Apache Kafka is a very popular system for publishing and consuming events. Its architecture is fundamentally different from most messaging systems, and combines speed with reliability.
Axon provides an extension dedicated to publishing and receiving event messages from Kafka. The Kafka Extension should be regarded as an alternative approach to distributing events, besides (the default) Axon Server. It's also possible to use the extension to stream events from Kafka to Axon server, or the other way around.
The implementation of the extension can be found here. The shared repository also contains a sample project using the extension.
To use the Kafka Extension components from Axon, make sure the axon-kafka module is available on the classpath. Using the extension requires setting up and configuring Kafka following your project's requirements. How this is achieved is outside of the scope of this reference guide and should be found in Kafka's documentation.
Note that Kafka is a perfectly fine event distribution mechanism, but it is not an event store. Along those lines this extension only provides the means to distributed Axon's events through Kafka. Due to this the extension cannot be used to event source aggregates, as this requires an event store implementation. We recommend using a built-for-purpose event store like Axon Server, or alternatively an RDBMS based (the JPA or JDBC implementations for example).

Publishing Events to Kafka

When Event Messages are published to an Event Bus (or Event Store), they can be forwarded to a Kafka topic using the KafkaPublisher. To achieve this it will utilize a Kafka Producer, retrieved through Axon's ProducerFactory. The KafkaPublisher in turn receives the events to publish from a KafkaEventPublisher.
Since the KafkaEventPublisher is an event message handler in Axon terms, we can provide it to any Event Processor to receive the published events. The choice of event processor which brings differing characteristics for event publication to Kafka:
  • Subscribing Event Processor - publication of messages to Kafka will occur in the same thread (and Unit of Work) which published the events to the event bus. This approach ensures failure to publish to Kafka enforces failure of the initial event publication on the event bus
  • Tracking Event Processor - publication of messages to Kafka is run in a different thread (and Unit of Work) than the one which published the events to the event bus. This approach ensures the event has been published on the event bus regardless of whether publication to Kafka works
When setting up event publication it is also important to take into account which ConfirmationMode is used. The ConfirmationMode influences the process of actually producing an event message on a Kafka topic, but also what kind of Producer the ProducerFactory will instantiate:
  • TRANSACTIONAL - This will require the Producer to start, commit and (in case of failure) rollback the transaction of publishing an event message. Alongside this, it will create a pool of Producer instances in the ProducerFactory to avoid continuous creation of new ones, requiring the user to provide a "transactional id prefix" to uniquely identify every Producer in the pool.
  • WAIT_FOR_ACK - Setting "WAIT_FOR_ACK" as the ConfirmationMode will require the Producer instance to wait for a default of 1 second (configurable on the KafkaPublisher) until the event message publication has been acknowledged. Alongside this, it will create a single, shareable Producer instance from within the ProducerFactory.
  • NONE - This is the default mode, which only ensures a single, shareable Producer instance from within the ProducerFactory.

