4. Reference

This part of the reference documentation details the various components that comprise Spring for Apache Kafka. The main chapter covers the core classes to develop a Kafka application with Spring.

4.1 Using Spring for Apache Kafka

4.1.1 Configuring Topics

If you define a KafkaAdmin bean in your application context, it can automatically add topics to the broker. Simply add a NewTopic @Bean for each topic to the application context.

@Bean
public KafkaAdmin admin() {
    Map<String, Object> configs = new HashMap<>();
    configs.put(AdminClientConfig.BOOTSTRAP_SERVERS_CONFIG,
            StringUtils.arrayToCommaDelimitedString(kafkaEmbedded().getBrokerAddresses()));
    return new KafkaAdmin(configs);
}

@Bean
public NewTopic topic1() {
    return new NewTopic("foo", 10, (short) 2);
}

@Bean
public NewTopic topic2() {
    return new NewTopic("bar", 10, (short) 2);
}

By default, if the broker is not available, a message will be logged, but the context will continue to load. You can programmatically invoke the admin’s initialize() method to try again later. If you wish this condition to be considered fatal, set the admin’s fatalIfBrokerNotAvailable property to true and the context will fail to initialize.

[Note]Note

If the broker supports it (1.0.0 or higher), the admin will increase the number of partitions if it is found that an existing topic has fewer partitions than the NewTopic.numPartitions.

For more advanced features, such as assigning partitions to replicas, you can use the AdminClient directly:

@Autowired
private KafkaAdmin admin;

...

    AdminClient client = AdminClient.create(admin.getConfig());
    ...
    client.close();

4.1.2 Sending Messages

KafkaTemplate

Overview

The KafkaTemplate wraps a producer and provides convenience methods to send data to kafka topics.

ListenableFuture<SendResult<K, V>> sendDefault(V data);

ListenableFuture<SendResult<K, V>> sendDefault(K key, V data);

ListenableFuture<SendResult<K, V>> sendDefault(Integer partition, K key, V data);

ListenableFuture<SendResult<K, V>> sendDefault(Integer partition, Long timestamp, K key, V data);

ListenableFuture<SendResult<K, V>> send(String topic, V data);

ListenableFuture<SendResult<K, V>> send(String topic, K key, V data);

ListenableFuture<SendResult<K, V>> send(String topic, Integer partition, K key, V data);

ListenableFuture<SendResult<K, V>> send(String topic, Integer partition, Long timestamp, K key, V data);

ListenableFuture<SendResult<K, V>> send(ProducerRecord<K, V> record);

ListenableFuture<SendResult<K, V>> send(Message<?> message);

Map<MetricName, ? extends Metric> metrics();

List<PartitionInfo> partitionsFor(String topic);

<T> T execute(ProducerCallback<K, V, T> callback);

// Flush the producer.

void flush();

interface ProducerCallback<K, V, T> {

    T doInKafka(Producer<K, V> producer);

}

The sendDefault API requires that a default topic has been provided to the template.

The API which take in a timestamp as a parameter will store this timestamp in the record. The behavior of the user provided timestamp is stored is dependent on the timestamp type configured on the Kafka topic. If the topic is configured to use CREATE_TIME then the user specified timestamp will be recorded or generated if not specified. If the topic is configured to use LOG_APPEND_TIME then the user specified timestamp will be ignored and broker will add in the local broker time.

The metrics and partitionsFor methods simply delegate to the same methods on the underlying Producer. The execute method provides direct access to the underlying Producer.

To use the template, configure a producer factory and provide it in the template’s constructor:

@Bean
public ProducerFactory<Integer, String> producerFactory() {
    return new DefaultKafkaProducerFactory<>(producerConfigs());
}

@Bean
public Map<String, Object> producerConfigs() {
    Map<String, Object> props = new HashMap<>();
    props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
    props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
    props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class);
    // See https://kafka.apache.org/documentation/#producerconfigs for more properties
    return props;
}

@Bean
public KafkaTemplate<Integer, String> kafkaTemplate() {
    return new KafkaTemplate<Integer, String>(producerFactory());
}

The template can also be configured using standard <bean/> definitions.

Then, to use the template, simply invoke one of its methods.

When using the methods with a Message<?> parameter, topic, partition and key information is provided in a message header:

  • KafkaHeaders.TOPIC
  • KafkaHeaders.PARTITION_ID
  • KafkaHeaders.MESSAGE_KEY
  • KafkaHeaders.TIMESTAMP

with the message payload being the data.

Optionally, you can configure the KafkaTemplate with a ProducerListener to get an async callback with the results of the send (success or failure) instead of waiting for the Future to complete.

public interface ProducerListener<K, V> {

    void onSuccess(String topic, Integer partition, K key, V value, RecordMetadata recordMetadata);

    void onError(String topic, Integer partition, K key, V value, Exception exception);

    boolean isInterestedInSuccess();

}

By default, the template is configured with a LoggingProducerListener which logs errors and does nothing when the send is successful.

onSuccess is only called if isInterestedInSuccess returns true.

For convenience, the abstract ProducerListenerAdapter is provided in case you only want to implement one of the methods. It returns false for isInterestedInSuccess.

Notice that the send methods return a ListenableFuture<SendResult>. You can register a callback with the listener to receive the result of the send asynchronously.

ListenableFuture<SendResult<Integer, String>> future = template.send("foo");
future.addCallback(new ListenableFutureCallback<SendResult<Integer, String>>() {

    @Override
    public void onSuccess(SendResult<Integer, String> result) {
        ...
    }

    @Override
    public void onFailure(Throwable ex) {
        ...
    }

});

The SendResult has two properties, a ProducerRecord and RecordMetadata; refer to the Kafka API documentation for information about those objects.

If you wish to block the sending thread, to await the result, you can invoke the future’s get() method. You may wish to invoke flush() before waiting or, for convenience, the template has a constructor with an autoFlush parameter which will cause the template to flush() on each send. Note, however that flushing will likely significantly reduce performance.

Examples

Non Blocking (Async). 

public void sendToKafka(final MyOutputData data) {
    final ProducerRecord<String, String> record = createRecord(data);

    ListenableFuture<SendResult<Integer, String>> future = template.send(record);
    future.addCallback(new ListenableFutureCallback<SendResult<Integer, String>>() {

        @Override
        public void onSuccess(SendResult<Integer, String> result) {
            handleSuccess(data);
        }

        @Override
        public void onFailure(Throwable ex) {
            handleFailure(data, record, ex);
        }

    });
}

Blocking (Sync). 

public void sendToKafka(final MyOutputData data) {
    final ProducerRecord<String, String> record = createRecord(data);

    try {
        template.send(record).get(10, TimeUnit.SECONDS);
        handleSuccess(data);
    }
    catch (ExecutionException e) {
        handleFailure(data, record, e.getCause());
    }
    catch (TimeoutException | InterruptedException e) {
        handleFailure(data, record, e);
    }
}

Transactions

The 0.11.0.0 client library added support for transactions. Spring for Apache Kafka adds support in several ways.

  • KafkaTransactionManager - used with normal Spring transaction support (@Transactional, TransactionTemplate etc).
  • Transactional KafkaMessageListenerContainer
  • Local transactions with KafkaTemplate

Transactions are enabled by providing the DefaultKafkaProducerFactory with a transactionIdPrefix. In that case, instead of managing a single shared Producer, the factory maintains a cache of transactional producers. When the user close() s a producer, it is returned to the cache for reuse instead of actually being closed. The transactional.id property of each producer is transactionIdPrefix + n, where n starts with 0 and is incremented for each new producer.

KafkaTransactionManager

The KafkaTransactionManager is an implementation of Spring Framework’s PlatformTransactionManager; it is provided with a reference to the producer factory in its constructor. If you provide a custom producer factory, it must support transactions - see ProducerFactory.transactionCapable().

You can use the KafkaTransactionManager with normal Spring transaction support (@Transactional, TransactionTemplate etc). If a transaction is active, any KafkaTemplate operations performed within the scope of the transaction will use the transaction’s Producer. The manager will commit or rollback the transaction depending on success or failure. The KafkaTemplate must be configured to use the same ProducerFactory as the transaction manager.

Transactional Listener Container

You can provide a listener container with a KafkaTransactionManager instance; when so configured, the container will start a transaction before invoking the listener. If the listener successfully processes the record (or records when using a BatchMessageListener), the container will send the offset(s) to the transaction using producer.sendOffsetsToTransaction()), before the transaction manager commits the transaction. If the listener throws an exception, the transaction is rolled back and the consumer is repositioned so that the rolled-back records will be retrieved on the next poll.

Transaction Synchronization

If you need to synchronize a Kafka transaction with some other transaction; simply configure the listener container with the appropriate transaction manager (one that supports synchronization, such as the DataSourceTransactionManager). Any operations performed on a transactional KafkaTemplate from the listener will participate in a single transaction. The Kafka transaction will be committed (or rolled back) immediately after the controlling transaction. Before exiting the listener, you should invoke one of the template’s sendOffsetsToTransaction methods (unless you use a ChainedKafkaTransactionManager - see below). For convenience, the listener container binds its consumer group id to the thread so, generally, you can use the first method:

void sendOffsetsToTransaction(Map<TopicPartition, OffsetAndMetadata> offsets);

void sendOffsetsToTransaction(Map<TopicPartition, OffsetAndMetadata> offsets, String consumerGroupId);

For example:

@Bean
KafkaMessageListenerContainer container(ConsumerFactory<String, String> cf,
            final KafkaTemplate template) {
    ContainerProperties props = new ContainerProperties("foo");
    props.setGroupId("group");
    props.setTransactionManager(new SomeOtherTransactionManager());
    ...
    props.setMessageListener((MessageListener<String, String>) m -> {
        template.send("foo", "bar");
        template.send("baz", "qux");
        template.sendOffsetsToTransaction(
            Collections.singletonMap(new TopicPartition(m.topic(), m.partition()),
                new OffsetAndMetadata(m.offset() + 1)));
    });
    return new KafkaMessageListenerContainer<>(cf, props);
}
[Note]Note

The offset to be committed is one greater than the offset of the record(s) processed by the listener.

[Important]Important

This should only be called when using transaction synchronization. When a listener container is configured to use a KafkaTransactionManager, it will take care of sending the offsets to the transaction.

ChainedKafkaTransactionManager

The ChainedKafkaTransactionManager was introduced in version 2.1.3. This is a subclass of ChainedTransactionManager that can have exactly one KafkaTransactionManager. Since it is a KafkaAwareTransactionManager, the container can send the offsets to the transaction in the same way as when the container is configured with a simple KafkaTransactionManager. This provides another mechanism for synchronizing transactions without having to send the offsets to the transaction in the listener code. Chain your transaction managers in the desired order and provide the ChainedTransactionManager in the ContainerProperties.

KafkaTemplate Local Transactions

You can use the KafkaTemplate to execute a series of operations within a local transaction.

boolean result = template.executeInTransaction(t -> {
    t.sendDefault("foo", "bar");
    t.sendDefault("baz", "qux");
    return true;
});

The argument in the callback is the template itself (this). If the callback exits normally, the transaction is committed; if an exception is thrown, the transaction is rolled-back.

[Note]Note

If there is a KafkaTransactionManager (or synchronized) transaction in process, it will not be used; a new "nested" transaction is used.

