Chapter 2. Deployment configuration
This chapter describes how to configure different aspects of the supported deployments:
- Kafka clusters
- Kafka Connect clusters
- Kafka Connect clusters with Source2Image support
- Kafka Mirror Maker
- Kafka Bridge
- OAuth 2.0 token-based authentication
- OAuth 2.0 token-based authorization
2.1. Kafka cluster configuration
The full schema of the Kafka
resource is described in the Section B.2, “Kafka
schema reference”. All labels that are applied to the desired Kafka
resource will also be applied to the OpenShift resources making up the Kafka cluster. This provides a convenient mechanism for resources to be labeled as required.
2.1.1. Sample Kafka YAML configuration
For help in understanding the configuration options available for your Kafka deployment, refer to sample YAML file provided here.
The sample shows only some of the possible configuration options, but those that are particularly important include:
- Resource requests (CPU / Memory)
- JVM options for maximum and minimum memory allocation
- Listeners (and authentication)
- Authentication
- Storage
- Rack awareness
- Metrics
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: replicas: 3 1 version: 1.6 2 resources: 3 requests: memory: 64Gi cpu: "8" limits: 4 memory: 64Gi cpu: "12" jvmOptions: 5 -Xms: 8192m -Xmx: 8192m listeners: 6 - name: plain 7 port: 9092 8 type: internal 9 tls: false 10 configuration: useServiceDnsDomain: true 11 - name: tls port: 9093 type: internal tls: true authentication: 12 type: tls - name: external 13 port: 9094 type: route tls: true configuration: brokerCertChainAndKey: 14 secretName: my-secret certificate: my-certificate.crt key: my-key.key authorization: 15 type: simple config: 16 auto.create.topics.enable: "false" offsets.topic.replication.factor: 3 transaction.state.log.replication.factor: 3 transaction.state.log.min.isr: 2 ssl.cipher.suites: "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384" 17 ssl.enabled.protocols: "TLSv1.2" ssl.protocol: "TLSv1.2" storage: 18 type: persistent-claim 19 size: 10000Gi 20 rack: 21 topologyKey: topology.kubernetes.io/zone metrics: 22 lowercaseOutputName: true rules: 23 # Special cases and very specific rules - pattern : kafka.server<type=(.+), name=(.+), clientId=(.+), topic=(.+), partition=(.*)><>Value name: kafka_server_$1_$2 type: GAUGE labels: clientId: "$3" topic: "$4" partition: "$5" # ... zookeeper: 24 replicas: 3 resources: requests: memory: 8Gi cpu: "2" limits: memory: 8Gi cpu: "2" jvmOptions: -Xms: 4096m -Xmx: 4096m storage: type: persistent-claim size: 1000Gi metrics: # ... entityOperator: 25 topicOperator: resources: requests: memory: 512Mi cpu: "1" limits: memory: 512Mi cpu: "1" userOperator: resources: requests: memory: 512Mi cpu: "1" limits: memory: 512Mi cpu: "1" kafkaExporter: 26 # ... cruiseControl: 27 # ...
- 1
- Replicas specifies the number of broker nodes.
- 2
- Kafka version, which can be changed by following the upgrade procedure.
- 3
- Resource requests specify the resources to reserve for a given container.
- 4
- Resource limits specify the maximum resources that can be consumed by a container.
- 5
- 6
- Listeners configure how clients connect to the Kafka cluster via bootstrap addresses. Listeners are configured as internal or external listeners for connection inside or outside the OpenShift cluster.
- 7
- Name to identify the listener. Must be unique within the Kafka cluster.
- 8
- 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.
- 9
- Listener type specified as
internal
, or for external listeners, asroute
,loadbalancer
,nodeport
oringress
. - 10
- Enables TLS encryption for each listener. Default is
false
. TLS encryption is not required forroute
listeners. - 11
- Defines whether the fully-qualified DNS names including the cluster service suffix (usually
.cluster.local
) are assigned. - 12
- Listener authentication mechanism specified as mutual TLS, SCRAM-SHA-512 or token-based OAuth 2.0.
- 13
- External listener configuration specifies how the Kafka cluster is exposed outside OpenShift, such as through a
route
,loadbalancer
ornodeport
. - 14
- Optional configuration for a Kafka listener certificate managed by an external Certificate Authority. The
brokerCertChainAndKey
property specifies aSecret
that holds a server certificate and a private key. Kafka listener certificates can also be configured for TLS listeners. - 15
- Authorization enables simple, OAUTH 2.0 or OPA authorization on the Kafka broker. Simple authorization uses the
AclAuthorizer
Kafka plugin. - 16
- Config specifies the broker configuration. Standard Apache Kafka configuration may be provided, restricted to those properties not managed directly by AMQ Streams.
- 17
- 18
- 19
- Storage size for persistent volumes may be increased and additional volumes may be added to JBOD storage.
- 20
- Persistent storage has additional configuration options, such as a storage
id
andclass
for dynamic volume provisioning. - 21
- Rack awareness is configured to spread replicas across different racks. A
topology
key must match the label of a cluster node. - 22
- 23
- Kafka rules for exporting metrics to a Grafana dashboard through the JMX Exporter. A set of rules provided with AMQ Streams may be copied to your Kafka resource configuration.
- 24
- ZooKeeper-specific configuration, which contains properties similar to the Kafka configuration.
- 25
- Entity Operator configuration, which specifies the configuration for the Topic Operator and User Operator.
- 26
- Kafka Exporter configuration, which is used to expose data as Prometheus metrics.
- 27
- Cruise Control configuration, which is used to rebalance the Kafka cluster.
2.1.2. Data storage considerations
An efficient data storage infrastructure is essential to the optimal performance of AMQ Streams.
Block storage is required. File storage, such as NFS, does not work with Kafka.
For your block storage, you can choose, for example:
- Cloud-based block storage solutions, such as Amazon Elastic Block Store (EBS)
- Local persistent volumes
- Storage Area Network (SAN) volumes accessed by a protocol such as Fibre Channel or iSCSI
AMQ Streams does not require OpenShift raw block volumes.
2.1.2.1. File systems
It is recommended that you configure your storage system to use the XFS file system. AMQ Streams is also compatible with the ext4 file system, but this might require additional configuration for best results.
2.1.2.2. Apache Kafka and ZooKeeper storage
Use separate disks for Apache Kafka and ZooKeeper.
Three types of data storage are supported:
- Ephemeral (Recommended for development only)
- Persistent
- JBOD (Just a Bunch of Disks, suitable for Kafka only)
For more information, see Kafka and ZooKeeper storage.
Solid-state drives (SSDs), though not essential, can improve the performance of Kafka in large clusters where data is sent to and received from multiple topics asynchronously. SSDs are particularly effective with ZooKeeper, which requires fast, low latency data access.
You do not need to provision replicated storage because Kafka and ZooKeeper both have built-in data replication.
2.1.3. Kafka and ZooKeeper storage types
As stateful applications, Kafka and ZooKeeper need to store data on disk. AMQ Streams supports three storage types for this data:
- Ephemeral
- Persistent
- JBOD storage
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.
The storage type cannot be changed after a Kafka cluster is deployed.
Additional resources
- For more information about ephemeral storage, see ephemeral storage schema reference.
- For more information about persistent storage, see persistent storage schema reference.
- For more information about JBOD storage, see JBOD schema reference.
-
For more information about the schema for
Kafka
, seeKafka
schema reference.
2.1.3.1. Ephemeral storage
Ephemeral storage uses the emptyDir
volumes to store data. To use ephemeral storage, the type
field should be set to ephemeral
.
emptyDir
volumes are not persistent and the data stored in them will be lost when the Pod is restarted. After the new pod is started, it has to recover all data from other nodes of the cluster. Ephemeral storage is not suitable for use with single node ZooKeeper clusters and for Kafka topics with replication factor 1, because it will lead to data loss.
An example of Ephemeral storage
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... storage: type: ephemeral # ... zookeeper: # ... storage: type: ephemeral # ...
2.1.3.1.1. Log directories
The ephemeral volume will be used by the Kafka brokers as log directories mounted into the following path:
/var/lib/kafka/data/kafka-log_idx_
-
Where
idx
is the Kafka broker pod index. For example/var/lib/kafka/data/kafka-log0
.
2.1.3.2. Persistent storage
Persistent storage uses Persistent Volume Claims to provision persistent volumes for storing data. Persistent Volume Claims can be used to provision volumes of many different types, depending on the Storage Class which will provision the volume. The data types which can be used with persistent volume claims include many types of SAN storage as well as Local persistent volumes.
To use persistent storage, the type
has to be set to persistent-claim
. Persistent storage supports additional configuration options:
id
(optional)-
Storage identification number. This option is mandatory for storage volumes defined in a JBOD storage declaration. Default is
0
. size
(required)- Defines the size of the persistent volume claim, for example, "1000Gi".
class
(optional)- The OpenShift 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
.
Increasing the size of persistent volumes in an existing AMQ Streams cluster is only supported in OpenShift 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 OpenShift 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.
Example fragment of persistent storage configuration with 1000Gi size
# ... storage: type: persistent-claim size: 1000Gi # ...
The following example demonstrates the use of a storage class.
Example fragment of persistent storage configuration with specific 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.
Example fragment of persistent storage configuration with selector
# ... storage: type: persistent-claim size: 1Gi selector: hdd-type: ssd deleteClaim: true # ...
2.1.3.2.1. Storage class overrides
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
:
Example AMQ Streams cluster using storage class overrides
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: labels: app: my-cluster name: my-cluster namespace: myproject spec: # ... kafka: replicas: 3 storage: deleteClaim: true size: 100Gi type: persistent-claim class: my-storage-class overrides: - broker: 0 class: my-storage-class-zone-1a - broker: 1 class: my-storage-class-zone-1b - broker: 2 class: my-storage-class-zone-1c # ... 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.
2.1.3.2.2. Persistent Volume Claim naming
When persistent storage is used, it creates Persistent Volume Claims with the following names:
data-cluster-name-kafka-idx
-
Persistent Volume Claim for the volume used for storing data for the Kafka broker pod
idx
. data-cluster-name-zookeeper-idx
-
Persistent Volume Claim for the volume used for storing data for the ZooKeeper node pod
idx
.
2.1.3.2.3. Log directories
The persistent volume will be used by the Kafka brokers as log directories mounted into the following path:
/var/lib/kafka/data/kafka-log_idx_
-
Where
idx
is the Kafka broker pod index. For example/var/lib/kafka/data/kafka-log0
.
2.1.3.3. Resizing persistent volumes
You can provision increased storage capacity by increasing the size of the persistent volumes used by an existing AMQ Streams cluster. Resizing persistent volumes is supported in clusters that use either a single persistent volume or multiple persistent volumes in a JBOD storage configuration.
You can increase but not decrease the size of persistent volumes. Decreasing the size of persistent volumes is not currently supported in OpenShift.
Prerequisites
- An OpenShift 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.
Procedure
In a
Kafka
resource, increase the size of the persistent volume allocated to the Kafka cluster, the ZooKeeper cluster, or both.-
To increase the volume size allocated to the Kafka cluster, edit the
spec.kafka.storage
property. To increase the volume size allocated to the ZooKeeper cluster, edit the
spec.zookeeper.storage
property.For example, to increase the volume size from
1000Gi
to2000Gi
:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... storage: type: persistent-claim size: 2000Gi class: my-storage-class # ... zookeeper: # ...
-
To increase the volume size allocated to the Kafka cluster, edit the
Create or update the resource.
Use
oc apply
:oc apply -f your-file
OpenShift 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.
Additional resources
For more information about resizing persistent volumes in OpenShift, see Resizing Persistent Volumes using Kubernetes.
2.1.3.4. JBOD storage overview
You can configure AMQ Streams to use JBOD, a data storage configuration of multiple disks or volumes. JBOD is one approach to providing increased data storage for Kafka brokers. It can also improve performance.
A JBOD configuration is described by one or more volumes, each of which can be either ephemeral or persistent. The rules and constraints for JBOD volume declarations are the same as those for ephemeral and persistent storage. For example, you cannot change the size of a persistent storage volume after it has been provisioned.
2.1.3.4.1. JBOD configuration
To use JBOD with AMQ Streams, 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.
2.1.3.4.2. JBOD and Persistent Volume Claims
When persistent storage is used to declare JBOD volumes, the naming scheme of the resulting Persistent Volume Claims is as follows:
data-id-cluster-name-kafka-idx
-
Where
id
is the ID of the volume used for storing data for Kafka broker podidx
.
2.1.3.4.3. Log directories
The JBOD volumes will be used by the Kafka brokers as log directories mounted into the following path:
/var/lib/kafka/data-id/kafka-log_idx_
-
Where
id
is the ID of the volume used for storing data for Kafka broker podidx
. For example/var/lib/kafka/data-0/kafka-log0
.
2.1.3.5. Adding volumes to JBOD storage
This procedure describes how to add volumes to a Kafka cluster configured to use JBOD storage. It cannot be applied to Kafka clusters configured to use any other storage type.
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.