Configuring Event Publication to Kafka

It is a several step process to configure Event publication to Kafka, which starts with the ProducerFactory. Axon provides the DefaultProducerFactory implementation of the ProducerFactory, which should be instantiated through the provided DefaultProducerFactory.Builder.
The builder has one hard requirement, which is the Producer configuration Map. The Map contains the settings to use for the Kafka Producer client, such as the Kafka instance locations. Please check the Kafka documentation for the possible settings and their values.
public class KafkaEventPublicationConfiguration {
// ...
public ProducerFactory<String, byte[]> producerFactory(Duration closeTimeout,
int producerCacheSize,
Map<String, Object> producerConfiguration,
ConfirmationMode confirmationMode,
String transactionIdPrefix) {
return DefaultProducerFactory.<String, byte[]>builder()
.closeTimeout(closeTimeout) // Defaults to "30" seconds
.producerCacheSize(producerCacheSize) // Defaults to "10"; only used for "TRANSACTIONAL" mode
.configuration(producerConfiguration) // Hard requirement
.confirmationMode(confirmationMode) // Defaults to a Confirmation Mode of "NONE"
.transactionalIdPrefix(transactionIdPrefix) // Hard requirement when in "TRANSACTIONAL" mode
// ...
The second infrastructure component to introduce is the KafkaPublisher, which has a hard requirement on the ProducerFactory. Additionally, this would be the place to define the Kafka topic upon which Axon event messages will be published. Note that the KafkaPublisher needs to be shutDown properly, to ensure all Producer instances are properly closed.
public class KafkaEventPublicationConfiguration {
// ...
public KafkaPublisher<String, byte[]> kafkaPublisher(String topic,
ProducerFactory<String, byte[]> producerFactory,
KafkaMessageConverter<String, byte[]> kafkaMessageConverter,
int publisherAckTimeout) {
return KafkaPublisher.<String, byte[]>builder()
.topic(topic) // Defaults to "Axon.Events"
.producerFactory(producerFactory) // Hard requirement
.messageConverter(kafkaMessageConverter) // Defaults to a "DefaultKafkaMessageConverter"
.publisherAckTimeout(publisherAckTimeout) // Defaults to "1000" milliseconds; only used for "WAIT_FOR_ACK" mode
// ...
Lastly, we need to provide Axon's event messages to the KafkaPublisher. To that end a KafkaEventPublisher should be instantiated through the builder pattern. Remember to add the KafkaEventPublisher to an event processor implementation of your choice. It is recommended to use the KafkaEventPublisher#DEFAULT_PROCESSING_GROUP as the processing group name of the event processor to distinguish it from other event processors.
public class KafkaEventPublicationConfiguration {
// ...
public KafkaEventPublisher<String, byte[]> kafkaEventPublisher(KafkaPublisher<String, byte[]> kafkaPublisher) {
return KafkaEventPublisher.<String, byte[]>builder()
.kafkaPublisher(kafkaPublisher) // Hard requirement
public void registerPublisherToEventProcessor(EventProcessingConfigurer eventProcessingConfigurer,
KafkaEventPublisher<String, byte[]> kafkaEventPublisher) {
String processingGroup = KafkaEventPublisher.DEFAULT_PROCESSING_GROUP;
eventProcessingConfigurer.registerEventHandler(configuration -> kafkaEventPublisher)
clazz -> clazz.isAssignableFrom(KafkaEventPublisher.class)
// Replace `registerSubscribingEventProcessor` for `registerTrackingEventProcessor` to use a tracking processor
// ...

Topic partition publication considerations

Kafka ensures message ordering on a topic-partition level, not on an entire topic. To control events of a certain group to be placed in a dedicated partition, based on aggregate identifier for example, the message converter's SequencingPolicy can be utilized.
The topic-partition pair events have been published in also has impact on event consumption. This extension mitigates any ordering concerns with the streamable solution, by ensuring a Consumer always receives all events of a topic to be able to perform a complete ordering. This guarantee is however not given when using the subscribable event consumption approach. The subscribable stream leaves all the ordering specifics in the hands of Kafka, which means the events should be published on a consistent partition to ensure ordering.

Consuming Events from Kafka

Event messages in an Axon application can be consumed through either a Subscribing or a Tracking Event Processor. Both options are maintained when it comes to consuming events from a Kafka topic, which from a set-up perspective translates to a SubscribableMessageSource or a StreamableKafkaMessageSource respectively, Both will be described in more detail later on, as we first shed light on the general requirements for event consumption in Axon through Kafka.
Both approaches use a similar mechanism to poll events with a Kafka Consumer, which breaks down to a combination of a ConsumerFactory and a Fetcher. The extension provides a DefaultConsumerFactory, whose sole requirement is a Map of configuration properties. The Map contains the settings to use for the Kafka Consumer client, such as the Kafka instance locations. Please check the Kafka documentation for the possible settings and their values.
public class KafkaEventConsumptionConfiguration {
// ...
public ConsumerFactory<String, byte[]> consumerFactory(Map<String, Object> consumerConfiguration) {
return new DefaultConsumerFactory<>(consumerConfiguration);
// ...
It is the Fetcher instance's job to retrieve the actual messages from Kafka by directing a Consumer instance it receives from the message source. You can draft up your own implementation or use the provided AsyncFetcher to this end. The AsyncFetcher doesn't need to be explicitly started, as it will react on the message source starting it. It does need to be shut down, to ensure any thread pool or active connections are properly closed.
public class KafkaEventConsumptionConfiguration {
// ...
public Fetcher<?, ?, ?> fetcher(long timeoutMillis,
ExecutorService executorService) {
return AsyncFetcher.builder()
.pollTimeout(timeoutMillis) // Defaults to "5000" milliseconds
.executorService(executorService) // Defaults to a cached thread pool executor
// ...

