The following properties are available for Kafka consumers only and must be prefixed with spring.cloud.stream.kafka.bindings.<channelName>.consumer..

Starting with version 3.0, when spring.cloud.stream.binding.<name>.consumer.batch-mode is set to true, all of the records received by polling the Kafka Consumer will be presented as a List<?> to the listener method. Otherwise, the method will be called with one record at a time. The size of the batch is controlled by Kafka consumer properties max.poll.records, min.fetch.bytes, fetch.max.wait.ms; refer to the Kafka documentation for more information.

Bear in mind that batch mode is not supported with @StreamListener - it only works with the newer functional programming model.

The following properties are available for Kafka producers only and must be prefixed with spring.cloud.stream.kafka.bindings.<channelName>.producer..

ResultMetadata meta = sendResultMsg.getHeaders().get(KafkaHeaders.RECORD_METADATA, RecordMetadata.class)

Failed sends go the producer error channel (if configured); see Error Channels. Default: null

+

In this section, we show the use of the preceding properties for specific scenarios.

By default, records are published to the Dead-Letter topic using the same partition as the original record. This means the Dead-Letter topic must have at least as many partitions as the original record.

To change this behavior, add a DlqPartitionFunction implementation as a @Bean to the application context. Only one such bean can be present. The function is provided with the consumer group, the failed ConsumerRecord and the exception. For example, if you always want to route to partition 0, you might use:

Because the framework cannot anticipate how users would want to dispose of dead-lettered messages, it does not provide any standard mechanism to handle them. If the reason for the dead-lettering is transient, you may wish to route the messages back to the original topic. However, if the problem is a permanent issue, that could cause an infinite loop. The sample Spring Boot application within this topic is an example of how to route those messages back to the original topic, but it moves them to a “parking lot” topic after three attempts. The application is another spring-cloud-stream application that reads from the dead-letter topic. It exits when no messages are received for 5 seconds.

The examples assume the original destination is so8400out and the consumer group is so8400.

There are a couple of strategies to consider:

The following code listings show the sample application:

Starting with Spring Cloud Stream 3.0.0, Kafka Streams binder allows the applications to be designed and developed using the functional programming style that is available in Java 8. This means that the applications can be concisely represented as a lambda expression of types java.util.function.Function or java.util.function.Consumer.

Let’s take a very basic example.

Albeit simple, this is a complete standalone Spring Boot application that is leveraging Kafka Streams for stream processing. This is a consumer application with no outbound binding and only a single inbound binding. The application consumes data and it simply logs the information from the KStream key and value on the standard output. The application contains the SpringBootApplication annotation and a method that is marked as Bean. The bean method is of type java.util.function.Consumer which is parameterized with KStream. Then in the implementation, we are returning a Consumer object that is essentially a lambda expression. Inside the lambda expression, the code for processing the data is provided.

In this application, there is a single input binding that is of type KStream. The binder creates this binding for the application with a name process-in-0, i.e. the name of the function bean name followed by a dash character (-) and the literal in followed by another dash and then the ordinal position of the parameter. You use this binding name to set other properties such as destination. For example, spring.cloud.stream.bindings.process-in-0.destination=my-topic.

Once built as a uber-jar (e.g., kstream-consumer-app.jar), you can run the above example like the following.

Here is another example, where it is a full processor with both input and output bindings. This is the classic word-count example in which the application receives data from a topic, the number of occurrences for each word is then computed in a tumbling time-window.

Here again, this is a complete Spring Boot application. The difference here from the first application is that the bean method is of type java.util.function.Function. The first parameterized type for the Function is for the input KStream and the second one is for the output. In the method body, a lambda expression is provided that is of type Function and as implementation, the actual business logic is given. Similar to the previously discussed Consumer based application, the input binding here is named as process-in-0 by default. For the output, the binding name is automatically also set to process-out-0.

Once built as an uber-jar (e.g., wordcount-processor.jar), you can run the above example like the following.

This application will consume messages from the Kafka topic words and the computed results are published to an output topic counts.

Spring Cloud Stream will ensure that the messages from both the incoming and outgoing topics are automatically bound as KStream objects. As a developer, you can exclusively focus on the business aspects of the code, i.e. writing the logic required in the processor. Setting up Kafka Streams specific configuration required by the Kafka Streams infrastructure is automatically handled by the framework.