ReplyingKafkaTemplate

Version 2.1.3 introduced a subclass of KafkaTemplate to provide request/reply semantics; the class is named ReplyingKafkaTemplate and has one method (in addition to those in the superclass):

RequestReplyFuture<K, V, R> sendAndReceive(ProducerRecord<K, V> record);

The result is a ListenableFuture that will asynchronously be populated with the result (or an exception, for a timeout). The result also has a property sendFuture which is the result of calling KafkaTemplate.send(); you can use this future to determine the result of the send operation.

The following Spring Boot application is an example of how to use the feature:

@SpringBootApplication
public class KRequestingApplication {

    public static void main(String[] args) {
        SpringApplication.run(KRequestingApplication.class, args).close();
    }

    @Bean
    public ApplicationRunner runner(ReplyingKafkaTemplate<String, String, String> template) {
        return args -> {
            ProducerRecord<String, String> record = new ProducerRecord<>("kRequests", "foo");
            record.headers().add(new RecordHeader(KafkaHeaders.REPLY_TOPIC, "kReplies".getBytes()));
            RequestReplyFuture<String, String, String> replyFuture = template.sendAndReceive(record);
            SendResult<String, String> sendResult = replyFuture.getSendFuture().get();
            System.out.println("Sent ok: " + sendResult.getRecordMetadata());
            ConsumerRecord<String, String> consumerRecord = replyFuture.get();
            System.out.println("Return value: " + consumerRecord.value());
        };
    }

    @Bean
    public ReplyingKafkaTemplate<String, String, String> kafkaTemplate(
            ProducerFactory<String, String> pf,
            KafkaMessageListenerContainer<String, String> replyContainer) {
        return new ReplyingKafkaTemplate<>(pf, replyContainer);
    }

    @Bean
    public KafkaMessageListenerContainer<String, String> replyContainer(
            ConsumerFactory<String, String> cf) {
        ContainerProperties containerProperties = new ContainerProperties("kReplies");
        return new KafkaMessageListenerContainer<>(cf, containerProperties);
    }

    @Bean
    public NewTopic kRequests() {
        return new NewTopic("kRequests", 10, (short) 2);
    }

    @Bean
    public NewTopic kReplies() {
        return new NewTopic("kReplies", 10, (short) 2);
    }

}

In addition to the reply topic header set by user code, the template sets a header KafkaHeaders.CORRELATION_ID which must be echoed back by the server side.

In this case, simple @KafkaListener application responds:

@SpringBootApplication
public class KReplyingApplication {

    public static void main(String[] args) {
        SpringApplication.run(KReplyingApplication.class, args);
    }

    @KafkaListener(id="server", topics = "kRequests")
    @SendTo // use default replyTo expression
    public String listen(String in) {
        System.out.println("Server received: " + in);
        return in.toUpperCase();
    }

    @Bean
    public NewTopic kRequests() {
        return new NewTopic("kRequests", 10, (short) 2);
    }

    @Bean // not required if Jackson is on the classpath
    public MessagingMessageConverter simpleMapperConverter() {
        MessagingMessageConverter messagingMessageConverter = new MessagingMessageConverter();
        messagingMessageConverter.setHeaderMapper(new SimpleKafkaHeaderMapper());
        return messagingMessageConverter;
    }

}

The @KafkaListener infrastructure echoes the correlation id and determines the reply topic.

See the section called “Forwarding Listener Results using @SendTo” for more information about sending replies; in this case we use the default header KafKaHeaders.REPLY_TOPIC to indicate which topic the reply goes to.

[Important]Important

If you have multiple client instances, each will need a dedicated reply topic for each instance. An alternative is to set the KafkaHeaders.REPLY_PARTITION and use a dedicated partition for each instance; the Header contains a 4 byte int (Big-endian). The server must use this header to route the reply to the correct topic (@KafkaListener does this). In this case, though, the reply container must not use Kafka’s group management feature and must be configured to listen on a fixed partition (using a TopicPartitionInitialOffset in its ContainerProperties constructor).

[Note]Note

The DefaultKafkaHeaderMapper requires Jackson to be on the classpath (for the @KafkaListener). If it is not available, the message converter has no header mapper, so you must configure a MessagingMessageConverter with a SimpleKafkaHeaderMapper as shown above.

4.1.3 Receiving Messages

Messages can be received by configuring a MessageListenerContainer and providing a Message Listener, or by using the @KafkaListener annotation.

Message Listeners

When using a Message Listener Container you must provide a listener to receive data. There are currently eight supported interfaces for message listeners:

public interface MessageListener<K, V> { 1

    void onMessage(ConsumerRecord<K, V> data);

}

public interface AcknowledgingMessageListener<K, V> { 2

    void onMessage(ConsumerRecord<K, V> data, Acknowledgment acknowledgment);

}

public interface ConsumerAwareMessageListener<K, V> extends MessageListener<K, V> { 3

    void onMessage(ConsumerRecord<K, V> data, Consumer<?, ?> consumer);

}

public interface AcknowledgingConsumerAwareMessageListener<K, V> extends MessageListener<K, V> { 4

    void onMessage(ConsumerRecord<K, V> data, Acknowledgment acknowledgment, Consumer<?, ?> consumer);

}

public interface BatchMessageListener<K, V> { 5

    void onMessage(List<ConsumerRecord<K, V>> data);

}

public interface BatchAcknowledgingMessageListener<K, V> { 6

    void onMessage(List<ConsumerRecord<K, V>> data, Acknowledgment acknowledgment);

}

public interface BatchConsumerAwareMessageListener<K, V> extends BatchMessageListener<K, V> { 7

    void onMessage(List<ConsumerRecord<K, V>> data, Consumer<?, ?> consumer);

}

public interface BatchAcknowledgingConsumerAwareMessageListener<K, V> extends BatchMessageListener<K, V> { 8

    void onMessage(List<ConsumerRecord<K, V>> data, Acknowledgment acknowledgment, Consumer<?, ?> consumer);

}

1

Use this for processing individual ConsumerRecord s received from the kafka consumer poll() operation when using auto-commit, or one of the container-managed commit methods.

2

Use this for processing individual ConsumerRecord s received from the kafka consumer poll() operation when using one of the manual commit methods.

3

Use this for processing individual ConsumerRecord s received from the kafka consumer poll() operation when using auto-commit, or one of the container-managed commit methods. Access to the Consumer object is provided.

4

Use this for processing individual ConsumerRecord s received from the kafka consumer poll() operation when using one of the manual commit methods. Access to the Consumer object is provided.

5

Use this for processing all ConsumerRecord s received from the kafka consumer poll() operation when using auto-commit, or one of the container-managed commit methods. AckMode.RECORD is not supported when using this interface since the listener is given the complete batch.

6

Use this for processing all ConsumerRecord s received from the kafka consumer poll() operation when using one of the manual commit methods.

7

Use this for processing all ConsumerRecord s received from the kafka consumer poll() operation when using auto-commit, or one of the container-managed commit methods. AckMode.RECORD is not supported when using this interface since the listener is given the complete batch. Access to the Consumer object is provided.

8

Use this for processing all ConsumerRecord s received from the kafka consumer poll() operation when using one of the manual commit methods. Access to the Consumer object is provided.

[Important]Important

The Consumer object is not thread-safe; you must only invoke its methods on the thread that calls the listener.

Message Listener Containers

Two MessageListenerContainer implementations are provided:

  • KafkaMessageListenerContainer
  • ConcurrentMessageListenerContainer

The KafkaMessageListenerContainer receives all message from all topics/partitions on a single thread. The ConcurrentMessageListenerContainer delegates to 1 or more KafkaMessageListenerContainer s to provide multi-threaded consumption.

KafkaMessageListenerContainer

The following constructors are available.

public KafkaMessageListenerContainer(ConsumerFactory<K, V> consumerFactory,
                    ContainerProperties containerProperties)

public KafkaMessageListenerContainer(ConsumerFactory<K, V> consumerFactory,
                    ContainerProperties containerProperties,
                    TopicPartitionInitialOffset... topicPartitions)

Each takes a ConsumerFactory and information about topics and partitions, as well as other configuration in a ContainerProperties object. The second constructor is used by the ConcurrentMessageListenerContainer (see below) to distribute TopicPartitionInitialOffset across the consumer instances. ContainerProperties has the following constructors:

public ContainerProperties(TopicPartitionInitialOffset... topicPartitions)

public ContainerProperties(String... topics)

public ContainerProperties(Pattern topicPattern)

The first takes an array of TopicPartitionInitialOffset arguments to explicitly instruct the container which partitions to use (using the consumer assign() method), and with an optional initial offset: a positive value is an absolute offset by default; a negative value is relative to the current last offset within a partition by default. A constructor for TopicPartitionInitialOffset is provided that takes an additional boolean argument. If this is true, the initial offsets (positive or negative) are relative to the current position for this consumer. The offsets are applied when the container is started. The second takes an array of topics and Kafka allocates the partitions based on the group.id property - distributing partitions across the group. The third uses a regex Pattern to select the topics.

To assign a MessageListener to a container, use the ContainerProps.setMessageListener method when creating the Container:

ContainerProperties containerProps = new ContainerProperties("topic1", "topic2");
containerProps.setMessageListener(new MessageListener<Integer, String>() {
    ...
});
DefaultKafkaConsumerFactory<Integer, String> cf =
                        new DefaultKafkaConsumerFactory<Integer, String>(consumerProps());
KafkaMessageListenerContainer<Integer, String> container =
                        new KafkaMessageListenerContainer<>(cf, containerProps);
return container;

Refer to the JavaDocs for ContainerProperties for more information about the various properties that can be set.

Since version 2.1.1, a new property logContainerConfig is available; when true, and INFO logging is enabled, each listener container will write a log message summarizing its configuration properties.

By default, logging of topic offset commits is performed with the DEBUG logging level. Starting with version 2.1.2, there is a new property in ContainerProperties called commitLogLevel which allows you to specify the log level for these messages. For example, to change the log level to INFO, use containerProperties.setCommitLogLevel(LogIfLevelEnabled.Level.INFO);.

ConcurrentMessageListenerContainer

The single constructor is similar to the first KafkaListenerContainer constructor:

public ConcurrentMessageListenerContainer(ConsumerFactory<K, V> consumerFactory,
                            ContainerProperties containerProperties)

It also has a property concurrency, e.g. container.setConcurrency(3) will create 3 KafkaMessageListenerContainer s.

For the first constructor, kafka will distribute the partitions across the consumers. For the second constructor, the ConcurrentMessageListenerContainer distributes the TopicPartition s across the delegate KafkaMessageListenerContainer s.

If, say, 6 TopicPartition s are provided and the concurrency is 3; each container will get 2 partitions. For 5 TopicPartition s, 2 containers will get 2 partitions and the third will get 1. If the concurrency is greater than the number of TopicPartitions, the concurrency will be adjusted down such that each container will get one partition.

[Note]Note

The client.id property (if set) will be appended with -n where n is the consumer instance according to the concurrency. This is required to provide unique names for MBeans when JMX is enabled.

Starting with version 1.3, the MessageListenerContainer provides an access to the metrics of the underlying KafkaConsumer. In case of ConcurrentMessageListenerContainer the metrics() method returns the metrics for all the target KafkaMessageListenerContainer instances. The metrics are grouped into the Map<MetricName, ? extends Metric> by the client-id provided for the underlying KafkaConsumer.

Committing Offsets

Several options are provided for committing offsets. If the enable.auto.commit consumer property is true, kafka will auto-commit the offsets according to its configuration. If it is false, the containers support the following AckMode s.