Consuming Events with a Subscribable Message Source

Using the SubscribableKafkaMessageSource means you are inclined to use a SubscribingEventProcessor to consume the events in your event handlers.
When using this source, Kafka's idea of pairing Consumer instances into "Consumer Groups" is used. This is strengthened by making the groupId upon source construction a hard requirement. To use a common groupId essentially means that the event-stream-workload can be shared on Kafka's terms, whereas a SubscribingEventProcessor typically works on its own accord regardless of the number of instances. The workload sharing can be achieved by having several application instances with the same groupId or by adjusting the consumer count through the SubscribableKafkaMessageSource's builder. The same benefit holds for resetting an event stream, which in Axon is reserved to the TrackingEventProcessor, but is now opened up through Kafka's own API's.
Although the SubscribableKafkaMessageSource thus provides the niceties the tracking event processor normally provides, it does come with two catches:
  1. 1.
    Axon's approach of the SequencingPolicy to deduce which thread receives which events is entirely lost.
    It is thus dependent on which topic-partition pairs are given to a Consumer for the events your handlers receives.
    From a usage perspective this means event message ordering is no longer guaranteed by Axon.
    It is thus the user's job to ensure events are published in the right topic-partition pair.
  2. 2.
    The API Axon provides for resets is entirely lost,
    since this API can only be correctly triggered through the TrackingEventProcessor#resetTokens operation
Due to the above it is recommended the user is knowledgeable about Kafka's specifics on message consumption.
When it comes to configuring a SubscribableKafkaMessageSource as a message source for a SubscribingEventProcessor, there is one additional requirement beside source creation and registration. The source should only start with polling for events as soon as all interested subscribing event processors have been subscribed to it. To ensure the SubscribableKafkaMessageSource#start() operation is called at the right point in the configuration lifecycle, the KafkaMessageSourceConfigurer should be utilized:
public class KafkaEventConsumptionConfiguration {
// ...
public KafkaMessageSourceConfigurer kafkaMessageSourceConfigurer(Configurer configurer) {
KafkaMessageSourceConfigurer kafkaMessageSourceConfigurer = new KafkaMessageSourceConfigurer();
return kafkaMessageSourceConfigurer;
public SubscribableKafkaMessageSource<String, byte[]> subscribableKafkaMessageSource(List<String> topics,
String groupId,
ConsumerFactory<String, byte[]> consumerFactory,
Fetcher<String, byte[], EventMessage<?>> fetcher,
KafkaMessageConverter<String, byte[]> messageConverter,
int consumerCount,
KafkaMessageSourceConfigurer kafkaMessageSourceConfigurer) {
SubscribableKafkaMessageSource<String, byte[]> subscribableKafkaMessageSource = SubscribableKafkaMessageSource.<String, byte[]>builder()
.topics(topics) // Defaults to a collection of "Axon.Events"
.groupId(groupId) // Hard requirement
.consumerFactory(consumerFactory) // Hard requirement
.fetcher(fetcher) // Hard requirement
.messageConverter(messageConverter) // Defaults to a "DefaultKafkaMessageConverter"
.consumerCount(consumerCount) // Defaults to a single Consumer
// Registering the source is required to tie into the Configurers lifecycle to start the source at the right stage
kafkaMessageSourceConfigurer.registerSubscribableSource(configuration -> subscribableKafkaMessageSource);
return subscribableKafkaMessageSource;
public void configureSubscribableKafkaSource(EventProcessingConfigurer eventProcessingConfigurer,
String processorName,
SubscribableKafkaMessageSource<String, byte[]> subscribableKafkaMessageSource) {
configuration -> subscribableKafkaMessageSource
// ...
The KafkaMessageSourceConfigurer is an Axon ModuleConfiguration which ties in to the start and end lifecycle of the application. It should receive the SubscribableKafkaMessageSource as a source which should start and stop. The KafkaMessageSourceConfigurer instance itself should be registered as a module to the main Configurer.
If only a single subscribing event processor will be subscribed to the kafka message source, SubscribableKafkaMessageSource.Builder#autoStart() can be toggled on. This will start the SubscribableKafkaMessageSource upon the first subscription.