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
- A Kafka cluster with JBOD storage
Procedure
Edit the
spec.kafka.storage.volumes
property in theKafka
resource. Add the new volumes to thevolumes
array. For example, add the new volume with id2
:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... storage: type: jbod volumes: - id: 0 type: persistent-claim size: 100Gi deleteClaim: false - id: 1 type: persistent-claim size: 100Gi deleteClaim: false - id: 2 type: persistent-claim size: 100Gi deleteClaim: false # ... zookeeper: # ...
Create or update the resource.
This can be done using
oc apply
:oc apply -f KAFKA-CONFIG-FILE
- Create new topics or reassign existing partitions to the new disks.
Additional resources
For more information about reassigning topics, see Section 2.1.24.2, “Partition reassignment”.
2.1.3.6. Removing volumes from JBOD storage
This procedure describes how to remove volumes from Kafka cluster configured to use JBOD storage. It cannot be applied to Kafka clusters configured to use any other storage type. The JBOD storage always has to contain at least one volume.
To avoid data loss, you have to move all partitions before removing the volumes.
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
- A Kafka cluster with JBOD storage with two or more volumes
Procedure
- Reassign all partitions from the disks which are you going to remove. Any data in partitions still assigned to the disks which are going to be removed might be lost.
Edit the
spec.kafka.storage.volumes
property in theKafka
resource. Remove one or more volumes from thevolumes
array. For example, remove the volumes with ids1
and2
:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... storage: type: jbod volumes: - id: 0 type: persistent-claim size: 100Gi deleteClaim: false # ... zookeeper: # ...
Create or update the resource.
This can be done using
oc apply
:oc apply -f your-file
Additional resources
For more information about reassigning topics, see Section 2.1.24.2, “Partition reassignment”.
2.1.4. Kafka broker replicas
A Kafka cluster can run with many brokers. You can configure the number of brokers used for the Kafka cluster in Kafka.spec.kafka.replicas
. The best number of brokers for your cluster has to be determined based on your specific use case.
2.1.4.1. Configuring the number of broker nodes
This procedure describes how to configure the number of Kafka broker nodes in a new cluster. It only applies to new clusters with no partitions. If your cluster already has topics defined, see Section 2.1.24, “Scaling clusters”.
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
- A Kafka cluster with no topics defined yet
Procedure
Edit the
replicas
property in theKafka
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... replicas: 3 # ... zookeeper: # ...
Create or update the resource.
This can be done using
oc apply
:oc apply -f your-file
Additional resources
If your cluster already has topics defined, see Section 2.1.24, “Scaling clusters”.
2.1.5. Kafka broker configuration
AMQ Streams allows you to customize the configuration of the Kafka brokers in your Kafka cluster. You can specify and configure most of the options listed in the "Broker Configs" section of the Apache Kafka documentation. You cannot configure options that are related to the following areas:
- Security (Encryption, Authentication, and Authorization)
- Listener configuration
- Broker ID configuration
- Configuration of log data directories
- Inter-broker communication
- ZooKeeper connectivity
These options are automatically configured by AMQ Streams.
For more information on broker configuration, see the KafkaClusterSpec
schema.
Listener configuration
You configure listeners for connecting to Kafka brokers. For more information on configuring listeners, see Listener configuration
Authorizing access to Kafka
You can configure your Kafka cluster to allow or decline actions executed by users. For more information on securing access to Kafka brokers, see Managing access to Kafka.
2.1.5.1. Configuring Kafka brokers
You can configure an existing Kafka broker, or create a new Kafka broker with a specified configuration.
Prerequisites
- An OpenShift cluster is available.
- The Cluster Operator is running.
Procedure
-
Open the YAML configuration file that contains the
Kafka
resource specifying the cluster deployment. In the
spec.kafka.config
property in theKafka
resource, enter one or more Kafka configuration settings. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... config: default.replication.factor: 3 offsets.topic.replication.factor: 3 transaction.state.log.replication.factor: 3 transaction.state.log.min.isr: 1 # ... zookeeper: # ...
Apply the new configuration to create or update the resource.
Use
oc apply
:oc apply -f kafka.yaml
where
kafka.yaml
is the YAML configuration file for the resource that you want to configure; for example,kafka-persistent.yaml
.
2.1.6. Listener configuration
Listeners are used to connect to Kafka brokers.
AMQ Streams 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 OpenShift cluster, or external listeners for connecting outside the OpenShift cluster.
Generic listener configuration
Each listener is defined as an array in the Kafka
resource.
For more information on listener configuration, see the GenericKafkaListener
schema reference.
Generic listener configuration replaces the previous approach to listener configuration using the KafkaListeners
schema reference, which is deprecated. However, you can convert the old format into the new format with backwards compatibility.
The KafkaListeners
schema uses sub-properties for plain
, tls
and external
listeners, with fixed ports for each. Because of the limits inherent in the architecture of the schema, it is only possible to configure three listeners, with configuration options limited to the type of listener.
With the GenericKafkaListener
schema, 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 OpenShift network to an outside network. In which case, you can configure internal listeners (using the useServiceDnsDomain
property) so that the OpenShift service DNS domain (typically .cluster.local
) is not used.
Configuring listeners to secure access to Kafka brokers
You can configure listeners for secure connection using authentication. For more information on securing access to Kafka brokers, see Managing access to Kafka.
Configuring external listeners for client access outside OpenShift
You can configure external listeners for client access outside an OpenShift environment using a specified connection mechanism, such as a loadbalancer. For more information on the configuration options for connecting an external client, see Configuring external listeners.
Listener certificates
You can provide your own server certificates, called Kafka listener certificates, for TLS listeners or external listeners which have TLS encryption enabled. For more information, see Kafka listener certificates.
2.1.7. ZooKeeper replicas
ZooKeeper clusters or ensembles usually run with an odd number of nodes, typically three, five, or seven.
The majority of nodes must be available in order to maintain an effective quorum. If the ZooKeeper cluster loses its quorum, it will stop responding to clients and the Kafka brokers will stop working. Having a stable and highly available ZooKeeper cluster is crucial for AMQ Streams.
- Three-node cluster
- A three-node ZooKeeper cluster requires at least two nodes to be up and running in order to maintain the quorum. It can tolerate only one node being unavailable.
- Five-node cluster
- A five-node ZooKeeper cluster requires at least three nodes to be up and running in order to maintain the quorum. It can tolerate two nodes being unavailable.
- Seven-node cluster
- A seven-node ZooKeeper cluster requires at least four nodes to be up and running in order to maintain the quorum. It can tolerate three nodes being unavailable.
For development purposes, it is also possible to run ZooKeeper with a single node.
Having more nodes does not necessarily mean better performance, as the costs to maintain the quorum will rise with the number of nodes in the cluster. Depending on your availability requirements, you can decide for the number of nodes to use.
2.1.7.1. Number of ZooKeeper nodes
The number of ZooKeeper nodes can be configured using the replicas
property in Kafka.spec.zookeeper
.
An example showing replicas configuration
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... replicas: 3 # ...
2.1.7.2. Changing the number of ZooKeeper replicas
Prerequisites
- An OpenShift cluster is available.
- The Cluster Operator is running.
Procedure
-
Open the YAML configuration file that contains the
Kafka
resource specifying the cluster deployment. In the
spec.zookeeper.replicas
property in theKafka
resource, enter the number of replicated ZooKeeper servers. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... replicas: 3 # ...
Apply the new configuration to create or update the resource.
Use
oc apply
:oc apply -f kafka.yaml
where
kafka.yaml
is the YAML configuration file for the resource that you want to configure; for example,kafka-persistent.yaml
.
2.1.8. ZooKeeper configuration
AMQ Streams allows you to customize the configuration of Apache ZooKeeper nodes. You can specify and configure most of the options listed in the ZooKeeper documentation.
Options which cannot be configured are those related to the following areas:
- Security (Encryption, Authentication, and Authorization)
- Listener configuration
- Configuration of data directories
- ZooKeeper cluster composition
These options are automatically configured by AMQ Streams.
2.1.8.1. ZooKeeper configuration
ZooKeeper nodes are configured using the config
property in Kafka.spec.zookeeper
. This property contains the ZooKeeper configuration options as keys. The values can be described using one of the following JSON types:
- String
- Number
- Boolean
Users can specify and configure the options listed in ZooKeeper documentation with the exception of those options which are managed directly by AMQ Streams. Specifically, all configuration options with keys equal to or starting with one of the following strings are forbidden:
-
server.
-
dataDir
-
dataLogDir
-
clientPort
-
authProvider
-
quorum.auth
-
requireClientAuthScheme
When one of the forbidden options is present in the config
property, it is ignored and a warning message is printed to the Cluster Operator log file. All other options are passed to ZooKeeper.
The Cluster Operator does not validate keys or values in the provided config
object. When invalid configuration is provided, the ZooKeeper cluster might not start or might become unstable. In such cases, the configuration in the Kafka.spec.zookeeper.config
object should be fixed and the Cluster Operator will roll out the new configuration to all ZooKeeper nodes.
Selected options have default values:
-
timeTick
with default value2000
-
initLimit
with default value5
-
syncLimit
with default value2
-
autopurge.purgeInterval
with default value1
These options will be automatically configured when they are not present in the Kafka.spec.zookeeper.config
property.
Use the three allowed ssl
configuration options for client connection using a specific cipher suite for a TLS version. A cipher suite combines algorithms for secure connection and data transfer.
Example ZooKeeper configuration
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... zookeeper: # ... config: autopurge.snapRetainCount: 3 autopurge.purgeInterval: 1 ssl.cipher.suites: "TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384" 1 ssl.enabled.protocols: "TLSv1.2" 2 ssl.protocol: "TLSv1.2" 3 # ...
- 1
- The cipher suite for TLS using a combination of
ECDHE
key exchange mechanism,RSA
authentication algorithm,AES
bulk encyption algorithm andSHA384
MAC algorithm. - 2
- The SSl protocol
TLSv1.2
is enabled. - 3
- Specifies the
TLSv1.2
protocol to generate the SSL context. Allowed values areTLSv1.1
andTLSv1.2
.
2.1.8.2. Configuring ZooKeeper
Prerequisites
- An OpenShift cluster is available.
- The Cluster Operator is running.
Procedure
-
Open the YAML configuration file that contains the
Kafka
resource specifying the cluster deployment. In the
spec.zookeeper.config
property in theKafka
resource, enter one or more ZooKeeper configuration settings. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... zookeeper: # ... config: autopurge.snapRetainCount: 3 autopurge.purgeInterval: 1 # ...
Apply the new configuration to create or update the resource.
Use
oc apply
:oc apply -f kafka.yaml
where
kafka.yaml
is the YAML configuration file for the resource that you want to configure; for example,kafka-persistent.yaml
.
2.1.9. ZooKeeper connection
ZooKeeper services are secured with encryption and authentication and are not intended to be used by external applications that are not part of AMQ Streams.
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.
2.1.9.1. Connecting to ZooKeeper from a terminal
Most Kafka CLI tools can connect directly to Kafka. So you should under normal circumstances not need to connect to ZooKeeper. In case it is needed, you can follow this procedure. Open a terminal inside a ZooKeeper container to use Kafka CLI tools that require a ZooKeeper connection.
Prerequisites
- An OpenShift cluster is available.
- A Kafka cluster is running.
- The Cluster Operator is running.
Procedure
Open the terminal using the OpenShift console or run the
exec
command from your CLI.For example:
oc exec -it my-cluster-zookeeper-0 -- bin/kafka-topics.sh --list --zookeeper localhost:12181
Be sure to use
localhost:12181
.You can now run Kafka commands to ZooKeeper.
2.1.10. Entity Operator
The Entity Operator is responsible for managing Kafka-related entities in a running Kafka cluster.
The Entity Operator comprises the:
- Topic Operator to manage Kafka topics
- User Operator to manage Kafka users
Through Kafka
resource configuration, the Cluster Operator can deploy the Entity Operator, including one or both operators, when deploying a Kafka cluster.
When deployed, the Entity Operator contains the operators according to the deployment configuration.
The operators are automatically configured to manage the topics and users of the Kafka cluster.
2.1.10.1. Entity Operator configuration properties
Use the entityOperator
property in Kafka.spec
to configure the Entity Operator.
The entityOperator
property supports several sub-properties:
-
tlsSidecar
-
topicOperator
-
userOperator
-
template
The tlsSidecar
property contains the configuration of the TLS sidecar container, which is used to communicate with ZooKeeper. For more information on configuring the TLS sidecar, see Section 2.1.19, “TLS sidecar”.
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 Section 2.6, “Customizing OpenShift 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 to configure the Entity Operator, see the EntityUserOperatorSpec
schema reference.
Example of basic configuration enabling both operators
apiVersion: kafka.strimzi.io/v1beta1 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.