The two examples we saw above have a single KStream input binding. In both cases, the bindings received the records from a single topic. If you want to multiplex multiple topics into a single KStream binding, you can provide comma separated Kafka topics as destinations below.

spring.cloud.stream.bindings.process-in-0.destination=topic-1,topic-2,topic-3

In addition, you can also provide topic patterns as destinations if you want to match topics against a regular exression.

spring.cloud.stream.bindings.process-in-0.destination=input.*

Although the functional programming model outlined above is the preferred approach, you can still use the classic StreamListener based approach if you prefer.

Here are some examples.

Following is the equivalent of the Word count example using StreamListener.

As you can see, this is a bit more verbose since you need to provide EnableBinding and the other extra annotations like StreamListener and SendTo to make it a complete application. EnableBinding is where you specify your binding interface that contains your bindings. In this case, we are using the stock KafkaStreamsProcessor binding interface that has the following contracts.

Binder will create bindings for the input KStream and output KStream since you are using a binding interface that contains those declarations.

In addition to the obvious differences in the programming model offered in the functional style, one particular thing that needs to be mentioned here is that the binding names are what you specify in the binding interface. For example, in the above application, since we are using KafkaStreamsProcessor, the binding names are input and output. Binding properties need to use those names. For instance spring.cloud.stream.bindings.input.destination, spring.cloud.stream.bindings.output.destination etc. Keep in mind that this is fundamentally different from the functional style since there the binder generates binding names for the application. This is because the application does not provide any binding interfaces in the functional model using EnableBinding.

Here is another example of a sink where we have two inputs.

Following is the StreamListener equivalent of the same BiFunction based processor that we saw above.

Finally, here is the StreamListener equivalent of the application with three inputs and curried functions.

You might notice that the above two examples are even more verbose since in addition to provide EnableBinding, you also need to write your own custom binding interface as well. Using the functional model, you can avoid all those ceremonial details.

Before we move on from looking at the general programming model offered by Kafka Streams binder, here is the StreamListener version of multiple output bindings.

To recap, we have reviewed the various programming model choices when using the Kafka Streams binder.

The binder provides binding capabilities for KStream, KTable and GlobalKTable on the input. KTable and GlobalKTable bindings are only available on the input. Binder supports both input and output bindings for KStream.

The upshot of the programming model of Kafka Streams binder is that the binder provides you the flexibility of going with a fully functional programming model or using the StreamListener based imperative approach.

Binder allows to have multiple Kafka Streams processors within a single Spring Cloud Stream application. You can have an application as below.

In this case, the binder will create 3 separate Kafka Streams objects with different application ID’s (more on this below). However, if you have more than one processor in the application, you have to tell Spring Cloud Stream, which functions need to be activated. Here is how you activate the functions.

spring.cloud.stream.function.definition: process;anotherProcess;yetAnotherProcess

If you want certain functions to be not activated right away, you can remove that from this list.

This is also true when you have a single Kafka Streams processor and other types of Function beans in the same application that is handled through a different binder (for e.g., a function bean that is based on the regular Kafka Message Channel binder)

Application id is a mandatory property that you need to provide for a Kafka Streams application. Spring Cloud Stream Kafka Streams binder allows you to configure this application id in multiple ways.

If you only have one single processor or StreamListener in the application, then you can set this at the binder level using the following property:

spring.cloud.stream.kafka.streams.binder.applicationId.

As a convenience, if you only have a single processor, you can also use spring.application.name as the property to delegate the application id.

If you have multiple Kafka Streams processors in the application, then you need to set the application id per processor. In the case of the functional model, you can attach it to each function as a property.

For e.g. imagine that you have the following functions.

and

Then you can set the application id for each, using the following binder level properties.

spring.cloud.stream.kafka.streams.binder.functions.process.applicationId

and

spring.cloud.stream.kafka.streams.binder.functions.anotherProcess.applicationId

In the case of StreamListener, you need to set this on the first input binding on the processor.

For e.g. imagine that you have the following two StreamListener based processors.

Then you must set the application id for this using the following binding property.

spring.cloud.stream.kafka.streams.bindings.input.consumer.applicationId

and

spring.cloud.stream.kafka.streams.bindings.anotherInput.consumer.applicationId

For function based model also, this approach of setting application id at the binding level will work. However, setting per function at the binder level as we have seen above is much easier if you are using the functional model.