The consumer poll() method will return one or more ConsumerRecords; the MessageListener is called for each record; the following describes the action taken by the container for each AckMode :

  • RECORD - commit the offset when the listener returns after processing the record.
  • BATCH - commit the offset when all the records returned by the poll() have been processed.
  • TIME - commit the offset when all the records returned by the poll() have been processed as long as the ackTime since the last commit has been exceeded.
  • COUNT - commit the offset when all the records returned by the poll() have been processed as long as ackCount records have been received since the last commit.
  • COUNT_TIME - similar to TIME and COUNT but the commit is performed if either condition is true.
  • MANUAL - the message listener is responsible to acknowledge() the Acknowledgment; after which, the same semantics as BATCH are applied.
  • MANUAL_IMMEDIATE - commit the offset immediately when the Acknowledgment.acknowledge() method is called by the listener.
[Note]Note

MANUAL, and MANUAL_IMMEDIATE require the listener to be an AcknowledgingMessageListener or a BatchAcknowledgingMessageListener; see Message Listeners.

The commitSync() or commitAsync() method on the consumer is used, depending on the syncCommits container property.

The Acknowledgment has this method:

public interface Acknowledgment {

    void acknowledge();

}

This gives the listener control over when offsets are committed.

Listener Container Auto Startup

The listener containers implement SmartLifecycle and autoStartup is true by default; the containers are started in a late phase (Integer.MAX-VALUE - 100). Other components that implement SmartLifecycle, that handle data from listeners, should be started in an earlier phase. The - 100 leaves room for later phases to enable components to be auto-started after the containers.

@KafkaListener Annotation

The @KafkaListener annotation provides a mechanism for simple POJO listeners:

public class Listener {

    @KafkaListener(id = "foo", topics = "myTopic", clientIdPrefix = "myClientId")
    public void listen(String data) {
        ...
    }

}

This mechanism requires an @EnableKafka annotation on one of your @Configuration classes and a listener container factory, which is used to configure the underlying ConcurrentMessageListenerContainer: by default, a bean with name kafkaListenerContainerFactory is expected.

@Configuration
@EnableKafka
public class KafkaConfig {

    @Bean
    KafkaListenerContainerFactory<ConcurrentMessageListenerContainer<Integer, String>>
                        kafkaListenerContainerFactory() {
        ConcurrentKafkaListenerContainerFactory<Integer, String> factory =
                                new ConcurrentKafkaListenerContainerFactory<>();
        factory.setConsumerFactory(consumerFactory());
        factory.setConcurrency(3);
        factory.getContainerProperties().setPollTimeout(3000);
        return factory;
    }

    @Bean
    public ConsumerFactory<Integer, String> consumerFactory() {
        return new DefaultKafkaConsumerFactory<>(consumerConfigs());
    }

    @Bean
    public Map<String, Object> consumerConfigs() {
        Map<String, Object> props = new HashMap<>();
        props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, embeddedKafka.getBrokersAsString());
        ...
        return props;
    }
}

Notice that to set container properties, you must use the getContainerProperties() method on the factory. It is used as a template for the actual properties injected into the container.

Starting with version 2.1.1, it is now possible to set the client.id property for consumers created by the annotation. The clientIdPrefix is suffixed with -n where n is an integer representing the container number when using concurrency.

You can also configure POJO listeners with explicit topics and partitions (and, optionally, their initial offsets):

@KafkaListener(id = "bar", topicPartitions =
        { @TopicPartition(topic = "topic1", partitions = { "0", "1" }),
          @TopicPartition(topic = "topic2", partitions = "0",
             partitionOffsets = @PartitionOffset(partition = "1", initialOffset = "100"))
        })
public void listen(ConsumerRecord<?, ?> record) {
    ...
}

Each partition can be specified in the partitions or partitionOffsets attribute, but not both.

When using manual AckMode, the listener can also be provided with the Acknowledgment; this example also shows how to use a different container factory.

@KafkaListener(id = "baz", topics = "myTopic",
          containerFactory = "kafkaManualAckListenerContainerFactory")
public void listen(String data, Acknowledgment ack) {
    ...
    ack.acknowledge();
}

Finally, metadata about the message is available from message headers, the following header names can be used for retrieving the headers of the message:

  • KafkaHeaders.RECEIVED_MESSAGE_KEY
  • KafkaHeaders.RECEIVED_TOPIC
  • KafkaHeaders.RECEIVED_PARTITION_ID
  • KafkaHeaders.RECEIVED_TIMESTAMP
  • KafkaHeaders.TIMESTAMP_TYPE
@KafkaListener(id = "qux", topicPattern = "myTopic1")
public void listen(@Payload String foo,
        @Header(KafkaHeaders.RECEIVED_MESSAGE_KEY) Integer key,
        @Header(KafkaHeaders.RECEIVED_PARTITION_ID) int partition,
        @Header(KafkaHeaders.RECEIVED_TOPIC) String topic,
        @Header(KafkaHeaders.RECEIVED_TIMESTAMP) long ts
        ) {
    ...
}

Starting with version 1.1, @KafkaListener methods can be configured to receive the entire batch of consumer records received from the consumer poll. To configure the listener container factory to create batch listeners, set the batchListener property:

@Bean
public KafkaListenerContainerFactory<?> batchFactory() {
    ConcurrentKafkaListenerContainerFactory<Integer, String> factory =
            new ConcurrentKafkaListenerContainerFactory<>();
    factory.setConsumerFactory(consumerFactory());
    factory.setBatchListener(true);  // <<<<<<<<<<<<<<<<<<<<<<<<<
    return factory;
}

To receive a simple list of payloads:

@KafkaListener(id = "list", topics = "myTopic", containerFactory = "batchFactory")
public void listen(List<String> list) {
    ...
}

The topic, partition, offset etc are available in headers which parallel the payloads:

@KafkaListener(id = "list", topics = "myTopic", containerFactory = "batchFactory")
public void listen(List<String> list,
        @Header(KafkaHeaders.RECEIVED_MESSAGE_KEY) List<Integer> keys,
        @Header(KafkaHeaders.RECEIVED_PARTITION_ID) List<Integer> partitions,
        @Header(KafkaHeaders.RECEIVED_TOPIC) List<String> topics,
        @Header(KafkaHeaders.OFFSET) List<Long> offsets) {
    ...
}

Alternatively you can receive a List of Message<?> objects with each offset, etc in each message, but it must be the only parameter (aside from an optional Acknowledgment when using manual commits) defined on the method:

@KafkaListener(id = "listMsg", topics = "myTopic", containerFactory = "batchFactory")
public void listen14(List<Message<?>> list) {
    ...
}

@KafkaListener(id = "listMsgAck", topics = "myTopic", containerFactory = "batchFactory")
public void listen15(List<Message<?>> list, Acknowledgment ack) {
    ...
}

No conversion is performed on the payloads in this case.

If the BatchMessagingMessageConverter is configured with a RecordMessageConverter, you can also add a generic type to the Message parameter and the payloads will be converted. See the section called “Payload Conversion with Batch Listeners” for more information.

You can also receive a list of ConsumerRecord<?, ?> objects but it must be the only parameter (aside from an optional Acknowledgment when using manual commits) defined on the method:

@KafkaListener(id = "listCRs", topics = "myTopic", containerFactory = "batchFactory")
public void listen(List<ConsumerRecord<Integer, String>> list) {
    ...
}

@KafkaListener(id = "listCRsAck", topics = "myTopic", containerFactory = "batchFactory")
public void listen(List<ConsumerRecord<Integer, String>> list, Acknowledgment ack) {
    ...
}

Starting with version 2.0, the id attribute (if present) is used as the Kafka group.id property, overriding the configured property in the consumer factory, if present. You can also set groupId explicitly, or set idIsGroup to false, to restore the previous behavior of using the consumer factory group.id.

You can use property placeholders or SpEL expressions within annotation properties, for example…​

@KafkaListener(topics = "${some.property}")

@KafkaListener(topics = "#{someBean.someProperty}",
    groupId = "#{someBean.someProperty}.group")

Starting with version 2.1.2, the SpEL expressions support a special token __listener which is a pseudo bean name which represents the current bean instance within which this annotation exists.

For example, given…​

@Bean
public Listener listener1() {
    return new Listener("topic1");
}

@Bean
public Listener listener2() {
    return new Listener("topic2");
}

…​we can use…​

public class Listener {

    private final String topic;

    public Listener(String topic) {
        this.topic = topic;
    }

    @KafkaListener(topics = "#{__listener.topic}",
        groupId = "#{__listener.topic}.group")
    public void listen(...) {
        ...
    }

    public String getTopic() {
        return this.topic;
    }

}

If, in the unlikely event that you have an actual bean called __listener, you can change the expression token using the beanRef attribute…​

@KafkaListener(beanRef = "__x", topics = "#{__x.topic}",
    groupId = "#{__x.topic}.group")

Container Thread Naming

Listener containers currently use two task executors, one to invoke the consumer and another which will be used to invoke the listener, when the kafka consumer property enable.auto.commit is false. You can provide custom executors by setting the consumerExecutor and listenerExecutor properties of the container’s ContainerProperties. When using pooled executors, be sure that enough threads are available to handle the concurrency across all the containers in which they are used. When using the ConcurrentMessageListenerContainer, a thread from each is used for each consumer (concurrency).

If you don’t provide a consumer executor, a SimpleAsyncTaskExecutor is used; this executor creates threads with names <beanName>-C-1 (consumer thread). For the ConcurrentMessageListenerContainer, the <beanName> part of the thread name becomes <beanName>-m, where m represents the consumer instance. n increments each time the container is started. So, with a bean name of container, threads in this container will be named container-0-C-1, container-1-C-1 etc., after the container is started the first time; container-0-C-2, container-1-C-2 etc., after a stop/start.

@KafkaListener on a Class

When using @KafkaListener at the class-level, you specify @KafkaHandler at the method level. When messages are delivered, the converted message payload type is used to determine which method to call.

@KafkaListener(id = "multi", topics = "myTopic")
static class MultiListenerBean {

    @KafkaHandler
    public void listen(String foo) {
        ...
    }

    @KafkaHandler
    public void listen(Integer bar) {
        ...
    }

    @KafkaHandler(isDefault = true`)
    public void listenDefault(Object object) {
        ...
    }

}

Starting with version 2.1.3, a @KafkaHandler method can be designated as the default method which is invoked if there is no match on other methods. At most one method can be so designated. When using @KafkaHandler methods, the payload must have already been converted to the domain object (so the match can be performed). Use a custom deserializer, the JsonDeserializer or the (String|Bytes)JsonMessageConverter with its TypePrecedence set to TYPE_ID - see Section 4.1.5, “Serialization/Deserialization and Message Conversion” for more information.

@KafkaListener Lifecycle Management

The listener containers created for @KafkaListener annotations are not beans in the application context. Instead, they are registered with an infrastructure bean of type KafkaListenerEndpointRegistry. This bean manages the containers' lifecycles; it will auto-start any containers that have autoStartup set to true. All containers created by all container factories must be in the same phase - see the section called “Listener Container Auto Startup” for more information. You can manage the lifecycle programmatically using the registry; starting/stopping the registry will start/stop all the registered containers. Or, you can get a reference to an individual container using its id attribute; you can set autoStartup on the annotation, which will override the default setting configured into the container factory.