2.1.10.2. Topic Operator configuration properties
Topic Operator deployment can be configured using additional options inside the topicOperator
object. The following properties are supported:
watchedNamespace
-
The OpenShift namespace in which the topic operator watches for
KafkaTopics
. Default is the namespace where the Kafka cluster is deployed. reconciliationIntervalSeconds
-
The interval between periodic reconciliations in seconds. Default
90
. zookeeperSessionTimeoutSeconds
-
The ZooKeeper session timeout in seconds. Default
20
. topicMetadataMaxAttempts
-
The number of attempts at getting topic metadata from Kafka. The time between each attempt is defined as an exponential back-off. Consider increasing this value when topic creation could take more time due to the number of partitions or replicas. Default
6
. image
-
The
image
property can be used to configure the container image which will be used. For more details about configuring custom container images, see Section 2.1.18, “Container images”. resources
-
The
resources
property configures the amount of resources allocated to the Topic Operator. For more details about resource request and limit configuration, see Section 2.1.11, “CPU and memory resources”. logging
-
The
logging
property configures the logging of the Topic Operator. For more details, see Section 2.1.10.4, “Operator loggers”.
Example of Topic Operator configuration
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... entityOperator: # ... topicOperator: watchedNamespace: my-topic-namespace reconciliationIntervalSeconds: 60 # ...
2.1.10.3. User Operator configuration properties
User Operator deployment can be configured using additional options inside the userOperator
object. The following properties are supported:
watchedNamespace
-
The OpenShift namespace in which the user operator watches for
KafkaUsers
. Default is the namespace where the Kafka cluster is deployed. reconciliationIntervalSeconds
-
The interval between periodic reconciliations in seconds. Default
120
. zookeeperSessionTimeoutSeconds
-
The ZooKeeper session timeout in seconds. Default
6
. image
-
The
image
property can be used to configure the container image which will be used. For more details about configuring custom container images, see Section 2.1.18, “Container images”. resources
-
The
resources
property configures the amount of resources allocated to the User Operator. For more details about resource request and limit configuration, see Section 2.1.11, “CPU and memory resources”. logging
-
The
logging
property configures the logging of the User Operator. For more details, see Section 2.1.10.4, “Operator loggers”.
Example of User Operator configuration
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... entityOperator: # ... userOperator: watchedNamespace: my-user-namespace reconciliationIntervalSeconds: 60 # ...
2.1.10.4. Operator loggers
The Topic Operator and User Operator have a configurable logger:
-
rootLogger.level
The operators use the Apache log4j2
logger implementation.
Use the logging
property in the Kafka
resource to configure loggers and logger levels.
You can set the log levels by specifying the logger and level directly (inline) or use a custom (external) ConfigMap. If a ConfigMap is used, you set logging.name
property to the name of the ConfigMap containing the external logging configuration. Inside the ConfigMap, the logging configuration is described using log4j2.properties
.
Here we see examples of inline
and external
logging.
Inline logging
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... entityOperator: # ... topicOperator: watchedNamespace: my-topic-namespace reconciliationIntervalSeconds: 60 logging: type: inline loggers: rootLogger.level: INFO # ... userOperator: watchedNamespace: my-topic-namespace reconciliationIntervalSeconds: 60 logging: type: inline loggers: rootLogger.level: INFO # ...
External logging
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... entityOperator: # ... topicOperator: watchedNamespace: my-topic-namespace reconciliationIntervalSeconds: 60 logging: type: external name: customConfigMap # ...
Additional resources
- Garbage collector (GC) logging can also be enabled (or disabled). For more information about GC logging, see Section 2.1.17.1, “JVM configuration”
- For more information about log levels, see Apache logging services.
2.1.10.5. Configuring the Entity Operator
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
entityOperator
property in theKafka
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... entityOperator: topicOperator: watchedNamespace: my-topic-namespace reconciliationIntervalSeconds: 60 userOperator: watchedNamespace: my-user-namespace reconciliationIntervalSeconds: 60
Create or update the resource.
This can be done using
oc apply
:oc apply -f your-file
2.1.11. CPU and memory resources
For every deployed container, AMQ Streams allows you to request specific resources and define the maximum consumption of those resources.
AMQ Streams supports two types of resources:
- CPU
- Memory
AMQ Streams uses the OpenShift syntax for specifying CPU and memory resources.
2.1.11.1. Resource limits and requests
Resource limits and requests are configured using the resources
property in the following resources:
-
Kafka.spec.kafka
-
Kafka.spec.zookeeper
-
Kafka.spec.entityOperator.topicOperator
-
Kafka.spec.entityOperator.userOperator
-
Kafka.spec.entityOperator.tlsSidecar
-
Kafka.spec.kafkaExporter
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
-
KafkaBridge.spec
Additional resources
- For more information about managing computing resources on OpenShift, see Managing Compute Resources for Containers.
2.1.11.1.1. Resource requests
Requests specify the resources to reserve for a given container. Reserving the resources ensures that they are always available.
If the resource request is for more than the available free resources in the OpenShift cluster, the pod is not scheduled.
Resources requests are specified in the requests
property. Resources requests currently supported by AMQ Streams:
-
cpu
-
memory
A request may be configured for one or more supported resources.
Example resource request configuration with all resources
# ... resources: requests: cpu: 12 memory: 64Gi # ...
2.1.11.1.2. Resource limits
Limits specify the maximum resources that can be consumed by a given container. The limit is not reserved and might not always be available. A container can use the resources up to the limit only when they are available. Resource limits should be always higher than the resource requests.
Resource limits are specified in the limits
property. Resource limits currently supported by AMQ Streams:
-
cpu
-
memory
A resource may be configured for one or more supported limits.
Example resource limits configuration
# ... resources: limits: cpu: 12 memory: 64Gi # ...
2.1.11.1.3. Supported CPU formats
CPU requests and limits are supported in the following formats:
-
Number of CPU cores as integer (
5
CPU core) or decimal (2.5
CPU core). -
Number or millicpus / millicores (
100m
) where 1000 millicores is the same1
CPU core.
Example CPU units
# ... resources: requests: cpu: 500m limits: cpu: 2.5 # ...
The computing power of 1 CPU core may differ depending on the platform where OpenShift is deployed.
Additional resources
- For more information on CPU specification, see the Meaning of CPU.
2.1.11.1.4. Supported memory formats
Memory requests and limits are specified in megabytes, gigabytes, mebibytes, and gibibytes.
-
To specify memory in megabytes, use the
M
suffix. For example1000M
. -
To specify memory in gigabytes, use the
G
suffix. For example1G
. -
To specify memory in mebibytes, use the
Mi
suffix. For example1000Mi
. -
To specify memory in gibibytes, use the
Gi
suffix. For example1Gi
.
An example of using different memory units
# ... resources: requests: memory: 512Mi limits: memory: 2Gi # ...
Additional resources
- For more details about memory specification and additional supported units, see Meaning of memory.
2.1.11.2. Configuring resource requests and limits
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
resources
property in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... resources: requests: cpu: "8" memory: 64Gi limits: cpu: "12" memory: 128Gi # ... zookeeper: # ...
Create or update the resource.
This can be done using
oc apply
:oc apply -f your-file
Additional resources
- For more information about the schema, see ResourceRequirements API reference.
2.1.12. Kafka loggers
Kafka has its own configurable loggers:
-
log4j.logger.org.I0Itec.zkclient.ZkClient
-
log4j.logger.org.apache.zookeeper
-
log4j.logger.kafka
-
log4j.logger.org.apache.kafka
-
log4j.logger.kafka.request.logger
-
log4j.logger.kafka.network.Processor
-
log4j.logger.kafka.server.KafkaApis
-
log4j.logger.kafka.network.RequestChannel$
-
log4j.logger.kafka.controller
-
log4j.logger.kafka.log.LogCleaner
-
log4j.logger.state.change.logger
-
log4j.logger.kafka.authorizer.logger
ZooKeeper also has a configurable logger:
-
zookeeper.root.logger
Kafka and ZooKeeper use the Apache log4j
logger implementation.
Operators use the Apache log4j2
logger implementation, so the logging configuration is described inside the ConfigMap using log4j2.properties
. For more information, see Section 2.1.10.4, “Operator loggers”.
Use the logging
property to configure loggers and logger levels.
You can set the log levels by specifying the logger and level directly (inline) or use a custom (external) ConfigMap. If a ConfigMap is used, you set logging.name
property to the name of the ConfigMap containing the external logging configuration. Inside the ConfigMap, the logging configuration is described using log4j.properties
.
Here we see examples of inline
and external
logging.
Inline logging
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: # ... kafka: # ... logging: type: inline loggers: kafka.root.logger.level: "INFO" # ... zookeeper: # ... logging: type: inline loggers: zookeeper.root.logger: "INFO" # ... entityOperator: # ... topicOperator: # ... logging: type: inline loggers: rootLogger.level: INFO # ... userOperator: # ... logging: type: inline loggers: rootLogger.level: INFO # ...
External logging
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: # ... logging: type: external name: customConfigMap # ...
Changes to both external and inline logging levels will be applied to Kafka brokers without a restart.
Additional resources
- Garbage collector (GC) logging can also be enabled (or disabled). For more information on garbage collection, see Section 2.1.17.1, “JVM configuration”
- For more information about log levels, see Apache logging services.
2.1.13. Kafka rack awareness
The rack awareness feature in AMQ Streams helps to spread the Kafka broker pods and Kafka topic replicas across different racks. Enabling rack awareness helps to improve availability of Kafka brokers and the topics they are hosting.
"Rack" might represent an availability zone, data center, or an actual rack in your data center.
2.1.13.1. Configuring rack awareness in Kafka brokers
Kafka rack awareness can be configured in the rack
property of Kafka.spec.kafka
. The rack
object has one mandatory field named topologyKey
. This key needs to match one of the labels assigned to the OpenShift cluster nodes. The label is used by OpenShift when scheduling the Kafka broker pods to nodes. If the OpenShift cluster is running on a cloud provider platform, that label should represent the availability zone where the node is running. Usually, the nodes are labeled with topology.kubernetes.io/zone
label (or failure-domain.beta.kubernetes.io/zone
on older OpenShift versions) that can be used as the topologyKey
value. For more information about OpenShift node labels, see Well-Known Labels, Annotations and Taints. This has the effect of spreading the broker pods across zones, and also setting the brokers' broker.rack
configuration parameter inside Kafka broker.
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
- Consult your OpenShift administrator regarding the node label that represents the zone / rack into which the node is deployed.
Edit the
rack
property in theKafka
resource using the label as the topology key.apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... rack: topologyKey: topology.kubernetes.io/zone # ...
Create or update the resource.
This can be done using
oc apply
:oc apply -f your-file
Additional resources
- For information about Configuring init container image for Kafka rack awareness, see Section 2.1.18, “Container images”.
2.1.14. Healthchecks
Healthchecks are periodical tests which verify the health of an application. When a Healthcheck probe fails, OpenShift assumes that the application is not healthy and attempts to fix it.
OpenShift supports two types of Healthcheck probes:
- Liveness probes
- Readiness probes
For more details about the probes, see Configure Liveness and Readiness Probes. Both types of probes are used in AMQ Streams components.
Users can configure selected options for liveness and readiness probes.
2.1.14.1. Healthcheck configurations
Liveness and readiness probes can be configured using the livenessProbe
and readinessProbe
properties in following resources:
-
Kafka.spec.kafka
-
Kafka.spec.zookeeper
-
Kafka.spec.entityOperator.tlsSidecar
-
Kafka.spec.entityOperator.topicOperator
-
Kafka.spec.entityOperator.userOperator
-
Kafka.spec.kafkaExporter
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
-
KafkaMirrorMaker.spec
-
KafkaBridge.spec
Both livenessProbe
and readinessProbe
support the following options:
-
initialDelaySeconds
-
timeoutSeconds
-
periodSeconds
-
successThreshold
-
failureThreshold
For more information about the livenessProbe
and readinessProbe
options, see Section B.45, “Probe
schema reference”.
An example of liveness and readiness probe configuration
# ... readinessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 # ...
2.1.14.2. Configuring healthchecks
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
livenessProbe
orreadinessProbe
property in theKafka
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... readinessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 # ... zookeeper: # ...
Create or update the resource.
This can be done using
oc apply
:oc apply -f your-file
2.1.15. Prometheus metrics
AMQ Streams supports Prometheus metrics using Prometheus JMX exporter to convert the JMX metrics supported by Apache Kafka and ZooKeeper to Prometheus metrics. When metrics are enabled, they are exposed on port 9404.
For more information about setting up and deploying Prometheus and Grafana, see Introducing Metrics to Kafka in the Deploying and Upgrading AMQ Streams on OpenShift guide.
2.1.15.1. Metrics configuration
Prometheus metrics are enabled by configuring the metrics
property in following resources:
-
Kafka.spec.kafka
-
Kafka.spec.zookeeper
-
KafkaConnect.spec
-
KafkaConnectS2I.spec
When the metrics
property is not defined in the resource, the Prometheus metrics will be disabled. To enable Prometheus metrics export without any further configuration, you can set it to an empty object ({}
).
Example of enabling metrics without any further configuration
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... metrics: {} # ... zookeeper: # ...
The metrics
property might contain additional configuration for the Prometheus JMX exporter.
Example of enabling metrics with additional Prometheus JMX Exporter configuration
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... metrics: lowercaseOutputName: true rules: - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*><>Count" name: "kafka_server_$1_$2_total" - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*, topic=(.+)><>Count" name: "kafka_server_$1_$2_total" labels: topic: "$3" # ... zookeeper: # ...
2.1.15.2. Configuring Prometheus metrics
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
metrics
property in theKafka
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... metrics: lowercaseOutputName: true # ...