For production deployments, it is highly recommended to explicitly specify the application ID through configuration. This is especially going to be very critical if you are auto scaling your application in which case you need to make sure that you are deploying each instance with the same application ID.

If the application does not provide an application ID, then in that case the binder will auto generate a static application ID for you. This is convenient in development scenarios as it avoids the need for explicitly providing the application ID. The generated application ID in this manner will be static over application restarts. In the case of functional model, the generated application ID will be the function bean name followed by the literal applicationID, for e.g process-applicationID if process if the function bean name. In the case of StreamListener, instead of using the function bean name, the generated application ID will be use the containing class name followed by the method name followed by the literal applicationId.

By default, the binder uses the strategy discussed above to generate the binding name when using the functional style, i.e. <function-bean-name>-<in>|<out>-[0..n], for e.g. process-in-0, process-out-0 etc. If you want to override those binding names, you can do that by specifying the following properties.

spring.cloud.stream.function.bindings.<default binding name>. Default binding name is the original binding name generated by the binder.

For e.g. lets say, you have this function.

Binder will generate bindings with names, process-in-0, process-in-1 and process-out-0. Now, if you want to change them to something else completely, maybe more domain specific binding names, then you can do so as below.

springc.cloud.stream.function.bindings.process-in-0=users

springc.cloud.stream.function.bindings.process-in-0=regions

and

spring.cloud.stream.function.bindings.process-out-0=clicks

After that, you must set all the binding level properties on these new binding names.

Please keep in mind that with the functional programming model described above, adhering to the default binding names make sense in most situations. The only reason you may still want to do this overriding is when you have larger number of configuration properties and you want to map the bindings to something more domain friendly.

When running Kafka Streams applications, you must provide the Kafka broker server information. If you don’t provide this information, the binder expects that you are running the broker at the default localhost:9092. If that is not the case, then you need to override that. There are a couple of ways to do that.

When it comes to the binder level property, it doesn’t matter if you use the broker property provided through the regular Kafka binder - spring.cloud.stream.kafka.binder.brokers. Kafka Streams binder will first check if Kafka Streams binder specific broker property is set (spring.cloud.stream.kafka.streams.binder.brokers) and if not found, it looks for spring.cloud.stream.kafka.binder.brokers.

Keys are always deserialized using native Serdes.

For values, by default, deserialization on the inbound is natively performed by Kafka. Please note that this is a major change on default behavior from previous versions of Kafka Streams binder where the deserialization was done by the framework.

Kafka Streams binder will try to infer matching Serde types by looking at the type signature of java.util.function.Function|Consumer or StreamListener. Here is the order that it matches the Serdes.

If none of the above strategies worked, then the applications must provide the `Serde`s through configuration. This can be configured in two ways - binding or default.

First the binder will look if a Serde is provided at the binding level. For e.g. if you have the following processor,

then, you can provide a binding level Serde using the following:

If you want the default key/value Serdes to be used for inbound deserialization, you can do so at the binder level.

If you don’t want the native decoding provided by Kafka, you can rely on the message conversion features that Spring Cloud Stream provides. Since native decoding is the default, in order to let Spring Cloud Stream deserialize the inbound value object, you need to explicitly disable native decoding.

For e.g. if you have the same BiFunction processor as above, then spring.cloud.stream.bindings.process-in-0.consumer.nativeDecoding: false You need to disable native decoding for all the inputs individually. Otherwise, native decoding will still be applied for those you do not disable.

By default, Spring Cloud Stream will use application/json as the content type and use an appropriate json message converter. You can use custom message converters by using the following property and an appropriate MessageConverter bean.

Outbound serialization pretty much follows the same rules as above for inbound deserialization. As with the inbound deserialization, one major change from the previous versions of Spring Cloud Stream is that the serialization on the outbound is handled by Kafka natively. Before 3.0 versions of the binder, this was done by the framework itself.

Keys on the outbound are always serialized by Kafka using a matching Serde that is inferred by the binder. If it can’t infer the type of the key, then that needs to be specified using configuration.

Value serdes are inferred using the same rules used for inbound deserialization. First it matches to see if the outbound type is from a provided bean in the application. If not, it checks to see if it matches with a Serde exposed by Kafka such as - Integer, Long, Short, Double, Float, byte[], UUID and String. If that doesnt’t work, then it falls back to JsonSerde provided by the Spring Kafka project, but first look at the default Serde configuration to see if there is a match. Keep in mind that all these happen transparently to the application. If none of these work, then the user has to provide the Serde to use by configuration.

