Chapter 3. Deployment configuration
This chapter describes how to configure different aspects of the supported deployments:
- Kafka clusters
- Kafka Connect clusters
- Kafka Connect clusters with Source2Image support
- Kafka Mirror Maker
3.1. Kafka cluster configuration
The full schema of the Kafka resource is described in the Section B.1, “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 those resources to be labelled in whatever way the user requires.
3.1.1. Kafka and Zookeeper storage
Kafka brokers and Zookeeper are stateful applications. They need to store data on disks. AMQ Streams allows you to configure the type of storage, which they want to use for Kafka and Zookeeper. Storage configuration is mandatory and has to be specified in every Kafka resource.
Storage can be configured using the storage property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper
AMQ Streams supports two types of storage:
- Ephemeral
- Persistent
The type of storage is specified in the type field.
Once the Kafka cluster is deployed, the storage cannot be changed.
3.1.1.1. 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/v1alpha1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
storage:
type: ephemeral
# ...
zookeeper:
# ...
storage:
type: ephemeral
# ...
3.1.1.2. 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:
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 a
matchLabelsfield which contains key:value pairs representing labels for selecting such a volume. delete-claim(optional)-
Boolean value which specifies if the Persistent Volume Claim has to be deleted when the cluster is undeployed. Default is
false.
Resizing persistent storage for existing AMQ Streams clusters is not currently supported. You must decide the necessary storage size before deploying the cluster.
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:
matchLabels:
"hdd-type": "ssd"
deleteClaim: true
# ...
Persistent Volume Claim naming
When the persistent storage is used, it will create 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.
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 the schema for
Kafka, seeKafkaschema reference.
3.1.2. 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.
3.1.2.1. 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 you should see Section 3.1.20, “Scaling clusters”.
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
- A Kafka cluster with no topics defined yet
Procedure
Edit the
replicasproperty in theKafkaresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... replicas: 3 # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
Additional resources
If your cluster already has topics defined see Section 3.1.20, “Scaling clusters”.
3.1.3. Kafka broker configuration
AMQ Streams allows you to customize the configuration of Apache Kafka brokers. You can specify and configure most of the options listed in Apache Kafka documentation.
The only options which cannot be configured are those 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.
3.1.3.1. Kafka broker configuration
Kafka broker can be configured using the config property in Kafka.spec.kafka.
This property should contain the Kafka broker configuration options as keys. The values could be in one of the following JSON types:
- String
- Number
- Boolean
Users can specify and configure the options listed in Apache Kafka 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:
-
listeners -
advertised. -
broker. -
listener. -
host.name -
port -
inter.broker.listener.name -
sasl. -
ssl. -
security. -
password. -
principal.builder.class -
log.dir -
zookeeper.connect -
zookeeper.set.acl -
authorizer. -
super.user
When one of the forbidden options is present in the config property, it will be ignored and a warning message will be printed to the Cluster Operator log file. All other options will be passed to Kafka.
The Cluster Operator does not validate keys or values in the provided config object. When invalid configuration is provided, the Kafka cluster might not start or might become unstable. In such cases, the configuration in the Kafka.spec.kafka.config object should be fixed and the cluster operator will roll out the new configuration to all Kafka brokers.
An example showing Kafka broker configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
config:
num.partitions: 1
num.recovery.threads.per.data.dir: 1
default.replication.factor: 3
offsets.topic.replication.factor: 3
transaction.state.log.replication.factor: 3
transaction.state.log.min.isr: 1
log.retention.hours: 168
log.segment.bytes: 1073741824
log.retention.check.interval.ms: 300000
num.network.threads: 3
num.io.threads: 8
socket.send.buffer.bytes: 102400
socket.receive.buffer.bytes: 102400
socket.request.max.bytes: 104857600
group.initial.rebalance.delay.ms: 0
# ...
3.1.3.2. Configuring Kafka brokers
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
configproperty in theKafkaresource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1alpha1 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: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.1.4. Kafka broker listeners
AMQ Streams allows users to configure the listeners which will be enabled in Kafka brokers. Two types of listeners are supported:
- Plain listener on port 9092 (without encryption)
- TLS listener on port 9093 (with encryption)
3.1.4.1. Mutual TLS authentication for clients
3.1.4.1.1. Mutual TLS authentication
Mutual authentication or two-way authentication is when both the server and the client present certificates. AMQ Streams can configure Kafka to use TLS (Transport Layer Security) to provide encrypted communication between Kafka brokers and clients either with or without mutual authentication. When you configure mutual authentication, the broker authenticates the client and the client authenticates the broker. Mutual TLS authentication is always used for the communication between Kafka brokers and Zookeeper pods.
In many common uses of TLS (such as the HTTPS protocol used between a web browser and a web server) the authentication is not mutual: Only one party to the communication gets proof of the identity of the other party.
TLS authentication is more commonly one-way, where only one party authenticates to another. For example, when the HTTPS protocol is used between a web browser and a web server, the authentication is not usually mutual and only the server gets proof of the identity of the browser.
3.1.4.1.2. When to use mutual TLS authentication for clients
Mutual TLS authentication is recommended for authenticating Kafka clients when:
- The client supports authentication using mutual TLS authentication
- It is necessary to use the TLS certificates rather than passwords
- You can reconfigure and restart client applications periodically so that they do not use expired certificates.
3.1.4.2. SCRAM-SHA authentication
SCRAM (Salted Challenge Response Authentication Mechanism) is an authentication protocol that can establish mutual authentication using passwords. AMQ Streams can configure Kafka to use SASL SCRMA-SHA-512 to provide authentication on both unencrypted and TLS-encrypted client connections. TLS authentication is always used internally between Kafka brokers and Zookeeper nodes. When used with a TLS client connection, the TLS protocol provides encryption, but is not used for authentication.
The following properties of SCRAM make it safe to use SCRAM-SHA even on unencrypted connections:
- The passwords are not sent in the clear over the communication channel. Instead the client and the server are each challenged by the other to offer proof that they know the password of the authenticating user.
- The server and client each generate a new challenge one each authentication exchange. This means that the exchange is resilient against replay attacks.
3.1.4.2.1. Supported SCRAM credentials
AMQ Streams supports SCRMA-SHA-512 only. When a KafkaUser.spec.authentication.type is configured with scram-sha-512 the User Operator will generate a random 12 character password consisting of upper and lowercase ASCII letters and numbers.
3.1.4.2.2. When to use SCRAM-SHA authentication for clients
SCRAM-SHA is recommended for authenticating Kafka clients when:
- The client supports authentication using SCRAM-SHA-512
- It is necessary to use passwords rather than the TLS certificates
- When you want to have authentication for unencrypted communication
3.1.4.3. Kafka listeners
You can configure Kafka broker listeners using the listeners property in the Kafka.spec.kafka resource. The listeners property contains three sub-properties:
-
plain -
tls -
external
When none of these properties are defined, the listener will be disabled.
An example of listeners property with all listeners enabled
# ...
listeners:
plain: {}
tls: {}
# ...
An example of listeners property with only the plain listener enabled
# ...
listeners:
plain: {}
# ...
3.1.4.3.1. External listener
The external listener is used to connect to a Kafka cluster from outside of an OpenShift environment. AMQ Streams supports three types of external listeners:
-
route -
loadbalancer -
nodeport
Exposing Kafka using OpenShift Routes
An external listener of type route exposes Kafka by using OpenShift Routes and the HAProxy router. A dedicated Route is created for every Kafka broker pod. An additional Route is created to serve as a Kafka bootstrap address. Kafka clients can use these Routes to connect to Kafka on port 443.
When exposing Kafka using OpenShift Routes, TLS encryption is always used.
For more information on using Routes to access Kafka, see Section 3.1.4.5, “Accessing Kafka using OpenShift routes”.
Exposing Kafka using loadbalancers
External listeners of type loadbalancer expose Kafka by using Loadbalancer type Services. A new loadbalancer service is created for every Kafka broker pod. An additional loadbalancer is created to serve as a Kafka bootstrap address. Loadbalancers listen to connections on port 9094.
By default, TLS encryption is enabled. To disable it, set the tls field to false.
For more information on using loadbalancers to access Kafka, see Section 3.1.4.6, “Accessing Kafka using loadbalancers routes”.
Exposing Kafka using node ports
External listeners of type nodeport expose Kafka by using NodePort type Services. When exposing Kafka in this way, Kafka clients connect directly to the nodes of OpenShift. You must enable access to the ports on the OpenShift nodes for each client (for example, in firewalls or security groups). Each Kafka broker pod is then accessible on a separate port. Additional NodePort type Service is created to serve as a Kafka bootstrap address.
When configuring the advertised addresses for the Kafka broker pods, AMQ Streams uses the address of the node on which the given pod is running. When selecting the node address, the different address types are used with the following priority:
- ExternalDNS
- ExternalIP
- Hostname
- InternalDNS
- InternalIP
By default, TLS encryption is enabled. To disable it, set the tls field to false.
TLS hostname verification is not currently supported when exposing Kafka clusters using node ports.
For more information on using node ports to access Kafka, see Section 3.1.4.7, “Accessing Kafka using node ports routes”.
3.1.4.3.2. Listener authentication
The listener sub-properties can also contain additional configuration. Both listeners support the authentication property. This is used to specify an authentication mechanism specific to that listener:
- mutual TLS authentication (only on the listeners with TLS encryption)
- SCRAM-SHA authentication
If no authentication property is specified then the listener does not authenticate clients which connect though that listener.
An example where the plain listener is configured for SCRAM-SHA authentication and the tls listener with mutual TLS authentication
# ...
listeners:
plain:
authentication:
type: scram-sha-512
tls:
authentication:
type: tls
external:
type: loadbalancer
tls: true
authentication:
type: tls
# ...
Authentication must be configured when using the User Operator to manage KafkaUsers.
3.1.4.4. Configuring Kafka listeners
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
-
Edit the
listenersproperty in theKafka.spec.kafkaresource.
An example configuration of the plain (unencrypted) listener without authentication:
+
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
spec:
kafka:
# ...
listeners:
plain: {}
# ...
zookeeper:
# ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
Additional resources
-
For more information about the schema, see
KafkaListenersschema reference.
3.1.4.5. Accessing Kafka using OpenShift routes
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Deploy Kafka cluster with an external listener enabled and configured to the type
route.An example configuration with an external listener configured to use
Routes:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... listeners: external: type: route # ... # ... zookeeper: # ...Create or update the resource.
oc apply -f your-fileFind the address of the bootstrap
Route.oc get routes _cluster-name_-kafka-bootstrap -o=jsonpath='{.status.ingress[0].host}{"\n"}'Use the address together with port 443 in your Kafka client as the bootstrap address.
Extract the public certificate of the broker certification authority
oc extract secret/_cluster-name_-cluster-ca-cert --keys=ca.crt --to=- > ca.crt
Use the extracted certificate in your Kafka client to configure TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication.
Additional resources
-
For more information about the schema, see
KafkaListenersschema reference.
3.1.4.6. Accessing Kafka using loadbalancers routes
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Deploy Kafka cluster with an external listener enabled and configured to the type
loadbalancer.An example configuration with an external listener configured to use loadbalancers:
apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... listeners: external: type: loadbalancer tls: true # ... # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-fileFind the hostname of the bootstrap loadbalancer.
On OpenShift this can be done using
oc get:oc get service cluster-name-kafka-external-bootstrap -o=jsonpath='{.status.loadBalancer.ingress[0].hostname}{"\n"}'If no hostname was found (nothing was returned by the command), use the loadbalancer IP address.
On OpenShift this can be done using
oc get:oc get service cluster-name-kafka-external-bootstrap -o=jsonpath='{.status.loadBalancer.ingress[0].ip}{"\n"}'Use the hostname or IP address together with port 9094 in your Kafka client as the bootstrap address.
Unless TLS encryption was disabled, extract the public certificate of the broker certification authority.
On OpenShift this can be done using
oc extract:oc extract secret/cluster-name-cluster-ca-cert --keys=ca.crt --to=- > ca.crtUse the extracted certificate in your Kafka client to configure TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication.
Additional resources
-
For more information about the schema, see
KafkaListenersschema reference.
3.1.4.7. Accessing Kafka using node ports routes
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Deploy Kafka cluster with an external listener enabled and configured to the type
nodeport.An example configuration with an external listener configured to use node ports:
apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... listeners: external: type: nodeport tls: true # ... # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-fileFind the port number of the bootstrap service.
On OpenShift this can be done using
oc get:oc get service cluster-name-kafka-external-bootstrap -o=jsonpath='{.spec.ports[0].nodePort}{"\n"}'The port should be used in the Kafka bootstrap address.
Find the address of the OpenShift node.
On OpenShift this can be done using
oc get:oc get node node-name -o=jsonpath='{range .status.addresses[*]}{.type}{"\t"}{.address}{"\n"}'If several different addresses are returned, select the address type you want based on the following order:
- ExternalDNS
- ExternalIP
- Hostname
- InternalDNS
InternalIP
Use the address with the port found in the previous step in the Kafka bootstrap address.
Unless TLS encryption was disabled, extract the public certificate of the broker certification authority.
On OpenShift this can be done using
oc extract:oc extract secret/cluster-name-cluster-ca-cert --keys=ca.crt --to=- > ca.crtUse the extracted certificate in your Kafka client to configure TLS connection. If you enabled any authentication, you will also need to configure SASL or TLS authentication.
Additional resources
-
For more information about the schema, see
KafkaListenersschema reference.
3.1.5. Authentication and Authorization
AMQ Streams supports authentication and authorization. Authentication can be configured independently for each listener. Authorization is always configured for the whole Kafka cluster.
3.1.5.1. Authentication
Authentication is configured as part of the listener configuration in the authentication property. When the authentication property is missing, no authentication will be enabled on given listener. The authentication mechanism which will be used is defined by the type field.
The supported authentication mechanisms are:
- TLS client authentication
- SASL SCRAM-SHA-512
3.1.5.1.1. TLS client authentication
TLS Client authentication can be enabled by specifying the type as tls. The TLS client authentication is supported only on the tls listener.
An example of authentication with type tls
# ... authentication: type: tls # ...
3.1.5.2. Configuring authentication in Kafka brokers
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
listenersproperty in theKafka.spec.kafkaresource. Add theauthenticationfield to the listeners where you want to enable authentication. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... listeners: tls: authentication: type: tls # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
Additional resources
- For more information about the supported authentication mechanisms, see authentication reference.
-
For more information about the schema for
Kafka, seeKafkaschema reference.
3.1.5.3. Authorization
Authorization can be configured using the authorization property in the Kafka.spec.kafka resource. When the authorization property is missing, no authorization will be enabled. When authorization is enabled it will be applied for all enabled listeners. The authorization method is defined by the type field.
Currently, the only supported authorization method is the Simple authorization.
3.1.5.3.1. Simple authorization
Simple authorization is using the SimpleAclAuthorizer plugin. SimpleAclAuthorizer is the default authorization plugin which is part of Apache Kafka. To enable simple authorization, the type field should be set to simple.
An example of Simple authorization
# ... authorization: type: simple # ...
3.1.5.4. Configuring authorization in Kafka brokers
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Add or edit the
authorizationproperty in theKafka.spec.kafkaresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... authorization: type: simple # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
Additional resources
- For more information about the supported authorization methods, see authorization reference.
-
For more information about the schema for
Kafka, seeKafkaschema reference.
3.1.6. Replicas
Zookeeper clusters or ensembles usually run with an odd number of nodes and always requires the majority of the nodes to be available in order to maintain a quorum. Maintaining a quorum is important because when the Zookeeper cluster loses a quorum, it will stop responding to clients. As a result, a Zookeeper cluster without a quorum will cause the Kafka brokers to stop working as well. This is why having a stable and highly available Zookeeper cluster is very important for AMQ Streams.
A Zookeeper cluster is usually deployed with three, five, or seven nodes.
- Three nodes
- Zookeeper cluster consisting of three nodes 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 nodes
- Zookeeper cluster consisting of five nodes requires at least three nodes to be up and running in order to maintain the quorum. It can tolerate two nodes being unavailable.
- Seven nodes
- Zookeeper cluster consisting of seven nodes 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.
3.1.6.1. 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/v1alpha1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
zookeeper:
# ...
replicas: 3
# ...
3.1.6.2. Changing number of replicas
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
replicasproperty in theKafkaresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... replicas: 3 # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.1.7. 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 Zookeeper documentation.
The only 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.
3.1.7.1. Zookeeper configuration
Zookeeper nodes can be configured using the config property in Kafka.spec.zookeeper. This property should contain the Zookeeper configuration options as keys. The values could be in 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 will be ignored and a warning message will be printed to the Custer Operator log file. All other options will be 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:
-
timeTickwith default value2000 -
initLimitwith default value5 -
syncLimitwith default value2 -
autopurge.purgeIntervalwith default value1
These options will be automatically configured when they are not present in the Kafka.spec.zookeeper.config property.