Create or update the resource.
This can be done using
oc apply
:oc apply -f your-file
2.1.16. JMX Options
AMQ Streams supports obtaining JMX metrics from the Kafka brokers by opening a JMX port on 9999. You can obtain various metrics about each Kafka broker, for example, usage data such as the BytesPerSecond
value or the request rate of the network of the broker. AMQ Streams supports opening a password and username protected JMX port or a non-protected JMX port.
2.1.16.1. Configuring JMX options
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
You can configure JMX options by using the jmxOptions
property in the following resources:
-
Kafka.spec.kafka
You can configure username and password protection for the JMX port that is opened on the Kafka brokers.
Securing the JMX Port
You can secure the JMX port to prevent unauthorized pods from accessing the port. Currently the JMX port can only be secured using a username and password. To enable security for the JMX port, set the type
parameter in the authentication
field to password
.:
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... jmxOptions: authentication: type: "password" # ... zookeeper: # ...
This allows you to deploy a pod internally into a cluster and obtain JMX metrics by using the headless service and specifying which broker you want to address. To get JMX metrics from broker 0 we address the headless service appending broker 0 in front of the headless service:
"<cluster-name>-kafka-0-<cluster-name>-<headless-service-name>"
If the JMX port is secured, you can get the username and password by referencing them from the JMX secret in the deployment of your pod.
Using an open JMX port
To disable security for the JMX port, do not fill in the authentication
field
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... jmxOptions: {} # ... zookeeper: # ...
This will just open the JMX Port on the headless service and you can follow a similar approach as described above to deploy a pod into the cluster. The only difference is that any pod will be able to read from the JMX port.
2.1.17. JVM Options
The following components of AMQ Streams run inside a Virtual Machine (VM):
- Apache Kafka
- Apache ZooKeeper
- Apache Kafka Connect
- Apache Kafka MirrorMaker
- AMQ Streams Kafka Bridge
JVM configuration options optimize the performance for different platforms and architectures. AMQ Streams allows you to configure some of these options.
2.1.17.1. JVM configuration
Use the jvmOptions
property to configure supported options for the JVM on which the component is running.
Supported JVM options help to optimize performance for different platforms and architectures.
2.1.17.2. Configuring JVM options
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
jvmOptions
property in theKafka
,KafkaConnect
,KafkaConnectS2I
,KafkaMirrorMaker
, orKafkaBridge
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... jvmOptions: "-Xmx": "8g" "-Xms": "8g" # ... zookeeper: # ...
Create or update the resource.
This can be done using
oc apply
:oc apply -f your-file
2.1.18. Container images
AMQ Streams allows you to configure container images which will be used for its components. Overriding container images is recommended only in special situations, where you need to use a different container registry. For example, because your network does not allow access to the container repository used by AMQ Streams. In such a case, you should either copy the AMQ Streams images or build them from the source. If the configured image is not compatible with AMQ Streams images, it might not work properly.
2.1.18.1. Container image configurations
Use the image
property to specify which container image to use.
Overriding container images is recommended only in special situations.
2.1.18.2. Configuring container images
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
image
property in theKafka
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... image: my-org/my-image:latest # ... zookeeper: # ...
Create or update the resource.
This can be done using
oc apply
:oc apply -f your-file
2.1.19. TLS sidecar
A sidecar is a container that runs in a pod but serves a supporting purpose. In AMQ Streams, the TLS sidecar uses TLS to encrypt and decrypt all communication between the various components and ZooKeeper.
The TLS sidecar is used in:
- Entity Operator
- Cruise Control
2.1.19.1. TLS sidecar configuration
The TLS sidecar can be configured using the tlsSidecar
property in:
-
Kafka.spec.kafka
-
Kafka.spec.zookeeper
-
Kafka.spec.entityOperator
The TLS sidecar supports the following additional options:
-
image
-
resources
-
logLevel
-
readinessProbe
-
livenessProbe
The resources
property can be used to specify the memory and CPU resources allocated for the TLS sidecar.
The image
property can be used to configure the container image which will be used. For more details about configuring custom container images, see Section 2.1.18, “Container images”.
The logLevel
property is used to specify the logging level. Following logging levels are supported:
- emerg
- alert
- crit
- err
- warning
- notice
- info
- debug
The default value is notice.
For more information about configuring the readinessProbe
and livenessProbe
properties for the healthchecks, see Section 2.1.14.1, “Healthcheck configurations”.
Example of TLS sidecar configuration
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... tlsSidecar: image: my-org/my-image:latest resources: requests: cpu: 200m memory: 64Mi limits: cpu: 500m memory: 128Mi logLevel: debug readinessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 # ... zookeeper: # ...
2.1.19.2. Configuring TLS sidecar
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
tlsSidecar
property in theKafka
resource. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... entityOperator: # ... tlsSidecar: resources: requests: cpu: 200m memory: 64Mi limits: cpu: 500m memory: 128Mi # ... cruiseControl: # ... tlsSidecar: resources: requests: cpu: 200m memory: 64Mi limits: cpu: 500m memory: 128Mi # ...
Create or update the resource.
This can be done using
oc apply
:oc apply -f your-file
2.1.20. Configuring pod scheduling
When two applications are scheduled to the same OpenShift node, both applications might use the same resources like disk I/O and impact performance. That can lead to performance degradation. Scheduling Kafka pods in a way that avoids sharing nodes with other critical workloads, using the right nodes or dedicated a set of nodes only for Kafka are the best ways how to avoid such problems.
2.1.20.1. Scheduling pods based on other applications
2.1.20.1.1. Avoid critical applications to share the node
Pod anti-affinity can be used to ensure that critical applications are never scheduled on the same disk. When running Kafka cluster, it is recommended to use pod anti-affinity to ensure that the Kafka brokers do not share the nodes with other workloads like databases.
2.1.20.1.2. Affinity
Affinity can be configured using the affinity
property in following resources:
-
Kafka.spec.kafka.template.pod
-
Kafka.spec.zookeeper.template.pod
-
Kafka.spec.entityOperator.template.pod
-
KafkaConnect.spec.template.pod
-
KafkaConnectS2I.spec.template.pod
-
KafkaBridge.spec.template.pod
The affinity configuration can include different types of affinity:
- Pod affinity and anti-affinity
- Node affinity
The format of the affinity
property follows the OpenShift specification. For more details, see the Kubernetes node and pod affinity documentation.
2.1.20.1.3. Configuring pod anti-affinity in Kafka components
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
affinity
property in the resource specifying the cluster deployment. Use labels to specify the pods which should not be scheduled on the same nodes. ThetopologyKey
should be set tokubernetes.io/hostname
to specify that the selected pods should not be scheduled on nodes with the same hostname. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... template: pod: affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: application operator: In values: - postgresql - mongodb topologyKey: "kubernetes.io/hostname" # ... zookeeper: # ...
Create or update the resource.
This can be done using
oc apply
:oc apply -f your-file
2.1.20.2. Scheduling pods to specific nodes
2.1.20.2.1. Node scheduling
The OpenShift 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 AMQ Streams components to use the right nodes.
OpenShift 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.
2.1.20.2.2. Affinity
Affinity can be configured using the affinity
property in following resources:
-
Kafka.spec.kafka.template.pod
-
Kafka.spec.zookeeper.template.pod
-
Kafka.spec.entityOperator.template.pod
-
KafkaConnect.spec.template.pod
-
KafkaConnectS2I.spec.template.pod
-
KafkaBridge.spec.template.pod
The affinity configuration can include different types of affinity:
- Pod affinity and anti-affinity
- Node affinity
The format of the affinity
property follows the OpenShift specification. For more details, see the Kubernetes node and pod affinity documentation.
2.1.20.2.3. Configuring node affinity in Kafka components
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Label the nodes where AMQ Streams components should be scheduled.
This can be done using
oc label
:oc label node your-node node-type=fast-network
Alternatively, some of the existing labels might be reused.
Edit the
affinity
property in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... template: pod: affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: node-type operator: In values: - fast-network # ... zookeeper: # ...
Create or update the resource.
This can be done using
oc apply
:oc apply -f your-file
2.1.20.3. Using dedicated nodes
2.1.20.3.1. Dedicated nodes
Cluster administrators can mark selected OpenShift nodes as tainted. Nodes with taints are excluded from regular scheduling and normal pods will not be scheduled to run on them. Only services which can tolerate the taint set on the node can be scheduled on it. The only other services running on such nodes will be system services such as log collectors or software defined networks.
Taints can be used to create dedicated nodes. Running Kafka and its components on dedicated nodes can have many advantages. There will be no other applications running on the same nodes which could cause disturbance or consume the resources needed for Kafka. That can lead to improved performance and stability.
To schedule Kafka pods on the dedicated nodes, configure node affinity and tolerations.
2.1.20.3.2. Affinity
Affinity can be configured using the affinity
property in following resources:
-
Kafka.spec.kafka.template.pod
-
Kafka.spec.zookeeper.template.pod
-
Kafka.spec.entityOperator.template.pod
-
KafkaConnect.spec.template.pod
-
KafkaConnectS2I.spec.template.pod
-
KafkaBridge.spec.template.pod
The affinity configuration can include different types of affinity:
- Pod affinity and anti-affinity
- Node affinity
The format of the affinity
property follows the OpenShift specification. For more details, see the Kubernetes node and pod affinity documentation.
2.1.20.3.3. Tolerations
Tolerations can be configured using the tolerations
property in following resources:
-
Kafka.spec.kafka.template.pod
-
Kafka.spec.zookeeper.template.pod
-
Kafka.spec.entityOperator.template.pod
-
KafkaConnect.spec.template.pod
-
KafkaConnectS2I.spec.template.pod
-
KafkaBridge.spec.template.pod
The format of the tolerations
property follows the OpenShift specification. For more details, see the Kubernetes taints and tolerations.
2.1.20.3.4. Setting up dedicated nodes and scheduling pods on them
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
- 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
oc adm taint
:oc adm taint node your-node dedicated=Kafka:NoSchedule
Additionally, add a label to the selected nodes as well.
This can be done using
oc label
:oc label node your-node dedicated=Kafka
Edit the
affinity
andtolerations
properties in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka spec: kafka: # ... template: pod: tolerations: - key: "dedicated" operator: "Equal" value: "Kafka" effect: "NoSchedule" affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: dedicated operator: In values: - Kafka # ... zookeeper: # ...
Create or update the resource.
This can be done using
oc apply
:oc apply -f your-file
2.1.21. Kafka Exporter
You can configure the Kafka
resource to automatically deploy Kafka Exporter in your cluster.
Kafka Exporter extracts data for analysis as Prometheus metrics, primarily data relating to offsets, consumer groups, consumer lag and topics.
For information on setting up Kafka Exporter and why it is important to monitor consumer lag for performance, see Kafka Exporter in the Deploying and Upgrading AMQ Streams on OpenShift guide.
2.1.22. Performing a rolling update of a Kafka cluster
This procedure describes how to manually trigger a rolling update of an existing Kafka cluster by using an OpenShift annotation.
Prerequisites
See the Deploying and Upgrading AMQ Streams on OpenShift guide for instructions on running a:
Procedure
Find the name of the
StatefulSet
that controls the Kafka pods you want to manually update.For example, if your Kafka cluster is named my-cluster, the corresponding
StatefulSet
is named my-cluster-kafka.Annotate the
StatefulSet
resource in OpenShift. For example, usingoc annotate
:oc annotate statefulset cluster-name-kafka strimzi.io/manual-rolling-update=true
-
Wait for the next reconciliation to occur (every two minutes by default). A rolling update of all pods within the annotated
StatefulSet
is triggered, as long as the annotation was detected by the reconciliation process. When the rolling update of all the pods is complete, the annotation is removed from theStatefulSet
.
2.1.23. Performing a rolling update of a ZooKeeper cluster
This procedure describes how to manually trigger a rolling update of an existing ZooKeeper cluster by using an OpenShift annotation.
Prerequisites
See the Deploying and Upgrading AMQ Streams on OpenShift guide for instructions on running a:
Procedure
Find the name of the
StatefulSet
that controls the ZooKeeper pods you want to manually update.For example, if your Kafka cluster is named my-cluster, the corresponding
StatefulSet
is named my-cluster-zookeeper.Annotate the
StatefulSet
resource in OpenShift. For example, usingoc annotate
:oc annotate statefulset cluster-name-zookeeper strimzi.io/manual-rolling-update=true
-
Wait for the next reconciliation to occur (every two minutes by default). A rolling update of all pods within the annotated
StatefulSet
is triggered, as long as the annotation was detected by the reconciliation process. When the rolling update of all the pods is complete, the annotation is removed from theStatefulSet
.
2.1.24. Scaling clusters
2.1.24.1. Scaling Kafka clusters
2.1.24.1.1. Adding brokers to a cluster
The primary way of increasing throughput for a topic is to increase the number of partitions for that topic. That works because the extra partitions allow the load of the topic to be shared between the different brokers in the cluster. However, in situations where every broker is constrained by a particular resource (typically I/O) using more partitions will not result in increased throughput. Instead, you need to add brokers to the cluster.