Lets say you are using the same BiFunction processor as above. Then you can configure outbound key/value Serdes as following.

If Serde inference fails, and no binding level Serdes are provided, then the binder falls back to the JsonSerde, but look at the default Serdes for a match.

Default serdes are configured in the same way as above where it is described under deserialization.

spring.cloud.stream.kafka.streams.binder.configuration.default.key.serde spring.cloud.stream.kafka.streams.binder.configuration.default.value.serde

If your application uses the branching feature and has multiple output bindings, then these have to be configured per binding. Once again, if the binder is capable of inferring the Serde types, you don’t need to do this configuration.

If you don’t want the native encoding provided by Kafka, but want to use the framework provided message conversion, then you need to explicitly disable native encoding since since native encoding is the default. For e.g. if you have the same BiFunction processor as above, then spring.cloud.stream.bindings.process-out-0.producer.nativeEncoding: false You need to disable native encoding for all the output individually in the case of branching. Otherwise, native encoding will still be applied for those you don’t disable.

When conversion is done by Spring Cloud Stream, by default, it will use application/json as the content type and use an appropriate json message converter. You can use custom message converters by using the following property and a corresponding MessageConverter bean.

When native encoding/decoding is disabled, binder will not do any inference as in the case of native Serdes. Applications need to explicitly provide all the configuration options. For that reason, it is generally advised to stay with the default options for de/serialization and stick with native de/serialization provided by Kafka Streams when you write Spring Cloud Stream Kafka Streams applications. The one scenario in which you must use message conversion capabilities provided by the framework is when your upstream producer is using a specific serialization strategy. In that case, you want to use a matching deserialization strategy as native mechanisms may fail. When relying on the default Serde mechanism, the applications must ensure that the binder has a way forward with correctly map the inbound and outbound with a proper Serde, as otherwise things might fail.

It is worth to mention that the data de/serialization approaches outlined above are only applicable on the edges of your processors, i.e. - inbound and outbound. Your business logic might still need to call Kafka Streams API’s that explicitly need Serde objects. Those are still the responsibility of the application and must be handled accordingly by the developer.

Kafka Streams binder allows to specify the deserialization exception handlers above using the following property.

or

In addition to the above two deserialization exception handlers, the binder also provides a third one for sending the erroneous records (poison pills) to a DLQ (dead letter queue) topic. Here is how you enable this DLQ exception handler.

When the above property is set, all the records in deserialization error are automatically sent to the DLQ topic.

You can set the topic name where the DLQ messages are published as below.

If this is set, then the error records are sent to the topic custom-dlq. If this is not set, then it will create a DLQ topic with the name error.<input-topic-name>.<application-id>. For instance, if your binding’s destination topic is inputTopic and the application ID is process-applicationId, then the default DLQ topic is error.inputTopic.process-applicationId. It is always recommended to explicitly create a DLQ topic for each input binding if it is your intention to enable DLQ.

The property spring.cloud.stream.kafka.streams.binder.deserializationExceptionHandler is applicable for the entire application. This implies that if there are multiple functions or StreamListener methods in the same application, this property is applied to all of them. However, if you have multiple processors or multiple input bindings within a single processor, then you can use the finer-grained DLQ control that the binder provides per input consumer binding.

If you have the following processor,

and you only want to enable DLQ on the first input binding and logAndSkip on the second binding, then you can do so on the consumer as below.

spring.cloud.stream.kafka.streams.bindings.process-in-0.consumer.deserializationExceptionHandler: sendToDlq spring.cloud.stream.kafka.streams.bindings.process-in-1.consumer.deserializationExceptionHandler: logAndSkip

Setting deserialization exception handlers this way has a higher precedence than setting at the binder level.

By default, records are published to the Dead-Letter topic using the same partition as the original record. This means the Dead-Letter topic must have at least as many partitions as the original record.

To change this behavior, add a DlqPartitionFunction implementation as a @Bean to the application context. Only one such bean can be present. The function is provided with the consumer group (which is the same as the application ID in most situations), the failed ConsumerRecord and the exception. For example, if you always want to route to partition 0, you might use:

A couple of things to keep in mind when using the exception handling feature in Kafka Streams binder.