Consuming Events with a Streamable Message Source

Using the StreamableKafkaMessageSource means you are inclined to use a TrackingEventProcessor to consume the events in your event handlers.
Whereas the subscribable kafka message source uses Kafka's idea of sharing the workload through multiple Consumer instances in the same "Consumer Group", the streamable approach doesn't use a consumer group, and assigns all available partitions.
public class KafkaEventConsumptionConfiguration {
// ...
public StreamableKafkaMessageSource<String, byte[]> streamableKafkaMessageSource(List<String> topics,
ConsumerFactory<String, byte[]> consumerFactory,
Fetcher<String, byte[], KafkaEventMessage> fetcher,
KafkaMessageConverter<String, byte[]> messageConverter,
int bufferCapacity) {
return StreamableKafkaMessageSource.<String, byte[]>builder()
.topics(topics) // Defaults to a collection of "Axon.Events"
.consumerFactory(consumerFactory) // Hard requirement
.fetcher(fetcher) // Hard requirement
.messageConverter(messageConverter) // Defaults to a "DefaultKafkaMessageConverter"
() -> new SortedKafkaMessageBuffer<>(bufferCapacity)) // Defaults to a "SortedKafkaMessageBuffer" with a buffer capacity of "1000"
public void configureStreamableKafkaSource(EventProcessingConfigurer eventProcessingConfigurer,
String processorName,
StreamableKafkaMessageSource<String, byte[]> streamableKafkaMessageSource) {
configuration -> streamableKafkaMessageSource
// ...
Note that as with any tracking event processor, the progress on the event stream is stored in a TrackingToken. Using the StreamableKafkaMessageSource means a KafkaTrackingToken containing topic-partition to offset pairs is stored in the TokenStore.

Customizing event message format

In the previous sections the KafkaMessageConverter<K, V> has been shown as a requirement for event production and consumption. The K is the format of the message's key, where the V stand for the message's value. The extension provides a DefaultKafkaMessageConverter which converts an axon EventMessage to a Kafka ProducerRecord, and an ConsumerRecord back into an EventMessage. This DefaultKafkaMessageConverter uses String as the key and byte[] as the value of the message to de-/serialize.
Albeit the default, this implementation allows for some customization, such as how the EventMessage's MetaData is mapped to Kafka headers. This is achieved by adjusting the "header value mapper" in the DefaultKafkaMessageConverter's builder.
The SequencingPolicy can be adjusted to change the behaviour of the record key being used. The default sequencing policy is the SequentialPerAggregatePolicy, which leads to the aggregate identifier of an event being the key of a ProducerRecord and ConsumerRecord.
The format of an event message defines an API between the producer and the consumer of the message. This API may change over time, leading to incompatibility between the event class' structure on the receiving side and the event structure of a message containing the old format. Axon addresses the topic of Event Versioning by introducing Event Upcasters. The DefaultKafkaMessageConverter supports this by provisioning an EventUpcasterChain and run the upcasting process on the MetaData and Payload of individual messages converted from ConsumerRecord before those are passed to the Serializer and converted into Event instances.
Note that the KafkaMessageConverter feeds the upcasters with messages one-by-one, limiting it to one-to-one or one-to-many upcasting only. Upcasters performing a many-to-one or many-to-many operation thus won't be able to operate inside the extension (yet).
Lastly, the Serializer used by the converter can be adjusted. See the Serializer section for more details on this.
public class KafkaMessageConversationConfiguration {
// ...
public KafkaMessageConverter<String, byte[]> kafkaMessageConverter(Serializer serializer,
SequencingPolicy<? super EventMessage<?>> sequencingPolicy,
BiFunction<String, Object, RecordHeader> headerValueMapper) {
return DefaultKafkaMessageConverter.builder()
.serializer(serializer) // Hard requirement
.sequencingPolicy(sequencingPolicy) // Defaults to a "SequentialPerAggregatePolicy"
.headerValueMapper(headerValueMapper) // Defaults to "HeaderUtils#byteMapper()"
// ...
Make sure to use an identical KafkaMessageConverter on both the producing and consuming end, as otherwise exception upon deserialization should be expected.