When you add an extra broker to the cluster, Kafka does not assign any partitions to it automatically. You must decide which partitions to move from the existing brokers to the new broker.
Once the partitions have been redistributed between all the brokers, the resource utilization of each broker should be reduced.
2.1.24.1.2. Removing brokers from a cluster
Because AMQ Streams uses StatefulSets
to manage broker pods, you cannot remove any pod from the cluster. You can only remove one or more of the highest numbered pods from the cluster. For example, in a cluster of 12 brokers the pods are named cluster-name-kafka-0
up to cluster-name-kafka-11
. If you decide to scale down by one broker, the cluster-name-kafka-11
will be removed.
Before you remove a broker from a cluster, ensure that it is not assigned to any partitions. You should also decide which of the remaining brokers will be responsible for each of the partitions on the broker being decommissioned. Once the broker has no assigned partitions, you can scale the cluster down safely.
2.1.24.2. Partition reassignment
The Topic Operator does not currently support reassigning replicas to different brokers, so it is necessary to connect directly to broker pods to reassign replicas to brokers.
Within a broker pod, the kafka-reassign-partitions.sh
utility allows you to reassign partitions to different brokers.
It has three different modes:
--generate
- Takes a set of topics and brokers and generates a reassignment JSON file which will result in the partitions of those topics being assigned to those brokers. Because this operates on whole topics, it cannot be used when you just need to reassign some of the partitions of some topics.
--execute
- Takes a reassignment JSON file and applies it to the partitions and brokers in the cluster. Brokers that gain partitions as a result become followers of the partition leader. For a given partition, once the new broker has caught up and joined the ISR (in-sync replicas) the old broker will stop being a follower and will delete its replica.
--verify
-
Using the same reassignment JSON file as the
--execute
step,--verify
checks whether all of the partitions in the file have been moved to their intended brokers. If the reassignment is complete, --verify also removes any throttles that are in effect. Unless removed, throttles will continue to affect the cluster even after the reassignment has finished.
It is only possible to have one reassignment running in a cluster at any given time, and it is not possible to cancel a running reassignment. If you need to cancel a reassignment, wait for it to complete and then perform another reassignment to revert the effects of the first reassignment. The kafka-reassign-partitions.sh
will print the reassignment JSON for this reversion as part of its output. Very large reassignments should be broken down into a number of smaller reassignments in case there is a need to stop in-progress reassignment.
2.1.24.2.1. Reassignment JSON file
The reassignment JSON file has a specific structure:
{
"version": 1,
"partitions": [
<PartitionObjects>
]
}
Where <PartitionObjects> is a comma-separated list of objects like:
{ "topic": <TopicName>, "partition": <Partition>, "replicas": [ <AssignedBrokerIds> ] }
Although Kafka also supports a "log_dirs"
property this should not be used in AMQ Streams.
The following is an example reassignment JSON file that assigns topic topic-a
, partition 4
to brokers 2
, 4
and 7
, and topic topic-b
partition 2
to brokers 1
, 5
and 7
:
{ "version": 1, "partitions": [ { "topic": "topic-a", "partition": 4, "replicas": [2,4,7] }, { "topic": "topic-b", "partition": 2, "replicas": [1,5,7] } ] }
Partitions not included in the JSON are not changed.
2.1.24.2.2. Reassigning partitions between JBOD volumes
When using JBOD storage in your Kafka cluster, you can choose to reassign the partitions between specific volumes and their log directories (each volume has a single log directory). To reassign a partition to a specific volume, add the log_dirs
option to <PartitionObjects> in the reassignment JSON file.
{ "topic": <TopicName>, "partition": <Partition>, "replicas": [ <AssignedBrokerIds> ], "log_dirs": [ <AssignedLogDirs> ] }
The log_dirs
object should contain the same number of log directories as the number of replicas specified in the replicas
object. The value should be either an absolute path to the log directory, or the any
keyword.
For example:
{ "topic": "topic-a", "partition": 4, "replicas": [2,4,7]. "log_dirs": [ "/var/lib/kafka/data-0/kafka-log2", "/var/lib/kafka/data-0/kafka-log4", "/var/lib/kafka/data-0/kafka-log7" ] }
2.1.24.3. Generating reassignment JSON files
This procedure describes how to generate a reassignment JSON file that reassigns all the partitions for a given set of topics using the kafka-reassign-partitions.sh
tool.
Prerequisites
- A running Cluster Operator
-
A
Kafka
resource - A set of topics to reassign the partitions of
Procedure
Prepare a JSON file named
topics.json
that lists the topics to move. It must have the following structure:{ "version": 1, "topics": [ <TopicObjects> ] }
where <TopicObjects> is a comma-separated list of objects like:
{ "topic": <TopicName> }
For example if you want to reassign all the partitions of
topic-a
andtopic-b
, you would need to prepare atopics.json
file like this:{ "version": 1, "topics": [ { "topic": "topic-a"}, { "topic": "topic-b"} ] }
Copy the
topics.json
file to one of the broker pods:cat topics.json | oc exec -c kafka <BrokerPod> -i -- \ /bin/bash -c \ 'cat > /tmp/topics.json'
Use the
kafka-reassign-partitions.sh
command to generate the reassignment JSON.oc exec <BrokerPod> -c kafka -it -- \ bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \ --topics-to-move-json-file /tmp/topics.json \ --broker-list <BrokerList> \ --generate
For example, to move all the partitions of
topic-a
andtopic-b
to brokers4
and7
oc exec <BrokerPod> -c kafka -it -- \ bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \ --topics-to-move-json-file /tmp/topics.json \ --broker-list 4,7 \ --generate
2.1.24.4. Creating reassignment JSON files manually
You can manually create the reassignment JSON file if you want to move specific partitions.
2.1.24.5. Reassignment throttles
Partition reassignment can be a slow process because it involves transferring large amounts of data between brokers. To avoid a detrimental impact on clients, you can throttle the reassignment process. This might cause the reassignment to take longer to complete.
- If the throttle is too low then the newly assigned brokers will not be able to keep up with records being published and the reassignment will never complete.
- If the throttle is too high then clients will be impacted.
For example, for producers, this could manifest as higher than normal latency waiting for acknowledgement. For consumers, this could manifest as a drop in throughput caused by higher latency between polls.
2.1.24.6. Scaling up a Kafka cluster
This procedure describes how to increase the number of brokers in a Kafka cluster.
Prerequisites
- An existing Kafka cluster.
-
A reassignment JSON file named
reassignment.json
that describes how partitions should be reassigned to brokers in the enlarged cluster.
Procedure
-
Add as many new brokers as you need by increasing the
Kafka.spec.kafka.replicas
configuration option. - Verify that the new broker pods have started.
Copy the
reassignment.json
file to the broker pod on which you will later execute the commands:cat reassignment.json | \ oc exec broker-pod -c kafka -i -- /bin/bash -c \ 'cat > /tmp/reassignment.json'
For example:
cat reassignment.json | \ oc exec my-cluster-kafka-0 -c kafka -i -- /bin/bash -c \ 'cat > /tmp/reassignment.json'
Execute the partition reassignment using the
kafka-reassign-partitions.sh
command line tool from the same broker pod.oc exec broker-pod -c kafka -it -- \ bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \ --reassignment-json-file /tmp/reassignment.json \ --execute
If you are going to throttle replication you can also pass the
--throttle
option with an inter-broker throttled rate in bytes per second. For example:oc exec my-cluster-kafka-0 -c kafka -it -- \ bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \ --reassignment-json-file /tmp/reassignment.json \ --throttle 5000000 \ --execute
This command will print out two reassignment JSON objects. The first records the current assignment for the partitions being moved. You should save this to a local file (not a file in the pod) in case you need to revert the reassignment later on. The second JSON object is the target reassignment you have passed in your reassignment JSON file.
If you need to change the throttle during reassignment you can use the same command line with a different throttled rate. For example:
oc exec my-cluster-kafka-0 -c kafka -it -- \ bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \ --reassignment-json-file /tmp/reassignment.json \ --throttle 10000000 \ --execute
Periodically verify whether the reassignment has completed using the
kafka-reassign-partitions.sh
command line tool from any of the broker pods. This is the same command as the previous step but with the--verify
option instead of the--execute
option.oc exec broker-pod -c kafka -it -- \ bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \ --reassignment-json-file /tmp/reassignment.json \ --verify
For example,
oc exec my-cluster-kafka-0 -c kafka -it -- \ bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \ --reassignment-json-file /tmp/reassignment.json \ --verify
-
The reassignment has finished when the
--verify
command reports each of the partitions being moved as completed successfully. This final--verify
will also have the effect of removing any reassignment throttles. You can now delete the revert file if you saved the JSON for reverting the assignment to their original brokers.
2.1.24.7. Scaling down a Kafka cluster
Additional resources
This procedure describes how to decrease the number of brokers in a Kafka cluster.
Prerequisites
- An existing Kafka cluster.
-
A reassignment JSON file named
reassignment.json
describing how partitions should be reassigned to brokers in the cluster once the broker(s) in the highest numberedPod(s)
have been removed.
Procedure
Copy the
reassignment.json
file to the broker pod on which you will later execute the commands:cat reassignment.json | \ oc exec broker-pod -c kafka -i -- /bin/bash -c \ 'cat > /tmp/reassignment.json'
For example:
cat reassignment.json | \ oc exec my-cluster-kafka-0 -c kafka -i -- /bin/bash -c \ 'cat > /tmp/reassignment.json'
Execute the partition reassignment using the
kafka-reassign-partitions.sh
command line tool from the same broker pod.oc exec broker-pod -c kafka -it -- \ bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \ --reassignment-json-file /tmp/reassignment.json \ --execute
If you are going to throttle replication you can also pass the
--throttle
option with an inter-broker throttled rate in bytes per second. For example:oc exec my-cluster-kafka-0 -c kafka -it -- \ bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \ --reassignment-json-file /tmp/reassignment.json \ --throttle 5000000 \ --execute
This command will print out two reassignment JSON objects. The first records the current assignment for the partitions being moved. You should save this to a local file (not a file in the pod) in case you need to revert the reassignment later on. The second JSON object is the target reassignment you have passed in your reassignment JSON file.
If you need to change the throttle during reassignment you can use the same command line with a different throttled rate. For example:
oc exec my-cluster-kafka-0 -c kafka -it -- \ bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \ --reassignment-json-file /tmp/reassignment.json \ --throttle 10000000 \ --execute
Periodically verify whether the reassignment has completed using the
kafka-reassign-partitions.sh
command line tool from any of the broker pods. This is the same command as the previous step but with the--verify
option instead of the--execute
option.oc exec broker-pod -c kafka -it -- \ bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \ --reassignment-json-file /tmp/reassignment.json \ --verify
For example,
oc exec my-cluster-kafka-0 -c kafka -it -- \ bin/kafka-reassign-partitions.sh --bootstrap-server localhost:9092 \ --reassignment-json-file /tmp/reassignment.json \ --verify
-
The reassignment has finished when the
--verify
command reports each of the partitions being moved as completed successfully. This final--verify
will also have the effect of removing any reassignment throttles. You can now delete the revert file if you saved the JSON for reverting the assignment to their original brokers. Once all the partition reassignments have finished, the broker(s) being removed should not have responsibility for any of the partitions in the cluster. You can verify this by checking that the broker’s data log directory does not contain any live partition logs. If the log directory on the broker contains a directory that does not match the extended regular expression
\.[a-z0-9]-delete$
then the broker still has live partitions and it should not be stopped.You can check this by executing the command:
oc exec my-cluster-kafka-0 -c kafka -it -- \ /bin/bash -c \ "ls -l /var/lib/kafka/kafka-log_<N>_ | grep -E '^d' | grep -vE '[a-zA-Z0-9.-]+\.[a-z0-9]+-delete$'"
where N is the number of the
Pod(s)
being deleted.If the above command prints any output then the broker still has live partitions. In this case, either the reassignment has not finished, or the reassignment JSON file was incorrect.
-
Once you have confirmed that the broker has no live partitions you can edit the
Kafka.spec.kafka.replicas
of yourKafka
resource, which will scale down theStatefulSet
, deleting the highest numbered brokerPod(s)
.
2.1.25. Deleting Kafka nodes manually
Additional resources
This procedure describes how to delete an existing Kafka node by using an OpenShift 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.
Deleting a PersistentVolumeClaim
can cause permanent data loss. The following procedure should only be performed if you have encountered storage issues.
Prerequisites
See the Deploying and Upgrading AMQ Streams on OpenShift guide for instructions on running a:
Procedure
Find the name of the
Pod
that you want to delete.For example, if the cluster is named cluster-name, the pods are named cluster-name-kafka-index, where index starts at zero and ends at the total number of replicas.
Annotate the
Pod
resource in OpenShift.Use
oc annotate
:oc annotate pod cluster-name-kafka-index strimzi.io/delete-pod-and-pvc=true
- Wait for the next reconciliation, when the annotated pod with the underlying persistent volume claim will be deleted and then recreated.
2.1.26. Deleting ZooKeeper nodes manually
This procedure describes how to delete an existing ZooKeeper node by using an OpenShift 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.
Deleting a PersistentVolumeClaim
can cause permanent data loss. The following procedure should only be performed if you have encountered storage issues.