An example showing Zookeeper configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
spec:
kafka:
# ...
zookeeper:
# ...
config:
autopurge.snapRetainCount: 3
autopurge.purgeInterval: 1
# ...
3.1.7.2. Configuring Zookeeper
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
configproperty in theKafkaresource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... zookeeper: # ... config: autopurge.snapRetainCount: 3 autopurge.purgeInterval: 1 # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.1.8. Entity Operator
The Entity Operator is responsible for managing different entities in a running Kafka cluster. The currently supported entities are:
- Kafka topics
- managed by the Topic Operator.
- Kafka users
- managed by the User Operator
Both Topic and User Operators can be deployed on their own. But the easiest way to deploy them is together with the Kafka cluster as part of the Entity Operator. The Entity Operator can include either one or both of them depending on the configuration. They will be automatically configured to manage the topics and users of the Kafka cluster with which they are deployed.
For more information about Topic Operator, see Section 4.2, “Topic Operator”. For more information about how to use Topic Operator to create or delete topics, see Chapter 5, Using the Topic Operator.
3.1.8.1. Configuration
The Entity Operator can be configured using the entityOperator property in Kafka.spec
The entityOperator property supports several sub-properties:
-
tlsSidecar -
affinity -
tolerations -
topicOperator -
userOperator
The tlsSidecar property can be used to configure the TLS sidecar container which is used to communicate with Zookeeper. For more details about configuring the TLS sidecar, see Section 3.1.16, “TLS sidecar”.
The affinity and tolerations properties can be used to configure how OpenShift schedules the Entity Operator pod. For more details about pod scheduling, see Section 3.1.17, “Configuring pod scheduling”.
The topicOperator property contains the configuration of the Topic Operator. When this option is missing, the Entity Operator will be deployed without the Topic Operator.
The userOperator property contains the configuration of the User Operator. When this option is missing, the Entity Operator will be deployed without the User Operator.
Example of basic configuration enabling both operators
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
zookeeper:
# ...
entityOperator:
topicOperator: {}
userOperator: {}
When both topicOperator and userOperator properties are missing, the Entity Operator will be not deployed.
3.1.8.1.1. Topic Operator
Topic Operator deployment can be configured using additional options inside the topicOperator object. Following options 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 is 90.
zookeeperSessionTimeoutSeconds- The Zookeeper session timeout in seconds. Default is 20 seconds.
topicMetadataMaxAttempts-
The number of attempts for getting topics metadata from Kafka. The time between each attempt is defined as an exponential back-off. You might want to increase this value when topic creation could take more time due to its many partitions or replicas. Default is
6. image-
The
imageproperty can be used to configure the container image which will be used. For more details about configuring custom container images, see Section 3.1.15, “Container images”. resources-
The
resourcesproperty configures the amount of resources allocated to the Topic Operator For more details about resource request and limit configuration, see Section 3.1.9, “CPU and memory resources”. logging-
The
loggingproperty configures the logging of the Topic Operator For more details about logging configuration, see Section 3.1.10, “Logging”.
Example of Topic Operator configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
zookeeper:
# ...
entityOperator:
# ...
topicOperator:
watchedNamespace: my-topic-namespace
reconciliationIntervalSeconds: 60
# ...
3.1.8.1.2. User Operator
User Operator deployment can be configured using additional options inside the userOperator object. Following options are supported:
watchedNamespace-
The OpenShift namespace in which the topic operator watches for
KafkaUsers. Default is the namespace where the Kafka cluster is deployed. reconciliationIntervalSeconds- The interval between periodic reconciliations in seconds. Default is 120.
zookeeperSessionTimeoutSeconds- The Zookeeper session timeout in seconds. Default is 6 seconds.
image-
The
imageproperty can be used to configure the container image which will be used. For more details about configuring custom container images, see Section 3.1.15, “Container images”. resources-
The
resourcesproperty configures the amount of resources allocated to the User Operator. For more details about resource request and limit configuration, see Section 3.1.9, “CPU and memory resources”. logging-
The
loggingproperty configures the logging of the User Operator. For more details about logging configuration, see Section 3.1.10, “Logging”.
Example of Topic Operator configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
zookeeper:
# ...
entityOperator:
# ...
userOperator:
watchedNamespace: my-user-namespace
reconciliationIntervalSeconds: 60
# ...
3.1.8.2. Configuring Entity Operator
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
entityOperatorproperty in theKafkaresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... entityOperator: topicOperator: watchedNamespace: my-topic-namespace reconciliationIntervalSeconds: 60 userOperator: watchedNamespace: my-user-namespace reconciliationIntervalSeconds: 60Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.1.9. CPU and memory resources
For every deployed container, AMQ Streams allows you to specify the resources which should be reserved for it and the maximum resources that can be consumed by it. AMQ Streams supports two types of resources:
- Memory
- CPU
AMQ Streams is using the OpenShift syntax for specifying CPU and memory resources.
3.1.9.1. Resource limits and requests
Resource limits and requests can be configured using the resources property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.kafka.tlsSidecar -
Kafka.spec.zookeeper -
Kafka.spec.zookeeper.tlsSidecar -
Kafka.spec.entityOperator.topicOperator -
Kafka.spec.entityOperator.userOperator -
Kafka.spec.entityOperator.tlsSidecar -
KafkaConnect.spec -
KafkaConnectS2I.spec
3.1.9.1.1. Resource requests
Requests specify the resources that will be reserved for a given container. Reserving the resources will ensure that they are always available.
If the resource request is for more than the available free resources in the OpenShift cluster, the pod will not be scheduled.
Resource requests can be specified in the request property. The resource requests currently supported by AMQ Streams are memory and CPU. Memory is specified under the property memory. CPU is specified under the property cpu.
An example showing resource request configuration
# ...
resources:
requests:
cpu: 12
memory: 64Gi
# ...
It is also possible to specify a resource request just for one of the resources:
An example showing resource request configuration with memory request only
# ...
resources:
requests:
memory: 64Gi
# ...
Or:
An example showing resource request configuration with CPU request only
# ...
resources:
requests:
cpu: 12
# ...
3.1.9.1.2. Resource limits
Limits specify the maximum resources that can be consumed by a given container. The limit is not reserved and might not be always available. The container can use the resources up to the limit only when they are available. The resource limits should be always higher than the resource requests.
Resource limits can be specified in the limits property. The resource limits currently supported by AMQ Streams are memory and CPU. Memory is specified under the property memory. CPU is specified under the property cpu.
An example showing resource limits configuration
# ...
resources:
limits:
cpu: 12
memory: 64Gi
# ...
It is also possible to specify the resource limit just for one of the resources:
An example showing resource limit configuration with memory request only
# ...
resources:
limits:
memory: 64Gi
# ...
Or:
An example showing resource limits configuration with CPU request only
# ...
resources:
requests:
cpu: 12
# ...
3.1.9.1.3. Supported CPU formats
CPU requests and limits are supported in the following formats:
-
Number of CPU cores as integer (
5CPU core) or decimal (2.5CPU core). -
Number or millicpus / millicores (
100m) where 1000 millicores is the same1CPU core.
An example of using different CPU units
# ...
resources:
requests:
cpu: 500m
limits:
cpu: 2.5
# ...
The amount of computing power of 1 CPU core might differ depending on the platform where the OpenShift is deployed.
For more details about the CPU specification, see the Meaning of CPU website.
3.1.9.1.4. Supported memory formats
Memory requests and limits are specified in megabytes, gigabytes, mebibytes, and gibibytes.
-
To specify memory in megabytes, use the
Msuffix. For example1000M. -
To specify memory in gigabytes, use the
Gsuffix. For example1G. -
To specify memory in mebibytes, use the
Misuffix. For example1000Mi. -
To specify memory in gibibytes, use the
Gisuffix. For example1Gi.
An example of using different memory units
# ...
resources:
requests:
memory: 512Mi
limits:
memory: 2Gi
# ...
For more details about the memory specification and additional supported units, see the Meaning of memory website.
3.1.9.1.5. Additional resources
- For more information about managing computing resources on OpenShift, see Managing Compute Resources for Containers.
3.1.9.2. Configuring resource requests and limits
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
resourcesproperty in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... resources: requests: cpu: "8" memory: 64Gi limits: cpu: "12" memory: 128Gi # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
Additional resources
-
For more information about the schema, see
Resourcesschema reference.
3.1.10. Logging
Logging enables you to diagnose error and performance issues of AMQ Streams. For the logging, various logger implementations are used. Kafka and Zookeeper use log4j logger and Topic Operator, User Operator, and other components use log4j2 logger.
This section provides information about different loggers and describes how to configure log levels.
You can set the log levels by specifying the loggers and their levels directly (inline) or by using a custom (external) config map.
3.1.10.1. Using inline logging setting
Procedure
Edit the YAML file to specify the loggers and their level for the required components. For example:
apiVersion: {KafkaApiVersion} kind: Kafka spec: kafka: # ... logging: type: inline loggers: logger.name: "INFO" # ...In the above example, the log level is set to INFO. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. For more information about the log levels, see log4j manual.
Create or update the Kafka resource in OpenShift.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.1.10.2. Using external ConfigMap for logging setting
Procedure
Edit the YAML file to specify the name of the
ConfigMapwhich should be used for the required components. For example:apiVersion: {KafkaApiVersion} kind: Kafka spec: kafka: # ... logging: type: external name: customConfigMap # ...Remember to place your custom ConfigMap under
log4j.propertieseventuallylog4j2.propertieskey.Create or update the Kafka resource in OpenShift.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.1.10.3. Loggers
AMQ Streams consists of several components. Each component has its own loggers and is configurable. This section provides information about loggers of various components.
Components and their loggers are listed below.
Kafka
-
kafka.root.logger.level -
log4j.logger.org.I0Itec.zkclient.ZkClient -
log4j.logger.org.apache.zookeeper -
log4j.logger.kafka -
log4j.logger.org.apache.kafka -
log4j.logger.kafka.request.logger -
log4j.logger.kafka.network.Processor -
log4j.logger.kafka.server.KafkaApis -
log4j.logger.kafka.network.RequestChannel$ -
log4j.logger.kafka.controller -
log4j.logger.kafka.log.LogCleaner -
log4j.logger.state.change.logger -
log4j.logger.kafka.authorizer.logger
-
Zookeeper
-
zookeeper.root.logger
-
Kafka Connect and Kafka Connect with Source2Image support
-
connect.root.logger.level -
log4j.logger.org.apache.zookeeper -
log4j.logger.org.I0Itec.zkclient -
log4j.logger.org.reflections
-
Kafka Mirror Maker
-
mirrormaker.root.logger
-
Topic Operator
-
rootLogger.level
-
User Operator
-
rootLogger.level
-
3.1.11. 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.
3.1.11.1. 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 failure-domain.beta.kubernetes.io/zone that can be easily used as the topologyKey value. This has the effect of spreading the broker pods across zones, and also setting the brokers' broker.rack configuration parameter inside Kafka broker.
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
- Consult your OpenShift administrator regarding the node label that represent the zone / rack into which the node is deployed.
Edit the
rackproperty in theKafkaresource using the label as the topology key.apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... rack: topologyKey: failure-domain.beta.kubernetes.io/zone # ...Create or update the resource.
On OpenShift 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 3.1.15, “Container images”.
3.1.12. Healthchecks
Healthchecks are periodical tests which verify that the application’s health. When the Healthcheck fails, OpenShift can assume that the application is not healthy and attempt 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
3.1.12.1. Healthcheck configurations
Liveness and readiness probes can be configured using the livenessProbe and readinessProbe properties in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
KafkaConnect.spec -
KafkaConnectS2I.spec
Both livenessProbe and readinessProbe support two additional options:
-
initialDelaySeconds -
timeoutSeconds
The initialDelaySeconds property defines the initial delay before the probe is tried for the first time. Default is 15 seconds.
The timeoutSeconds property defines timeout of the probe. Default is 5 seconds.
An example of liveness and readiness probe configuration
# ... readinessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 # ...
3.1.12.2. Configuring healthchecks
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
livenessProbeorreadinessProbeproperty in theKafka,KafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... readinessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.1.13. 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.
3.1.13.1. Metrics configuration
Prometheus metrics can be 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/v1alpha1
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/v1alpha1
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:
# ...
3.1.13.2. Configuring Prometheus metrics
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
metricsproperty in theKafka,KafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... metrics: lowercaseOutputName: true # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.1.14. JVM Options
Apache Kafka and Apache Zookeeper are running inside of a Java Virtual Machine (JVM). JVM has many configuration options to optimize the performance for different platforms and architectures. AMQ Streams allows configuring some of these options.
3.1.14.1. JVM configuration
JVM options can be configured using the jvmOptions property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
KafkaConnect.spec -
KafkaConnectS2I.spec
Only a selected subset of available JVM options can be configured. The following options are supported:
-Xms and -Xmx
-Xms configures the minimum initial allocation heap size when the JVM starts. -Xmx configures the maximum heap size.
The units accepted by JVM settings such as -Xmx and -Xms are those accepted by the JDK java binary in the corresponding image. Accordingly, 1g or 1G means 1,073,741,824 bytes, and Gi is not a valid unit suffix. This is in contrast to the units used for memory requests and limits, which follow the OpenShift convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes
The default values used for -Xms and -Xmx depends on whether there is a memory request limit configured for the container:
- If there is a memory limit then the JVM’s minimum and maximum memory will be set to a value corresponding to the limit.
-
If there is no memory limit then the JVM’s minimum memory will be set to
128Mand the JVM’s maximum memory will not be defined. This allows for the JVM’s memory to grow as-needed, which is ideal for single node environments in test and development.
Setting -Xmx explicitly requires some care:
-
The JVM’s overall memory usage will be approximately 4 × the maximum heap, as configured by
-Xmx. -
If
-Xmxis set without also setting an appropriate OpenShift memory limit, it is possible that the container will be killed should the OpenShift node experience memory pressure (from other Pods running on it). -
If
-Xmxis set without also setting an appropriate OpenShift memory request, it is possible that the container will be scheduled to a node with insufficient memory. In this case, the container will not start but crash (immediately if-Xmsis set to-Xmx, or some later time if not).
When setting -Xmx explicitly, it is recommended to:
- set the memory request and the memory limit to the same value,
-
use a memory request that is at least 4.5 × the
-Xmx, -
consider setting
-Xmsto the same value as-Xms.
Containers doing lots of disk I/O (such as Kafka broker containers) will need to leave some memory available for use as operating system page cache. On such containers, the requested memory should be significantly higher than the memory used by the JVM.
Example fragment configuring -Xmx and -Xms
# ... jvmOptions: "-Xmx": "2g" "-Xms": "2g" # ...
In the above example, the JVM will use 2 GiB (=2,147,483,648 bytes) for its heap. Its total memory usage will be approximately 8GiB.
Setting the same value for initial (-Xms) and maximum (-Xmx) heap sizes avoids the JVM having to allocate memory after startup, at the cost of possibly allocating more heap than is really needed. For Kafka and Zookeeper pods such allocation could cause unwanted latency. For Kafka Connect avoiding over allocation may be the most important concern, especially in distributed mode where the effects of over-allocation will be multiplied by the number of consumers.
-server
-server enables the server JVM. This option can be set to true or false.
Example fragment configuring -server
# ... jvmOptions: "-server": true # ...
When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.
-XX
-XX object can be used for configuring advanced runtime options of a JVM. The -server and -XX options are used to configure the KAFKA_JVM_PERFORMANCE_OPTS option of Apache Kafka.
Example showing the use of the -XX object
jvmOptions:
"-XX":
"UseG1GC": true,
"MaxGCPauseMillis": 20,
"InitiatingHeapOccupancyPercent": 35,
"ExplicitGCInvokesConcurrent": true,
"UseParNewGC": false
The example configuration above will result in the following JVM options:
-XX:+UseG1GC -XX:MaxGCPauseMillis=20 -XX:InitiatingHeapOccupancyPercent=35 -XX:+ExplicitGCInvokesConcurrent -XX:-UseParNewGC
When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.
3.1.14.2. Configuring JVM options
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
jvmOptionsproperty in theKafka,KafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... jvmOptions: "-Xmx": "8g" "-Xms": "8g" # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.1.15. 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.
3.1.15.1. Container image configurations
Container image which should be used for given components can be specified using the image property in:
-
Kafka.spec.kafka -
Kafka.spec.kafka.tlsSidecar -
Kafka.spec.zookeeper -
Kafka.spec.zookeeper.tlsSidecar -
Kafka.spec.entityOperator.topicOperator -
Kafka.spec.entityOperator.userOperator -
Kafka.spec.entityOperator.tlsSidecar -
KafkaConnect.spec -
KafkaConnectS2I.spec
The image specified in the component-specific custom resource will be used during deployment. If the image field is missing, the image specified in the Cluster Operator configuration will be used. If the image name is not defined in the Cluster Operator configuration, then the default value will be used.
For Kafka brokers:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/kafka:latestcontainer image.
-
Container image specified in the
For Kafka broker TLS sidecar:
-
Container image specified in the
STRIMZI_DEFAULT_TLS_SIDECAR_KAFKA_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/kafka-stunnel:latestcontainer image.
