Spring Cloud Stream Core

This section goes into more detail about how you can work with Spring Cloud Stream. It covers topics such as creating and running stream applications.

1. Introducing Spring Cloud Stream

Spring Cloud Stream is a framework for building message-driven microservice applications. Spring Cloud Stream builds upon Spring Boot to create standalone, production-grade Spring applications, and uses Spring Integration to provide connectivity to message brokers. It provides opinionated configuration of middleware from several vendors, introducing the concepts of persistent publish-subscribe semantics, consumer groups, and partitions.

You can add the @EnableBinding annotation to your application to get immediate connectivity to a message broker, and you can add @StreamListener to a method to cause it to receive events for stream processing. The following is a simple sink application which receives external messages.

@SpringBootApplication
@EnableBinding(Sink.class)
public class VoteRecordingSinkApplication {

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

  @StreamListener(Sink.INPUT)
  public void processVote(Vote vote) {
      votingService.recordVote(vote);
  }
}

The @EnableBinding annotation takes one or more interfaces as parameters (in this case, the parameter is a single Sink interface). An interface declares input and/or output channels. Spring Cloud Stream provides the interfaces Source, Sink, and Processor; you can also define your own interfaces.

The following is the definition of the Sink interface:

public interface Sink {
  String INPUT = "input";

  @Input(Sink.INPUT)
  SubscribableChannel input();
}

The @Input annotation identifies an input channel, through which received messages enter the application; the @Output annotation identifies an output channel, through which published messages leave the application. The @Input and @Output annotations can take a channel name as a parameter; if a name is not provided, the name of the annotated method will be used.

Spring Cloud Stream will create an implementation of the interface for you. You can use this in the application by autowiring it, as in the following example of a test case.

@RunWith(SpringJUnit4ClassRunner.class)
@SpringApplicationConfiguration(classes = VoteRecordingSinkApplication.class)
@WebAppConfiguration
@DirtiesContext
public class StreamApplicationTests {

  @Autowired
  private Sink sink;

  @Test
  public void contextLoads() {
    assertNotNull(this.sink.input());
  }
}

2. Main Concepts

Spring Cloud Stream provides a number of abstractions and primitives that simplify the writing of message-driven microservice applications. This section gives an overview of the following:

Spring Cloud Stream’s application model
The Binder abstraction
Persistent publish-subscribe support
Consumer group support
Partitioning support
A pluggable Binder API

2.1. Application Model

A Spring Cloud Stream application consists of a middleware-neutral core. The application communicates with the outside world through input and output channels injected into it by Spring Cloud Stream. Channels are connected to external brokers through middleware-specific Binder implementations.

Figure 1. Spring Cloud Stream Application

2.1.1. Fat JAR

Spring Cloud Stream applications can be run in standalone mode from your IDE for testing. To run a Spring Cloud Stream application in production, you can create an executable (or "fat") JAR by using the standard Spring Boot tooling provided for Maven or Gradle.

2.2. The Binder Abstraction

Spring Cloud Stream provides Binder implementations for Kafka and Rabbit MQ. Spring Cloud Stream also includes a TestSupportBinder, which leaves a channel unmodified so that tests can interact with channels directly and reliably assert on what is received. You can use the extensible API to write your own Binder.

Spring Cloud Stream uses Spring Boot for configuration, and the Binder abstraction makes it possible for a Spring Cloud Stream application to be flexible in how it connects to middleware. For example, deployers can dynamically choose, at runtime, the destinations (e.g., the Kafka topics or RabbitMQ exchanges) to which channels connect. Such configuration can be provided through external configuration properties and in any form supported by Spring Boot (including application arguments, environment variables, and application.yml or application.properties files). In the sink example from the Introducing Spring Cloud Stream section, setting the application property spring.cloud.stream.bindings.input.destination to raw-sensor-data will cause it to read from the raw-sensor-data Kafka topic, or from a queue bound to the raw-sensor-data RabbitMQ exchange.

Spring Cloud Stream automatically detects and uses a binder found on the classpath. You can easily use different types of middleware with the same code: just include a different binder at build time. For more complex use cases, you can also package multiple binders with your application and have it choose the binder, and even whether to use different binders for different channels, at runtime.

2.3. Persistent Publish-Subscribe Support

Communication between applications follows a publish-subscribe model, where data is broadcast through shared topics. This can be seen in the following figure, which shows a typical deployment for a set of interacting Spring Cloud Stream applications.

Figure 2. Spring Cloud Stream Publish-Subscribe

Data reported by sensors to an HTTP endpoint is sent to a common destination named raw-sensor-data. From the destination, it is independently processed by a microservice application that computes time-windowed averages and by another microservice application that ingests the raw data into HDFS. In order to process the data, both applications declare the topic as their input at runtime.

The publish-subscribe communication model reduces the complexity of both the producer and the consumer, and allows new applications to be added to the topology without disruption of the existing flow. For example, downstream from the average-calculating application, you can add an application that calculates the highest temperature values for display and monitoring. You can then add another application that interprets the same flow of averages for fault detection. Doing all communication through shared topics rather than point-to-point queues reduces coupling between microservices.