Prerequisites
See the Deploying and Upgrading AMQ Streams on OpenShift guide for instructions on running a:
Procedure
Find the name of the
Pod
that you want to delete.For example, if the cluster is named cluster-name, the pods are named cluster-name-zookeeper-index, where index starts at zero and ends at the total number of replicas.
Annotate the
Pod
resource in OpenShift.Use
oc annotate
:oc annotate pod cluster-name-zookeeper-index strimzi.io/delete-pod-and-pvc=true
- Wait for the next reconciliation, when the annotated pod with the underlying persistent volume claim will be deleted and then recreated.
2.1.27. Maintenance time windows for rolling updates
Maintenance time windows allow you to schedule certain rolling updates of your Kafka and ZooKeeper clusters to start at a convenient time.
2.1.27.1. Maintenance time windows overview
In most cases, the Cluster Operator only updates your Kafka or ZooKeeper clusters in response to changes to the corresponding Kafka
resource. This enables you to plan when to apply changes to a Kafka
resource to minimize the impact on Kafka client applications.
However, some updates to your Kafka and ZooKeeper clusters can happen without any corresponding change to the Kafka
resource. For example, the Cluster Operator will need to perform a rolling restart if a CA (Certificate Authority) certificate that it manages is close to expiry.
While a rolling restart of the pods should not affect availability of the service (assuming correct broker and topic configurations), it could affect performance of the Kafka client applications. Maintenance time windows allow you to schedule such spontaneous rolling updates of your Kafka and ZooKeeper clusters to start at a convenient time. If maintenance time windows are not configured for a cluster then it is possible that such spontaneous rolling updates will happen at an inconvenient time, such as during a predictable period of high load.
2.1.27.2. Maintenance time window definition
You configure maintenance time windows by entering an array of strings in the Kafka.spec.maintenanceTimeWindows
property. Each string is a cron expression interpreted as being in UTC (Coordinated Universal Time, which for practical purposes is the same as Greenwich Mean Time).
The following example configures a single maintenance time window that starts at midnight and ends at 01:59am (UTC), on Sundays, Mondays, Tuesdays, Wednesdays, and Thursdays:
# ... maintenanceTimeWindows: - "* * 0-1 ? * SUN,MON,TUE,WED,THU *" # ...
In practice, maintenance windows should be set in conjunction with the Kafka.spec.clusterCa.renewalDays
and Kafka.spec.clientsCa.renewalDays
properties of the Kafka
resource, to ensure that the necessary CA certificate renewal can be completed in the configured maintenance time windows.
AMQ Streams 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.
Additional resources
- For more information about the Cluster Operator configuration, see Section 5.1.1, “Cluster Operator configuration”.
2.1.27.3. Configuring a maintenance time window
You can configure a maintenance time window for rolling updates triggered by supported processes.
Prerequisites
- An OpenShift cluster.
- The Cluster Operator is running.
Procedure
Add or edit the
maintenanceTimeWindows
property in theKafka
resource. For example to allow maintenance between 0800 and 1059 and between 1400 and 1559 you would set themaintenanceTimeWindows
as shown below:apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... maintenanceTimeWindows: - "* * 8-10 * * ?" - "* * 14-15 * * ?"
Create or update the resource.
This can be done using
oc apply
:oc apply -f your-file
Additional resources
- Performing a rolling update of a Kafka cluster, see Section 2.1.22, “Performing a rolling update of a Kafka cluster”
- Performing a rolling update of a ZooKeeper cluster, see Section 2.1.23, “Performing a rolling update of a ZooKeeper cluster”
2.1.28. Renewing CA certificates manually
Cluster and clients CA certificates auto-renew at the start of their respective certificate renewal periods. If Kafka.spec.clusterCa.generateCertificateAuthority
and Kafka.spec.clientsCa.generateCertificateAuthority
are set to false
, the CA certificates do not auto-renew.
You can 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.
Prerequisites
- The Cluster Operator is running.
- A Kafka cluster in which CA certificates and private keys are installed.
Procedure
Apply the
strimzi.io/force-renew
annotation to theSecret
that contains the CA certificate that you want to renew.Table 2.1. Annotation for the Secret that forces renewal of certificates Certificate Secret Annotate command Cluster CA
KAFKA-CLUSTER-NAME-cluster-ca-cert
oc annotate secret KAFKA-CLUSTER-NAME-cluster-ca-cert strimzi.io/force-renew=true
Clients CA
KAFKA-CLUSTER-NAME-clients-ca-cert
oc annotate secret KAFKA-CLUSTER-NAME-clients-ca-cert strimzi.io/force-renew=true
At the next reconciliation the Cluster Operator will generate a new CA certificate for the
Secret
that you annotated. If maintenance time windows are configured, the Cluster Operator will generate the new CA certificate at the first reconciliation within the next maintenance time window.Client applications must reload the cluster and clients CA certificates that were renewed by the Cluster Operator.
Check the period the CA certificate is valid:
For example, using an
openssl
command:oc get secret CA-CERTIFICATE-SECRET -o 'jsonpath={.data.CA-CERTIFICATE}' | base64 -d | openssl x509 -subject -issuer -startdate -enddate -noout
CA-CERTIFICATE-SECRET is the name of the
Secret
, which isKAFKA-CLUSTER-NAME-cluster-ca-cert
for the cluster CA certificate andKAFKA-CLUSTER-NAME-clients-ca-cert
for the clients CA certificate.CA-CERTIFICATE is the name of the CA certificate, such as
jsonpath={.data.ca\.crt}
.The command returns a
notBefore
andnotAfter
date, which is the validity period for the CA certificate.For example, for a cluster CA certificate:
subject=O = io.strimzi, CN = cluster-ca v0 issuer=O = io.strimzi, CN = cluster-ca v0 notBefore=Jun 30 09:43:54 2020 GMT notAfter=Jun 30 09:43:54 2021 GMT
Delete old certificates from the Secret.
When components are using the new certificates, older certificates might still be active. Delete the old certificates to remove any potential security risk.
2.1.29. Replacing private keys
You can replace the private keys used by the cluster CA and clients CA certificates. When a private key is replaced, the Cluster Operator generates a new CA certificate for the new private key.
Prerequisites
- The Cluster Operator is running.
- A Kafka cluster in which CA certificates and private keys are installed.
Procedure
Apply the
strimzi.io/force-replace
annotation to theSecret
that contains the private key that you want to renew.Table 2.2. Commands for replacing private keys Private key for Secret Annotate command Cluster CA
<cluster-name>-cluster-ca
oc annotate secret <cluster-name>-cluster-ca strimzi.io/force-replace=true
Clients CA
<cluster-name>-clients-ca
oc annotate secret <cluster-name>-clients-ca strimzi.io/force-replace=true
At the next reconciliation the Cluster Operator will:
-
Generate a new private key for the
Secret
that you annotated - Generate a new CA certificate
If maintenance time windows are configured, the Cluster Operator will generate the new private key and CA certificate at the first reconciliation within the next maintenance time window.
Client applications must reload the cluster and clients CA certificates that were renewed by the Cluster Operator.
Additional resources
2.1.30. List of resources created as part of Kafka cluster
The following resources are created by the Cluster Operator in the OpenShift cluster:
Shared resources
cluster-name-cluster-ca
- Secret with the Cluster CA used to encrypt the cluster communication.
cluster-name-cluster-ca-cert
- Secret with the Cluster CA public key. This key can be used to verify the identity of the Kafka brokers.
cluster-name-clients-ca
- Secret with the Clients CA private key used to sign user certiticates
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.
Zookeeper nodes
cluster-name-zookeeper
- StatefulSet which is in charge of managing the ZooKeeper node pods.
cluster-name-zookeeper-idx
- Pods created by the Zookeeper StatefulSet.
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-zookeeper
- Service account used by the Zookeeper nodes.
cluster-name-zookeeper
- Pod Disruption Budget configured for the ZooKeeper nodes.
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.
Kafka brokers
cluster-name-kafka
- StatefulSet which is in charge of managing the Kafka broker pods.
cluster-name-kafka-idx
- Pods created by the Kafka StatefulSet.
cluster-name-kafka-brokers
- Service needed to have DNS resolve the Kafka broker pods IP addresses directly.
cluster-name-kafka-bootstrap
- Service can be used as bootstrap servers for Kafka clients.
cluster-name-kafka-external-bootstrap
- Bootstrap service for clients connecting from outside of the OpenShift cluster. This resource will be created only when external listener is enabled.
cluster-name-kafka-pod-id
- Service used to route traffic from outside of the OpenShift cluster to individual pods. This resource will be created only when external listener is enabled.
cluster-name-kafka-external-bootstrap
-
Bootstrap route for clients connecting from outside of the OpenShift cluster. This resource will be created only when external listener is enabled and set to type
route
. cluster-name-kafka-pod-id
-
Route for traffic from outside of the OpenShift cluster to individual pods. This resource will be created only when external listener is enabled and set to type
route
. cluster-name-kafka-config
- ConfigMap which contains the Kafka ancillary configuration and is mounted as a volume by the Kafka broker pods.
cluster-name-kafka-brokers
- Secret with Kafka broker keys.
cluster-name-kafka
- Service account used by the Kafka brokers.
cluster-name-kafka
- Pod Disruption Budget configured for the Kafka brokers.
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 will be 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 will be created only if persistent storage is selected for provisioning persistent volumes to store data. data-id-cluster-name-kafka-idx
-
Persistent Volume Claim for the volume
id
used for storing data for the Kafka broker podidx
. This resource is only created if persistent storage is selected for JBOD volumes when provisioning persistent volumes to store data.
Entity Operator
These resources are only created if the Entity Operator is deployed using the Cluster Operator.
cluster-name-entity-operator
- Deployment with Topic and User Operators.
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-operator-certs
- Secret with Entity Operator keys for communication with Kafka and ZooKeeper.
cluster-name-entity-operator
- Service account used by the Entity Operator.
strimzi-cluster-name-topic-operator
- Role binding used by the Entity Operator.
strimzi-cluster-name-user-operator
- Role binding used by the Entity Operator.
Kafka Exporter
These resources are only created if the Kafka Exporter is deployed using the Cluster Operator.
cluster-name-kafka-exporter
- Deployment with Kafka Exporter.
cluster-name-kafka-exporter-random-string
- Pod created by the Kafka Exporter deployment.
cluster-name-kafka-exporter
- Service used to collect consumer lag metrics.
cluster-name-kafka-exporter
- Service account used by the Kafka Exporter.
Cruise Control
These resources are only created only if Cruise Control was deployed using the Cluster Operator.
cluster-name-cruise-control
- Deployment with 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-cruise-control
- Service used to communicate with Cruise Control.
cluster-name-cruise-control
- Service account used by Cruise Control.
cluster-name-network-policy-cruise-control
- Network policy managing access to the Cruise Control service.
JMXTrans
These resources are only created if JMXTrans is deployed using the Cluster Operator.
cluster-name-jmxtrans
- Deployment with 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.
cluster-name-jmxtrans
- Service account used by JMXTrans.
2.2. Kafka Connect/S2I cluster configuration
This section describes how to configure a Kafka Connect or Kafka Connect with Source-to-Image (S2I) deployment in your AMQ Streams 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.
If you are using Kafka Connect, you configure either the KafkaConnect
or the KafkaConnectS2I
resource. Use the KafkaConnectS2I
resource if you are using the Source-to-Image (S2I) framework to deploy Kafka Connect.
-
The full schema of the
KafkaConnect
resource is described in Section B.79, “KafkaConnect
schema reference”. -
The full schema of the
KafkaConnectS2I
resource is described in Section B.95, “KafkaConnectS2I
schema reference”.
Additional resources
2.2.1. Configuring Kafka Connect
Use Kafka Connect to set up external data connections to your Kafka cluster.
Use the properties of the KafkaConnect
or KafkaConnectS2I
resource to configure your Kafka Connect deployment. The example shown in this procedure is for the KafkaConnect
resource, but the properties are the same for the KafkaConnectS2I
resource.
Kafka connector configuration
KafkaConnector
resources allow you to create and manage connector instances for Kafka Connect in an OpenShift-native way.
In the configuration, you enable KafkaConnectors
for a Kafka Connect cluster by adding the strimzi.io/use-connector-resources
annotation. You can also specify external configuration for Kafka Connect connectors through the externalConfiguration
property.
Connectors are created, reconfigured, and deleted using the Kafka Connect HTTP REST interface, or by using KafkaConnectors
. For more information on these methods, see Creating and managing connectors in the Deploying and Upgrading AMQ Streams on OpenShift guide.
The connector configuration is passed to Kafka Connect as part of an HTTP request and stored within Kafka itself. ConfigMaps and Secrets are standard OpenShift 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 (this keeps the configuration separate and more secure, if needed). This method applies especially to confidential data, such as usernames, passwords, or certificates.