-
Container image specified in the
For Zookeeper nodes:
-
Container image specified in the
STRIMZI_DEFAULT_ZOOKEEPER_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/zookeeper:latestcontainer image.
-
Container image specified in the
For Zookeeper node TLS sidecar:
-
Container image specified in the
STRIMZI_DEFAULT_TLS_SIDECAR_ZOOKEEPER_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/zookeeper-stunnel:latestcontainer image.
-
Container image specified in the
For Topic Operator:
-
Container image specified in the
STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGEenvironment variable from the Cluster Operator configuration.
-
Container image specified in the
For User Operator:
-
Container image specified in the
STRIMZI_DEFAULT_USER_OPERATOR_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/user-operator:latestcontainer image.
-
Container image specified in the
For Entity Operator TLS sidecar:
-
Container image specified in the
STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/entity-operator-stunnel:latestcontainer image.
-
Container image specified in the
For Kafka Connect:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/kafka-connect:latestcontainer image.
-
Container image specified in the
For Kafka Connect with Source2image support:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_CONNECT_S2I_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/kafka-connect-s2i:latestcontainer image.
-
Container image specified in the
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 case, you should either copy the AMQ Streams images or build them from source. In case the configured image is not compatible with AMQ Streams images, it might not work properly.
Example of container image configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
image: my-org/my-image:latest
# ...
zookeeper:
# ...
3.1.15.2. Configuring container images
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
imageproperty in theKafka,KafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... image: my-org/my-image:latest # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.1.16. TLS sidecar
Sidecar is a container which is running in a pod and serves an auxiliary purpose. The purpose of the TLS sidecar is to encrypt or decrypt the communication between AMQ Streams components and Zookeeper since Zookeeper does not support TLS encryption natively. Zookeeper does not support TLS encryption natively. Therefore AMQ Streams uses the sidecar to add the TLS support.
The TLS sidecar is currrently being used in:
- Kafka brokers
- Zookeeper
- Entity Operator
3.1.16.1. 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 three additional options:
-
image -
resources -
logLevel
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 3.1.15, “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.
Example of TLS sidecar configuration
apiVersion: kafka.strimzi.io/v1alpha1
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
# ...
zookeeper:
# ...
3.1.16.2. Configuring TLS sidecar
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
tlsSidecarproperty in theKafkaresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... tlsSidecar: resources: requests: cpu: 200m memory: 64Mi limits: cpu: 500m memory: 128Mi # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.1.17. Configuring pod scheduling
When two application 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.
3.1.17.1. Scheduling pods based on other applications
3.1.17.1.1. 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.
3.1.17.1.2. Affinity
Affinity can be configured using the affinity property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
Kafka.spec.entityOperator -
KafkaConnect.spec -
KafkaConnectS2I.spec
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.
3.1.17.1.3. Configuring pod anti-affinity in Kafka components
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
affinityproperty in the resource specifying the cluster deployment. Use labels to specify the pods which should not be scheduled on the same nodes. ThetopologyKeyshould be set tokubernetes.io/hostnameto specify that the selected pods should not be scheduled on nodes with the same hostname. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: application operator: In values: - postgresql - mongodb topologyKey: "kubernetes.io/hostname" # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.1.17.2. Scheduling pods to specific nodes
3.1.17.2.1. 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.
3.1.17.2.2. Affinity
Affinity can be configured using the affinity property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
Kafka.spec.entityOperator -
KafkaConnect.spec -
KafkaConnectS2I.spec
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.
3.1.17.2.3. 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.
On OpenShift this can be done using
oc label:oc label node your-node node-type=fast-networkAlternatively, some of the existing labels might be reused.
Edit the
affinityproperty in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: node-type operator: In values: - fast-network # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.1.17.3. Using dedicated nodes
3.1.17.3.1. 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.
3.1.17.3.2. Affinity
Affinity can be configured using the affinity property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
Kafka.spec.entityOperator -
KafkaConnect.spec -
KafkaConnectS2I.spec
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.
3.1.17.3.3. Tolerations
Tolerations ca be configured using the tolerations property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
Kafka.spec.entityOperator -
KafkaConnect.spec -
KafkaConnectS2I.spec
The format of the tolerations property follows the OpenShift specification. For more details, see the Kubernetes taints and tolerations.
3.1.17.3.4. 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
On OpenShift this can be done using
oc adm taint:oc adm taint node your-node dedicated=Kafka:NoScheduleAdditionally, add a label to the selected nodes as well.
On OpenShift this can be done using
oc label:oc label node your-node dedicated=KafkaEdit the
affinityandtolerationsproperties in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... 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.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.1.18. 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
- A running Kafka cluster.
- A running Cluster Operator.
Procedure
Find the name of the
StatefulSetthat controls the Kafka pods you want to manually update.For example, if your Kafka cluster is named my-cluster, the corresponding
StatefulSetis named my-cluster-kafka.Annotate a
StatefulSetresource in OpenShift.+ On OpenShift, use
oc annotate:oc annotate statefulset cluster-name-kafka operator.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
StatefulSetis triggered, as long as the annotation was detected by the reconciliation process. Once the rolling update of all the pods is complete, the annotation is removed from theStatefulSet.
Additional resources
- For more information about deploying the Cluster Operator, see Section 2.2, “Cluster Operator”.
- For more information about deploying the Kafka cluster on OpenShift, see Section 2.3.1, “Deploying the Kafka cluster to OpenShift”.
3.1.19. 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
- A running Zookeeper cluster.
- A running Cluster Operator.
Procedure
Find the name of the
StatefulSetthat controls the Zookeeper pods you want to manually update.For example, if your Kafka cluster is named my-cluster, the corresponding
StatefulSetis named my-cluster-zookeeper.Annotate a
StatefulSetresource in OpenShift.+ On OpenShift, use
oc annotate:oc annotate statefulset cluster-name-zookeeper operator.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
StatefulSetis triggered, as long as the annotation was detected by the reconciliation process. Once the rolling update of all the pods is complete, the annotation is removed from theStatefulSet.
Additional resources
- For more information about deploying the Cluster Operator, see Section 2.2, “Cluster Operator”.
- For more information about deploying the Zookeeper cluster, see Section 2.3.1, “Deploying the Kafka cluster to OpenShift”.
3.1.20. Scaling clusters
3.1.20.1. Scaling Kafka clusters
3.1.20.1.1. 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.
3.1.20.1.2. 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.
3.1.20.2. 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
--executestep,--verifychecks 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.
3.1.20.2.1. 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 Red Hat 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.
3.1.20.3. 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
Kafkaresource - A set of topics to reassign the partitions of
Procedure
Prepare a JSON file named
topics.jsonthat 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-aandtopic-b, you would need to prepare atopics.jsonfile like this:{ "version": 1, "topics": [ { "topic": "topic-a"}, { "topic": "topic-b"} ] }Copy the
topics.jsonfile to one of the broker pods:On OpenShift:
cat topics.json | oc rsh -c kafka <BrokerPod> /bin/bash -c \ 'cat > /tmp/topics.json'
Use the
kafka-reassign-partitions.sh`command to generate the reassignment JSON.On OpenShift:
oc rsh -c kafka <BrokerPod> \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --topics-to-move-json-file /tmp/topics.json \ --broker-list <BrokerList> \ --generate
For example, to move all the partitions of
topic-aandtopic-bto brokers4and7oc rsh -c kafka _<BrokerPod>_ \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --topics-to-move-json-file /tmp/topics.json \ --broker-list 4,7 \ --generate
3.1.20.4. Creating reassignment JSON files manually
You can manually create the reassignment JSON file if you want to move specific partitions.
3.1.20.5. 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.
3.1.20.6. 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.jsonthat 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.replicasconfiguration option. - Verify that the new broker pods have started.
Copy the
reassignment.jsonfile to the broker pod on which you will later execute the commands:On OpenShift:
cat reassignment.json | \ oc rsh -c kafka broker-pod /bin/bash -c \ 'cat > /tmp/reassignment.json'
For example:
cat reassignment.json | \ oc rsh -c kafka my-cluster-kafka-0 /bin/bash -c \ 'cat > /tmp/reassignment.json'
Execute the partition reassignment using the
kafka-reassign-partitions.shcommand line tool from the same broker pod.On OpenShift:
oc rsh -c kafka broker-pod \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --reassignment-json-file /tmp/reassignment.json \ --executeIf you are going to throttle replication you can also pass the
--throttleoption with an inter-broker throttled rate in bytes per second. For example:On OpenShift:
oc rsh -c kafka my-cluster-kafka-0 \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --reassignment-json-file /tmp/reassignment.json \ --throttle 5000000 \ --execute
This command will print out two reassignment JSON objects. The first records the current assignment for the partitions being moved. You should save this to a local file (not a file in the pod) in case you need to revert the reassignment later on. The second JSON object is the target reassignment you have passed in your reassignment JSON file.
If you need to change the throttle during reassignment you can use the same command line with a different throttled rate. For example:
On OpenShift:
oc rsh -c kafka my-cluster-kafka-0 \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --reassignment-json-file /tmp/reassignment.json \ --throttle 10000000 \ --execute
Periodically verify whether the reassignment has completed using the
kafka-reassign-partitions.shcommand line tool from any of the broker pods. This is the same command as the previous step but with the--verifyoption instead of the--executeoption.On OpenShift:
oc rsh -c kafka broker-pod \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --reassignment-json-file /tmp/reassignment.json \ --verifyFor example, on {OpenShift},
oc rsh -c kafka my-cluster-kafka-0 \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --reassignment-json-file /tmp/reassignment.json \ --verify
-
The reassignment has finished when the
--verifycommand reports each of the partitions being moved as completed successfully. This final--verifywill 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.
3.1.20.7. 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.jsondescribing 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.jsonfile to the broker pod on which you will later execute the commands:On OpenShift:
cat reassignment.json | \ oc rsh -c kafka broker-pod /bin/bash -c \ 'cat > /tmp/reassignment.json'
For example:
cat reassignment.json | \ oc rsh -c kafka my-cluster-kafka-0 /bin/bash -c \ 'cat > /tmp/reassignment.json'
Execute the partition reassignment using the
kafka-reassign-partitions.shcommand line tool from the same broker pod.On OpenShift:
oc rsh -c kafka broker-pod \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --reassignment-json-file /tmp/reassignment.json \ --executeIf you are going to throttle replication you can also pass the
--throttleoption with an inter-broker throttled rate in bytes per second. For example:On OpenShift:
oc rsh -c kafka my-cluster-kafka-0 \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --reassignment-json-file /tmp/reassignment.json \ --throttle 5000000 \ --execute
This command will print out two reassignment JSON objects. The first records the current assignment for the partitions being moved. You should save this to a local file (not a file in the pod) in case you need to revert the reassignment later on. The second JSON object is the target reassignment you have passed in your reassignment JSON file.
If you need to change the throttle during reassignment you can use the same command line with a different throttled rate. For example:
On OpenShift:
oc rsh -c kafka my-cluster-kafka-0 \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --reassignment-json-file /tmp/reassignment.json \ --throttle 10000000 \ --execute
Periodically verify whether the reassignment has completed using the
kafka-reassign-partitions.shcommand line tool from any of the broker pods. This is the same command as the previous step but with the--verifyoption instead of the--executeoption.On OpenShift:
oc rsh -c kafka broker-pod \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --reassignment-json-file /tmp/reassignment.json \ --verifyFor example, on {OpenShift},
oc rsh -c kafka my-cluster-kafka-0 \ bin/kafka-reassign-partitions.sh --zookeeper localhost:2181 \ --reassignment-json-file /tmp/reassignment.json \ --verify
-
The reassignment has finished when the
--verifycommand reports each of the partitions being moved as completed successfully. This final--verifywill 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 rsh <BrokerN> -c kafka /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.replicasof yourKafkaresource, which will scale down theStatefulSet, deleting the highest numbered brokerPod(s).
3.1.21. 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
- A running Kafka cluster.
- A running Cluster Operator.
Procedure
-
Find the name of the
Podthat 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
Podresource in OpenShift.+ On OpenShift use
oc annotate:oc annotate pod cluster-name-kafka-index operator.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.
Additional resources
- For more information about deploying the Cluster Operator, see Section 2.2, “Cluster Operator”.
- For more information about deploying the Kafka cluster on OpenShift, see Section 2.3.1, “Deploying the Kafka cluster to OpenShift”.
3.1.22. 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
- A running Zookeeper cluster.
- A running Cluster Operator.
Procedure
-
Find the name of the
Podthat 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
Podresource in OpenShift.+ On OpenShift use
oc annotate:oc annotate pod cluster-name-zookeeper-index operator.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.
Additional resources
- For more information about deploying the Cluster Operator, see Section 2.2, “Cluster Operator”.
- For more information about deploying the Zookeeper cluster on OpenShift, see Section 2.3.1, “Deploying the Kafka cluster to OpenShift”.
3.1.23. List of resources created as part of Kafka cluster
The following resources will created by the Cluster Operator in the OpenShift cluster:
cluster-name-kafka- StatefulSet which is in charge of managing the Kafka broker pods.
cluster-name-kafka-brokers- Service needed to have DNS resolve the Kafka broker pods IP addresses directly.
cluster-name-kafka-bootstrap- Service can be used as bootstrap servers for Kafka clients.
cluster-name-kafka-external-bootstrap- Bootstrap service for clients connecting from outside of the 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.
strimzi-namespace-name-cluster-name-kafka-init- Cluster role binding used by the Kafka brokers.
cluster-name-zookeeper- StatefulSet which is in charge of managing the Zookeeper node pods.
cluster-name-zookeeper-nodes- Service needed to have DNS resolve the Zookeeper pods IP addresses directly.
cluster-name-zookeeper-client- Service used by Kafka brokers to connect to Zookeeper nodes as clients.
cluster-name-zookeeper-config- ConfigMap which contains the Zookeeper ancillary configuration and is mounted as a volume by the Zookeeper node pods.
cluster-name-zookeeper-nodes- Secret with Zookeeper node keys.
cluster-name-entity-operator- Deployment with Topic and User Operators. This resource will be created only if Cluster Operator deployed Entity Operator.
cluster-name-entity-topic-operator-config- Configmap with ancillary configuration for Topic Operators. This resource will be created only if Cluster Operator deployed Entity Operator.
cluster-name-entity-user-operator-config- Configmap with ancillary configuration for User Operators. This resource will be created only if Cluster Operator deployed Entity Operator.
cluster-name-entity-operator-certs- Secret with Entitiy operators keys for communication with Kafka and Zookeeper. This resource will be created only if Cluster Operator deployed Entity Operator.
cluster-name-entity-operator- Service account used by the Entity Operator.
strimzi-cluster-name-topic-operator- Role binding used by the Entity Operator.
strimzi-cluster-name-user-operator- Role binding used by the Entity Operator.
cluster-name-cluster-ca- Secret with the Cluster CA used to encrypt the cluster communication.
cluster-name-cluster-ca-cert- Secret with the Cluster CA public key. This key can be used to verify the identity of the Kafka brokers.
cluster-name-clients-ca- Secret with the Clients CA used to encrypt the communication between Kafka brokers and Kafka clients.
cluster-name-clients-ca-cert- Secret with the Clients CA public key. This key can be used to verify the identity of the Kafka brokers.
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-cluster-name-zookeeper-idx-
Persistent Volume Claim for the volume used for storing data for the Zookeeper node pod
idx. This resource will be created only if persistent storage is selected for provisioning persistent volumes to store data.
3.2. Kafka Connect cluster configuration
The full schema of the KafkaConnect resource is described in the Section B.29, “KafkaConnect schema reference”. All labels that are applied to the desired KafkaConnect resource will also be applied to the OpenShift resources making up the Kafka Connect cluster. This provides a convenient mechanism for those resources to be labelled in whatever way the user requires.
3.2.1. Replicas
Kafka Connect clusters can run with a different number of nodes. The number of nodes is defined in the KafkaConnect and KafkaConnectS2I resources. Running Kafka Connect cluster with multiple nodes can provide better availability and scalability. However, when running Kafka Connect on OpenShift it is not absolutely necessary to run multiple nodes of Kafka Connect for high availability. When the node where Kafka Connect is deployed to crashes, OpenShift will automatically take care of rescheduling the Kafka Connect pod to a different node. However, running Kafka Connect with multiple nodes can provide faster failover times, because the other nodes will be already up and running.
3.2.1.1. Configuring the number of nodes
Number of Kafka Connect nodes can be configured using the replicas property in KafkaConnect.spec and KafkaConnectS2I.spec.
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
replicasproperty in theKafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaConnectS2I metadata: name: my-cluster spec: # ... replicas: 3 # ...
Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.2.2. Bootstrap servers
Kafka Connect cluster always works together with a Kafka cluster. The Kafka cluster is specified in the form of a list of bootstrap servers. On OpenShift, the list must ideally contain the Kafka cluster bootstrap service which is named cluster-name-kafka-bootstrap and a port of 9092 for plain traffic or 9093 for encrypted traffic.