Unlike the support for deserialization exception handlers as described above, the binder does not provide such first class mechanisms for handling production exceptions. However, you still can configure production exception handlers using the StreamsBuilderFactoryBean customizer which you can find more details about, in a subsequent section below.

As mentioned above, the binder does not provide a first class way to register global state stores as a feature. For that, you need to use the customizer. Here is how that can be done.

Again, if you have multiple processors, you want to attach the global state store to the right StreamsBuilder by filtering out the other StreamsBuilderFactoryBean objects using the application id as outlined above.

In the error handling section, we indicated that the binder does not provide a first class way to deal with production exceptions. Though that is the case, you can still use the StreamsBuilderFacotryBean customizer to register production exception handlers. See below.

Once again, if you have multiple processors, you may want to set it appropriately against the correct StreamsBuilderFactoryBean. You may also add such production exception handlers using the configuration property (See below for more on that), but this is an option if you choose to go with a programmatic approach.

The following properties are available at the binder level and must be prefixed with spring.cloud.stream.kafka.streams.binder.

For more information about all the properties that may go into streams configuration, see StreamsConfig JavaDocs in Apache Kafka Streams docs. All configuration that you can set from StreamsConfig can be set through this. When using this property, it is applicable against the entire application since this is a binder level property. If you have more than processors in the application, all of them will acquire these properties. In the case of properties like application.id, this will become problematic and therefore you have to carefully examine how the properties from StreamsConfig are mapped using this binder level configuration property.

The following properties are only available for Kafka Streams producers and must be prefixed with spring.cloud.stream.kafka.streams.bindings.<binding name>.producer. For convenience, if there are multiple output bindings and they all require a common value, that can be configured by using the prefix spring.cloud.stream.kafka.streams.default.producer..

streamPartitionerBeanName: Custom outbound partitioner bean name to be used at the consumer. Applications can provide custom StreamPartitioner as a Spring bean and the name of this bean can be provided to the producer to use instead of the default one.

+ Default: See the discussion above on outbound partition support.

The following properties are available for Kafka Streams consumers and must be prefixed with spring.cloud.stream.kafka.streams.bindings.<binding-name>.consumer. For convenience, if there are multiple input bindings and they all require a common value, that can be configured by using the prefix spring.cloud.stream.kafka.streams.default.consumer..

Note: Using resetOffsets on the consumer does not have any effect on Kafka Streams binder. Unlike the message channel based binder, Kafka Streams binder does not seek to beginning or end on demand.

In Kafka Streams, you can control of the number of threads a processor can create using the num.stream.threads property. This, you can do using the various configuration options described above under binder, functions, producer or consumer level. You can also use the concurrency property that core Spring Cloud Stream provides for this purpose. When using this, you need to use it on the consumer. When you have more than one input bindings either in a function or StreamListener, set this on the first input binding. For e.g. when setting spring.cloud.stream.bindings.process-in-0.consumer.concurrency, it will be translated as num.stream.threads by the binder. If you have multiple processors and one processor defines binding level concurrency, but not the others, those ones with no binding level concurrency will default back to the binder wide property specified through spring.cloud.stream.binder.configuration.num.stream.threads. If this binder configuration is not available, then the application will use the default set by Kafka Streams.

We recommend the m2eclipe eclipse plugin when working with eclipse. If you don’t already have m2eclipse installed it is available from the "eclipse marketplace".

Unfortunately m2e does not yet support Maven 3.3, so once the projects are imported into Eclipse you will also need to tell m2eclipse to use the .settings.xml file for the projects. If you do not do this you may see many different errors related to the POMs in the projects. Open your Eclipse preferences, expand the Maven preferences, and select User Settings. In the User Settings field click Browse and navigate to the Spring Cloud project you imported selecting the .settings.xml file in that project. Click Apply and then OK to save the preference changes.

If you prefer not to use m2eclipse you can generate eclipse project metadata using the following command:

The generated eclipse projects can be imported by selecting import existing projects from the file menu.

[[contributing] == Contributing

Spring Cloud is released under the non-restrictive Apache 2.0 license, and follows a very standard Github development process, using Github tracker for issues and merging pull requests into master. If you want to contribute even something trivial please do not hesitate, but follow the guidelines below.