport is currently not supported.
|
Note
|
The Kafka bootstrap addresses listed in the status do not signify that those endpoints or the Kafka cluster is in a ready state. |
You can access status information for a resource from the command line. For more information, see 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 the status
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
or status.observedGeneration
, to fine-tune the status information you wish to see.
For more information about using JSONPath, see JSONPath support.
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
to true
:
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 to ReconciliationPaused
at the lastTransitionTime
.
apiVersion: 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.
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.
Since the Strimzi Drain Cleaner will handle the eviction instead of Kubernetes, you need to 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==
# ...
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.
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.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
name: my-topic
labels:
strimzi.io/cluster: my-cluster
spec:
partitions: 1
replicas: 3
config:
# ...
min.insync.replicas: 2
# ...
If you don’t want to include ZooKeeper, you can remove the --zookeeper
command option from the Strimzi Drain Cleaner Deployment
configuration file.
apiVersion: apps/v1
kind: Deployment
spec:
# ...
template:
spec:
serviceAccountName: strimzi-drain-cleaner
containers:
- name: strimzi-drain-cleaner
# ...
command:
- "/application"
- "-Dquarkus.http.host=0.0.0.0"
- "--kafka"
- "--zookeeper" (1)
# ...
Remove this option to exclude ZooKeeper from Strimzi Drain Cleaner operations.
Configure a pod disruption budget of 0
(zero) for your Kafka deployment using template
settings in the Kafka
resource.
apiVersion: 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.
You can use cert-manager
in the deployment process.
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, apply the resources in the /install/drain-cleaner/kubernetes
directory.
kubectl apply -f ./install/drain-cleaner/kubernetes
The resources are configured with TLS certificates that have already been generated. Use these certificates if you are not changing any configuration for the Strimzi Drain Cleaner, such as namespaces, service names, or pod names. Otherwise, you can generate and use your own certificates.
Note
|
You can use the build script and files provided in webhook-certificates
to generate your own certificates. The script uses the CFSSL and openSSL tools to generate the certificates.
After you have generated the certificates, you need to add them to the 040-Secret.yaml and 070-ValidatingWebhookConfiguration.yaml files.
|
To run the Drain Cleaner on OpenShift, apply the resources in the /install/drain-cleaner/openshift
directory.
kubectl apply -f ./install/drain-cleaner/openshift
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.
INFO ... 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.
INFO 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
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.
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:
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.
kubectl annotate statefulset <cluster_name>-kafka strimzi.io/manual-rolling-update=true
kubectl annotate statefulset <cluster_name>-zookeeper strimzi.io/manual-rolling-update=true
kubectl 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.
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.
apiVersion: kafka.strimzi.io/v1beta2
kind: KafkaTopic
metadata:
name: my-topic
labels:
strimzi.io/cluster: my-cluster
spec:
partitions: 1
replicas: 3
config:
# ...
min.insync.replicas: 2
# ...
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 the Pod
.
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.
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:
#...
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
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.
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
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. |
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.
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:0.31.0-kafka-3.2.1 --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 the KafkaTopic
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
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 the Kafka
resource.
The plugin properties are shown in this example configuration.
apiVersion: 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>
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
and KafkaConnect
ClusterRoles
and ClusterRoleBindings
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:
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 creating Roles
and RoleBindings
in the namespace that the operator runs in
The User Operator can manage KafkaUsers
, by creating Roles
and RoleBindings
in the namespace that the operator runs in
The failure domain of a Node
is discovered by Strimzi, by creating a ClusterRoleBinding
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
.
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.
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.
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.
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", "");
Use configuration properties to optimize the performance of Kafka brokers, producers and consumers.
A minimum set of configuration properties is required, but you can add or adjust properties to change how producers and consumers interact with Kafka brokers. For example, you can tune latency and throughput of messages so that clients can respond to data in real time.
You might start by analyzing metrics to gauge where to make your initial configurations, then make incremental changes and further comparisons of metrics until you have the configuration you need.
For more information about Apache Kafka configuration properties, see the Apache Kafka documentation.
The following tools help with Kafka tuning:
Cruise Control generates optimization proposals that you can use to assess and implement a cluster rebalance
Kafka Static Quota plugin sets limits on brokers
Rack configuration spreads broker partitions across racks and allows consumers to fetch data from the nearest replica
When you deploy Strimzi on Kubernetes, you can specify broker configuration through the config
property of the Kafka
custom resource.
However, certain broker configuration options are managed directly by Strimzi.