@Autowired
private KafkaListenerEndpointRegistry registry;

...

@KafkaListener(id = "myContainer", topics = "myTopic", autoStartup = "false")
public void listen(...) { ... }

...

    registry.getListenerContainer("myContainer").start();

Rebalance Listeners

ContainerProperties has a property consumerRebalanceListener which takes an implementation of the Kafka client’s ConsumerRebalanceListener interface. If this property is not provided, the container will configure a simple logging listener that logs rebalance events under the INFO level. The framework also adds a sub-interface ConsumerAwareRebalanceListener:

public interface ConsumerAwareRebalanceListener extends ConsumerRebalanceListener {

    void onPartitionsRevokedBeforeCommit(Consumer<?, ?> consumer, Collection<TopicPartition> partitions);

    void onPartitionsRevokedAfterCommit(Consumer<?, ?> consumer, Collection<TopicPartition> partitions);

    void onPartitionsAssigned(Consumer<?, ?> consumer, Collection<TopicPartition> partitions);

}

Notice that there are two callbacks when partitions are revoked: the first is called immediately; the second is called after any pending offsets are committed. This is useful if you wish to maintain offsets in some external repository; for example:

containerProperties.setConsumerRebalanceListener(new ConsumerAwareRebalanceListener() {

    @Override
    public void onPartitionsRevokedBeforeCommit(Consumer<?, ?> consumer, Collection<TopicPartition> partitions) {
        // acknowledge any pending Acknowledgments (if using manual acks)
    }

    @Override
    public void onPartitionsRevokedAfterCommit(Consumer<?, ?> consumer, Collection<TopicPartition> partitions) {
        // ...
            store(consumer.position(partition));
        // ...
    }

    @Override
    public void onPartitionsAssigned(Collection<TopicPartition> partitions) {
        // ...
            consumer.seek(partition, offsetTracker.getOffset() + 1);
        // ...
    }
});

Forwarding Listener Results using @SendTo

Starting with version 2.0, if you also annotate a @KafkaListener with a @SendTo annotation and the method invocation returns a result, the result will be forwared to the topic specified by the @SendTo.

The @SendTo value can have several forms:

  • @SendTo("someTopic") routes to the literal topic
  • @SendTo("#{someExpression}") routes to the topic determined by evaluating the expression once during application context initialization.
  • @SendTo("!{someExpression}") routes to the topic determined by evaluating the expression at runtime. The #root object for the evaluation has 3 properties:
  • request - the inbound ConsumerRecord (or ConsumerRecords object for a batch listener))
  • source - the org.springframework.messaging.Message<?> converted from the request.
  • result - the method return result.
  • @SendTo (no properties) - this is treated as !{source.headers['kafka_replyTopic']} (since version 2.1.3).

The result of the expression evaluation must be a String representing the topic name.

@KafkaListener(topics = "annotated21")
@SendTo("!{request.value()}") // runtime SpEL
public String replyingListener(String in) {
    ...
}

@KafkaListener(topics = "annotated22")
@SendTo("#{myBean.replyTopic}") // config time SpEL
public Collection<String> replyingBatchListener(List<String> in) {
    ...
}

@KafkaListener(topics = "annotated23", errorHandler = "replyErrorHandler")
@SendTo("annotated23reply") // static reply topic definition
public String replyingListenerWithErrorHandler(String in) {
    ...
}
...
@KafkaListener(topics = "annotated25")
@SendTo("annotated25reply1")
public class MultiListenerSendTo {

    @KafkaHandler
    public String foo(String in) {
        ...
    }

    @KafkaHandler
    @SendTo("!{'annotated25reply2'}")
    public String bar(@Payload(required = false) KafkaNull nul,
            @Header(KafkaHeaders.RECEIVED_MESSAGE_KEY) int key) {
        ...
    }

}

When using @SendTo, the ConcurrentKafkaListenerContainerFactory must be configured with a KafkaTemplate in its replyTemplate property, to perform the send. NOTE: unless you are using request/reply semantics only the simple send(topic, value) method is used, so you may wish to create a subclass to generate the partition and/or key:

@Bean
public KafkaTemplate<String, String> myReplyingTemplate() {
    return new KafkaTemplate<Integer, String>(producerFactory()) {

        @Override
        public ListenableFuture<SendResult<String, String>> send(String topic, String data) {
            return super.send(topic, partitionForData(data), keyForData(data), data);
        }

        ...

    };
}

When using request/reply semantics, the target partition can be requested by the sender.

[Note]Note

You can annotate a @KafkaListener method with @SendTo even if no result is returned. This is to allow the configuration of an errorHandler that can forward information about a failed message delivery to some topic.

@KafkaListener(id = "voidListenerWithReplyingErrorHandler", topics = "someTopic",
        errorHandler = "voidSendToErrorHandler")
@SendTo("failures")
public void voidListenerWithReplyingErrorHandler(String in) {
    throw new RuntimeException("fail");
}

@Bean
public KafkaListenerErrorHandler voidSendToErrorHandler() {
    return (m, e) -> {
        return ... // some information about the failure and input data
    };
}

See Section 4.1.8, “Handling Exceptions” for more information.

Filtering Messages

In certain scenarios, such as rebalancing, a message may be redelivered that has already been processed. The framework cannot know whether such a message has been processed or not, that is an application-level function. This is known as the Idempotent Receiver pattern and Spring Integration provides an implementation thereof.

The Spring for Apache Kafka project also provides some assistance by means of the FilteringMessageListenerAdapter class, which can wrap your MessageListener. This class takes an implementation of RecordFilterStrategy where you implement the filter method to signal that a message is a duplicate and should be discarded.

A FilteringAcknowledgingMessageListenerAdapter is also provided for wrapping an AcknowledgingMessageListener. This has an additional property ackDiscarded which indicates whether the adapter should acknowledge the discarded record; it is true by default.

When using @KafkaListener, set the RecordFilterStrategy (and optionally ackDiscarded) on the container factory and the listener will be wrapped in the appropriate filtering adapter.

In addition, a FilteringBatchMessageListenerAdapter is provided, for when using a batch message listener.

Retrying Deliveries

If your listener throws an exception, the default behavior is to invoke the ErrorHandler, if configured, or logged otherwise.

[Note]Note

Two error handler interfaces are provided ErrorHandler and BatchErrorHandler; the appropriate type must be configured to match the Message Listener.

To retry deliveries, a convenient listener adapter RetryingMessageListenerAdapter is provided.

It can be configured with a RetryTemplate and RecoveryCallback<Void> - see the spring-retry project for information about these components. If a recovery callback is not provided, the exception is thrown to the container after retries are exhausted. In that case, the ErrorHandler will be invoked, if configured, or logged otherwise.

When using @KafkaListener, set the RetryTemplate (and optionally recoveryCallback) on the container factory and the listener will be wrapped in the appropriate retrying adapter.

The contents of the RetryContext passed into the RecoveryCallback will depend on the type of listener. The context will always have an attribute record which is the record for which the failure occurred. If your listener is acknowledging and/or consumer aware, additional attributes acknowledgment and/or consumer will be available. For convenience, the RetryingAcknowledgingMessageListenerAdapter provides static constants for these keys. See its javadocs for more information.

A retry adapter is not provided for any of the batch message listeners because the framework has no knowledge of where, in a batch, the failure occurred. Users wishing retry capabilities, when using a batch listener, are advised to use a RetryTemplate within the listener itself.

Stateful Retry

It is important to understand that the retry discussed above suspends the consumer thread (if a BackOffPolicy is used); there are no calls to Consumer.poll() during the retries. Kafka has two properties to determine consumer health; the session.timeout.ms is used to determine if the consumer is active. Since version 0.10.1.0 heartbeats are sent on a background thread so a slow consumer no longer affects that. max.poll.interval.ms (default 5 minutes) is used to determine if a consumer appears to be hung (taking too long to process records from the last poll). If the time between poll() s exceeds this, the broker will revoke the assigned partitions and perform a rebalance. For lengthy retry sequences, with back off, this can easily happen.

Since version 2.1.3, you can avoid this problem by using stateful retry in conjunction with a SeekToCurrentErrorHandler. In this case, each delivery attempt will throw the exception back to the container and the error handler will re-seek the unprocessed offsets and the same message will be redelivered by the next poll(). This avoids the problem of exceeding the max.poll.interval.ms property (as long as an individual delay between attempts does not exceed it). So, when using an ExponentialBackOffPolicy, it’s important to ensure that the maxInterval is rather less than the max.poll.interval.ms property. To enable stateful retry, use the RetryingMessageListenerAdapter constructor that takes a stateful boolean argument (set it to true). When configuring using the listener container factory (for @KafkaListener s), set the factory’s statefulRetry property to true.

Detecting Idle and Non-Responsive Consumers

While efficient, one problem with asynchronous consumers is detecting when they are idle - users might want to take some action if no messages arrive for some period of time.

You can configure the listener container to publish a ListenerContainerIdleEvent when some time passes with no message delivery. While the container is idle, an event will be published every idleEventInterval milliseconds.

To configure this feature, set the idleEventInterval on the container:

@Bean
public KafKaMessageListenerContainer(ConnectionFactory connectionFactory) {
    ContainerProperties containerProps = new ContainerProperties("topic1", "topic2");
    ...
    containerProps.setIdleEventInterval(60000L);
    ...
    KafKaMessageListenerContainer<String, String> container = new KafKaMessageListenerContainer<>(...);
    return container;
}

Or, for a @KafkaListener…​

@Bean
public ConcurrentKafkaListenerContainerFactory kafkaListenerContainerFactory() {
    ConcurrentKafkaListenerContainerFactory<String, String> factory =
                new ConcurrentKafkaListenerContainerFactory<>();
    ...
    factory.getContainerProperties().setIdleEventInterval(60000L);
    ...
    return factory;
}

In each of these cases, an event will be published once per minute while the container is idle.

In addition, if the broker is unreachable (at the time of writing), the consumer poll() method does not exit, so no messages are received, and idle events can’t be generated. To solve this issue, the container will publish a NonResponsiveConsumerEvent if a poll does not return within 3x the pollInterval property. By default, this check is performed once every 30 seconds in each container. You can modify the behavior by setting the monitorInterval and noPollThreshold properties in the ContainerProperties when configuring the listener container. Receiving such an event will allow you to stop the container(s), thus waking the consumer so it can terminate.

Event Consumption

You can capture these events by implementing ApplicationListener - either a general listener, or one narrowed to only receive this specific event. You can also use @EventListener, introduced in Spring Framework 4.2.

The following example combines the @KafkaListener and @EventListener into a single class. It’s important to understand that the application listener will get events for all containers so you may need to check the listener id if you want to take specific action based on which container is idle. You can also use the @EventListener condition for this purpose.

The events have 5 properties:

  • source - the listener container instance
  • id - the listener id (or container bean name)
  • idleTime - the time the container had been idle when the event was published
  • topicPartitions - the topics/partitions that the container was assigned at the time the event was generated
  • consumer - a reference to the kafka Consumer object; for example, if the consumer was previously pause() d, it can be resume() d when the event is received.

Starting with version 2.1.5, the idle event has a boolean property paused which indicates whether the consumer is currently paused; see Section 4.1.4, “Pausing/Resuming Listener Containers” for more information.