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
See the Deploying and Upgrading AMQ Streams on OpenShift guide for instructions on running a:
Procedure
Edit the
spec
properties for theKafkaConnect
orKafkaConnectS2I
resource.The properties you can configure are shown in this example configuration:
apiVersion: kafka.strimzi.io/v1beta1 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 externalConfiguration: 8 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: 9 requests: cpu: "1" memory: 2Gi limits: cpu: "2" memory: 2Gi logging: 10 type: inline loggers: log4j.rootLogger: "INFO" readinessProbe: 11 initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 metrics: 12 lowercaseOutputName: true lowercaseOutputLabelNames: true rules: - pattern: kafka.connect<type=connect-worker-metrics><>([a-z-]+) name: kafka_connect_worker_$1 help: "Kafka Connect JMX metric worker" type: GAUGE - pattern: kafka.connect<type=connect-worker-rebalance-metrics><>([a-z-]+) name: kafka_connect_worker_rebalance_$1 help: "Kafka Connect JMX metric rebalance information" type: GAUGE jvmOptions: 13 "-Xmx": "1g" "-Xms": "1g" image: my-org/my-image:latest 14 template: 15 pod: affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: application operator: In values: - postgresql - mongodb topologyKey: "kubernetes.io/hostname" connectContainer: 16 env: - name: JAEGER_SERVICE_NAME value: my-jaeger-service - name: JAEGER_AGENT_HOST value: jaeger-agent-name - name: JAEGER_AGENT_PORT value: "6831"
- 1
- Use
KafkaConnect
orKafkaConnectS2I
, as required. - 2
- Enables
KafkaConnectors
for the Kafka Connect cluster. - 3
- 4
- Authentication for the Kafka Connect cluster, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-512 or PLAIN mechanism. By default, Kafka Connect connects to Kafka brokers using a plain text connection.
- 5
- Bootstrap server for connection to the Kafka Connect cluster.
- 6
- 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.
- 7
- Kafka Connect configuration of workers (not connectors). Standard Apache Kafka configuration may be provided, restricted to those properties not managed directly by AMQ Streams.
- 8
- External configuration for Kafka connectors using environment variables, as shown here, or volumes.
- 9
- Requests for reservation of supported resources, currently
cpu
andmemory
, and limits to specify the maximum resources that can be consumed. - 10
- Specified Kafka Connect loggers and log levels added directly (
inline
) or indirectly (external
) through a ConfigMap. A custom ConfigMap must be placed under thelog4j.properties
orlog4j2.properties
key. For the Kafka Connectlog4j.rootLogger
logger, you can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. - 11
- Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).
- 12
- Prometheus metrics, which are enabled with configuration for the Prometheus JMX exporter in this example. You can enable metrics without further configuration using
metrics: {}
. - 13
- JVM configuration options to optimize performance for the Virtual Machine (VM) running Kafka Connect.
- 14
- ADVANCED OPTION: Container image configuration, which is recommended only in special situations.
- 15
- Template customization. Here a pod is scheduled with anti-affinity, so the pod is not scheduled on nodes with the same hostname.
- 16
- Environment variables are also set for distributed tracing using Jaeger.
Create or update the resource:
oc apply -f KAFKA-CONNECT-CONFIG-FILE
- If authorization is enabled for Kafka Connect, configure Kafka Connect users to enable access to the Kafka Connect consumer group and topics.
2.2.2. Kafka Connect configuration for multiple instances
If you are running multiple instances of Kafka Connect, you have to change the default configuration of the following config
properties:
apiVersion: kafka.strimzi.io/v1beta1 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 # ... # ...
Values for the three topics must be the same for all Kafka Connect instances with the same group.id
.
Unless you change the default settings, each Kafka Connect instance connecting to the same Kafka cluster is deployed with the same values. What happens, in effect, is all instances are coupled to run in a cluster and use the same topics.
If multiple Kafka Connect clusters try to use the same topics, Kafka Connect will not work as expected and generate errors.
If you wish to run multiple Kafka Connect instances, change the values of these properties for each instance.
2.2.3. Configuring Kafka Connect user authorization
This procedure describes how to authorize user access to Kafka Connect.
When any type of authorization is being used in Kafka, a Kafka Connect user requires read/write access rights to the consumer group and the internal topics of Kafka Connect.
The properties for the consumer group and internal topics are automatically configured by AMQ Streams, or they can be specified explicitly in the spec
of the KafkaConnect
or KafkaConnectS2I
resource.
Example configuration properties in the KafkaConnect
resource
apiVersion: kafka.strimzi.io/v1beta1 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 # ... # ...
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.
The default values for the consumer group and topics will differ when running multiple instances.
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
authorization
property in theKafkaUser
resource to provide access rights to the user.In the following example, access rights are configured for the Kafka Connect topics and consumer group using
literal
name values:Property Name offset.storage.topic
connect-cluster-offsets
status.storage.topic
connect-cluster-status
config.storage.topic
connect-cluster-configs
group
connect-cluster
apiVersion: kafka.strimzi.io/v1beta1 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.
oc apply -f KAFKA-USER-CONFIG-FILE
2.2.4. List of Kafka Connect cluster resources
The following resources are created by the Cluster Operator in the OpenShift cluster:
- connect-cluster-name-connect
- Deployment which is in charge to create the Kafka Connect worker node pods.
- connect-cluster-name-connect-api
- Service which exposes the REST interface for managing the Kafka Connect cluster.
- connect-cluster-name-config
- ConfigMap which contains the Kafka Connect ancillary configuration and is mounted as a volume by the Kafka broker pods.
- connect-cluster-name-connect
- Pod Disruption Budget configured for the Kafka Connect worker nodes.
2.2.5. List of Kafka Connect (S2I) cluster resources
The following resources are created by the Cluster Operator in the OpenShift cluster:
- connect-cluster-name-connect-source
- ImageStream which is used as the base image for the newly-built Docker images.
- connect-cluster-name-connect
- BuildConfig which is responsible for building the new Kafka Connect Docker images.
- connect-cluster-name-connect
- ImageStream where the newly built Docker images will be pushed.
- connect-cluster-name-connect
- DeploymentConfig which is in charge of creating the Kafka Connect worker node pods.
- connect-cluster-name-connect-api
- Service which exposes the REST interface for managing the Kafka Connect cluster.
- connect-cluster-name-config
- ConfigMap which contains the Kafka Connect ancillary configuration and is mounted as a volume by the Kafka broker pods.
- connect-cluster-name-connect
- Pod Disruption Budget configured for the Kafka Connect worker nodes.
2.2.6. Integrating with Debezium for change data capture
Red Hat Debezium is a distributed change data capture platform. It captures row-level changes in databases, creates change event records, and streams the records to Kafka topics. Debezium is built on Apache Kafka. You can deploy and integrate Debezium with AMQ Streams. Following a deployment of AMQ Streams, you deploy Debezium as a connector configuration through Kafka Connect. Debezium passes change event records to AMQ Streams on OpenShift. Applications can read these change event streams and access the change events in the order in which they occurred.
Debezium has multiple uses, including:
- Data replication
- Updating caches and search indexes
- Simplifying monolithic applications
- Data integration
- Enabling streaming queries
To capture database changes, deploy Kafka Connect with a Debezium database connector . You configure a KafkaConnector
resource to define the connector instance.
For more information on deploying Debezium with AMQ Streams, refer to the product documentation. The Debezium documentation includes a Getting Started with Debezium guide that guides you through the process of setting up the services and connector required to view change event records for database updates.
2.3. Kafka MirrorMaker cluster configuration
This chapter describes how to configure a Kafka MirrorMaker deployment in your AMQ Streams cluster to replicate data between Kafka clusters.
You can use AMQ Streams 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.
The following procedure shows how the resource is configured:
The full schema of the KafkaMirrorMaker
resource is described in the KafkaMirrorMaker schema reference.
2.3.1. Configuring Kafka MirrorMaker
Use the properties of the KafkaMirrorMaker
resource to configure your Kafka MirrorMaker deployment.
You can configure access control for producers and consumers using TLS or SASL authentication. This procedure shows a configuration that uses TLS encryption and authentication on the consumer and producer side.
Prerequisites
See the Deploying and Upgrading AMQ Streams on OpenShift guide for instructions on running a:
- Source and target Kafka clusters must be available
Procedure
Edit the
spec
properties for theKafkaMirrorMaker
resource.The properties you can configure are shown in this example configuration:
apiVersion: kafka.strimzi.io/v1beta1 kind: KafkaMirrorMaker metadata: name: my-mirror-maker spec: replicas: 3 1 consumer: bootstrapServers: my-source-cluster-kafka-bootstrap:9092 2 groupId: "my-group" 3 numStreams: 2 4 offsetCommitInterval: 120000 5 tls: 6 trustedCertificates: - secretName: my-source-cluster-ca-cert certificate: ca.crt authentication: 7 type: tls certificateAndKey: secretName: my-source-secret certificate: public.crt key: private.key config: 8 max.poll.records: 100 receive.buffer.bytes: 32768 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 whitelist: "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 metrics: 18 lowercaseOutputName: true rules: - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*><>Count" name: "kafka_server_$1_$2_total" - pattern: "kafka.server<type=(.+), name=(.+)PerSec\\w*, topic=(.+)><>Count" name: "kafka_server_$1_$2_total" labels: topic: "$3" jvmOptions: 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
- 1
- 2
- Bootstrap servers for consumer and producer.
- 3
- 4
- 5
- 6
- 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.
- 7
- Authentication for consumer or producer, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-512 or PLAIN mechanism.
- 8
- 9
- SSL properties for external listeners to run with a specific cipher suite for a TLS version.
- 10
- Hostname verification is enabled by setting to
HTTPS
. An empty string disables the verification. - 11
- If the
abortOnSendFailure
property is set totrue
, Kafka MirrorMaker will exit and the container will restart following a send failure for a message. - 12
- SSL properties for external listeners to run with a specific cipher suite for a TLS version.
- 13
- Hostname verification is enabled by setting to
HTTPS
. An empty string disables the verification. - 14
- A whitelist of topics mirrored from source to target Kafka cluster.
- 15
- Requests for reservation of supported resources, currently
cpu
andmemory
, and limits to specify the maximum resources that can be consumed. - 16
- Specified loggers and log levels added directly (
inline
) or indirectly (external
) through a ConfigMap. A custom ConfigMap must be placed under thelog4j.properties
orlog4j2.properties
key. MirrorMaker has a single logger calledmirrormaker.root.logger
. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. - 17
- Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).
- 18
- Prometheus metrics, which are enabled with configuration for the Prometheus JMX exporter in this example. You can enable metrics without further configuration using
metrics: {}
. - 19
- JVM configuration options to optimize performance for the Virtual Machine (VM) running Kafka MirrorMaker.
- 20
- ADVANCED OPTION: Container image configuration, which is recommended only in special situations.
- 21
- Template customization. Here a pod is scheduled with anti-affinity, so the pod is not scheduled on nodes with the same hostname.
- 22
- Environment variables are also set for distributed tracing using Jaeger.
- 23
WarningWith the
abortOnSendFailure
property set tofalse
, the producer attempts to send the next message in a topic. The original message might be lost, as there is no attempt to resend a failed message.Create or update the resource:
oc apply -f <your-file>
2.3.2. List of Kafka MirrorMaker cluster resources
The following resources are created by the Cluster Operator in the OpenShift cluster:
- <mirror-maker-name>-mirror-maker
- Deployment which is responsible for creating the Kafka MirrorMaker pods.
- <mirror-maker-name>-config
- ConfigMap which contains ancillary configuration for the Kafka MirrorMaker, and is mounted as a volume by the Kafka broker pods.
- <mirror-maker-name>-mirror-maker
- Pod Disruption Budget configured for the Kafka MirrorMaker worker nodes.
2.4. Kafka MirrorMaker 2.0 cluster configuration
This section describes how to configure a Kafka MirrorMaker 2.0 deployment in your AMQ Streams 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.
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.
How MirrorMaker 2.0 replicates data is described here:
The following procedure shows how the resource is configured for MirrorMaker 2.0:
The full schema of the KafkaMirrorMaker2
resource is described in the KafkaMirrorMaker2 schema reference.
2.4.1. MirrorMaker 2.0 data replication
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. A MirrorMaker 2.0 MirrorSourceConnector
replicates topics from a source cluster to a 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.
Figure 2.1. Replication across two clusters
2.4.2. Cluster configuration
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.
2.4.2.1. Bidirectional replication (active/active)
The MirrorMaker 2.0 architecture supports bidirectional replication in an active/active cluster configuration.
Each cluster replicates the data of the other cluster using the concept of source and remote topics. As the same topics are stored in each cluster, remote topics are automatically renamed by MirrorMaker 2.0 to represent the source cluster. The name of the originating cluster is prepended to the name of the topic.
Figure 2.2. Topic renaming
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.
2.4.2.2. Unidirectional replication (active/passive)
The MirrorMaker 2.0 architecture supports unidirectional replication in an active/passive cluster configuration.
You can use an active/passive cluster configuration to make backups or migrate data to another cluster. In this situation, you might not want automatic renaming of remote topics.
You can override automatic renaming by adding IdentityReplicationPolicy
to the source connector configuration of the KafkaMirrorMaker2
resource. With this configuration applied, topics retain their original names.
2.4.2.3. Topic configuration synchronization
Topic configuration is automatically synchronized between source and target clusters. By synchronizing configuration properties, the need for rebalancing is reduced.
2.4.2.4. Data integrity
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.