Configuration in Spring Boot

This extension can be added as a Spring Boot starter dependency to your project using group id org.axonframework.extensions.kafka and artifact id axon-kafka-spring-boot-starter. When using the auto configuration, the following components will be created for you automatically:
Generic Components:
  • A DefaultKafkaMessageConverter using the configured eventSerializer (which defaults to XStreamSerializer).
    Uses a String for the keys and a byte[] for the record's values
Producer Components:
  • A DefaultProducerFactory using a String for the keys and a byte[] for the record's values.
    This creates a ProducerFactory in confirmation mode "NONE", as is specified here.
    The axon.kafka.publisher.confirmation-mode should be adjusted to change this mode,
    where the "TRANSACTIONAL" mode requires axon.kafka.producer.transaction-id-prefix property to be provided.
    If the axon.kafka.producer.transaction-id-prefix is non-null and non-empty,
    it is assumed a "TRANSACTIONAL" confirmation mode is desired
  • A KafkaPublisher.
    Uses a Producer instance from the ProducerFactory to publish events to the configured Kafka topic.
  • A KafkaEventPublisher. Used to provide events to the KafkaPublisher and to assign a processor name
    and processing group called __axon-kafka-event-publishing-group to it. Defaults to a SubscribingEventProcessor.
    If a TrackingEventProcessor is desired, the axon.kafka.producer.event-processor-mode should be set to tracking
Consumer Components:
  • A DefaultConsumerFactory using a String for the keys and a byte[] for the record's values
  • An AsyncFetcher. To adjust the Fetcher's poll timeout, the axon.kafka.fetcher.poll-timeout can be set.
  • A StreamableKafkaMessageSource which can be used for TrackingEventProcessor instances
When using the Spring Boot auto configuration be mindful to provide an application.properties file. The Kafka extension configuration specifics should be placed under prefix axon.kafka. On this level, the bootstrapServers (defaults to localhost:9092) and default-topic used by the producing and consuming side can be defined.
The DefaultProducerFactory and DefaultConsumerFactory expects a Map of configuration properties, which correspond to Kafka Producer and Consumer specific properties respectively. As such, Axon itself passes along these properties without using them directly itself. The application.properties file provides a number of named properties under the axon.kafka.producer. and axon.kafka.consumer. prefixes. If the property you are looking for is not predefined in Axon KafkaProperties file, you are always able to introduce properties in a map style.
# This is a sample properties file to configure the Kafka Extension
bootstrap-servers: localhost:9092
client-id: kafka-axon-example
default-topic: local.event
security.protocol: PLAINTEXT
confirmation-mode: transactional
transaction-id-prefix: kafka-sample
retries: 0
event-processor-mode: subscribing
# For additional unnamed properties, add them to the `properties` map like so
some-key: [some-value]
poll-timeout: 3000
enable-auto-commit: true
auto-commit-interval: 3000
event-processor-mode: tracking
# For additional unnamed properties, add them to the `properties` map like so
some-key: [some-value]
Auto configuring a SubscribableKafkaMessageSource
The auto configured StreamableKafkaMessageSource can be toggled off by setting the axon.kafka.consumer.event-processing-mode to subscribing.
Note that this does not create a SubscribableKafkaMessageSource for you out of the box. To set up a subscribable message, we recommend to read this section.