As such, you cannot configure the following options:
broker.id
to specify the ID of the Kafka broker
log.dirs
directories for log data
zookeeper.connect
configuration to connect Kafka with ZooKeeper
listeners
to expose the Kafka cluster to clients
authorization
mechanisms to allow or decline actions executed by users
authentication
mechanisms to prove the identity of users requiring access to Kafka
Broker IDs start from 0 (zero) and correspond to the number of broker replicas.
Log directories are mounted to /var/lib/kafka/data/kafka-logIDX
based on the spec.kafka.storage
configuration in the Kafka
custom resource.
IDX is the Kafka broker pod index.
For a list of exclusions, see the KafkaClusterSpec
schema reference.
Use configuration properties to optimize the performance of Kafka brokers. You can use standard Kafka broker configuration options, except for properties managed directly by Strimzi.
A typical broker configuration will include settings for properties related to topics, threads and logs.
# ...
num.partitions=1
default.replication.factor=3
offsets.topic.replication.factor=3
transaction.state.log.replication.factor=3
transaction.state.log.min.isr=2
log.retention.hours=168
log.segment.bytes=1073741824
log.retention.check.interval.ms=300000
num.network.threads=3
num.io.threads=8
num.recovery.threads.per.data.dir=1
socket.send.buffer.bytes=102400
socket.receive.buffer.bytes=102400
socket.request.max.bytes=104857600
group.initial.rebalance.delay.ms=0
zookeeper.connection.timeout.ms=6000
# ...
Basic topic properties set the default number of partitions and replication factor for topics, which will apply to topics that are created without these properties being explicitly set, including when topics are created automatically.
# ...
num.partitions=1
auto.create.topics.enable=false
default.replication.factor=3
min.insync.replicas=2
replica.fetch.max.bytes=1048576
# ...
For high availability environments, it is advisable to increase the replication factor to at least 3 for topics and set the minimum number of in-sync replicas required to 1 less than the replication factor.
The auto.create.topics.enable
property is enabled by default so that topics that do not already exist are created automatically when needed by producers and consumers.
If you are using automatic topic creation, you can set the default number of partitions for topics using num.partitions
.
Generally, however, this property is disabled so that more control is provided over topics through explicit topic creation.
For data durability, you should also set min.insync.replicas
in your topic configuration and message delivery acknowledgments using acks=all
in your producer configuration.
Use replica.fetch.max.bytes
to set the maximum size, in bytes, of messages fetched by each follower that replicates the leader partition.
Change this value according to the average message size and throughput. When considering the total memory allocation required for read/write buffering, the memory available must also be able to accommodate the maximum replicated message size when multiplied by all followers.
The delete.topic.enable
property is enabled by default to allow topics to be deleted.
In a production environment, you should disable this property to avoid accidental topic deletion, resulting in data loss.
You can, however, temporarily enable it and delete topics and then disable it again.
Note
|
When running Strimzi on Kubernetes, the Topic Operator can provide operator-style topic management. You can use the KafkaTopic resource to create topics.
For topics created using the KafkaTopic resource, the replication factor is set using spec.replicas .
If delete.topic.enable is enabled, you can also delete topics using the KafkaTopic resource.
|
# ...
auto.create.topics.enable=false
delete.topic.enable=true
# ...
If you are using transactions to enable atomic writes to partitions from producers, the state of the transactions is stored in the internal __transaction_state
topic.
By default, the brokers are configured with a replication factor of 3 and a minimum of 2 in-sync replicas for this topic, which means that a minimum of three brokers are required in your Kafka cluster.
# ...
transaction.state.log.replication.factor=3
transaction.state.log.min.isr=2
# ...
Similarly, the internal __consumer_offsets
topic, which stores consumer state, has default settings for the number of partitions and replication factor.
# ...
offsets.topic.num.partitions=50
offsets.topic.replication.factor=3
# ...
Do not reduce these settings in production. You can increase the settings in a production environment. As an exception, you might want to reduce the settings in a single-broker test environment.
Network threads handle requests to the Kafka cluster, such as produce and fetch requests from client applications. Produce requests are placed in a request queue. Responses are placed in a response queue.
The number of network threads per listener should reflect the replication factor and the levels of activity from client producers and consumers interacting with the Kafka cluster. If you are going to have a lot of requests, you can increase the number of threads, using the amount of time threads are idle to determine when to add more threads.
To reduce congestion and regulate the request traffic, you can limit the number of requests allowed in the request queue. When the requ