The event is normally published on the consumer thread, so it is safe to interact with the Consumer object.

public class Listener {

    @KafkaListener(id = "qux", topics = "annotated")
    public void listen4(@Payload String foo, Acknowledgment ack) {
        ...
    }

    @EventListener(condition = "event.listenerId.startsWith('qux-')")
    public void eventHandler(ListenerContainerIdleEvent event) {
        ...
    }

}
[Important]Important

Event listeners will see events for all containers; so, in the example above, we narrow the events received based on the listener ID. Since containers created for the @KafkaListener support concurrency, the actual containers are named id-n where the n is a unique value for each instance to support the concurrency. Hence we use startsWith in the condition.

[Caution]Caution

If you wish to use the idle event to stop the lister container, you should not call container.stop() on the thread that calls the listener - it will cause delays and unnecessary log messages. Instead, you should hand off the event to a different thread that can then stop the container. Also, you should not stop() the container instance in the event if it is a child container, you should stop the concurrent container instead.

Current Positions when Idle

Note that you can obtain the current positions when idle is detected by implementing ConsumerSeekAware in your listener; see onIdleContainer() in `the section called “Seeking to a Specific Offset”.

Topic/Partition Initial Offset

There are several ways to set the initial offset for a partition.

When manually assigning partitions, simply set the initial offset (if desired) in the configured TopicPartitionInitialOffset arguments (see the section called “Message Listener Containers”). You can also seek to a specific offset at any time.

When using group management where the broker assigns partitions:

  • For a new group.id, the initial offset is determined by the auto.offset.reset consumer property (earliest or latest).
  • For an existing group id, the initial offset is the current offset for that group id. You can, however, seek to a specific offset during initialization (or at any time thereafter).

Seeking to a Specific Offset

In order to seek, your listener must implement ConsumerSeekAware which has the following methods:

void registerSeekCallback(ConsumerSeekCallback callback);

void onPartitionsAssigned(Map<TopicPartition, Long> assignments, ConsumerSeekCallback callback);

void onIdleContainer(Map<TopicPartition, Long> assignments, ConsumerSeekCallback callback);

The first is called when the container is started; this callback should be used when seeking at some arbitrary time after initialization. You should save a reference to the callback; if you are using the same listener in multiple containers (or in a ConcurrentMessageListenerContainer) you should store the callback in a ThreadLocal or some other structure keyed by the listener Thread.

When using group management, the second method is called when assignments change. You can use this method, for example, for setting initial offsets for the partitions, by calling the callback; you must use the callback argument, not the one passed into registerSeekCallback. This method will never be called if you explicitly assign partitions yourself; use the TopicPartitionInitialOffset in that case.

The callback has these methods:

void seek(String topic, int partition, long offset);

void seekToBeginning(String topic, int partition);

void seekToEnd(String topic, int partition);

You can also perform seek operations from onIdleContainer() when an idle container is detected; see the section called “Detecting Idle and Non-Responsive Consumers” for how to enable idle container detection.

To arbitrarily seek at runtime, use the callback reference from the registerSeekCallback for the appropriate thread.

4.1.4 Pausing/Resuming Listener Containers

Version 2.1.3 added pause() and resume() methods to listener containers. Previously, you could pause a consumer within a ConsumerAwareMessageListener and resume it by listening for ListenerContainerIdleEvent s, which provide access to the Consumer object. While you could pause a consumer in an idle container via an event listener, in some cases this was not thread-safe since there is no guarantee that the event listener is invoked on the consumer thread. To safely pause/resume consumers, you should use the methods on the listener containers. pause() takes effect just before the next poll(); resume takes effect, just after the current poll() returns. When a container is paused, it continues to poll() the consumer, avoiding a rebalance if group management is being used, but will not retrieve any records; refer to the Kafka documentation for more information.

Starting with version 2.1.5, you can call isPauseRequested() to see if pause() has been called. However, the consumers might not have actually paused yet; isConsumerPaused() will return true if all Consumer s have actually paused.

In addition, also since 2.1.5, ConsumerPausedEvent s and ConsumerResumedEvent s are published with the container as the source property and the TopicPatition s involved in the partitions s property.

4.1.5 Serialization/Deserialization and Message Conversion

Overview

Apache Kafka provides a high-level API for serializing/deserializing record values as well as their keys. It is present with the org.apache.kafka.common.serialization.Serializer<T> and org.apache.kafka.common.serialization.Deserializer<T> abstractions with some built-in implementations. Meanwhile we can specify simple (de)serializer classes using Producer and/or Consumer configuration properties, e.g.:

props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, IntegerDeserializer.class);
props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
...
props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, IntegerSerializer.class);
props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class);

for more complex or particular cases, the KafkaConsumer, and therefore KafkaProducer, provides overloaded constructors to accept (De)Serializer instances for keys and/or values, respectively.

To meet this API, the DefaultKafkaProducerFactory and DefaultKafkaConsumerFactory also provide properties to allow to inject a custom (De)Serializer to target Producer/Consumer.

For this purpose, Spring for Apache Kafka also provides JsonSerializer/JsonDeserializer implementations based on the Jackson JSON object mapper. The JsonSerializer is quite simple and just allows writing any Java object as a JSON byte[], the JsonDeserializer requires an additional Class<?> targetType argument to allow the deserialization of a consumed byte[] to the proper target object.

JsonDeserializer<Bar> barDeserializer = new JsonDeserializer<>(Bar.class);

Both JsonSerializer and JsonDeserializer can be customized with an ObjectMapper. You can also extend them to implement some particular configuration logic in the configure(Map<String, ?> configs, boolean isKey) method.

Starting with version 2.1, type information can be conveyed in record Headers, allowing the handling of multiple types. In addition, the serializer/deserializer can be configured using Kafka properties.

  • JsonSerializer.ADD_TYPE_INFO_HEADERS (default true); set to false to disable this feature on the JsonSerializer (sets the addTypeInfo property).
  • JsonDeserializer.KEY_DEFAULT_TYPE; fallback type for deserialization of keys if no header information is present.
  • JsonDeserializer.VALUE_DEFAULT_TYPE; fallback type for deserialization of values if no header information is present.
  • JsonDeserializer.TRUSTED_PACKAGES (default java.util, java.lang); comma-delimited list of package patterns allowed for deserialization; * means deserialize all.

Although the Serializer/Deserializer API is quite simple and flexible from the low-level Kafka Consumer and Producer perspective, you might need more flexibility at the Spring Messaging level, either when using @KafkaListener or Spring Integration. To easily convert to/from org.springframework.messaging.Message, Spring for Apache Kafka provides a MessageConverter abstraction with the MessagingMessageConverter implementation and its StringJsonMessageConverter and BytesJsonMessageConverter customization. The MessageConverter can be injected into KafkaTemplate instance directly and via AbstractKafkaListenerContainerFactory bean definition for the @KafkaListener.containerFactory() property:

@Bean
public KafkaListenerContainerFactory<?> kafkaJsonListenerContainerFactory() {
    ConcurrentKafkaListenerContainerFactory<Integer, String> factory =
        new ConcurrentKafkaListenerContainerFactory<>();
    factory.setConsumerFactory(consumerFactory());
    factory.setMessageConverter(new StringJsonMessageConverter());
    return factory;
}
...
@KafkaListener(topics = "jsonData",
                containerFactory = "kafkaJsonListenerContainerFactory")
public void jsonListener(Foo foo) {
...
}

When using a @KafkaListener, the parameter type is provided to the message converter to assist with the conversion.

[Note]Note

This type inference can only be achieved when the @KafkaListener annotation is declared at the method level. With a class-level @KafkaListener, the payload type is used to select which @KafkaHandler method to invoke so it must already have been converted before the method can be chosen.

[Note]Note

When using the StringJsonMessageConverter, you should use a StringDeserializer in the kafka consumer configuration and StringSerializer in the kafka producer configuration, when using Spring Integration or the KafkaTemplate.send(Message<?> message) method. When using the BytesJsonMessageConverter, you should use a BytesDeserializer in the kafka consumer configuration and BytesSerializer in the kafka producer configuration, when using Spring Integration or the KafkaTemplate.send(Message<?> message) method. Generally, the BytesJsonMessageConverter is more efficient because it avoids a String to/from byte[] conversion.

Payload Conversion with Batch Listeners

Starting with version 1.3.2, you can also use a StringJsonMessageConverter or BytesJsonMessageConverter within a BatchMessagingMessageConverter for converting batch messages, when using a batch listener container factory. See Section 4.1.5, “Serialization/Deserialization and Message Conversion” for more information.

By default, the type for the conversion is inferred from the listener argument. If you configure the (Bytes|String)JsonMessageConverter with a DefaultJackson2TypeMapper that has its TypePrecedence set to TYPE_ID (instead of the default INFERRED), then the converter will use type information in headers (if present) instead. This allows, for example, listener methods to be declared with interfaces instead of concrete classes. Also, the type converter supports mapping so the deserialization can be to a different type than the source (as long as the data is compatible). This is also useful when using class-level @KafkaListener s where the payload must have already been converted, to determine which method to invoke.

@Bean
public KafkaListenerContainerFactory<?> kafkaListenerContainerFactory() {
    ConcurrentKafkaListenerContainerFactory<Integer, String> factory =
            new ConcurrentKafkaListenerContainerFactory<>();
    factory.setConsumerFactory(consumerFactory());
    factory.setBatchListener(true);
    factory.setMessageConverter(new BatchMessagingMessageConverter(converter()));
    return factory;
}

@Bean
public StringJsonMessageConverter converter() {
    return new StringJsonMessageConverter();
}

Note that for this to work, the method signature for the conversion target must be a container object with a single generic parameter type, such as:

@KafkaListener(topics = "blc1")
public void listen(List<Foo> foos, @Header(KafkaHeaders.OFFSET) List<Long> offsets) {
    ...
}

Notice that you can still access the batch headers too.

If the batch converter has a record converter that supports it, you can also receive a list of messages where the payloads are converted according to the generic type:

@KafkaListener(topics = "blc3", groupId = "blc3")
public void listen1(List<Message<Foo>> fooMessages) {
    ...
}

ConversionService Customization

Starting with version 2.1.1, the org.springframework.core.convert.ConversionService used by the default o.s.messaging.handler.annotation.support.MessageHandlerMethodFactory to resolve parameters for the invocation of a listener method is supplied with all beans implementing any of the following interfaces:

  • org.springframework.core.convert.converter.Converter
  • org.springframework.core.convert.converter.GenericConverter
  • org.springframework.format.Formatter

This allows you to further customize listener deserialization without changing the default configuration for ConsumerFactory and KafkaListenerContainerFactory.

[Important]Important

Setting a custom MessageHandlerMethodFactory on the KafkaListenerEndpointRegistrar through a KafkaListenerConfigurer bean will disable this feature.

4.1.6 Message Headers

The 0.11.0.0 client introduced support for headers in messages. Spring for Apache Kafka version 2.0 now supports mapping these headers to/from spring-messaging MessageHeaders.

[Note]Note

Previous versions mapped ConsumerRecord and ProducerRecord to spring-messaging Message<?> where the value property is mapped to/from the payload and other properties (topic, partition, etc) were mapped to headers. This is still the case but additional, arbitrary, headers can now be mapped.