The list of bootstrap servers can be configured in the bootstrapServers property in KafkaConnect.spec and KafkaConnectS2I.spec. The servers should be a comma-separated list containing one or more Kafka brokers or a service pointing to Kafka brokers specified as a hostname:_port_ pairs.
When using Kafka Connect with a Kafka cluster not managed by AMQ Streams, you can specify the bootstrap servers list according to the configuration of a given cluster.
3.2.2.1. Configuring bootstrap servers
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
bootstrapServersproperty in theKafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaConnect metadata: name: my-cluster spec: # ... bootstrapServers: my-cluster-kafka-bootstrap:9092 # ...
Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.2.3. Connecting to Kafka brokers using TLS
By default, Kafka Connect will try to connect to Kafka brokers using a plain text connection. If you would prefer to use TLS additional configuration will be necessary.
3.2.3.1. TLS support in Kafka Connect
TLS support is configured in the tls property in KafkaConnect.spec and KafkaConnectS2I.spec. The tls property contains a list of secrets with key names under which the certificates are stored. The certificates should be stored in X509 format.
An example showing TLS configuration with multiple certificates
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaConnect
metadata:
name: my-cluster
spec:
# ...
tls:
trustedCertificates:
- secretName: my-secret
certificate: ca.crt
- secretName: my-other-secret
certificate: certificate.crt
# ...
When multiple certificates are stored in the same secret, it can be listed multiple times.
An example showing TLS configuration with multiple certificates from the same secret
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaConnectS2I
metadata:
name: my-cluster
spec:
# ...
tls:
trustedCertificates:
- secretName: my-secret
certificate: ca.crt
- secretName: my-secret
certificate: ca2.crt
# ...
3.2.3.2. Configuring TLS in Kafka Connect
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Find out the name of the secret with the certificate which should be used for TLS Server Authentication and the key under which the certificate is stored in the secret. If such secret does not exist yet, prepare the certificate in a file and create the secret.
On OpenShift this can be done using
oc create:oc create secret generic my-secret --from-file=my-file.crt
Edit the
tlsproperty in theKafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaConnect metadata: name: my-connect spec: # ... tls: trustedCertificates: - secretName: my-cluster-cluster-cert certificate: ca.crt # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.2.4. Connecting to Kafka brokers with Authentication
By default, Kafka Connect will try to connect to Kafka brokers without any authentication. Authentication can be enabled in the KafkaConnect and KafkaConnectS2I resources.
3.2.4.1. Authentication support in Kafka Connect
Authentication can be configured in the authentication property in KafkaConnect.spec and KafkaConnectS2I.spec. The authentication property specifies the type of the authentication mechanisms which should be used and additional configuration details depending on the mechanism. The currently supported authentication types are:
- TLS client authentication
- SASL based authentication using SCRAM-SHA-512 mechanism
3.2.4.1.1. TLS Client Authentication
To use the TLS client authentication, set the type property to the value tls. TLS client authentication is using TLS certificate to authenticate. The certificate has to be specified in the certificateAndKey property. It is always loaded from an OpenShift secret. Inside the secret, it has to be stored in the X509 format under two different keys: for public and private keys.
TLS client authentication can be used only with TLS connections. For more details about TLS configuration in Kafka Connect see Section 3.2.3, “Connecting to Kafka brokers using TLS”.
An example showing TLS client authentication configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaConnect
metadata:
name: my-cluster
spec:
# ...
authentication:
type: tls
certificateAndKey:
secretName: my-secret
certificate: public.crt
key: private.key
# ...
3.2.4.1.2. SCRAM-SHA-512 authentication
To use the authentication using the SCRAM-SHA-512 SASL mechanism, set the type property to the value scram-sha-512. SCRAM-SHA-512 uses a username and password to authenticate. Specify the username in the username property. Specify the password as a link to a Secret containing the password in the passwordSecret property. It has to specify the name of the Secret containing the password and the name of the key under which the password is stored inside the Secret.
An example showing SCRAM-SHA-512 client authentication configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaConnect
metadata:
name: my-cluster
spec:
# ...
authentication:
type: scram-sha-512
username: my-connect-user
passwordSecret:
secretName: my-connect-user
password: password
# ...
3.2.4.2. Configuring TLS client authentication in Kafka Connect
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Find out the name of the
Secretwith the public and private keys which should be used for TLS Client Authentication and the keys under which they are stored in theSecret. If such aSecretdoes not exist yet, prepare the keys in a file and create theSecret.On OpenShift this can be done using
oc create:oc create secret generic my-secret --from-file=my-public.crt --from-file=my-private.key
Edit the
authenticationproperty in theKafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaConnect metadata: name: my-connect spec: # ... authentication: type: tls certificateAndKey: secretName: my-secret certificate: my-public.crt key: my-private.key # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.2.4.3. Configuring SCRAM-SHA-512 authentication in Kafka Connect
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
- Username of the user which should be used for authentication
Procedure
Find out the name of the
Secretwith the password which should be used for authentication and the key under which the password is stored in theSecret. If such aSecretdoes not exist yet, prepare a file with the password and create theSecret.On OpenShift this can be done using
oc create:echo -n '1f2d1e2e67df' > my-password.txt oc create secret generic my-secret --from-file=my-password.txt
Edit the
authenticationproperty in theKafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaConnect metadata: name: my-connect spec: # ... authentication: type: scram-sha-512 username: _my-username_ passwordSecret: secretName: _my-secret_ password: _my-password.txt_ # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.2.5. Kafka Connect configuration
AMQ Streams allows you to customize the configuration of Apache Kafka Connect nodes by editing most of the options listed in Apache Kafka documentation.
The only options which cannot be configured are those related to the following areas:
- Kafka cluster bootstrap address
- Security (Encryption, Authentication, and Authorization)
- Listener / REST interface configuration
- Plugin path configuration
These options are automatically configured by AMQ Streams.
3.2.5.1. Kafka Connect configuration
Kafka Connect can be configured using the config property in KafkaConnect.spec and KafkaConnectS2I.spec. This property should contain the Kafka Connect configuration options as keys. The values could be in one of the following JSON types:
- String
- Number
- Boolean
Users can specify and configure the options listed in the Apache Kafka 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:
-
ssl. -
sasl. -
security. -
listeners -
plugin.path -
rest. -
bootstrap.servers
When one of the forbidden options is present in the config property, it will be ignored and a warning message will be printed to the Custer Operator log file. All other options will be passed to Kafka Connect.
The Cluster Operator does not validate keys or values in the provided config object. When an invalid configuration is provided, the Kafka Connect cluster might not start or might become unstable. In such cases, the configuration in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config object should be fixed and the cluster operator will roll out the new configuration to all Kafka Connect nodes.
Selected options have default values:
-
group.idwith default valueconnect-cluster -
offset.storage.topicwith default valueconnect-cluster-offsets -
config.storage.topicwith default valueconnect-cluster-configs -
status.storage.topicwith default valueconnect-cluster-status -
key.converterwith default valueorg.apache.kafka.connect.json.JsonConverter -
value.converterwith default valueorg.apache.kafka.connect.json.JsonConverter -
internal.key.converterwith default valueorg.apache.kafka.connect.json.JsonConverter -
internal.value.converterwith default valueorg.apache.kafka.connect.json.JsonConverter -
internal.key.converter.schemas.enablewith default valuefalse -
internal.value.converter.schemas.enablewith default valuefalse
These options will be automatically configured in case they are not present in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config properties.
An example showing Kafka Connect configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaConnect
metadata:
name: my-connect
spec:
# ...
config:
group.id: my-connect-cluster
offset.storage.topic: my-connect-cluster-offsets
config.storage.topic: my-connect-cluster-configs
status.storage.topic: my-connect-cluster-status
key.converter: org.apache.kafka.connect.json.JsonConverter
value.converter: org.apache.kafka.connect.json.JsonConverter
key.converter.schemas.enable: true
value.converter.schemas.enable: true
internal.key.converter: org.apache.kafka.connect.json.JsonConverter
internal.value.converter: org.apache.kafka.connect.json.JsonConverter
internal.key.converter.schemas.enable: false
internal.value.converter.schemas.enable: false
config.storage.replication.factor: 3
offset.storage.replication.factor: 3
status.storage.replication.factor: 3
# ...
3.2.5.2. Configuring Kafka Connect
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
configproperty in theKafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaConnect metadata: name: my-connect spec: # ... config: group.id: my-connect-cluster offset.storage.topic: my-connect-cluster-offsets config.storage.topic: my-connect-cluster-configs status.storage.topic: my-connect-cluster-status key.converter: org.apache.kafka.connect.json.JsonConverter value.converter: org.apache.kafka.connect.json.JsonConverter key.converter.schemas.enable: true value.converter.schemas.enable: true internal.key.converter: org.apache.kafka.connect.json.JsonConverter internal.value.converter: org.apache.kafka.connect.json.JsonConverter internal.key.converter.schemas.enable: false internal.value.converter.schemas.enable: false config.storage.replication.factor: 3 offset.storage.replication.factor: 3 status.storage.replication.factor: 3 # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.2.6. CPU and memory resources
For every deployed container, AMQ Streams allows you to specify the resources which should be reserved for it and the maximum resources that can be consumed by it. AMQ Streams supports two types of resources:
- Memory
- CPU
AMQ Streams is using the OpenShift syntax for specifying CPU and memory resources.
3.2.6.1. Resource limits and requests
Resource limits and requests can be configured using the resources property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.kafka.tlsSidecar -
Kafka.spec.zookeeper -
Kafka.spec.zookeeper.tlsSidecar -
Kafka.spec.entityOperator.topicOperator -
Kafka.spec.entityOperator.userOperator -
Kafka.spec.entityOperator.tlsSidecar -
KafkaConnect.spec -
KafkaConnectS2I.spec
3.2.6.1.1. Resource requests
Requests specify the resources that will be reserved for a given container. Reserving the resources will ensure that they are always available.
If the resource request is for more than the available free resources in the OpenShift cluster, the pod will not be scheduled.
Resource requests can be specified in the request property. The resource requests currently supported by AMQ Streams are memory and CPU. Memory is specified under the property memory. CPU is specified under the property cpu.
An example showing resource request configuration
# ...
resources:
requests:
cpu: 12
memory: 64Gi
# ...
It is also possible to specify a resource request just for one of the resources:
An example showing resource request configuration with memory request only
# ...
resources:
requests:
memory: 64Gi
# ...
Or:
An example showing resource request configuration with CPU request only
# ...
resources:
requests:
cpu: 12
# ...
3.2.6.1.2. Resource limits
Limits specify the maximum resources that can be consumed by a given container. The limit is not reserved and might not be always available. The container can use the resources up to the limit only when they are available. The resource limits should be always higher than the resource requests.
Resource limits can be specified in the limits property. The resource limits currently supported by AMQ Streams are memory and CPU. Memory is specified under the property memory. CPU is specified under the property cpu.
An example showing resource limits configuration
# ...
resources:
limits:
cpu: 12
memory: 64Gi
# ...
It is also possible to specify the resource limit just for one of the resources:
An example showing resource limit configuration with memory request only
# ...
resources:
limits:
memory: 64Gi
# ...
Or:
An example showing resource limits configuration with CPU request only
# ...
resources:
requests:
cpu: 12
# ...
3.2.6.1.3. Supported CPU formats
CPU requests and limits are supported in the following formats:
-
Number of CPU cores as integer (
5CPU core) or decimal (2.5CPU core). -
Number or millicpus / millicores (
100m) where 1000 millicores is the same1CPU core.
An example of using different CPU units
# ...
resources:
requests:
cpu: 500m
limits:
cpu: 2.5
# ...
The amount of computing power of 1 CPU core might differ depending on the platform where the OpenShift is deployed.
For more details about the CPU specification, see the Meaning of CPU website.
3.2.6.1.4. Supported memory formats
Memory requests and limits are specified in megabytes, gigabytes, mebibytes, and gibibytes.
-
To specify memory in megabytes, use the
Msuffix. For example1000M. -
To specify memory in gigabytes, use the
Gsuffix. For example1G. -
To specify memory in mebibytes, use the
Misuffix. For example1000Mi. -
To specify memory in gibibytes, use the
Gisuffix. For example1Gi.
An example of using different memory units
# ...
resources:
requests:
memory: 512Mi
limits:
memory: 2Gi
# ...
For more details about the memory specification and additional supported units, see the Meaning of memory website.
3.2.6.1.5. Additional resources
- For more information about managing computing resources on OpenShift, see Managing Compute Resources for Containers.
3.2.6.2. Configuring resource requests and limits
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
resourcesproperty in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... resources: requests: cpu: "8" memory: 64Gi limits: cpu: "12" memory: 128Gi # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
Additional resources
-
For more information about the schema, see
Resourcesschema reference.
3.2.7. Logging
Logging enables you to diagnose error and performance issues of AMQ Streams. For the logging, various logger implementations are used. Kafka and Zookeeper use log4j logger and Topic Operator, User Operator, and other components use log4j2 logger.
This section provides information about different loggers and describes how to configure log levels.
You can set the log levels by specifying the loggers and their levels directly (inline) or by using a custom (external) config map.
3.2.7.1. Using inline logging setting
Procedure
Edit the YAML file to specify the loggers and their level for the required components. For example:
apiVersion: {KafkaApiVersion} kind: Kafka spec: kafka: # ... logging: type: inline loggers: logger.name: "INFO" # ...In the above example, the log level is set to INFO. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. For more information about the log levels, see log4j manual.
Create or update the Kafka resource in OpenShift.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.2.7.2. Using external ConfigMap for logging setting
Procedure
Edit the YAML file to specify the name of the
ConfigMapwhich should be used for the required components. For example:apiVersion: {KafkaApiVersion} kind: Kafka spec: kafka: # ... logging: type: external name: customConfigMap # ...Remember to place your custom ConfigMap under
log4j.propertieseventuallylog4j2.propertieskey.Create or update the Kafka resource in OpenShift.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.2.7.3. Loggers
AMQ Streams consists of several components. Each component has its own loggers and is configurable. This section provides information about loggers of various components.
Components and their loggers are listed below.
Kafka
-
kafka.root.logger.level -
log4j.logger.org.I0Itec.zkclient.ZkClient -
log4j.logger.org.apache.zookeeper -
log4j.logger.kafka -
log4j.logger.org.apache.kafka -
log4j.logger.kafka.request.logger -
log4j.logger.kafka.network.Processor -
log4j.logger.kafka.server.KafkaApis -
log4j.logger.kafka.network.RequestChannel$ -
log4j.logger.kafka.controller -
log4j.logger.kafka.log.LogCleaner -
log4j.logger.state.change.logger -
log4j.logger.kafka.authorizer.logger
-
Zookeeper
-
zookeeper.root.logger
-
Kafka Connect and Kafka Connect with Source2Image support
-
connect.root.logger.level -
log4j.logger.org.apache.zookeeper -
log4j.logger.org.I0Itec.zkclient -
log4j.logger.org.reflections
-
Kafka Mirror Maker
-
mirrormaker.root.logger
-
Topic Operator
-
rootLogger.level
-
User Operator
-
rootLogger.level
-
3.2.8. Healthchecks
Healthchecks are periodical tests which verify that the application’s health. When the Healthcheck fails, OpenShift can assume that the application is not healthy and attempt 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
3.2.8.1. Healthcheck configurations
Liveness and readiness probes can be configured using the livenessProbe and readinessProbe properties in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
KafkaConnect.spec -
KafkaConnectS2I.spec
Both livenessProbe and readinessProbe support two additional options:
-
initialDelaySeconds -
timeoutSeconds
The initialDelaySeconds property defines the initial delay before the probe is tried for the first time. Default is 15 seconds.
The timeoutSeconds property defines timeout of the probe. Default is 5 seconds.
An example of liveness and readiness probe configuration
# ... readinessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 # ...
3.2.8.2. Configuring healthchecks
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
livenessProbeorreadinessProbeproperty in theKafka,KafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... readinessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.2.9. 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.
3.2.9.1. Metrics configuration
Prometheus metrics can be 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/v1alpha1
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/v1alpha1
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:
# ...
3.2.9.2. Configuring Prometheus metrics
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
metricsproperty in theKafka,KafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... metrics: lowercaseOutputName: true # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.2.10. JVM Options
Apache Kafka and Apache Zookeeper are running inside of a Java Virtual Machine (JVM). JVM has many configuration options to optimize the performance for different platforms and architectures. AMQ Streams allows configuring some of these options.
3.2.10.1. JVM configuration
JVM options can be configured using the jvmOptions property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
KafkaConnect.spec -
KafkaConnectS2I.spec
Only a selected subset of available JVM options can be configured. The following options are supported:
-Xms and -Xmx
-Xms configures the minimum initial allocation heap size when the JVM starts. -Xmx configures the maximum heap size.
