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.
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.
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The admin does not alter existing topics; it will log (INFO) if the number of partitions don’t match. |
The KafkaTemplate wraps a producer and provides convenience methods to send data to kafka topics.
Both asynchronous and synchronous methods are provided, with the async methods returning a Future.
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"); ... 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.
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).
KafkaMessageListenerContainer
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.
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.
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.
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.
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); }
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The offset to be committed is one greater than the offset of the record(s) processed by the listener. |
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This should only be called when using transaction synchronization.
When a listener container is configured to use a |
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.
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If there is a |
Messages can be received by configuring a MessageListenerContainer and providing a Message Listener, or by
using the @KafkaListener annotation.
When using a Message Listener Container you must provide a listener to receive data. There are currently four supported interfaces for message listeners:
public interface MessageListener<K, V> {}void onMessage(ConsumerRecord<K, V> data); } public interface AcknowledgingMessageListener<K, V> {}
void onMessage(ConsumerRecord<K, V> data, Acknowledgment acknowledgment); } public interface BatchMessageListener<K, V> {}
void onMessage(List<ConsumerRecord<K, V>> data); } public interface BatchAcknowledgingMessageListener<K, V> {}
void onMessage(List<ConsumerRecord<K, V>> data, Acknowledgment acknowledgment); }
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Use this for processing individual | |
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Use this for processing individual | |
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Use this for processing all | |
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Use this for processing all |
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.
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.
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.
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The |
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.
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 :
poll() have been processed.
poll() have been processed as long as the ackTime
since the last commit has been exceeded.
poll() have been processed as long as ackCount
records have been received since the last commit.
acknowledge() the Acknowledgment;
after which, the same semantics as BATCH are applied.
Acknowledgment.acknowledge() method is called by the
listener.
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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.
The @KafkaListener annotation provides a mechanism for simple POJO listeners:
public class Listener { @KafkaListener(id = "foo", topics = "myTopic") 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.
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_MESSAGE_KEY
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) { ... }
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.
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.
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) { ... } }
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.
Finally, FilteringBatchMessageListenerAdapter and FilteringBatchAcknowledgingMessageListenerAdapter are provided, for when using a batch message listener.
If your listener throws an exception, the default behavior is to invoke the ErrorHandler, if configured, or logged otherwise.
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Two error handler interfaces are provided |
To retry deliveries, convenient listener adapters - RetryingMessageListenerAdapter and RetryingAcknowledgingMessageListenerAdapter are provided, depending on whether you are using a MessageListener or an AcknowledgingMessageListener.
These 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 the additional acknowledgment attribute is provided.
For convenience, the AbstractRetryingMessageListenerAdapter 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.
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.
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 4 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
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) {
this.event = event;
eventLatch.countDown();
}
}
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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 |
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If you wish to use the idle event to stop the lister container, you should not call |
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”.
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:
group.id, the initial offset is determined by the auto.offset.reset consumer property (earliest or latest).
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 Asynchronous Consumers” for how to enable idle container detection.
To arbitrarily seek at runtime, use the callback reference from the registerSeekCallback for the appropriate thread.
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.
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 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.
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This type inference can only be achieved when the |
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When using the |
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.
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Previous versions 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.
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By default, the mapper will only deserialize classes in |
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).
When using Log Compaction, it is possible to send and receive messages with null payloads which identifies the deletion of a key.
Starting with version 1.0.3, this is now fully supported.
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; you will usually also need the key so your application knows 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, some additional configuration is needed - a @KafkaHandler method with a KafkaNull payload:
@KafkaListener(id = "multi", topics = "myTopic") static class MultiListenerBean { private final CountDownLatch latch1 = new CountDownLatch(2); @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) { ... } }
By default, if an annotated listener method throws an exception, it is thrown to the container, and the message will handled according to the container configuration. Nothing is returned to the sender.
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.
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", "kafka-client-1@EXAMPLE.COM"); jaasConfig.setOptions(options); return jaasConfig; }
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.
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. KStreamBuilder builder = ...; // when using the Kafka Streams DSL // // OR // TopologyBuilder builder = ...; // when using the Processor API // 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: KStreamBuilder (which extends TopologyBuilder as well) 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 KStreamBuilder 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 KStreamBuilder 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.
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 KStreamBuilderFactoryBean.
This is an AbstractFactoryBean implementation to expose a KStreamBuilder singleton instance as a bean:
@Bean public FactoryBean<KStreamBuilder> myKStreamBuilder(StreamsConfig streamsConfig) { return new KStreamBuilderFactoryBean(streamsConfig); }
The KStreamBuilderFactoryBean 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 KStreamBuilder before the application context is refreshed, when we use default autoStartup = true on the KStreamBuilderFactoryBean.
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(KStreamBuilder 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 KStreamBuilderFactoryBean bean directly using factory bean (&) prefix.
Since KStreamBuilderFactoryBean 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 KStreamBuilderFactoryBean s, if you would like to control lifecycles for KStream instances separately.
You can specify KafkaStreams.StateListener and Thread.UncaughtExceptionHandler options on the KStreamBuilderFactoryBean which are delegated to the internal KafkaStreams instance.
That internal KafkaStreams instance can be accessed via KStreamBuilderFactoryBean.getKafkaStreams() if you need to perform some KafkaStreams operations directly.
You can autowire KStreamBuilderFactoryBean bean by type, but you should be sure that you use full type in the bean definition, for example:
@Bean public KStreamBuilderFactoryBean myKStreamBuilder(StreamsConfig streamsConfig) { return new KStreamBuilderFactoryBean(streamsConfig); } ... @Autowired private KStreamBuilderFactoryBean myKStreamBuilderFactoryBean;
Or add @Qualifier for injection by name If you use interface bean definition:
@Bean public FactoryBean<KStreamBuilder> myKStreamBuilder(StreamsConfig streamsConfig) { return new KStreamBuilderFactoryBean(streamsConfig); } ... @Autowired @Qualifier("&myKStreamBuilder") private KStreamBuilderFactoryBean myKStreamBuilderFactoryBean;
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");
To configure the Kafka Streams environment, the KStreamBuilderFactoryBean 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 KStreamBuilder bean with the defaultKStreamBuilder name will be declare in the application context automatically.
Any additional KStreamBuilderFactoryBean beans can be declared and used as well.
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(KStreamBuilder 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; } }
The spring-kafka-test jar contains some useful utilities to assist with testing your applications.
o.s.kafka.test.utils.KafkaUtils 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 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 server is started by JUnit, it sets a system property spring.embedded.kafka.brokers to the address of the broker(s).
A convenient constant KafkaEmbedded.SPRING_EMBEDDED_KAFKA_BROKERS is 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.
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 kafkaEmbedded; @Test public void someTest() { Map<String, Object> consumerProps = KafkaTestUtils.consumerProps("testGroup", "true", this.kafkaEmbedded); 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 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); }
/** * @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); }
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"));
}
}