Apache Kafka headers have a simple API:

public interface Header {

    String key();

    byte[] value();

}

The KafkaHeaderMapper strategy is provided to map header entries between Kafka Headers and MessageHeaders:

public interface KafkaHeaderMapper {

    void fromHeaders(MessageHeaders headers, Headers target);

    void toHeaders(Headers source, Map<String, Object> target);

}

The DefaultKafkaHeaderMapper maps the key to the MessageHeaders header name and, in order to support rich header types, for outbound messages, JSON conversion is performed. A "special" header, with key, spring_json_header_types contains a JSON map of <key>:<type>. This header is used on the inbound side to provide appropriate conversion of each header value to the original type.

On the inbound side, all Kafka Header s are mapped to MessageHeaders. On the outbound side, by default, all MessageHeaders are mapped except id, timestamp, and the headers that map to ConsumerRecord properties.

You can specify which headers are to be mapped for outbound messages, by providing patterns to the mapper.

public DefaultKafkaHeaderMapper() {
    ...
}

public DefaultKafkaHeaderMapper(ObjectMapper objectMapper) {
    ...
}

public DefaultKafkaHeaderMapper(String... patterns) {
    ...
}

public DefaultKafkaHeaderMapper(ObjectMapper objectMapper, String... patterns) {
    ...
}

The first constructor will use a default Jackson ObjectMapper and map most headers, as discussed above. The second constructor will use the provided Jackson ObjectMapper and map most headers, as discussed above. The third constructor will use a default Jackson ObjectMapper and map headers according to the provided patterns. The third constructor will use the provided Jackson ObjectMapper and map headers according to the provided patterns.

Patterns are rather simple and can contain either a leading or trailing wildcard *, or both, e.g. *.foo.*. Patterns can be negated with a leading !. The first pattern that matches a header name wins (positive or negative).

When providing your own patterns, it is recommended to include !id and !timestamp since these headers are read-only on the inbound side.

[Important]Important

By default, the mapper will only deserialize classes in java.lang and java.util. You can trust other (or all) packages by adding trusted packages using the addTrustedPackages method. If you are receiving messages from untrusted sources, you may wish to add just those packages that you trust. To trust all packages use mapper.addTrustedPackages("*").

The DefaultKafkaHeaderMapper is used in the MessagingMessageConverter and BatchMessagingMessageConverter by default, as long as Jackson is on the class path.

With the batch converter, the converted headers are available in the KafkaHeaders.BATCH_CONVERTED_HEADERS as a List<Map<String, Object>> where the map in a position of the list corresponds to the data position in the payload.

If the converter has no converter (either because Jackson is not present, or it is explicitly set to null), the headers from the consumer record are provided unconverted in the KafkaHeaders.NATIVE_HEADERS header (a Headers object, or a List<Headers> in the case of the batch converter, where the position in the list corresponds to the data position in the payload).

[Important]Important

The Jackson ObjectMapper (even if provided) will be enhanced to support deserializing org.springframework.util.MimeType objects, often used in the spring-messaging contentType header. If you don’t wish your mapper to be enhanced in this way, for some reason, you should subclass the DefaultKafkaHeaderMapper and override getObjectMapper() to return your mapper.

4.1.7 Null Payloads and Log Compaction Tombstone Records

When using Log Compaction, it is possible to send and receive messages with null payloads which identifies the deletion of a key.

It is also possible to receive null values for other reasons - such as a Deserializer that might return null when it can’t deserialize a value.

To send a null payload using the KafkaTemplate simply pass null into the value argument of the send() methods. One exception to this is the send(Message<?> message) variant. Since spring-messaging Message<?> cannot have a null payload, a special payload type KafkaNull is used and the framework will send null. For convenience, the static KafkaNull.INSTANCE is provided.

When using a message listener container, the received ConsumerRecord will have a null value().

To configure the @KafkaListener to handle null payloads, you must use the @Payload annotation with required = false; if it’s a tombstone message for a compacted log, you will usually also need the key so your application can determine which key was "deleted":

@KafkaListener(id = "deletableListener", topics = "myTopic")
public void listen(@Payload(required = false) String value, @Header(KafkaHeaders.RECEIVED_MESSAGE_KEY) String key) {
    // value == null represents key deletion
}

When using a class-level @KafkaListener with multiple @KafkaHandler methods, some additional configuration is needed - a @KafkaHandler method with a KafkaNull payload:

@KafkaListener(id = "multi", topics = "myTopic")
static class MultiListenerBean {

    @KafkaHandler
    public void listen(String foo) {
        ...
    }

    @KafkaHandler
    public void listen(Integer bar) {
        ...
    }

    @KafkaHandler
    public void delete(@Payload(required = false) KafkaNull nul, @Header(KafkaHeaders.RECEIVED_MESSAGE_KEY) int key) {
        ...
    }

}

Note that the argument will be null not a KafkaNull.

4.1.8 Handling Exceptions

Listener Error Handlers

Starting with version 2.0, the @KafkaListener annotation has a new attribute: errorHandler.

This attribute is not configured by default.

Use the errorHandler to provide the bean name of a KafkaListenerErrorHandler implementation. This functional interface has one method:

@FunctionalInterface
public interface KafkaListenerErrorHandler {

    Object handleError(Message<?> message, ListenerExecutionFailedException exception) throws Exception;

}

As you can see, you have access to the spring-messaging Message<?> object produced by the message converter and the exception that was thrown by the listener, wrapped in a ListenerExecutionFailedException. The error handler can throw the original or a new exception which will be thrown to the container. Anything returned by the error handler is ignored.

It has a sub-interface ConsumerAwareListenerErrorHandler that has access to the consumer object, via the method:

Object handleError(Message<?> message, ListenerExecutionFailedException exception, Consumer<?, ?> consumer);

If your error handler implements this interface you can, for example, adjust the offsets accordingly. For example, to reset the offset to replay the failed message, you could do something like the following; note however, these are simplistic implementations and you would probably want more checking in the error handler.

@Bean
public ConsumerAwareListenerErrorHandler listen3ErrorHandler() {
    return (m, e, c) -> {
        this.listen3Exception = e;
        MessageHeaders headers = m.getHeaders();
        c.seek(new org.apache.kafka.common.TopicPartition(
                headers.get(KafkaHeaders.RECEIVED_TOPIC, String.class),
                headers.get(KafkaHeaders.RECEIVED_PARTITION_ID, Integer.class)),
                headers.get(KafkaHeaders.OFFSET, Long.class));
        return null;
    };
}

And for a batch listener:

@Bean
public ConsumerAwareListenerErrorHandler listen10ErrorHandler() {
    return (m, e, c) -> {
        this.listen10Exception = e;
        MessageHeaders headers = m.getHeaders();
        List<String> topics = headers.get(KafkaHeaders.RECEIVED_TOPIC, List.class);
        List<Integer> partitions = headers.get(KafkaHeaders.RECEIVED_PARTITION_ID, List.class);
        List<Long> offsets = headers.get(KafkaHeaders.OFFSET, List.class);
        Map<TopicPartition, Long> offsetsToReset = new HashMap<>();
        for (int i = 0; i < topics.size(); i++) {
            int index = i;
            offsetsToReset.compute(new TopicPartition(topics.get(i), partitions.get(i)),
                    (k, v) -> v == null ? offsets.get(index) : Math.min(v, offsets.get(index)));
        }
        offsetsToReset.forEach((k, v) -> c.seek(k, v));
        return null;
    };
}

This resets each topic/partition in the batch to the lowest offset in the batch.

Container Error Handlers

You can specify a global error handler used for all listeners in the container factory.

@Bean
public KafkaListenerContainerFactory<ConcurrentMessageListenerContainer<Integer, String>>
        kafkaListenerContainerFactory() {
    ConcurrentKafkaListenerContainerFactory<Integer, String> factory =
            new ConcurrentKafkaListenerContainerFactory<>();
    ...
    factory.getContainerProperties().setErrorHandler(myErrorHandler);
    ...
    return factory;
}

or

@Bean
public KafkaListenerContainerFactory<ConcurrentMessageListenerContainer<Integer, String>>
        kafkaListenerContainerFactory() {
    ConcurrentKafkaListenerContainerFactory<Integer, String> factory =
            new ConcurrentKafkaListenerContainerFactory<>();
    ...
    factory.getContainerProperties().setBatchErrorHandler(myBatchErrorHandler);
    ...
    return factory;
}

By default, if an annotated listener method throws an exception, it is thrown to the container, and the message will be handled according to the container configuration.

Consumer-Aware Container Error Handlers

The container-level error handlers (ErrorHandler and BatchErrorHandler) have sub-interfaces ConsumerAwareErrorHandler and ConsumerAwareBatchErrorHandler with method signatures:

void handle(Exception thrownException, ConsumerRecord<?, ?> data, Consumer<?, ?> consumer);

void handle(Exception thrownException, ConsumerRecords<?, ?> data, Consumer<?, ?> consumer);

respectively.

Similar to the @KafkaListener error handlers, you can reset the offsets as needed based on the data that failed.

[Note]Note

Unlike the listener-level error handlers, however, you should set the container property ackOnError to false when making adjustments; otherwise any pending acks will be applied after your repositioning.

Seek To Current Container Error Handlers

If an ErrorHandler implements RemainingRecordsErrorHandler, the error handler is provided with the failed record and any unprocessed records retrieved by the previous poll(). Those records will not be passed to the listener after the handler exits.

@FunctionalInterface
public interface RemainingRecordsErrorHandler extends ConsumerAwareErrorHandler {

    void handle(Exception thrownException, List<ConsumerRecord<?, ?>> records, Consumer<?, ?> consumer);

}

This allows implementations to seek all unprocessed topic/partitions so the current record (and the others remaining) will be retrieved by the next poll. The SeekToCurrentErrorHandler does exactly this.

The container will commit any pending offset commits before calling the error handler.

To configure the listener container with this handler, add it to the ContainerProperties.

For example, with the @KafkaListener container factory:

@Bean
public ConcurrentKafkaListenerContainerFactory<String, String> kafkaListenerContainerFactory() {
    ConcurrentKafkaListenerContainerFactory<String, String> factory = new ConcurrentKafkaListenerContainerFactory();
    factory.setConsumerFactory(consumerFactory());
    factory.getContainerProperties().setAckOnError(false);
    factory.getContainerProperties().setErrorHandler(new SeekToCurrentErrorHandler());
    factory.getContainerProperties().setAckMode(AckMode.RECORD);
    return factory;
}

As an example; if the poll returns 6 records (2 from each partition 0, 1, 2) and the listener throws an exception on the fourth record, the container will have acknowledged the first 3 by committing their offsets. The SeekToCurrentErrorHandler will seek to offset 1 for partition 1 and offset 0 for partition 2. The next poll() will return the 3 unprocessed records.

If the AckMode was BATCH, the container commits the offsets for the first 2 partitions before calling the error handler.

The SeekToCurrentBatchErrorHandler seeks each partition to the first record in each partition in the batch so the whole batch is replayed.

After seeking, an exception wrapping the ListenerExecutionFailedException is thrown. This is to cause the transaction to roll back (if transactions are enabled).

Container Stopping Error Handlers

The ContainerStoppingErrorHandler (used with record listeners) will stop the container if the listener throws an exception. When the AckMode is RECORD, offsets for already processed records will be committed. When the AckMode is any manual, offsets for already acknowledged records will be committed. When the AckMode is BATCH, the entire batch will be replayed when the container is restarted, unless transactions are enabled in which case only the unprocessed records will be re-fetched.

The ContainerStoppingBatchErrorHandler (used with batch listeners) will stop the container and the entire batch will be replayed when the container is restarted.