The units accepted by JVM settings such as -Xmx and -Xms are those accepted by the JDK java binary in the corresponding image. Accordingly, 1g or 1G means 1,073,741,824 bytes, and Gi is not a valid unit suffix. This is in contrast to the units used for memory requests and limits, which follow the OpenShift convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes
The default values used for -Xms and -Xmx depends on whether there is a memory request limit configured for the container:
- If there is a memory limit then the JVM’s minimum and maximum memory will be set to a value corresponding to the limit.
-
If there is no memory limit then the JVM’s minimum memory will be set to
128Mand the JVM’s maximum memory will not be defined. This allows for the JVM’s memory to grow as-needed, which is ideal for single node environments in test and development.
Setting -Xmx explicitly requires some care:
-
The JVM’s overall memory usage will be approximately 4 × the maximum heap, as configured by
-Xmx. -
If
-Xmxis set without also setting an appropriate OpenShift memory limit, it is possible that the container will be killed should the OpenShift node experience memory pressure (from other Pods running on it). -
If
-Xmxis set without also setting an appropriate OpenShift memory request, it is possible that the container will be scheduled to a node with insufficient memory. In this case, the container will not start but crash (immediately if-Xmsis set to-Xmx, or some later time if not).
When setting -Xmx explicitly, it is recommended to:
- set the memory request and the memory limit to the same value,
-
use a memory request that is at least 4.5 × the
-Xmx, -
consider setting
-Xmsto the same value as-Xms.
Containers doing lots of disk I/O (such as Kafka broker containers) will need to leave some memory available for use as operating system page cache. On such containers, the requested memory should be significantly higher than the memory used by the JVM.
Example fragment configuring -Xmx and -Xms
# ... jvmOptions: "-Xmx": "2g" "-Xms": "2g" # ...
In the above example, the JVM will use 2 GiB (=2,147,483,648 bytes) for its heap. Its total memory usage will be approximately 8GiB.
Setting the same value for initial (-Xms) and maximum (-Xmx) heap sizes avoids the JVM having to allocate memory after startup, at the cost of possibly allocating more heap than is really needed. For Kafka and Zookeeper pods such allocation could cause unwanted latency. For Kafka Connect avoiding over allocation may be the most important concern, especially in distributed mode where the effects of over-allocation will be multiplied by the number of consumers.
-server
-server enables the server JVM. This option can be set to true or false.
Example fragment configuring -server
# ... jvmOptions: "-server": true # ...
When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.
-XX
-XX object can be used for configuring advanced runtime options of a JVM. The -server and -XX options are used to configure the KAFKA_JVM_PERFORMANCE_OPTS option of Apache Kafka.
Example showing the use of the -XX object
jvmOptions:
"-XX":
"UseG1GC": true,
"MaxGCPauseMillis": 20,
"InitiatingHeapOccupancyPercent": 35,
"ExplicitGCInvokesConcurrent": true,
"UseParNewGC": false
The example configuration above will result in the following JVM options:
-XX:+UseG1GC -XX:MaxGCPauseMillis=20 -XX:InitiatingHeapOccupancyPercent=35 -XX:+ExplicitGCInvokesConcurrent -XX:-UseParNewGC
When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.
3.2.10.2. Configuring JVM options
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
jvmOptionsproperty in theKafka,KafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... jvmOptions: "-Xmx": "8g" "-Xms": "8g" # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.2.11. 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.
3.2.11.1. Container image configurations
Container image which should be used for given components can be specified using the image property in:
-
Kafka.spec.kafka -
Kafka.spec.kafka.tlsSidecar -
Kafka.spec.zookeeper -
Kafka.spec.zookeeper.tlsSidecar -
Kafka.spec.entityOperator.topicOperator -
Kafka.spec.entityOperator.userOperator -
Kafka.spec.entityOperator.tlsSidecar -
KafkaConnect.spec -
KafkaConnectS2I.spec
The image specified in the component-specific custom resource will be used during deployment. If the image field is missing, the image specified in the Cluster Operator configuration will be used. If the image name is not defined in the Cluster Operator configuration, then the default value will be used.
For Kafka brokers:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/kafka:latestcontainer image.
-
Container image specified in the
For Kafka broker TLS sidecar:
-
Container image specified in the
STRIMZI_DEFAULT_TLS_SIDECAR_KAFKA_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/kafka-stunnel:latestcontainer image.
-
Container image specified in the
For Zookeeper nodes:
-
Container image specified in the
STRIMZI_DEFAULT_ZOOKEEPER_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/zookeeper:latestcontainer image.
-
Container image specified in the
For Zookeeper node TLS sidecar:
-
Container image specified in the
STRIMZI_DEFAULT_TLS_SIDECAR_ZOOKEEPER_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/zookeeper-stunnel:latestcontainer image.
-
Container image specified in the
For Topic Operator:
-
Container image specified in the
STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGEenvironment variable from the Cluster Operator configuration.
-
Container image specified in the
For User Operator:
-
Container image specified in the
STRIMZI_DEFAULT_USER_OPERATOR_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/user-operator:latestcontainer image.
-
Container image specified in the
For Entity Operator TLS sidecar:
-
Container image specified in the
STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/entity-operator-stunnel:latestcontainer image.
-
Container image specified in the
For Kafka Connect:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/kafka-connect:latestcontainer image.
-
Container image specified in the
For Kafka Connect with Source2image support:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_CONNECT_S2I_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/kafka-connect-s2i:latestcontainer image.
-
Container image specified in the
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 case, you should either copy the AMQ Streams images or build them from source. In case the configured image is not compatible with AMQ Streams images, it might not work properly.
Example of container image configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
image: my-org/my-image:latest
# ...
zookeeper:
# ...
3.2.11.2. Configuring container images
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
imageproperty in theKafka,KafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... image: my-org/my-image:latest # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.2.12. Configuring pod scheduling
When two application 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.
3.2.12.1. Scheduling pods based on other applications
3.2.12.1.1. 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.
3.2.12.1.2. Affinity
Affinity can be configured using the affinity property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
Kafka.spec.entityOperator -
KafkaConnect.spec -
KafkaConnectS2I.spec
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.
3.2.12.1.3. Configuring pod anti-affinity in Kafka components
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
affinityproperty in the resource specifying the cluster deployment. Use labels to specify the pods which should not be scheduled on the same nodes. ThetopologyKeyshould be set tokubernetes.io/hostnameto specify that the selected pods should not be scheduled on nodes with the same hostname. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: application operator: In values: - postgresql - mongodb topologyKey: "kubernetes.io/hostname" # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.2.12.2. Scheduling pods to specific nodes
3.2.12.2.1. 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.
3.2.12.2.2. Affinity
Affinity can be configured using the affinity property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
Kafka.spec.entityOperator -
KafkaConnect.spec -
KafkaConnectS2I.spec
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.
3.2.12.2.3. 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.
On OpenShift this can be done using
oc label:oc label node your-node node-type=fast-networkAlternatively, some of the existing labels might be reused.
Edit the
affinityproperty in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: node-type operator: In values: - fast-network # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.2.12.3. Using dedicated nodes
3.2.12.3.1. 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.
3.2.12.3.2. Affinity
Affinity can be configured using the affinity property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
Kafka.spec.entityOperator -
KafkaConnect.spec -
KafkaConnectS2I.spec
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.
3.2.12.3.3. Tolerations
Tolerations ca be configured using the tolerations property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
Kafka.spec.entityOperator -
KafkaConnect.spec -
KafkaConnectS2I.spec
The format of the tolerations property follows the OpenShift specification. For more details, see the Kubernetes taints and tolerations.
3.2.12.3.4. 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
On OpenShift this can be done using
oc adm taint:oc adm taint node your-node dedicated=Kafka:NoScheduleAdditionally, add a label to the selected nodes as well.
On OpenShift this can be done using
oc label:oc label node your-node dedicated=KafkaEdit the
affinityandtolerationsproperties in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... 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.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.2.13. List of resources created as part of Kafka Connect cluster
The following resources will 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.
3.3. Kafka Connect cluster with Source2Image support
The full schema of the KafkaConnectS2I resource is described in the Section B.37, “KafkaConnectS2I schema reference”. All labels that are applied to the desired KafkaConnectS2I resource will also be applied to the OpenShift resources making up the Kafka Connect cluster with Source2Image support. This provides a convenient mechanism for those resources to be labelled in whatever way the user requires.
3.3.1. Replicas
Kafka Connect clusters can run with a different number of nodes. The number of nodes is defined in the KafkaConnect and KafkaConnectS2I resources. Running Kafka Connect cluster with multiple nodes can provide better availability and scalability. However, when running Kafka Connect on OpenShift it is not absolutely necessary to run multiple nodes of Kafka Connect for high availability. When the node where Kafka Connect is deployed to crashes, OpenShift will automatically take care of rescheduling the Kafka Connect pod to a different node. However, running Kafka Connect with multiple nodes can provide faster failover times, because the other nodes will be already up and running.
3.3.1.1. Configuring the number of nodes
Number of Kafka Connect nodes can be configured using the replicas property in KafkaConnect.spec and KafkaConnectS2I.spec.
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
replicasproperty in theKafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaConnectS2I metadata: name: my-cluster spec: # ... replicas: 3 # ...
Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.3.2. Bootstrap servers
Kafka Connect cluster always works together with a Kafka cluster. The Kafka cluster is specified in the form of a list of bootstrap servers. On OpenShift, the list must ideally contain the Kafka cluster bootstrap service which is named cluster-name-kafka-bootstrap and a port of 9092 for plain traffic or 9093 for encrypted traffic.
The list of bootstrap servers can be configured in the bootstrapServers property in KafkaConnect.spec and KafkaConnectS2I.spec. The servers should be a comma-separated list containing one or more Kafka brokers or a service pointing to Kafka brokers specified as a hostname:_port_ pairs.
When using Kafka Connect with a Kafka cluster not managed by AMQ Streams, you can specify the bootstrap servers list according to the configuration of a given cluster.
3.3.2.1. Configuring bootstrap servers
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
bootstrapServersproperty in theKafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaConnect metadata: name: my-cluster spec: # ... bootstrapServers: my-cluster-kafka-bootstrap:9092 # ...
Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.3.3. Connecting to Kafka brokers using TLS
By default, Kafka Connect will try to connect to Kafka brokers using a plain text connection. If you would prefer to use TLS additional configuration will be necessary.
3.3.3.1. TLS support in Kafka Connect
TLS support is configured in the tls property in KafkaConnect.spec and KafkaConnectS2I.spec. The tls property contains a list of secrets with key names under which the certificates are stored. The certificates should be stored in X509 format.
An example showing TLS configuration with multiple certificates
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaConnect
metadata:
name: my-cluster
spec:
# ...
tls:
trustedCertificates:
- secretName: my-secret
certificate: ca.crt
- secretName: my-other-secret
certificate: certificate.crt
# ...
When multiple certificates are stored in the same secret, it can be listed multiple times.
An example showing TLS configuration with multiple certificates from the same secret
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaConnectS2I
metadata:
name: my-cluster
spec:
# ...
tls:
trustedCertificates:
- secretName: my-secret
certificate: ca.crt
- secretName: my-secret
certificate: ca2.crt
# ...
3.3.3.2. Configuring TLS in Kafka Connect
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Find out the name of the secret with the certificate which should be used for TLS Server Authentication and the key under which the certificate is stored in the secret. If such secret does not exist yet, prepare the certificate in a file and create the secret.
On OpenShift this can be done using
oc create:oc create secret generic my-secret --from-file=my-file.crt
Edit the
tlsproperty in theKafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaConnect metadata: name: my-connect spec: # ... tls: trustedCertificates: - secretName: my-cluster-cluster-cert certificate: ca.crt # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.3.4. Connecting to Kafka brokers with Authentication
By default, Kafka Connect will try to connect to Kafka brokers without any authentication. Authentication can be enabled in the KafkaConnect and KafkaConnectS2I resources.
3.3.4.1. Authentication support in Kafka Connect
Authentication can be configured in the authentication property in KafkaConnect.spec and KafkaConnectS2I.spec. The authentication property specifies the type of the authentication mechanisms which should be used and additional configuration details depending on the mechanism. The currently supported authentication types are:
- TLS client authentication
- SASL based authentication using SCRAM-SHA-512 mechanism
3.3.4.1.1. TLS Client Authentication
To use the TLS client authentication, set the type property to the value tls. TLS client authentication is using TLS certificate to authenticate. The certificate has to be specified in the certificateAndKey property. It is always loaded from an OpenShift secret. Inside the secret, it has to be stored in the X509 format under two different keys: for public and private keys.
TLS client authentication can be used only with TLS connections. For more details about TLS configuration in Kafka Connect see Section 3.3.3, “Connecting to Kafka brokers using TLS”.
An example showing TLS client authentication configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaConnect
metadata:
name: my-cluster
spec:
# ...
authentication:
type: tls
certificateAndKey:
secretName: my-secret
certificate: public.crt
key: private.key
# ...
3.3.4.1.2. SCRAM-SHA-512 authentication
To use the authentication using the SCRAM-SHA-512 SASL mechanism, set the type property to the value scram-sha-512. SCRAM-SHA-512 uses a username and password to authenticate. Specify the username in the username property. Specify the password as a link to a Secret containing the password in the passwordSecret property. It has to specify the name of the Secret containing the password and the name of the key under which the password is stored inside the Secret.
An example showing SCRAM-SHA-512 client authentication configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaConnect
metadata:
name: my-cluster
spec:
# ...
authentication:
type: scram-sha-512
username: my-connect-user
passwordSecret:
secretName: my-connect-user
password: password
# ...
3.3.4.2. Configuring TLS client authentication in Kafka Connect
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Find out the name of the
Secretwith the public and private keys which should be used for TLS Client Authentication and the keys under which they are stored in theSecret. If such aSecretdoes not exist yet, prepare the keys in a file and create theSecret.On OpenShift this can be done using
oc create:oc create secret generic my-secret --from-file=my-public.crt --from-file=my-private.key
Edit the
authenticationproperty in theKafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaConnect metadata: name: my-connect spec: # ... authentication: type: tls certificateAndKey: secretName: my-secret certificate: my-public.crt key: my-private.key # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.3.4.3. Configuring SCRAM-SHA-512 authentication in Kafka Connect
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
- Username of the user which should be used for authentication
Procedure
Find out the name of the
Secretwith the password which should be used for authentication and the key under which the password is stored in theSecret. If such aSecretdoes not exist yet, prepare a file with the password and create theSecret.On OpenShift this can be done using
oc create:echo -n '1f2d1e2e67df' > my-password.txt oc create secret generic my-secret --from-file=my-password.txt
Edit the
authenticationproperty in theKafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaConnect metadata: name: my-connect spec: # ... authentication: type: scram-sha-512 username: _my-username_ passwordSecret: secretName: _my-secret_ password: _my-password.txt_ # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.3.5. Kafka Connect configuration
AMQ Streams allows you to customize the configuration of Apache Kafka Connect nodes by editing most of the options listed in Apache Kafka documentation.
The only options which cannot be configured are those related to the following areas:
- Kafka cluster bootstrap address
- Security (Encryption, Authentication, and Authorization)
- Listener / REST interface configuration
- Plugin path configuration
These options are automatically configured by AMQ Streams.
3.3.5.1. Kafka Connect configuration
Kafka Connect can be configured using the config property in KafkaConnect.spec and KafkaConnectS2I.spec. This property should contain the Kafka Connect configuration options as keys. The values could be in one of the following JSON types:
- String
- Number
- Boolean
Users can specify and configure the options listed in the Apache Kafka 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:
-
ssl. -
sasl. -
security. -
listeners -
plugin.path -
rest. -
bootstrap.servers
When one of the forbidden options is present in the config property, it will be ignored and a warning message will be printed to the Custer Operator log file. All other options will be passed to Kafka Connect.
The Cluster Operator does not validate keys or values in the provided config object. When an invalid configuration is provided, the Kafka Connect cluster might not start or might become unstable. In such cases, the configuration in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config object should be fixed and the cluster operator will roll out the new configuration to all Kafka Connect nodes.
Selected options have default values:
-
group.idwith default valueconnect-cluster -
offset.storage.topicwith default valueconnect-cluster-offsets -
config.storage.topicwith default valueconnect-cluster-configs -
status.storage.topicwith default valueconnect-cluster-status -
key.converterwith default valueorg.apache.kafka.connect.json.JsonConverter -
value.converterwith default valueorg.apache.kafka.connect.json.JsonConverter -
internal.key.converterwith default valueorg.apache.kafka.connect.json.JsonConverter -
internal.value.converterwith default valueorg.apache.kafka.connect.json.JsonConverter -
internal.key.converter.schemas.enablewith default valuefalse -
internal.value.converter.schemas.enablewith default valuefalse
These options will be automatically configured in case they are not present in the KafkaConnect.spec.config or KafkaConnectS2I.spec.config properties.