While the concept of publish-subscribe messaging is not new, Spring Cloud Stream takes the extra step of making it an opinionated choice for its application model. By using native middleware support, Spring Cloud Stream also simplifies use of the publish-subscribe model across different platforms.

2.4. Consumer Groups

While the publish-subscribe model makes it easy to connect applications through shared topics, the ability to scale up by creating multiple instances of a given application is equally important. When doing this, different instances of an application are placed in a competing consumer relationship, where only one of the instances is expected to handle a given message.

Spring Cloud Stream models this behavior through the concept of a consumer group. (Spring Cloud Stream consumer groups are similar to and inspired by Kafka consumer groups.) Each consumer binding can use the spring.cloud.stream.bindings.<channelName>.group property to specify a group name. For the consumers shown in the following figure, this property would be set as spring.cloud.stream.bindings.<channelName>.group=hdfsWrite or spring.cloud.stream.bindings.<channelName>.group=average.

Figure 3. Spring Cloud Stream Consumer Groups

All groups which subscribe to a given destination receive a copy of published data, but only one member of each group receives a given message from that destination. By default, when a group is not specified, Spring Cloud Stream assigns the application to an anonymous and independent single-member consumer group that is in a publish-subscribe relationship with all other consumer groups.

2.4.1. Durability

Consistent with the opinionated application model of Spring Cloud Stream, consumer group subscriptions are durable. That is, a binder implementation ensures that group subscriptions are persistent, and once at least one subscription for a group has been created, the group will receive messages, even if they are sent while all applications in the group are stopped.

Anonymous subscriptions are non-durable by nature. For some binder implementations (e.g., RabbitMQ), it is possible to have non-durable group subscriptions.

In general, it is preferable to always specify a consumer group when binding an application to a given destination. When scaling up a Spring Cloud Stream application, you must specify a consumer group for each of its input bindings. This prevents the application’s instances from receiving duplicate messages (unless that behavior is desired, which is unusual).

2.5. Partitioning Support

Spring Cloud Stream provides support for partitioning data between multiple instances of a given application. In a partitioned scenario, the physical communication medium (e.g., the broker topic) is viewed as being structured into multiple partitions. One or more producer application instances send data to multiple consumer application instances and ensure that data identified by common characteristics are processed by the same consumer instance.

Spring Cloud Stream provides a common abstraction for implementing partitioned processing use cases in a uniform fashion. Partitioning can thus be used whether the broker itself is naturally partitioned (e.g., Kafka) or not (e.g., RabbitMQ).

Figure 4. Spring Cloud Stream Partitioning

Partitioning is a critical concept in stateful processing, where it is critiical, for either performance or consistency reasons, to ensure that all related data is processed together. For example, in the time-windowed average calculation example, it is important that all measurements from any given sensor are processed by the same application instance.

To set up a partitioned processing scenario, you must configure both the data-producing and the data-consuming ends.

3. Programming Model

This section describes Spring Cloud Stream’s programming model. Spring Cloud Stream provides a number of predefined annotations for declaring bound input and output channels as well as how to listen to channels.

3.1. Declaring and Binding Channels

3.1.1. Triggering Binding Via `@EnableBinding`

You can turn a Spring application into a Spring Cloud Stream application by applying the @EnableBinding annotation to one of the application’s configuration classes. The @EnableBinding annotation itself is meta-annotated with @Configuration and triggers the configuration of Spring Cloud Stream infrastructure:

...
@Import(...)
@Configuration
@EnableIntegration
public @interface EnableBinding {
    ...
    Class<?>[] value() default {};
}

The @EnableBinding annotation can take as parameters one or more interface classes that contain methods which represent bindable components (typically message channels).

The @EnableBinding annotation is only required on your Configuration classes, you can provide as many binding interfaces as you need, for instance: @EnableBinding(value={Orders.class, Payment.class}. Where both Order and Payment interfaces would declare @Input and @Output channels.

3.1.2. `@Input` and `@Output`

A Spring Cloud Stream application can have an arbitrary number of input and output channels defined in an interface as @Input and @Output methods:

public interface Barista {

    @Input
    SubscribableChannel orders();

    @Output
    MessageChannel hotDrinks();

    @Output
    MessageChannel coldDrinks();
}

Using this interface as a parameter to @EnableBinding will trigger the creation of three bound channels named orders, hotDrinks, and coldDrinks, respectively.

@EnableBinding(Barista.class)
public class CafeConfiguration {

   ...
}

In Spring Cloud Stream, the bindable MessageChannel components are the Spring Messaging MessageChannel (for outbound) and its extension SubscribableChannel (for inbound). Using the same mechanism other bindable components can be supported. KStream support in Spring Cloud Stream Kafka binder is one such example where KStream is used as inbound/outbound bindable components. In this documentation, we will continue to refer to MessageChannels as the bindable components.