After the container stops, an exception wrapping the ListenerExecutionFailedException is thrown. This is to cause the transaction to roll back (if transactions are enabled).

After Rollback Processor

When using transactions, if the listener container throws an exception (and an error handler, if present, throws an exception), the transaction is rolled back. By default, any unprocessed records (including the failed record) will be re-fetched on the next poll. This is achieved by performing seek operations in the DefaultAfterRollbackProcessor. With a batch listener, the entire batch of records will be reprocessed (the container has no knowledge of which record in the batch failed). To modify this behavior, configure the listener container with a custom AfterRollbackProcessor. For example, with a record-based listener, you might want to keep track of the failed record and give up after some number of attempts - perhaps by publishing it to a dead-letter topic.

4.1.9 Kerberos

Starting with version 2.0 a KafkaJaasLoginModuleInitializer class has been added to assist with Kerberos configuration. Simply add this bean, with the desired configuration, to your application context.

@Bean
public KafkaJaasLoginModuleInitializer jaasConfig() throws IOException {
    KafkaJaasLoginModuleInitializer jaasConfig = new KafkaJaasLoginModuleInitializer();
    jaasConfig.setControlFlag("REQUIRED");
    Map<String, String> options = new HashMap<>();
    options.put("useKeyTab", "true");
    options.put("storeKey", "true");
    options.put("keyTab", "/etc/security/keytabs/kafka_client.keytab");
    options.put("principal", "[email protected]");
    jaasConfig.setOptions(options);
    return jaasConfig;
}

4.2 Kafka Streams Support

4.2.1 Introduction

Starting with version 1.1.4, Spring for Apache Kafka provides first class support for Kafka Streams. For using it from a Spring application, the kafka-streams jar must be present on classpath. It is an optional dependency of the spring-kafka project and isn’t downloaded transitively.

4.2.2 Basics

The reference Apache Kafka Streams documentation suggests this way of using the API:

// Use the builders to define the actual processing topology, e.g. to specify
// from which input topics to read, which stream operations (filter, map, etc.)
// should be called, and so on.

StreamsBuilder builder = ...;  // when using the Kafka Streams DSL

// Use the configuration to tell your application where the Kafka cluster is,
// which serializers/deserializers to use by default, to specify security settings,
// and so on.
StreamsConfig config = ...;

KafkaStreams streams = new KafkaStreams(builder, config);

// Start the Kafka Streams instance
streams.start();

// Stop the Kafka Streams instance
streams.close();

So, we have two main components: StreamsBuilder with an API to build KStream (or KTable) instances and KafkaStreams to manage their lifecycle. Note: all KStream instances exposed to a KafkaStreams instance by a single StreamsBuilder will be started and stopped at the same time, even if they have a fully different logic. In other words all our streams defined by a StreamsBuilder are tied with a single lifecycle control. Once a KafkaStreams instance has been closed via streams.close() it cannot be restarted, and a new KafkaStreams instance to restart stream processing must be created instead.

4.2.3 Spring Management

To simplify the usage of Kafka Streams from the Spring application context perspective and utilize the lifecycle management via container, the Spring for Apache Kafka introduces StreamsBuilderFactoryBean. This is an AbstractFactoryBean implementation to expose a StreamsBuilder singleton instance as a bean:

@Bean
public FactoryBean<StreamsBuilderFactoryBean> myKStreamBuilder(StreamsConfig streamsConfig) {
    return new StreamsBuilderFactoryBean(streamsConfig);
}

The StreamsBuilderFactoryBean also implements SmartLifecycle to manage lifecycle of an internal KafkaStreams instance. Similar to the Kafka Streams API, the KStream instances must be defined before starting the KafkaStreams, and that also applies for the Spring API for Kafka Streams. Therefore we have to declare KStream s on the StreamsBuilder before the application context is refreshed, when we use default autoStartup = true on the StreamsBuilderFactoryBean. For example, KStream can be just as a regular bean definition, meanwhile the Kafka Streams API is used without any impacts:

@Bean
public KStream<?, ?> kStream(StreamsBuilder kStreamBuilder) {
    KStream<Integer, String> stream = kStreamBuilder.stream(STREAMING_TOPIC1);
    // Fluent KStream API
    return stream;
}

If you would like to control lifecycle manually (e.g. stop and start by some condition), you can reference the StreamsBuilderFactoryBean bean directly using factory bean (&) prefix. Since StreamsBuilderFactoryBean utilize its internal KafkaStreams instance, it is safe to stop and restart it again - a new KafkaStreams is created on each start(). Also consider using different StreamsBuilderFactoryBean s, if you would like to control lifecycles for KStream instances separately.

You also can specify KafkaStreams.StateListener, Thread.UncaughtExceptionHandler and StateRestoreListener options on the StreamsBuilderFactoryBean which are delegated to the internal KafkaStreams instance. Also apart from setting those options indirectly on StreamsBuilderFactoryBean, starting with version 2.1.5, a KafkaStreamsCustomizer callback interface can be used to configure inner KafkaStreams instance. Note that KafkaStreamsCustomizer will override the options which are given via StreamsBuilderFactoryBean. That internal KafkaStreams instance can be accessed via StreamsBuilderFactoryBean.getKafkaStreams() if you need to perform some KafkaStreams operations directly. You can autowire StreamsBuilderFactoryBean bean by type, but you should be sure that you use full type in the bean definition, for example:

@Bean
public StreamsBuilderFactoryBean myKStreamBuilder(StreamsConfig streamsConfig) {
    return new StreamsBuilderFactoryBean(streamsConfig);
}
...
@Autowired
private StreamsBuilderFactoryBean myKStreamBuilderFactoryBean;

Or add @Qualifier for injection by name if you use interface bean definition:

@Bean
public FactoryBean<StreamsBuilder> myKStreamBuilder(StreamsConfig streamsConfig) {
    return new StreamsBuilderFactoryBean(streamsConfig);
}
...
@Autowired
@Qualifier("&myKStreamBuilder")
private StreamsBuilderFactoryBean myKStreamBuilderFactoryBean;

4.2.4 JSON Serdes

For serializing and deserializing data when reading or writing to topics or state stores in JSON format, Spring Kafka provides a JsonSerde implementation using JSON, delegating to the JsonSerializer and JsonDeserializer described in the serialization/deserialization section. The JsonSerde provides the same configuration options via its constructor (target type and/or ObjectMapper). In the following example we use the JsonSerde to serialize and deserialize the Foo payload of a Kafka stream - the JsonSerde can be used in a similar fashion wherever an instance is required.

stream.through(Serdes.Integer(), new JsonSerde<>(Foo.class), "foos");
[Important]Important

Since Kafka Streams do not support headers, the addTypeInfo property on the JsonSerializer is ignored.

4.2.5 Configuration

To configure the Kafka Streams environment, the StreamsBuilderFactoryBean requires a Map of particular properties or a StreamsConfig instance. See Apache Kafka documentation for all possible options.

To avoid boilerplate code for most cases, especially when you develop micro services, Spring for Apache Kafka provides the @EnableKafkaStreams annotation, which should be placed alongside with @Configuration. Only you need is to declare StreamsConfig bean with the defaultKafkaStreamsConfig name. A StreamsBuilder bean with the defaultKafkaStreamsBuilder name will be declare in the application context automatically. Any additional StreamsBuilderFactoryBean beans can be declared and used as well.

By default, when the factory bean is stopped, the KafkaStreams.cleanUp() method is called. Starting with version 2.1.2, the factory bean has additional constructors, taking a CleanupConfig object that has properties to allow you to control whether the cleanUp() method is called during start(), stop(), or neither.

4.2.6 Kafka Streams Example

Putting it all together:

@Configuration
@EnableKafka
@EnableKafkaStreams
public static class KafkaStreamsConfiguration {

    @Bean(name = KafkaStreamsDefaultConfiguration.DEFAULT_STREAMS_CONFIG_BEAN_NAME)
    public StreamsConfig kStreamsConfigs() {
        Map<String, Object> props = new HashMap<>();
        props.put(StreamsConfig.APPLICATION_ID_CONFIG, "testStreams");
        props.put(StreamsConfig.KEY_SERDE_CLASS_CONFIG, Serdes.Integer().getClass().getName());
        props.put(StreamsConfig.VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());
        props.put(StreamsConfig.TIMESTAMP_EXTRACTOR_CLASS_CONFIG, WallclockTimestampExtractor.class.getName());
        return new StreamsConfig(props);
    }

    @Bean
    public KStream<Integer, String> kStream(StreamsBuilder kStreamBuilder) {
        KStream<Integer, String> stream = kStreamBuilder.stream("streamingTopic1");
        stream
                .mapValues(String::toUpperCase)
                .groupByKey()
                .reduce((String value1, String value2) -> value1 + value2,
                		TimeWindows.of(1000),
                		"windowStore")
                .toStream()
                .map((windowedId, value) -> new KeyValue<>(windowedId.key(), value))
                .filter((i, s) -> s.length() > 40)
                .to("streamingTopic2");

        stream.print();

        return stream;
    }

}

4.3 Testing Applications

4.3.1 Introduction

The spring-kafka-test jar contains some useful utilities to assist with testing your applications.

[Note]Note

See Appendix A, Override Dependencies to use the 1.1.x kafka-clients if you wish to use the 1.1.x kafka-clients jar with version 2.1.x.

4.3.2 JUnit

o.s.kafka.test.utils.KafkaTestUtils provides some static methods to set up producer and consumer properties:

/**
 * Set up test properties for an {@code <Integer, String>} consumer.
 * @param group the group id.
 * @param autoCommit the auto commit.
 * @param embeddedKafka a {@link KafkaEmbedded} instance.
 * @return the properties.
 */
public static Map<String, Object> consumerProps(String group, String autoCommit,
                                       KafkaEmbedded embeddedKafka) { ... }

/**
 * Set up test properties for an {@code <Integer, String>} producer.
 * @param embeddedKafka a {@link KafkaEmbedded} instance.
 * @return the properties.
 */
public static Map<String, Object> senderProps(KafkaEmbedded embeddedKafka) { ... }

A JUnit @Rule is provided that creates an embedded Kafka and an embedded Zookeeper server.

/**
 * Create embedded Kafka brokers.
 * @param count the number of brokers.
 * @param controlledShutdown passed into TestUtils.createBrokerConfig.
 * @param topics the topics to create (2 partitions per).
 */
public KafkaEmbedded(int count, boolean controlledShutdown, String... topics) { ... }

/**
 *
 * Create embedded Kafka brokers.
 * @param count the number of brokers.
 * @param controlledShutdown passed into TestUtils.createBrokerConfig.
 * @param partitions partitions per topic.
 * @param topics the topics to create.
 */
public KafkaEmbedded(int count, boolean controlledShutdown, int partitions, String... topics) { ... }

The embedded kafka class has a utility method allowing you to consume for all the topics it created:

Map<String, Object> consumerProps = KafkaTestUtils.consumerProps("testT", "false", embeddedKafka);
DefaultKafkaConsumerFactory<Integer, String> cf = new DefaultKafkaConsumerFactory<Integer, String>(
        consumerProps);
Consumer<Integer, String> consumer = cf.createConsumer();
embeddedKafka.consumeFromAllEmbeddedTopics(consumer);

The KafkaTestUtils has some utility methods to fetch results from the consumer:

/**
 * Poll the consumer, expecting a single record for the specified topic.
 * @param consumer the consumer.
 * @param topic the topic.
 * @return the record.
 * @throws org.junit.ComparisonFailure if exactly one record is not received.
 */
public static <K, V> ConsumerRecord<K, V> getSingleRecord(Consumer<K, V> consumer, String topic) { ... }

/**
 * Poll the consumer for records.
 * @param consumer the consumer.
 * @return the records.
 */
public static <K, V> ConsumerRecords<K, V> getRecords(Consumer<K, V> consumer) { ... }

Usage:

...
template.sendDefault(0, 2, "bar");
ConsumerRecord<Integer, String> received = KafkaTestUtils.getSingleRecord(consumer, "topic");
...