An example showing Kafka Connect configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaConnect
metadata:
name: my-connect
spec:
# ...
config:
group.id: my-connect-cluster
offset.storage.topic: my-connect-cluster-offsets
config.storage.topic: my-connect-cluster-configs
status.storage.topic: my-connect-cluster-status
key.converter: org.apache.kafka.connect.json.JsonConverter
value.converter: org.apache.kafka.connect.json.JsonConverter
key.converter.schemas.enable: true
value.converter.schemas.enable: true
internal.key.converter: org.apache.kafka.connect.json.JsonConverter
internal.value.converter: org.apache.kafka.connect.json.JsonConverter
internal.key.converter.schemas.enable: false
internal.value.converter.schemas.enable: false
config.storage.replication.factor: 3
offset.storage.replication.factor: 3
status.storage.replication.factor: 3
# ...
3.3.5.2. Configuring Kafka Connect
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
configproperty in theKafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaConnect metadata: name: my-connect spec: # ... config: group.id: my-connect-cluster offset.storage.topic: my-connect-cluster-offsets config.storage.topic: my-connect-cluster-configs status.storage.topic: my-connect-cluster-status key.converter: org.apache.kafka.connect.json.JsonConverter value.converter: org.apache.kafka.connect.json.JsonConverter key.converter.schemas.enable: true value.converter.schemas.enable: true internal.key.converter: org.apache.kafka.connect.json.JsonConverter internal.value.converter: org.apache.kafka.connect.json.JsonConverter internal.key.converter.schemas.enable: false internal.value.converter.schemas.enable: false config.storage.replication.factor: 3 offset.storage.replication.factor: 3 status.storage.replication.factor: 3 # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.3.6. CPU and memory resources
For every deployed container, AMQ Streams allows you to specify the resources which should be reserved for it and the maximum resources that can be consumed by it. AMQ Streams supports two types of resources:
- Memory
- CPU
AMQ Streams is using the OpenShift syntax for specifying CPU and memory resources.
3.3.6.1. Resource limits and requests
Resource limits and requests can be configured using the resources property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.kafka.tlsSidecar -
Kafka.spec.zookeeper -
Kafka.spec.zookeeper.tlsSidecar -
Kafka.spec.entityOperator.topicOperator -
Kafka.spec.entityOperator.userOperator -
Kafka.spec.entityOperator.tlsSidecar -
KafkaConnect.spec -
KafkaConnectS2I.spec
3.3.6.1.1. Resource requests
Requests specify the resources that will be reserved for a given container. Reserving the resources will ensure that they are always available.
If the resource request is for more than the available free resources in the OpenShift cluster, the pod will not be scheduled.
Resource requests can be specified in the request property. The resource requests currently supported by AMQ Streams are memory and CPU. Memory is specified under the property memory. CPU is specified under the property cpu.
An example showing resource request configuration
# ...
resources:
requests:
cpu: 12
memory: 64Gi
# ...
It is also possible to specify a resource request just for one of the resources:
An example showing resource request configuration with memory request only
# ...
resources:
requests:
memory: 64Gi
# ...
Or:
An example showing resource request configuration with CPU request only
# ...
resources:
requests:
cpu: 12
# ...
3.3.6.1.2. Resource limits
Limits specify the maximum resources that can be consumed by a given container. The limit is not reserved and might not be always available. The container can use the resources up to the limit only when they are available. The resource limits should be always higher than the resource requests.
Resource limits can be specified in the limits property. The resource limits currently supported by AMQ Streams are memory and CPU. Memory is specified under the property memory. CPU is specified under the property cpu.
An example showing resource limits configuration
# ...
resources:
limits:
cpu: 12
memory: 64Gi
# ...
It is also possible to specify the resource limit just for one of the resources:
An example showing resource limit configuration with memory request only
# ...
resources:
limits:
memory: 64Gi
# ...
Or:
An example showing resource limits configuration with CPU request only
# ...
resources:
requests:
cpu: 12
# ...
3.3.6.1.3. Supported CPU formats
CPU requests and limits are supported in the following formats:
-
Number of CPU cores as integer (
5CPU core) or decimal (2.5CPU core). -
Number or millicpus / millicores (
100m) where 1000 millicores is the same1CPU core.
An example of using different CPU units
# ...
resources:
requests:
cpu: 500m
limits:
cpu: 2.5
# ...
The amount of computing power of 1 CPU core might differ depending on the platform where the OpenShift is deployed.
For more details about the CPU specification, see the Meaning of CPU website.
3.3.6.1.4. Supported memory formats
Memory requests and limits are specified in megabytes, gigabytes, mebibytes, and gibibytes.
-
To specify memory in megabytes, use the
Msuffix. For example1000M. -
To specify memory in gigabytes, use the
Gsuffix. For example1G. -
To specify memory in mebibytes, use the
Misuffix. For example1000Mi. -
To specify memory in gibibytes, use the
Gisuffix. For example1Gi.
An example of using different memory units
# ...
resources:
requests:
memory: 512Mi
limits:
memory: 2Gi
# ...
For more details about the memory specification and additional supported units, see the Meaning of memory website.
3.3.6.1.5. Additional resources
- For more information about managing computing resources on OpenShift, see Managing Compute Resources for Containers.
3.3.6.2. Configuring resource requests and limits
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
resourcesproperty in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... resources: requests: cpu: "8" memory: 64Gi limits: cpu: "12" memory: 128Gi # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
Additional resources
-
For more information about the schema, see
Resourcesschema reference.
3.3.7. Logging
Logging enables you to diagnose error and performance issues of AMQ Streams. For the logging, various logger implementations are used. Kafka and Zookeeper use log4j logger and Topic Operator, User Operator, and other components use log4j2 logger.
This section provides information about different loggers and describes how to configure log levels.
You can set the log levels by specifying the loggers and their levels directly (inline) or by using a custom (external) config map.
3.3.7.1. Using inline logging setting
Procedure
Edit the YAML file to specify the loggers and their level for the required components. For example:
apiVersion: {KafkaApiVersion} kind: Kafka spec: kafka: # ... logging: type: inline loggers: logger.name: "INFO" # ...In the above example, the log level is set to INFO. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. For more information about the log levels, see log4j manual.
Create or update the Kafka resource in OpenShift.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.3.7.2. Using external ConfigMap for logging setting
Procedure
Edit the YAML file to specify the name of the
ConfigMapwhich should be used for the required components. For example:apiVersion: {KafkaApiVersion} kind: Kafka spec: kafka: # ... logging: type: external name: customConfigMap # ...Remember to place your custom ConfigMap under
log4j.propertieseventuallylog4j2.propertieskey.Create or update the Kafka resource in OpenShift.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.3.7.3. Loggers
AMQ Streams consists of several components. Each component has its own loggers and is configurable. This section provides information about loggers of various components.
Components and their loggers are listed below.
Kafka
-
kafka.root.logger.level -
log4j.logger.org.I0Itec.zkclient.ZkClient -
log4j.logger.org.apache.zookeeper -
log4j.logger.kafka -
log4j.logger.org.apache.kafka -
log4j.logger.kafka.request.logger -
log4j.logger.kafka.network.Processor -
log4j.logger.kafka.server.KafkaApis -
log4j.logger.kafka.network.RequestChannel$ -
log4j.logger.kafka.controller -
log4j.logger.kafka.log.LogCleaner -
log4j.logger.state.change.logger -
log4j.logger.kafka.authorizer.logger
-
Zookeeper
-
zookeeper.root.logger
-
Kafka Connect and Kafka Connect with Source2Image support
-
connect.root.logger.level -
log4j.logger.org.apache.zookeeper -
log4j.logger.org.I0Itec.zkclient -
log4j.logger.org.reflections
-
Kafka Mirror Maker
-
mirrormaker.root.logger
-
Topic Operator
-
rootLogger.level
-
User Operator
-
rootLogger.level
-
3.3.8. Healthchecks
Healthchecks are periodical tests which verify that the application’s health. When the Healthcheck fails, OpenShift can assume that the application is not healthy and attempt 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
3.3.8.1. Healthcheck configurations
Liveness and readiness probes can be configured using the livenessProbe and readinessProbe properties in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
KafkaConnect.spec -
KafkaConnectS2I.spec
Both livenessProbe and readinessProbe support two additional options:
-
initialDelaySeconds -
timeoutSeconds
The initialDelaySeconds property defines the initial delay before the probe is tried for the first time. Default is 15 seconds.
The timeoutSeconds property defines timeout of the probe. Default is 5 seconds.
An example of liveness and readiness probe configuration
# ... readinessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 # ...
3.3.8.2. Configuring healthchecks
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
livenessProbeorreadinessProbeproperty in theKafka,KafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... readinessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.3.9. 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.
3.3.9.1. Metrics configuration
Prometheus metrics can be 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/v1alpha1
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/v1alpha1
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:
# ...
3.3.9.2. Configuring Prometheus metrics
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
metricsproperty in theKafka,KafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... metrics: lowercaseOutputName: true # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.3.10. JVM Options
Apache Kafka and Apache Zookeeper are running inside of a Java Virtual Machine (JVM). JVM has many configuration options to optimize the performance for different platforms and architectures. AMQ Streams allows configuring some of these options.
3.3.10.1. JVM configuration
JVM options can be configured using the jvmOptions property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
KafkaConnect.spec -
KafkaConnectS2I.spec
Only a selected subset of available JVM options can be configured. The following options are supported:
-Xms and -Xmx
-Xms configures the minimum initial allocation heap size when the JVM starts. -Xmx configures the maximum heap size.
The units accepted by JVM settings such as -Xmx and -Xms are those accepted by the JDK java binary in the corresponding image. Accordingly, 1g or 1G means 1,073,741,824 bytes, and Gi is not a valid unit suffix. This is in contrast to the units used for memory requests and limits, which follow the OpenShift convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes
The default values used for -Xms and -Xmx depends on whether there is a memory request limit configured for the container:
- If there is a memory limit then the JVM’s minimum and maximum memory will be set to a value corresponding to the limit.
-
If there is no memory limit then the JVM’s minimum memory will be set to
128Mand the JVM’s maximum memory will not be defined. This allows for the JVM’s memory to grow as-needed, which is ideal for single node environments in test and development.
Setting -Xmx explicitly requires some care:
-
The JVM’s overall memory usage will be approximately 4 × the maximum heap, as configured by
-Xmx. -
If
-Xmxis set without also setting an appropriate OpenShift memory limit, it is possible that the container will be killed should the OpenShift node experience memory pressure (from other Pods running on it). -
If
-Xmxis set without also setting an appropriate OpenShift memory request, it is possible that the container will be scheduled to a node with insufficient memory. In this case, the container will not start but crash (immediately if-Xmsis set to-Xmx, or some later time if not).
When setting -Xmx explicitly, it is recommended to:
- set the memory request and the memory limit to the same value,
-
use a memory request that is at least 4.5 × the
-Xmx, -
consider setting
-Xmsto the same value as-Xms.
Containers doing lots of disk I/O (such as Kafka broker containers) will need to leave some memory available for use as operating system page cache. On such containers, the requested memory should be significantly higher than the memory used by the JVM.
Example fragment configuring -Xmx and -Xms
# ... jvmOptions: "-Xmx": "2g" "-Xms": "2g" # ...
In the above example, the JVM will use 2 GiB (=2,147,483,648 bytes) for its heap. Its total memory usage will be approximately 8GiB.
Setting the same value for initial (-Xms) and maximum (-Xmx) heap sizes avoids the JVM having to allocate memory after startup, at the cost of possibly allocating more heap than is really needed. For Kafka and Zookeeper pods such allocation could cause unwanted latency. For Kafka Connect avoiding over allocation may be the most important concern, especially in distributed mode where the effects of over-allocation will be multiplied by the number of consumers.
-server
-server enables the server JVM. This option can be set to true or false.
Example fragment configuring -server
# ... jvmOptions: "-server": true # ...
When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.
-XX
-XX object can be used for configuring advanced runtime options of a JVM. The -server and -XX options are used to configure the KAFKA_JVM_PERFORMANCE_OPTS option of Apache Kafka.
Example showing the use of the -XX object
jvmOptions:
"-XX":
"UseG1GC": true,
"MaxGCPauseMillis": 20,
"InitiatingHeapOccupancyPercent": 35,
"ExplicitGCInvokesConcurrent": true,
"UseParNewGC": false
The example configuration above will result in the following JVM options:
-XX:+UseG1GC -XX:MaxGCPauseMillis=20 -XX:InitiatingHeapOccupancyPercent=35 -XX:+ExplicitGCInvokesConcurrent -XX:-UseParNewGC
When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.
3.3.10.2. Configuring JVM options
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
jvmOptionsproperty in theKafka,KafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... jvmOptions: "-Xmx": "8g" "-Xms": "8g" # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.3.11. 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.
3.3.11.1. Container image configurations
Container image which should be used for given components can be specified using the image property in:
-
Kafka.spec.kafka -
Kafka.spec.kafka.tlsSidecar -
Kafka.spec.zookeeper -
Kafka.spec.zookeeper.tlsSidecar -
Kafka.spec.entityOperator.topicOperator -
Kafka.spec.entityOperator.userOperator -
Kafka.spec.entityOperator.tlsSidecar -
KafkaConnect.spec -
KafkaConnectS2I.spec
The image specified in the component-specific custom resource will be used during deployment. If the image field is missing, the image specified in the Cluster Operator configuration will be used. If the image name is not defined in the Cluster Operator configuration, then the default value will be used.
For Kafka brokers:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/kafka:latestcontainer image.
-
Container image specified in the
For Kafka broker TLS sidecar:
-
Container image specified in the
STRIMZI_DEFAULT_TLS_SIDECAR_KAFKA_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/kafka-stunnel:latestcontainer image.
-
Container image specified in the
For Zookeeper nodes:
-
Container image specified in the
STRIMZI_DEFAULT_ZOOKEEPER_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/zookeeper:latestcontainer image.
-
Container image specified in the
For Zookeeper node TLS sidecar:
-
Container image specified in the
STRIMZI_DEFAULT_TLS_SIDECAR_ZOOKEEPER_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/zookeeper-stunnel:latestcontainer image.
-
Container image specified in the
For Topic Operator:
-
Container image specified in the
STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGEenvironment variable from the Cluster Operator configuration.
-
Container image specified in the
For User Operator:
-
Container image specified in the
STRIMZI_DEFAULT_USER_OPERATOR_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/user-operator:latestcontainer image.
-
Container image specified in the
For Entity Operator TLS sidecar:
-
Container image specified in the
STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/entity-operator-stunnel:latestcontainer image.
-
Container image specified in the
For Kafka Connect:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/kafka-connect:latestcontainer image.
-
Container image specified in the
For Kafka Connect with Source2image support:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_CONNECT_S2I_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/kafka-connect-s2i:latestcontainer image.
-
Container image specified in the
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 case, you should either copy the AMQ Streams images or build them from source. In case the configured image is not compatible with AMQ Streams images, it might not work properly.
Example of container image configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
image: my-org/my-image:latest
# ...
zookeeper:
# ...
3.3.11.2. Configuring container images
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
imageproperty in theKafka,KafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... image: my-org/my-image:latest # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.3.12. Configuring pod scheduling
When two application 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.
3.3.12.1. Scheduling pods based on other applications
3.3.12.1.1. 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.
3.3.12.1.2. Affinity
Affinity can be configured using the affinity property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
Kafka.spec.entityOperator -
KafkaConnect.spec -
KafkaConnectS2I.spec
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.
3.3.12.1.3. Configuring pod anti-affinity in Kafka components
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
affinityproperty in the resource specifying the cluster deployment. Use labels to specify the pods which should not be scheduled on the same nodes. ThetopologyKeyshould be set tokubernetes.io/hostnameto specify that the selected pods should not be scheduled on nodes with the same hostname. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: application operator: In values: - postgresql - mongodb topologyKey: "kubernetes.io/hostname" # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.3.12.2. Scheduling pods to specific nodes
3.3.12.2.1. 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.
3.3.12.2.2. Affinity
Affinity can be configured using the affinity property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
Kafka.spec.entityOperator -
KafkaConnect.spec -
KafkaConnectS2I.spec
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.
3.3.12.2.3. 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.
On OpenShift this can be done using
oc label:oc label node your-node node-type=fast-networkAlternatively, some of the existing labels might be reused.
Edit the
affinityproperty in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: node-type operator: In values: - fast-network # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.3.12.3. Using dedicated nodes
3.3.12.3.1. 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.
3.3.12.3.2. Affinity
Affinity can be configured using the affinity property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
Kafka.spec.entityOperator -
KafkaConnect.spec -
KafkaConnectS2I.spec
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.
3.3.12.3.3. Tolerations
Tolerations ca be configured using the tolerations property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
Kafka.spec.entityOperator -
KafkaConnect.spec -
KafkaConnectS2I.spec
The format of the tolerations property follows the OpenShift specification. For more details, see the Kubernetes taints and tolerations.
3.3.12.3.4. 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
On OpenShift this can be done using
oc adm taint:oc adm taint node your-node dedicated=Kafka:NoScheduleAdditionally, add a label to the selected nodes as well.