2.4.2.5. Offset tracking
MirrorMaker 2.0 tracks offsets for consumer groups using internal topics.
- The offset sync topic maps the source and target offsets for replicated topic partitions from record metadata
- The checkpoint topic maps the last committed offset in the source and target cluster for replicated topic partitions in each consumer group
Offsets for the checkpoint topic are tracked at predetermined intervals through configuration. Both topics enable replication to be fully restored from the correct offset position on failover.
MirrorMaker 2.0 uses its MirrorCheckpointConnector
to emit checkpoints for offset tracking.
2.4.2.6. Connectivity checks
A heartbeat internal topic checks connectivity between clusters.
The heartbeat topic is replicated from the source cluster.
Target clusters use the topic to check:
- The connector managing connectivity between clusters is running
- The source cluster is available
MirrorMaker 2.0 uses its MirrorHeartbeatConnector
to emit heartbeats that perform these checks.
2.4.3. ACL rules synchronization
ACL access to remote topics is possible if you are not using the User Operator.
If AclAuthorizer
is being used, without the User Operator, ACL rules that manage access to brokers also apply to remote topics. Users that can read a source topic can read its remote equivalent.
OAuth 2.0 authorization does not support access to remote topics in this way.
2.4.4. Synchronizing data between Kafka clusters using MirrorMaker 2.0
Use MirrorMaker 2.0 to synchronize data between Kafka clusters through configuration.
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
Use the properties of the KafkaMirrorMaker2
resource to configure your Kafka MirrorMaker 2.0 deployment.
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/v1alpha1 kind: KafkaMirrorMaker2 metadata: name: my-mirror-maker2 spec: version: 2.6.0 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.
Prerequisites
See the Deploying and Upgrading AMQ Streams on OpenShift guide for instructions on running a:
- Source and target Kafka clusters must be available
Procedure
Edit the
spec
properties for theKafkaMirrorMaker2
resource.The properties you can configure are shown in this example configuration:
apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaMirrorMaker2 metadata: name: my-mirror-maker2 spec: version: 2.6.0 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 config: replication.factor: 1 20 offset-syncs.topic.replication.factor: 1 21 sync.topic.acls.enabled: "false" 22 replication.policy.separator: "" 23 replication.policy.class: "io.strimzi.kafka.connect.mirror.IdentityReplicationPolicy" 24 heartbeatConnector: 25 config: heartbeats.topic.replication.factor: 1 26 checkpointConnector: 27 config: checkpoints.topic.replication.factor: 1 28 topicsPattern: ".*" 29 groupsPattern: "group1|group2|group3" 30 resources: 31 requests: cpu: "1" memory: 2Gi limits: cpu: "2" memory: 2Gi logging: 32 type: inline loggers: connect.root.logger.level: "INFO" readinessProbe: 33 initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 jvmOptions: 34 "-Xmx": "1g" "-Xms": "1g" image: my-org/my-image:latest 35 template: 36 pod: affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: application operator: In values: - postgresql - mongodb topologyKey: "kubernetes.io/hostname" connectContainer: 37 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 38 externalConfiguration: 39 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
- 1
- The Kafka Connect version.
- 2
- 3
- Cluster alias for Kafka Connect.
- 4
- Specification for the Kafka clusters being synchronized.
- 5
- Cluster alias for the source Kafka cluster.
- 6
- Authentication for the source cluster, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-512 or PLAIN mechanism.
- 7
- Bootstrap server for connection to the source Kafka cluster.
- 8
- 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.
- 9
- Cluster alias for the target Kafka cluster.
- 10
- Authentication for the target Kafka cluster is configured in the same way as for the source Kafka cluster.
- 11
- Bootstrap server for connection to the target Kafka cluster.
- 12
- Kafka Connect configuration. Standard Apache Kafka configuration may be provided, restricted to those properties not managed directly by AMQ Streams.
- 13
- SSL properties for external listeners to run with a specific cipher suite for a TLS version.
- 14
- Hostname verification is enabled by setting to
HTTPS
. An empty string disables the verification. - 15
- TLS encryption for the target Kafka cluster is configured in the same way as for the source Kafka cluster.
- 16
- 17
- Cluster alias for the source cluster used by the MirrorMaker 2.0 connectors.
- 18
- Cluster alias for the target cluster used by the MirrorMaker 2.0 connectors.
- 19
- Configuration for the
MirrorSourceConnector
that creates remote topics. Theconfig
overrides the default configuration options. - 20
- Replication factor for mirrored topics created at the target cluster.
- 21
- Replication factor for the
MirrorSourceConnector
offset-syncs
internal topic that maps the offsets of the source and target clusters. - 22
- When ACL rules synchronization is enabled, ACLs are applied to synchronized topics. The default is
true
. - 23
- Defines the separator used for the renaming of remote topics.
- 24
- 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.
- 25
- Configuration for the
MirrorHeartbeatConnector
that performs connectivity checks. Theconfig
overrides the default configuration options. - 26
- Replication factor for the heartbeat topic created at the target cluster.
- 27
- Configuration for the
MirrorCheckpointConnector
that tracks offsets. Theconfig
overrides the default configuration options. - 28
- Replication factor for the checkpoints topic created at the target cluster.
- 29
- Topic replication from the source cluster defined as regular expression patterns. Here we request all topics.
- 30
- Consumer group replication from the source cluster defined as regular expression patterns. Here we request three consumer groups by name. You can use comma-separated lists.
- 31
- Requests for reservation of supported resources, currently
cpu
andmemory
, and limits to specify the maximum resources that can be consumed. - 32
- Specified Kafka Connect loggers and log levels added directly (
inline
) or indirectly (external
) through a ConfigMap. A custom ConfigMap must be placed under thelog4j.properties
orlog4j2.properties
key. For the Kafka Connectlog4j.rootLogger
logger, you can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. - 33
- Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).
- 34
- JVM configuration options to optimize performance for the Virtual Machine (VM) running Kafka MirrorMaker.
- 35
- ADVANCED OPTION: Container image configuration, which is recommended only in special situations.
- 36
- Template customization. Here a pod is scheduled with anti-affinity, so the pod is not scheduled on nodes with the same hostname.
- 37
- Environment variables are also set for distributed tracing using Jaeger.
- 38
- 39
- External configuration for an OpenShift Secret mounted to Kafka MirrorMaker as an environment variable.
Create or update the resource:
oc apply -f <your-file>
2.5. Kafka Bridge cluster configuration
This section describes how to configure a Kafka Bridge deployment in your AMQ Streams 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 Section B.121, “KafkaBridge
schema reference”.
2.5.1. Configuring the Kafka Bridge
Use the Kafka Bridge to make HTTP-based requests to the Kafka cluster.
Use the properties of the KafkaBridge
resource to configure your Kafka Bridge deployment.
In order to prevent issues arising when client consumer requests are processed by different Kafka Bridge instances, address-based routing must be employed to ensure that requests are routed to the right Kafka Bridge instance. Additionally, each independent Kafka Bridge instance must have a replica. A Kafka Bridge instance has its own state which is not shared with another instances.
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
See the Deploying and Upgrading AMQ Streams on OpenShift guide for instructions on running a:
Procedure
Edit the
spec
properties for theKafkaBridge
resource.The properties you can configure are shown in this example configuration:
apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaBridge metadata: name: my-bridge spec: replicas: 3 1 bootstrapServers: my-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"
- 1
- 2
- Bootstrap server for connection to the target Kafka cluster.
- 3
- 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.
- 4
- Authentication for the Kafka Bridge cluster, using the TLS mechanism, as shown here, using OAuth bearer tokens, or a SASL-based SCRAM-SHA-512 or PLAIN mechanism. By default, the Kafka Bridge connects to Kafka brokers without authentication.
- 5
- HTTP access to Kafka brokers.
- 6
- CORS access specifying selected resources and access methods. Additional HTTP headers in requests describe the origins that are permitted access to the Kafka cluster.
- 7
- Consumer configuration options.
- 8
- Producer configuration options.
- 9
- Requests for reservation of supported resources, currently
cpu
andmemory
, and limits to specify the maximum resources that can be consumed. - 10
- Specified Kafka Bridge loggers and log levels added directly (
inline
) or indirectly (external
) through a ConfigMap. A custom ConfigMap must be placed under thelog4j.properties
orlog4j2.properties
key. For the Kafka Bridge loggers, you can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. - 11
- JVM configuration options to optimize performance for the Virtual Machine (VM) running the Kafka Bridge.
- 12
- Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness).
- 13
- ADVANCED OPTION: Container image configuration, which is recommended only in special situations.
- 14
- Template customization. Here a pod is scheduled with anti-affinity, so the pod is not scheduled on nodes with the same hostname.
- 15
- Environment variables are also set for distributed tracing using Jaeger.
Create or update the resource:
oc apply -f KAFKA-BRIDGE-CONFIG-FILE
2.5.2. List of Kafka Bridge cluster resources
The following resources are created by the Cluster Operator in the OpenShift cluster:
- bridge-cluster-name-bridge
- Deployment which is in charge to create the Kafka Bridge worker node pods.
- bridge-cluster-name-bridge-service
- Service which exposes the REST interface of the Kafka Bridge cluster.
- bridge-cluster-name-bridge-config
- ConfigMap which contains the Kafka Bridge ancillary configuration and is mounted as a volume by the Kafka broker pods.
- bridge-cluster-name-bridge
- Pod Disruption Budget configured for the Kafka Bridge worker nodes.
2.6. Customizing OpenShift resources
AMQ Streams creates several OpenShift resources, such as Deployments
, StatefulSets
, Pods
, and Services
, which are managed by AMQ Streams operators. Only the operator that is responsible for managing a particular OpenShift resource can change that resource. If you try to manually change an operator-managed OpenShift resource, the operator will revert your changes back.
However, changing an operator-managed OpenShift 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 such changes using the template
property in the AMQ Streams custom resources. The template
property is supported in the following resources. The API reference provides more details about the customizable fields.
Kafka.spec.kafka
-
See Section B.53, “
KafkaClusterTemplate
schema reference” Kafka.spec.zookeeper
-
See Section B.63, “
ZookeeperClusterTemplate
schema reference” Kafka.spec.entityOperator
-
See Section B.68, “
EntityOperatorTemplate
schema reference” Kafka.spec.kafkaExporter
-
See Section B.74, “
KafkaExporterTemplate
schema reference” Kafka.spec.cruiseControl
-
See Section B.71, “
CruiseControlTemplate
schema reference” KafkaConnect.spec
-
See Section B.88, “
KafkaConnectTemplate
schema reference” KafkaConnectS2I.spec
-
See Section B.88, “
KafkaConnectTemplate
schema reference” KafkaMirrorMaker.spec
-
See Section B.119, “
KafkaMirrorMakerTemplate
schema reference” KafkaMirrorMaker2.spec
-
See Section B.88, “
KafkaConnectTemplate
schema reference” KafkaBridge.spec
-
See Section B.128, “
KafkaBridgeTemplate
schema reference” KafkaUser.spec
-
See Section B.112, “
KafkaUserTemplate
schema reference”
In the following example, the template
property is used to modify the labels in a Kafka broker’s StatefulSet
:
apiVersion: kafka.strimzi.io/v1beta1 kind: Kafka metadata: name: my-cluster labels: app: my-cluster spec: kafka: # ... template: statefulset: metadata: labels: mylabel: myvalue # ...
2.6.1. Customizing the image pull policy
AMQ Streams 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.
Additional resources
- For more information about Cluster Operator configuration, see Section 5.1, “Using the Cluster Operator”.
- For more information about Image Pull Policies, see Disruptions.
2.7. External logging
When setting the logging levels for a resource, you can specify them inline directly in the spec.logging
property of the resource YAML:
spec: # ... logging: type: inline loggers: kafka.root.logger.level: "INFO"
Or you can specify external logging:
spec:
# ...
logging:
type: external
name: customConfigMap
With external logging, logging properties are defined in a ConfigMap. The name of the ConfigMap is referenced in the spec.logging.name
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.
2.7.1. Creating a ConfigMap for logging
To use a ConfigMap to define logging properties, you create the ConfigMap and then reference it as part of the logging definition in the spec
of a resource.
The ConfigMap must contain the appropriate logging configuration.
-
log4j.properties
for Kafka components, ZooKeeper, and the Kafka Bridge -
log4j2.properties
for the Topic Operator and User Operator
The configuration must be placed under these properties.
Here we demonstrate how a ConfigMap defines a root logger for a Kafka resource.
Procedure
Create the ConfigMap.
You can create the ConfigMap as a YAML file or from a properties file using
oc
at the command line.ConfigMap example with a root logger definition for Kafka:
kind: ConfigMap apiVersion: kafka.strimzi.io/v1beta1 metadata: name: logging-configmap data: log4j.properties: kafka.root.logger.level="INFO"
From the command line, using a properties file:
oc create configmap logging-configmap --from-file=log4j.properties
The properties file defines the logging configuration:
# Define the logger kafka.root.logger.level="INFO" # ...
Define external logging in the
spec
of the resource, setting thelogging.name
to the name of the ConfigMap.spec: # ... logging: type: external name: logging-configmap
Create or update the resource.
oc apply -f kafka.yaml