When the embedded Kafka and embedded Zookeeper server are started by JUnit, a system property spring.embedded.kafka.brokers is set to the address of the Kafka broker(s) and a system property spring.embedded.zookeeper.connect is set to the address of Zookeeper. Convenient constants KafkaEmbedded.SPRING_EMBEDDED_KAFKA_BROKERS and KafkaEmbedded.SPRING_EMBEDDED_ZOOKEEPER_CONNECT are provided for this property.

With the KafkaEmbedded.brokerProperties(Map<String, String>) you can provide additional properties for the Kafka server(s). See Kafka Config for more information about possible broker properties.

4.3.3 Using the Same Broker(s) for Multiple Test Classes

There is no built-in support for this, but it can be achieved with something similar to the following:

public final class KafkaEmbeddedHolder {

	private static KafkaEmbedded kafkaEmbedded = new KafkaEmbedded(1, false);

	private static boolean started;

	public static KafkaEmbedded getKafkaEmbedded() {
		if (!started) {
			try {
				kafkaEmbedded.before();
			}
			catch (Exception e) {
				throw new KafkaException(e);
			}
			started = true;
		}
		return kafkaEmbedded;
	}

	private KafkaEmbeddedHolder() {
		super();
	}

}

And then, in each test class:

static {
    KafkaEmbeddedHolder.getKafkaEmbedded().addTopics(topic1, topic2);
}

private static KafkaEmbedded embeddedKafka = KafkaEmbeddedHolder.getKafkaEmbedded();
[Important]Important

This example provides no mechanism for shutting down the broker(s) when all tests are complete. This could be a problem if, say, you run your tests in a gradle daemon. You should not use this technique in such a situation, or use something to call destroy() on the KafkaEmbedded when your tests are complete.

4.3.4 @EmbeddedKafka Annotation

It is generally recommended to use the rule as a @ClassRule to avoid starting/stopping the broker between tests (and use a different topic for each test). Starting with version 2.0, if you are using Spring’s test application context caching, you can also declare a KafkaEmbedded bean, so a single broker can be used across multiple test classes. The JUnit ExternalResource before()/after() lifecycle is wrapped to the afterPropertiesSet() and destroy() Spring infrastructure hooks. For convenience a test class level @EmbeddedKafka annotation is provided with the purpose to register KafkaEmbedded bean:

@RunWith(SpringRunner.class)
@DirtiesContext
@EmbeddedKafka(partitions = 1,
         topics = {
                 KafkaStreamsTests.STREAMING_TOPIC1,
                 KafkaStreamsTests.STREAMING_TOPIC2 })
public class KafkaStreamsTests {

    @Autowired
    private KafkaEmbedded embeddedKafka;

    @Test
    public void someTest() {
        Map<String, Object> consumerProps = KafkaTestUtils.consumerProps("testGroup", "true", this.embeddedKafka);
        consumerProps.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");
        ConsumerFactory<Integer, String> cf = new DefaultKafkaConsumerFactory<>(consumerProps);
        Consumer<Integer, String> consumer = cf.createConsumer();
        this.embeddedKafka.consumeFromAnEmbeddedTopic(consumer, KafkaStreamsTests.STREAMING_TOPIC2);
        ConsumerRecords<Integer, String> replies = KafkaTestUtils.getRecords(consumer);
        assertThat(replies.count()).isGreaterThanOrEqualTo(1);
    }

    @Configuration
    @EnableKafkaStreams
    public static class KafkaStreamsConfiguration {

        @Value("${" + KafkaEmbedded.SPRING_EMBEDDED_KAFKA_BROKERS + "}")
        private String brokerAddresses;

        @Bean(name = KafkaStreamsDefaultConfiguration.DEFAULT_STREAMS_CONFIG_BEAN_NAME)
        public StreamsConfig kStreamsConfigs() {
            Map<String, Object> props = new HashMap<>();
            props.put(StreamsConfig.APPLICATION_ID_CONFIG, "testStreams");
            props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, this.brokerAddresses);
            return new StreamsConfig(props);
        }

    }

}

The topics, brokerProperties, and brokerPropertiesLocation attributes of @EmbeddedKafka support property placeholder resolutions:

@TestPropertySource(locations = "classpath:/test.properties")
@EmbeddedKafka(topics = { "any-topic", "${kafka.topics.another-topic}" },
        brokerProperties = { "log.dir=${kafka.broker.logs-dir}",
                            "listeners=PLAINTEXT://localhost:${kafka.broker.port}",
                            "auto.create.topics.enable=${kafka.broker.topics-enable:true}" }
        brokerPropertiesLocation = "classpath:/broker.properties")

In the example above, the property placeholders ${kafka.topics.another-topic}, ${kafka.broker.logs-dir}, and ${kafka.broker.port} are resolved from the Spring Environment. In addition the broker properties are loaded from the broker.properties classpath resource specified by the brokerPropertiesLocation. Property placeholders are resolved for the brokerPropertiesLocation URL and for any property placeholders found in the resource. Properties defined by brokerProperties override properties found in brokerPropertiesLocation.

4.3.5 Hamcrest Matchers

The o.s.kafka.test.hamcrest.KafkaMatchers provides the following matchers:

/**
 * @param key the key
 * @param <K> the type.
 * @return a Matcher that matches the key in a consumer record.
 */
public static <K> Matcher<ConsumerRecord<K, ?>> hasKey(K key) { ... }

/**
 * @param value the value.
 * @param <V> the type.
 * @return a Matcher that matches the value in a consumer record.
 */
public static <V> Matcher<ConsumerRecord<?, V>> hasValue(V value) { ... }

/**
 * @param partition the partition.
 * @return a Matcher that matches the partition in a consumer record.
 */
public static Matcher<ConsumerRecord<?, ?>> hasPartition(int partition) { ... }

/**
 * Matcher testing the timestamp of a {@link ConsumerRecord} asssuming the topic has been set with
 * {@link org.apache.kafka.common.record.TimestampType#CREATE_TIME CreateTime}.
 *
 * @param ts timestamp of the consumer record.
 * @return a Matcher that matches the timestamp in a consumer record.
 */
public static Matcher<ConsumerRecord<?, ?>> hasTimestamp(long ts) {
  return hasTimestamp(TimestampType.CREATE_TIME, ts);
}

/**
 * Matcher testing the timestamp of a {@link ConsumerRecord}
 * @param type timestamp type of the record
 * @param ts timestamp of the consumer record.
 * @return a Matcher that matches the timestamp in a consumer record.
 */
public static Matcher<ConsumerRecord<?, ?>> hasTimestamp(TimestampType type, long ts) {
  return new ConsumerRecordTimestampMatcher(type, ts);
}

4.3.6 AssertJ Conditions

/**
 * @param key the key
 * @param <K> the type.
 * @return a Condition that matches the key in a consumer record.
 */
public static <K> Condition<ConsumerRecord<K, ?>> key(K key) { ... }

/**
 * @param value the value.
 * @param <V> the type.
 * @return a Condition that matches the value in a consumer record.
 */
public static <V> Condition<ConsumerRecord<?, V>> value(V value) { ... }

/**
 * @param partition the partition.
 * @return a Condition that matches the partition in a consumer record.
 */
public static Condition<ConsumerRecord<?, ?>> partition(int partition) { ... }

/**
 * @param value the timestamp.
 * @return a Condition that matches the timestamp value in a consumer record.
 */
public static Condition<ConsumerRecord<?, ?>> timestamp(long value) {
  return new ConsumerRecordTimestampCondition(TimestampType.CREATE_TIME, value);
}

/**
 * @param type the type of timestamp
 * @param value the timestamp.
 * @return a Condition that matches the timestamp value in a consumer record.
 */
public static Condition<ConsumerRecord<?, ?>> timestamp(TimestampType type, long value) {
  return new ConsumerRecordTimestampCondition(type, value);
}

4.3.7 Example

Putting it all together:

public class KafkaTemplateTests {

    private static final String TEMPLATE_TOPIC = "templateTopic";

    @ClassRule
    public static KafkaEmbedded embeddedKafka = new KafkaEmbedded(1, true, TEMPLATE_TOPIC);

    @Test
    public void testTemplate() throws Exception {
        Map<String, Object> consumerProps = KafkaTestUtils.consumerProps("testT", "false",
            embeddedKafka);
        DefaultKafkaConsumerFactory<Integer, String> cf =
                            new DefaultKafkaConsumerFactory<Integer, String>(consumerProps);
        ContainerProperties containerProperties = new ContainerProperties(TEMPLATE_TOPIC);
        KafkaMessageListenerContainer<Integer, String> container =
                            new KafkaMessageListenerContainer<>(cf, containerProperties);
        final BlockingQueue<ConsumerRecord<Integer, String>> records = new LinkedBlockingQueue<>();
        container.setupMessageListener(new MessageListener<Integer, String>() {

        	@Override
        	public void onMessage(ConsumerRecord<Integer, String> record) {
                System.out.println(record);
                records.add(record);
            }

        });
        container.setBeanName("templateTests");
        container.start();
        ContainerTestUtils.waitForAssignment(container, embeddedKafka.getPartitionsPerTopic());
        Map<String, Object> senderProps =
                            KafkaTestUtils.senderProps(embeddedKafka.getBrokersAsString());
        ProducerFactory<Integer, String> pf =
                            new DefaultKafkaProducerFactory<Integer, String>(senderProps);
        KafkaTemplate<Integer, String> template = new KafkaTemplate<>(pf);
        template.setDefaultTopic(TEMPLATE_TOPIC);
        template.sendDefault("foo");
        assertThat(records.poll(10, TimeUnit.SECONDS), hasValue("foo"));
        template.sendDefault(0, 2, "bar");
        ConsumerRecord<Integer, String> received = records.poll(10, TimeUnit.SECONDS);
        assertThat(received, hasKey(2));
        assertThat(received, hasPartition(0));
        assertThat(received, hasValue("bar"));
        template.send(TEMPLATE_TOPIC, 0, 2, "baz");
        received = records.poll(10, TimeUnit.SECONDS);
        assertThat(received, hasKey(2));
        assertThat(received, hasPartition(0));
        assertThat(received, hasValue("baz"));
    }

}

The above uses the hamcrest matchers; with AssertJ, the final part looks like this…​

assertThat(records.poll(10, TimeUnit.SECONDS)).has(value("foo"));
template.sendDefault(0, 2, "bar");
ConsumerRecord<Integer, String> received = records.poll(10, TimeUnit.SECONDS);
assertThat(received).has(key(2));
assertThat(received).has(partition(0));
assertThat(received).has(value("bar"));
template.send(TEMPLATE_TOPIC, 0, 2, "baz");
received = records.poll(10, TimeUnit.SECONDS);
assertThat(received).has(key(2));
assertThat(received).has(partition(0));
assertThat(received).has(value("baz"));