On OpenShift this can be done using
oc label:oc label node your-node dedicated=KafkaEdit the
affinityandtolerationsproperties in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... 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.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.3.13. List of resources created as part of Kafka Connect cluster with Source2Image support
The following resources will 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.
3.3.14. Using OpenShift builds and S2I to create new images
OpenShift supports builds, which can be used together with the Source-to-Image (S2I) framework to create new container images. An OpenShift build takes a builder image with S2I support together with source code and binaries provided by the user and uses them to build a new container image. The newly created container image is stored in OpenShift’s local container image repository and can be used in deployments. AMQ Streams provides a Kafka Connect builder image, which can be found on Red Hat Container Catalog as registry.access.redhat.com/amqstreams-1/amqstreams10-kafkaconnects2i-openshift:1.0.0 with this S2I support. It takes user-provided binaries (with plugins and connectors) and creates a new Kafka Connect image. This enhanced Kafka Connect image can be used with the Kafka Connect deployment.
The S2I deployment provided as an OpenShift template. It can be deployed from the template using the command-line or the OpenShift console.
Procedure
Create a Kafka Connect S2I cluster from the command-line
oc apply -f examples/kafka-connect/kafka-connect-s2i.yaml
Once the cluster is deployed, a new build can be triggered from the command-line by creating a directory with Kafka Connect plugins:
$ tree ./my-plugins/ ./my-plugins/ ├── debezium-connector-mongodb │ ├── bson-3.4.2.jar │ ├── CHANGELOG.md │ ├── CONTRIBUTE.md │ ├── COPYRIGHT.txt │ ├── debezium-connector-mongodb-0.7.1.jar │ ├── debezium-core-0.7.1.jar │ ├── LICENSE.txt │ ├── mongodb-driver-3.4.2.jar │ ├── mongodb-driver-core-3.4.2.jar │ └── README.md ├── debezium-connector-mysql │ ├── CHANGELOG.md │ ├── CONTRIBUTE.md │ ├── COPYRIGHT.txt │ ├── debezium-connector-mysql-0.7.1.jar │ ├── debezium-core-0.7.1.jar │ ├── LICENSE.txt │ ├── mysql-binlog-connector-java-0.13.0.jar │ ├── mysql-connector-java-5.1.40.jar │ ├── README.md │ └── wkb-1.0.2.jar └── debezium-connector-postgres ├── CHANGELOG.md ├── CONTRIBUTE.md ├── COPYRIGHT.txt ├── debezium-connector-postgres-0.7.1.jar ├── debezium-core-0.7.1.jar ├── LICENSE.txt ├── postgresql-42.0.0.jar ├── protobuf-java-2.6.1.jar └── README.md
Start a new image build using the prepared directory:
oc start-build my-connect-cluster-connect --from-dir ./my-plugins/
NoteThe name of the build will be changed according to the cluster name of the deployed Kafka Connect cluster.
- Once the build is finished, the new image will be used automatically by the Kafka Connect deployment.
3.4. Kafka Mirror Maker configuration
The full schema of the KafkaMirrorMaker resource is described in the Section B.50, “KafkaMirrorMaker schema reference”. All labels that apply to the desired KafkaMirrorMaker resource will also be applied to the OpenShift resources making up Mirror Maker. This provides a convenient mechanism for those resources to be labelled in whatever way the user requires.
3.4.1. Replicas
It is possible to run multiple Mirror Maker replicas. The number of replicas is defined in the KafkaMirrorMaker resource. You can run multiple Mirror Maker replicas to provide better availability and scalability. However, when running Kafka Mirror Maker on OpenShift it is not absolutely necessary to run multiple replicas of the Kafka Mirror Maker for high availability. When the node where the Kafka Mirror Maker has deployed crashes, OpenShift will automatically reschedule the Kafka Mirror Maker pod to a different node. However, running Kafka Mirror Maker with multiple replicas can provide faster failover times as the other nodes will be up and running.
3.4.1.1. Configuring the number of replicas
The number of Kafka Mirror Maker replicas can be configured using the replicas property in KafkaMirrorMaker.spec.
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
replicasproperty in theKafkaMirrorMakerresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaMirrorMaker metadata: name: my-mirror-maker spec: # ... replicas: 3 # ...
Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f <your-file>
3.4.2. Bootstrap servers
Kafka Mirror Maker always works together with two Kafka clusters (source and target). The source and the target Kafka clusters are specified in the form of two lists of comma-separated list of <hostname>:<port> pairs. The bootstrap server lists can refer to Kafka clusters which do not need to be deployed in the same OpenShift cluster. They can even refer to any Kafka cluster not deployed by AMQ Streams or even deployed by AMQ Streams but on a different OpenShift cluster and accessible from outside.
If on the same OpenShift cluster, each list must ideally contain the Kafka cluster bootstrap service which is named <cluster-name>-kafka-bootstrap and a port of 9092 for plain traffic or 9093 for encrypted traffic. If deployed by AMQ Streams but on different OpenShift clusters, the list content depends on the way used for exposing the clusters (routes, nodeports or loadbalancers).
The list of bootstrap servers can be configured in the KafkaMirrorMaker.spec.consumer.bootstrapServers and KafkaMirrorMaker.spec.producer.bootstrapServers properties. The servers should be a comma-separated list containing one or more Kafka brokers or a Service pointing to Kafka brokers specified as a <hostname>:<port> pairs.
When using Kafka Mirror Maker with a Kafka cluster not managed by AMQ Streams, you can specify the bootstrap servers list according to the configuration of the given cluster.
3.4.2.1. Configuring bootstrap servers
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
KafkaMirrorMaker.spec.consumer.bootstrapServersandKafkaMirrorMaker.spec.producer.bootstrapServersproperties. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaMirrorMaker metadata: name: my-mirror-maker spec: # ... consumer: bootstrapServers: my-source-cluster-kafka-bootstrap:9092 # ... producer: bootstrapServers: my-target-cluster-kafka-bootstrap:9092Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f <your-file>
3.4.3. Whitelist
You specify the list topics that the Kafka Mirror Maker has to mirror from the source to the target Kafka cluster in the KafkaMirrorMaker resource using the whitelist option. It allows any regular expression from the simplest case with a single topic name to complex patterns. For example, you can mirror topics A and B using "A|B" or all topics using "*". You can also pass multiple regular expressions separated by commas to the Kafka Mirror Maker.
3.4.3.1. Configuring the topics whitelist
Specify the list topics that have to be mirrored by the Kafka Mirror Maker from source to target Kafka cluster using the whitelist property in KafkaMirrorMaker.spec.
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
whitelistproperty in theKafkaMirrorMakerresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaMirrorMaker metadata: name: my-mirror-maker spec: # ... whitelist: "my-topic|other-topic" # ...
Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f <your-file>
3.4.4. Consumer group identifier
The Kafka Mirror Maker uses Kafka consumer to consume messages and it behaves like any other Kafka consumer client. It is in charge to consume the messages from the source Kafka cluster which will be mirrored to the target Kafka cluster. The consumer needs to be part of a consumer group for being assigned partitions.
3.4.4.1. Configuring the consumer group identifier
The consumer group identifier can be configured in the KafkaMirrorMaker.spec.consumer.groupId property.
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
KafkaMirrorMaker.spec.consumer.groupIdproperty. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaMirrorMaker metadata: name: my-mirror-maker spec: # ... consumer: groupId: "my-group" # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f <your-file>
3.4.5. Number of consumer streams
You can increase the throughput in mirroring topics by increase the number of consumer threads. More consumer threads will belong to the same configured consumer group. The topic partitions will be assigned across these consumer threads which will consume messages in parallel.
3.4.5.1. Configuring the number of consumer streams
The number of consumer streams can be configured using the KafkaMirrorMaker.spec.consumer.numStreams property.
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
KafkaMirrorMaker.spec.consumer.numStreamsproperty. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaMirrorMaker metadata: name: my-mirror-maker spec: # ... consumer: numStreams: 2 # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f <your-file>
3.4.6. Connecting to Kafka brokers using TLS
By default, Kafka Mirror Maker will try to connect to Kafka brokers, in the source and target clusters, using a plain text connection. You must make additional configurations to use TLS.
3.4.6.1. TLS support in Kafka Mirror Maker
TLS support is configured in the tls sub-property of consumer and producer properties in KafkaMirrorMaker.spec. The tls property contains a list of secrets with key names under which the certificates are stored. The certificates should be stored in X.509 format.
An example showing TLS configuration with multiple certificates
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaMirrorMaker
metadata:
name: my-mirror-maker
spec:
# ...
consumer:
tls:
trustedCertificates:
- secretName: my-source-secret
certificate: ca.crt
- secretName: my-other-source-secret
certificate: certificate.crt
# ...
producer:
tls:
trustedCertificates:
- secretName: my-target-secret
certificate: ca.crt
- secretName: my-other-target-secret
certificate: certificate.crt
# ...
When multiple certificates are stored in the same secret, it can be listed multiple times.
An example showing TLS configuration with multiple certificates from the same secret
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaMirrorMaker
metadata:
name: my-mirror-maker
spec:
# ...
consumer:
tls:
trustedCertificates:
- secretName: my-source-secret
certificate: ca.crt
- secretName: my-source-secret
certificate: ca2.crt
# ...
producer:
tls:
trustedCertificates:
- secretName: my-target-secret
certificate: ca.crt
- secretName: my-target-secret
certificate: ca2.crt
# ...
3.4.6.2. Configuring TLS encryption in Kafka Mirror Maker
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
As the Kafka Mirror Maker connects to two Kafka clusters (source and target), you can choose to configure TLS for one or both the clusters. The following steps describe how to configure TLS on the consumer side for connecting to the source Kafka cluster:
Find out the name of the secret with the certificate which should be used for TLS Server Authentication and the key under which the certificate is stored in the secret. If such secret does not exist yet, prepare the certificate in a file and create the secret.
On OpenShift this can be done using
oc create:oc create secret generic <my-secret> --from-file=<my-file.crt>
Edit the
KafkaMirrorMaker.spec.consumer.tlsproperty. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaMirrorMaker metadata: name: my-mirror-maker spec: # ... consumer: tls: trustedCertificates: - secretName: my-cluster-cluster-cert certificate: ca.crt # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f <your-file>
Repeat the above steps for configuring TLS on the target Kafka cluster. In this case, the secret containing the certificate has to be configured in the KafkaMirrorMaker.spec.producer.tls property.
3.4.7. Connecting to Kafka brokers with Authentication
By default, Kafka Mirror Maker will try to connect to Kafka brokers without any authentication. Authentication can be enabled in the KafkaMirrorMaker resource.
3.4.7.1. Authentication support in Kafka Mirror Maker
Authentication can be configured in the KafkaMirrorMaker.spec.consumer.authentication and KafkaMirrorMaker.spec.producer.authentication properties. The authentication property specifies the type of the authentication method which should be used and additional configuration details depending on the mechanism. The currently supported authentication types are:
- TLS client authentication
- SASL based authentication using SCRAM-SHA-512 mechanism
3.4.7.1.1. TLS Client Authentication
To use the TLS client authentication, set the type property to the value tls. The TLS client authentication uses TLS certificate to authenticate. The certificate has to be specified in the certificateAndKey property. It is always loaded from an OpenShift secret. Inside the secret, it has to be stored in the X.509 format separately as public and private keys.
TLS client authentication can be used only with TLS connections. For more details about TLS configuration in Kafka Mirror Maker see Section 3.4.6, “Connecting to Kafka brokers using TLS”.
An example showing TLS client authentication configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaMirrorMaker
metadata:
name: my-mirror-maker
spec:
# ...
consumer:
authentication:
type: tls
certificateAndKey:
secretName: my-source-secret
certificate: public.crt
key: private.key
# ...
producer:
authentication:
type: tls
certificateAndKey:
secretName: my-target-secret
certificate: public.crt
key: private.key
# ...
3.4.7.1.2. SCRAM-SHA-512 authentication
To use the authentication using the SCRAM-SHA-512 SASL mechanism, set the type property to the value scram-sha-512. It is possible to use it only if the broker listener, clients are connecting to, is configured to use it. SCRAM-SHA-512 uses a username and password to authenticate. Specify the username in the username property. Specify the password as a link to a Secret containing the password in the passwordSecret property. It has to specify the name of the Secret containing the password and the name of the key under which the password is stored inside the Secret.
An example showing SCRAM-SHA-512 client authentication configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaMirrorMaker
metadata:
name: my-mirror-maker
spec:
# ...
consumer:
authentication:
type: scram-sha-512
username: my-source-user
passwordSecret:
secretName: my-source-user
password: password
# ...
producer:
authentication:
type: scram-sha-512
username: my-producer-user
passwordSecret:
secretName: my-producer-user
password: password
# ...
3.4.7.2. Configuring TLS client authentication in Kafka Mirror Maker
Prerequisites
- An OpenShift cluster
-
A running Cluster Operator with a
tlslistener withtlsauthentication enabled
Procedure
As the Kafka Mirror Maker connects to two Kafka clusters (source and target), you can choose to configure TLS client authentication for one or both the clusters. The following steps describe how to configure TLS client authentication on the consumer side for connecting to the source Kafka cluster:
Find out the name of the
Secretwith the public and private keys which should be used for TLS Client Authentication and the keys under which they are stored in theSecret. If such aSecretdoes not exist yet, prepare the keys in a file and create theSecret.On OpenShift this can be done using
oc create:oc create secret generic <my-secret> --from-file=<my-public.crt> --from-file=<my-private.key>
Edit the
KafkaMirrorMaker.spec.consumer.authenticationproperty. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaMirrorMaker metadata: name: my-mirror-maker spec: # ... consumer: authentication: type: tls certificateAndKey: secretName: my-secret certificate: my-public.crt key: my-private.key # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f <your-file>
Repeat the above steps for configuring TLS client authentication on the target Kafka cluster. In this case, the secret containing the certificate has to be configured in the KafkaMirrorMaker.spec.producer.authentication property.
3.4.7.3. Configuring SCRAM-SHA-512 authentication in Kafka Mirror Maker
Prerequisites
- An OpenShift cluster
-
A running Cluster Operator with a
listenerconfigured for SCRAM-SHA-512 authentication - Username to be used for authentication
Procedure
As the Kafka Mirror Maker connects to two Kafka clusters (source and target), you can choose to configure SCRAM-SHA-512 authentication for one or both the clusters. The following steps describe how to configure SCRAM-SHA-512 authentication on the consumer side for connecting to the source Kafka cluster:
Find out the name of the
Secretwith the password which should be used for authentication and the key under which the password is stored in theSecret. If such aSecretdoes not exist yet, prepare a file with the password and create theSecret.On OpenShift this can be done using
oc create:echo -n '1f2d1e2e67df' > <my-password.txt> oc create secret generic <my-secret> --from-file=<my-password.txt>
Edit the
KafkaMirrorMaker.spec.consumer.authenticationproperty. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaMirrorMaker metadata: name: my-mirror-maker spec: # ... consumer: authentication: type: scram-sha-512 username: _<my-username>_ passwordSecret: secretName: _<my-secret>_ password: _<my-password.txt>_ # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f <your-file>
Repeat the above steps for configuring SCRAM-SHA-512 authentication on the target Kafka cluster. In this case, the secret containing the certificate has to be configured in the KafkaMirrorMaker.spec.producer.authentication property.
3.4.8. Kafka Mirror Maker configuration
AMQ Streams allows you to customize the configuration of the Kafka Mirror Maker by editing most of the options for the related consumer and producer. Producer options are listed in Apache Kafka documentation. Consumer options are listed in Apache Kafka documentation.
The only options which cannot be configured are those related to the following areas:
- Kafka cluster bootstrap address
- Security (Encryption, Authentication, and Authorization)
- Consumer group identifier
These options are automatically configured by AMQ Streams.
3.4.8.1. Kafka Mirror Maker configuration
Kafka Mirror Maker can be configured using the config sub-property in KafkaMirrorMaker.spec.consumer and KafkaMirrorMaker.spec.producer. This property should contain the Kafka Mirror Maker consumer and producer configuration options as keys. The values could be in one of the following JSON types:
- String
- Number
- Boolean
Users can specify and configure the options listed in the Apache Kafka documentation and Apache Kafka 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:
-
ssl. -
sasl. -
security. -
bootstrap.servers -
group.id
When one of the forbidden options is present in the config property, it will be ignored and a warning message will be printed to the Custer Operator log file. All other options will be passed to Kafka Mirror Maker.
The Cluster Operator does not validate keys or values in the provided config object. When an invalid configuration is provided, the Kafka Mirror Maker might not start or might become unstable. In such cases, the configuration in the KafkaMirrorMaker.spec.consumer.config or KafkaMirrorMaker.spec.producer.config object should be fixed and the cluster operator will roll out the new configuration for Kafka Mirror Maker.
An example showing Kafka Mirror Maker configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: KafkaMirroMaker
metadata:
name: my-mirror-maker
spec:
# ...
consumer:
config:
max.poll.records: 100
receive.buffer.bytes: 32768
producer:
config:
compression.type: gzip
batch.size: 8192
# ...
3.4.8.2. Configuring Kafka Mirror Maker
Prerequisites
- Two running Kafka clusters (source and target)
- A running Cluster Operator
Procedure
Edit the
KafkaMirrorMaker.spec.consumer.configandKafkaMirrorMaker.spec.producer.configproperties. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: KafkaMirroMaker metadata: name: my-mirror-maker spec: # ... consumer: config: max.poll.records: 100 receive.buffer.bytes: 32768 producer: config: compression.type: gzip batch.size: 8192 # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f <your-file>
3.4.9. CPU and memory resources
For every deployed container, AMQ Streams allows you to specify the resources which should be reserved for it and the maximum resources that can be consumed by it. AMQ Streams supports two types of resources:
- Memory
- CPU
AMQ Streams is using the OpenShift syntax for specifying CPU and memory resources.
3.4.9.1. Resource limits and requests
Resource limits and requests can be configured using the resources property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.kafka.tlsSidecar -
Kafka.spec.zookeeper -
Kafka.spec.zookeeper.tlsSidecar -
Kafka.spec.entityOperator.topicOperator -
Kafka.spec.entityOperator.userOperator -
Kafka.spec.entityOperator.tlsSidecar -
KafkaConnect.spec -
KafkaConnectS2I.spec
3.4.9.1.1. Resource requests
Requests specify the resources that will be reserved for a given container. Reserving the resources will ensure that they are always available.
If the resource request is for more than the available free resources in the OpenShift cluster, the pod will not be scheduled.
Resource requests can be specified in the request property. The resource requests currently supported by AMQ Streams are memory and CPU. Memory is specified under the property memory. CPU is specified under the property cpu.
An example showing resource request configuration
# ...
resources:
requests:
cpu: 12
memory: 64Gi
# ...
It is also possible to specify a resource request just for one of the resources:
An example showing resource request configuration with memory request only
# ...
resources:
requests:
memory: 64Gi
# ...
Or:
An example showing resource request configuration with CPU request only
# ...
resources:
requests:
cpu: 12
# ...
3.4.9.1.2. Resource limits
Limits specify the maximum resources that can be consumed by a given container. The limit is not reserved and might not be always available. The container can use the resources up to the limit only when they are available. The resource limits should be always higher than the resource requests.
Resource limits can be specified in the limits property. The resource limits currently supported by AMQ Streams are memory and CPU. Memory is specified under the property memory. CPU is specified under the property cpu.
An example showing resource limits configuration
# ...
resources:
limits:
cpu: 12
memory: 64Gi
# ...
It is also possible to specify the resource limit just for one of the resources:
An example showing resource limit configuration with memory request only
# ...
resources:
limits:
memory: 64Gi
# ...
Or:
An example showing resource limits configuration with CPU request only
# ...
resources:
requests:
cpu: 12
# ...
3.4.9.1.3. Supported CPU formats
CPU requests and limits are supported in the following formats:
-
Number of CPU cores as integer (
5CPU core) or decimal (2.5CPU core). -
Number or millicpus / millicores (
100m) where 1000 millicores is the same1CPU core.
An example of using different CPU units
# ...
resources:
requests:
cpu: 500m
limits:
cpu: 2.5
# ...
The amount of computing power of 1 CPU core might differ depending on the platform where the OpenShift is deployed.
For more details about the CPU specification, see the Meaning of CPU website.
3.4.9.1.4. Supported memory formats
Memory requests and limits are specified in megabytes, gigabytes, mebibytes, and gibibytes.
-
To specify memory in megabytes, use the
Msuffix. For example1000M. -
To specify memory in gigabytes, use the
Gsuffix. For example1G. -
To specify memory in mebibytes, use the
Misuffix. For example1000Mi. -
To specify memory in gibibytes, use the
Gisuffix. For example1Gi.
An example of using different memory units
# ...
resources:
requests:
memory: 512Mi
limits:
memory: 2Gi
# ...
For more details about the memory specification and additional supported units, see the Meaning of memory website.
3.4.9.1.5. Additional resources
- For more information about managing computing resources on OpenShift, see Managing Compute Resources for Containers.
3.4.9.2. Configuring resource requests and limits
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
resourcesproperty in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... resources: requests: cpu: "8" memory: 64Gi limits: cpu: "12" memory: 128Gi # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
Additional resources
-
For more information about the schema, see
Resourcesschema reference.
3.4.10. Logging
Logging enables you to diagnose error and performance issues of AMQ Streams. For the logging, various logger implementations are used. Kafka and Zookeeper use log4j logger and Topic Operator, User Operator, and other components use log4j2 logger.
This section provides information about different loggers and describes how to configure log levels.
You can set the log levels by specifying the loggers and their levels directly (inline) or by using a custom (external) config map.
3.4.10.1. Using inline logging setting
Procedure
Edit the YAML file to specify the loggers and their level for the required components. For example:
apiVersion: {KafkaApiVersion} kind: Kafka spec: kafka: # ... logging: type: inline loggers: logger.name: "INFO" # ...In the above example, the log level is set to INFO. You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. For more information about the log levels, see log4j manual.
Create or update the Kafka resource in OpenShift.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.4.10.2. Using external ConfigMap for logging setting
Procedure
Edit the YAML file to specify the name of the
ConfigMapwhich should be used for the required components. For example:apiVersion: {KafkaApiVersion} kind: Kafka spec: kafka: # ... logging: type: external name: customConfigMap # ...Remember to place your custom ConfigMap under
log4j.propertieseventuallylog4j2.propertieskey.Create or update the Kafka resource in OpenShift.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.4.10.3. Loggers
AMQ Streams consists of several components. Each component has its own loggers and is configurable. This section provides information about loggers of various components.
Components and their loggers are listed below.
Kafka
-
kafka.root.logger.level -
log4j.logger.org.I0Itec.zkclient.ZkClient -
log4j.logger.org.apache.zookeeper -
log4j.logger.kafka -
log4j.logger.org.apache.kafka -
log4j.logger.kafka.request.logger -
log4j.logger.kafka.network.Processor -
log4j.logger.kafka.server.KafkaApis -
log4j.logger.kafka.network.RequestChannel$ -
log4j.logger.kafka.controller -
log4j.logger.kafka.log.LogCleaner -
log4j.logger.state.change.logger -
log4j.logger.kafka.authorizer.logger
-
Zookeeper
-
zookeeper.root.logger
-
Kafka Connect and Kafka Connect with Source2Image support
-
connect.root.logger.level -
log4j.logger.org.apache.zookeeper -
log4j.logger.org.I0Itec.zkclient -
log4j.logger.org.reflections
-
Kafka Mirror Maker
-
mirrormaker.root.logger
-
Topic Operator
-
rootLogger.level
-
User Operator
-
rootLogger.level
-
3.4.11. 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.
3.4.11.1. Metrics configuration
Prometheus metrics can be 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/v1alpha1
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/v1alpha1
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:
# ...
3.4.11.2. Configuring Prometheus metrics
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
metricsproperty in theKafka,KafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... metrics: lowercaseOutputName: true # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.4.12. JVM Options
Apache Kafka and Apache Zookeeper are running inside of a Java Virtual Machine (JVM). JVM has many configuration options to optimize the performance for different platforms and architectures. AMQ Streams allows configuring some of these options.
3.4.12.1. JVM configuration
JVM options can be configured using the jvmOptions property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
KafkaConnect.spec -
KafkaConnectS2I.spec
Only a selected subset of available JVM options can be configured. The following options are supported:
-Xms and -Xmx
-Xms configures the minimum initial allocation heap size when the JVM starts. -Xmx configures the maximum heap size.
The units accepted by JVM settings such as -Xmx and -Xms are those accepted by the JDK java binary in the corresponding image. Accordingly, 1g or 1G means 1,073,741,824 bytes, and Gi is not a valid unit suffix. This is in contrast to the units used for memory requests and limits, which follow the OpenShift convention where 1G means 1,000,000,000 bytes, and 1Gi means 1,073,741,824 bytes
The default values used for -Xms and -Xmx depends on whether there is a memory request limit configured for the container:
- If there is a memory limit then the JVM’s minimum and maximum memory will be set to a value corresponding to the limit.
-
If there is no memory limit then the JVM’s minimum memory will be set to
128Mand the JVM’s maximum memory will not be defined. This allows for the JVM’s memory to grow as-needed, which is ideal for single node environments in test and development.
Setting -Xmx explicitly requires some care:
-
The JVM’s overall memory usage will be approximately 4 × the maximum heap, as configured by
-Xmx. -
If
-Xmxis set without also setting an appropriate OpenShift memory limit, it is possible that the container will be killed should the OpenShift node experience memory pressure (from other Pods running on it). -
If
-Xmxis set without also setting an appropriate OpenShift memory request, it is possible that the container will be scheduled to a node with insufficient memory. In this case, the container will not start but crash (immediately if-Xmsis set to-Xmx, or some later time if not).
When setting -Xmx explicitly, it is recommended to:
- set the memory request and the memory limit to the same value,
-
use a memory request that is at least 4.5 × the
-Xmx, -
consider setting
-Xmsto the same value as-Xms.
Containers doing lots of disk I/O (such as Kafka broker containers) will need to leave some memory available for use as operating system page cache. On such containers, the requested memory should be significantly higher than the memory used by the JVM.
Example fragment configuring -Xmx and -Xms
# ... jvmOptions: "-Xmx": "2g" "-Xms": "2g" # ...
In the above example, the JVM will use 2 GiB (=2,147,483,648 bytes) for its heap. Its total memory usage will be approximately 8GiB.
Setting the same value for initial (-Xms) and maximum (-Xmx) heap sizes avoids the JVM having to allocate memory after startup, at the cost of possibly allocating more heap than is really needed. For Kafka and Zookeeper pods such allocation could cause unwanted latency. For Kafka Connect avoiding over allocation may be the most important concern, especially in distributed mode where the effects of over-allocation will be multiplied by the number of consumers.
-server
-server enables the server JVM. This option can be set to true or false.
Example fragment configuring -server
# ... jvmOptions: "-server": true # ...
When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.
-XX
-XX object can be used for configuring advanced runtime options of a JVM. The -server and -XX options are used to configure the KAFKA_JVM_PERFORMANCE_OPTS option of Apache Kafka.
Example showing the use of the -XX object
jvmOptions:
"-XX":
"UseG1GC": true,
"MaxGCPauseMillis": 20,
"InitiatingHeapOccupancyPercent": 35,
"ExplicitGCInvokesConcurrent": true,
"UseParNewGC": false
The example configuration above will result in the following JVM options:
-XX:+UseG1GC -XX:MaxGCPauseMillis=20 -XX:InitiatingHeapOccupancyPercent=35 -XX:+ExplicitGCInvokesConcurrent -XX:-UseParNewGC
When neither of the two options (-server and -XX) is specified, the default Apache Kafka configuration of KAFKA_JVM_PERFORMANCE_OPTS will be used.
3.4.12.2. Configuring JVM options
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
jvmOptionsproperty in theKafka,KafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... jvmOptions: "-Xmx": "8g" "-Xms": "8g" # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.4.13. 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.
3.4.13.1. Container image configurations
Container image which should be used for given components can be specified using the image property in:
-
Kafka.spec.kafka -
Kafka.spec.kafka.tlsSidecar -
Kafka.spec.zookeeper -
Kafka.spec.zookeeper.tlsSidecar -
Kafka.spec.entityOperator.topicOperator -
Kafka.spec.entityOperator.userOperator -
Kafka.spec.entityOperator.tlsSidecar -
KafkaConnect.spec -
KafkaConnectS2I.spec
The image specified in the component-specific custom resource will be used during deployment. If the image field is missing, the image specified in the Cluster Operator configuration will be used. If the image name is not defined in the Cluster Operator configuration, then the default value will be used.
For Kafka brokers:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/kafka:latestcontainer image.
-
Container image specified in the
For Kafka broker TLS sidecar:
-
Container image specified in the
STRIMZI_DEFAULT_TLS_SIDECAR_KAFKA_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/kafka-stunnel:latestcontainer image.
-
Container image specified in the
For Zookeeper nodes:
-
Container image specified in the
STRIMZI_DEFAULT_ZOOKEEPER_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/zookeeper:latestcontainer image.
-
Container image specified in the
For Zookeeper node TLS sidecar:
-
Container image specified in the
STRIMZI_DEFAULT_TLS_SIDECAR_ZOOKEEPER_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/zookeeper-stunnel:latestcontainer image.
-
Container image specified in the
For Topic Operator:
-
Container image specified in the
STRIMZI_DEFAULT_TOPIC_OPERATOR_IMAGEenvironment variable from the Cluster Operator configuration.
-
Container image specified in the
For User Operator:
-
Container image specified in the
STRIMZI_DEFAULT_USER_OPERATOR_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/user-operator:latestcontainer image.
-
Container image specified in the
For Entity Operator TLS sidecar:
-
Container image specified in the
STRIMZI_DEFAULT_TLS_SIDECAR_ENTITY_OPERATOR_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/entity-operator-stunnel:latestcontainer image.
-
Container image specified in the
For Kafka Connect:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_CONNECT_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/kafka-connect:latestcontainer image.
-
Container image specified in the
For Kafka Connect with Source2image support:
-
Container image specified in the
STRIMZI_DEFAULT_KAFKA_CONNECT_S2I_IMAGEenvironment variable from the Cluster Operator configuration. -
strimzi/kafka-connect-s2i:latestcontainer image.
-
Container image specified in the
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 case, you should either copy the AMQ Streams images or build them from source. In case the configured image is not compatible with AMQ Streams images, it might not work properly.
Example of container image configuration
apiVersion: kafka.strimzi.io/v1alpha1
kind: Kafka
metadata:
name: my-cluster
spec:
kafka:
# ...
image: my-org/my-image:latest
# ...
zookeeper:
# ...
3.4.13.2. Configuring container images
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
imageproperty in theKafka,KafkaConnectorKafkaConnectS2Iresource. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: # ... image: my-org/my-image:latest # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.4.14. Configuring pod scheduling
When two application 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.
3.4.14.1. Scheduling pods based on other applications
3.4.14.1.1. 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.
3.4.14.1.2. Affinity
Affinity can be configured using the affinity property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
Kafka.spec.entityOperator -
KafkaConnect.spec -
KafkaConnectS2I.spec
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.
3.4.14.1.3. Configuring pod anti-affinity in Kafka components
Prerequisites
- An OpenShift cluster
- A running Cluster Operator
Procedure
Edit the
affinityproperty in the resource specifying the cluster deployment. Use labels to specify the pods which should not be scheduled on the same nodes. ThetopologyKeyshould be set tokubernetes.io/hostnameto specify that the selected pods should not be scheduled on nodes with the same hostname. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - labelSelector: matchExpressions: - key: application operator: In values: - postgresql - mongodb topologyKey: "kubernetes.io/hostname" # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.4.14.2. Scheduling pods to specific nodes
3.4.14.2.1. 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.
3.4.14.2.2. Affinity
Affinity can be configured using the affinity property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
Kafka.spec.entityOperator -
KafkaConnect.spec -
KafkaConnectS2I.spec
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.
3.4.14.2.3. 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.
On OpenShift this can be done using
oc label:oc label node your-node node-type=fast-networkAlternatively, some of the existing labels might be reused.
Edit the
affinityproperty in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: node-type operator: In values: - fast-network # ... zookeeper: # ...Create or update the resource.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.4.14.3. Using dedicated nodes
3.4.14.3.1. 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.
3.4.14.3.2. Affinity
Affinity can be configured using the affinity property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
Kafka.spec.entityOperator -
KafkaConnect.spec -
KafkaConnectS2I.spec
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.
3.4.14.3.3. Tolerations
Tolerations ca be configured using the tolerations property in following resources:
-
Kafka.spec.kafka -
Kafka.spec.zookeeper -
Kafka.spec.entityOperator -
KafkaConnect.spec -
KafkaConnectS2I.spec
The format of the tolerations property follows the OpenShift specification. For more details, see the Kubernetes taints and tolerations.
3.4.14.3.4. 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
On OpenShift this can be done using
oc adm taint:oc adm taint node your-node dedicated=Kafka:NoScheduleAdditionally, add a label to the selected nodes as well.
On OpenShift this can be done using
oc label:oc label node your-node dedicated=KafkaEdit the
affinityandtolerationsproperties in the resource specifying the cluster deployment. For example:apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka spec: kafka: # ... 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.
On OpenShift this can be done using
oc apply:oc apply -f your-file
3.4.15. List of resources created as part of Kafka Mirror Maker
The following resources will created by the Cluster Operator in the OpenShift cluster:
- <mirror-maker-name>-mirror-maker
- Deployment which is in charge to create the Kafka Mirror Maker pods.
- <mirror-maker-name>-config
- ConfigMap which contains the Kafka Mirror Maker ancillary configuration and is mounted as a volume by the Kafka broker pods.

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