Customizing Channel Names

Using the @Input and @Output annotations, you can specify a customized channel name for the channel, as shown in the following example:

public interface Barista {
    ...
    @Input("inboundOrders")
    SubscribableChannel orders();
}

In this example, the created bound channel will be named inboundOrders.

`Source`, `Sink`, and `Processor`

For easy addressing of the most common use cases, which involve either an input channel, an output channel, or both, Spring Cloud Stream provides three predefined interfaces out of the box.

Source can be used for an application which has a single outbound channel.

public interface Source {

  String OUTPUT = "output";

  @Output(Source.OUTPUT)
  MessageChannel output();

}

Sink can be used for an application which has a single inbound channel.

public interface Sink {

  String INPUT = "input";

  @Input(Sink.INPUT)
  SubscribableChannel input();

}

Processor can be used for an application which has both an inbound channel and an outbound channel.

public interface Processor extends Source, Sink {
}

Spring Cloud Stream provides no special handling for any of these interfaces; they are only provided out of the box.

3.1.3. Accessing Bound Channels

Injecting the Bound Interfaces

For each bound interface, Spring Cloud Stream will generate a bean that implements the interface. Invoking a @Input-annotated or @Output-annotated method of one of these beans will return the relevant bound channel.

The bean in the following example sends a message on the output channel when its hello method is invoked. It invokes output() on the injected Source bean to retrieve the target channel.

@Component
public class SendingBean {

    private Source source;

    @Autowired
    public SendingBean(Source source) {
        this.source = source;
    }

    public void sayHello(String name) {
         source.output().send(MessageBuilder.withPayload(name).build());
    }
}

Injecting Channels Directly

Bound channels can be also injected directly:

@Component
public class SendingBean {

    private MessageChannel output;

    @Autowired
    public SendingBean(MessageChannel output) {
        this.output = output;
    }

    public void sayHello(String name) {
         output.send(MessageBuilder.withPayload(name).build());
    }
}

If the name of the channel is customized on the declaring annotation, that name should be used instead of the method name. Given the following declaration:

public interface CustomSource {
    ...
    @Output("customOutput")
    MessageChannel output();
}

The channel will be injected as shown in the following example:

@Component
public class SendingBean {

    private MessageChannel output;

    @Autowired
    public SendingBean(@Qualifier("customOutput") MessageChannel output) {
        this.output = output;
    }

    public void sayHello(String name) {
         this.output.send(MessageBuilder.withPayload(name).build());
    }
}

3.1.4. Producing and Consuming Messages

You can write a Spring Cloud Stream application using either Spring Integration annotations or Spring Cloud Stream’s @StreamListener annotation. The @StreamListener annotation is modeled after other Spring Messaging annotations (such as @MessageMapping, @JmsListener, @RabbitListener, etc.) but adds content type management and type coercion features.

Native Spring Integration Support

Because Spring Cloud Stream is based on Spring Integration, Stream completely inherits Integration’s foundation and infrastructure as well as the component itself. For example, you can attach the output channel of a Source to a MessageSource:

@EnableBinding(Source.class)
public class TimerSource {

  @Value("${format}")
  private String format;

  @Bean
  @InboundChannelAdapter(value = Source.OUTPUT, poller = @Poller(fixedDelay = "${fixedDelay}", maxMessagesPerPoll = "1"))
  public MessageSource<String> timerMessageSource() {
    return () -> new GenericMessage<>(new SimpleDateFormat(format).format(new Date()));
  }
}

Or you can use a processor’s channels in a transformer:

@EnableBinding(Processor.class)
public class TransformProcessor {
  @Transformer(inputChannel = Processor.INPUT, outputChannel = Processor.OUTPUT)
  public Object transform(String message) {
    return message.toUpperCase();
  }
}

It’s important to understant that when you consume from the same binding using @StreamListener a pubsub model is used, where each method annotated with @StreamListener receives it’s own copy of the message, each one has its own consumer group. However, if you share a bindable channel as an input for @Aggregator, @Transformer or @ServiceActivator, those will consume in a competing model, no individual consumer group is created for each subscription.

Spring Integration Error Channel Support

Spring Cloud Stream supports publishing error messages received by the Spring Integration global error channel. Error messages sent to the errorChannel can be published to a specific destination at the broker by configuring a binding for the outbound target named error. For example, to publish error messages to a broker destination named "myErrors", provide the following property: spring.cloud.stream.bindings.error.destination=myErrors.

Message Channel Binders and Error Channels

Starting with version 1.3, some MessageChannel - based binders publish errors to a discrete error channel for each destination. In addition, these error channels are bridged to the global Spring Integration errorChannel mentioned above. You can therefore consume errors for specific destinations and/or for all destinations, using a standard Spring Integration flow (IntegrationFlow, @ServiceActivator, etc).

On the consumer side, the listener thread catches any exceptions and forwards an ErrorMessage to the destination’s error channel. The payload of the message is a MessagingException with the normal failedMessage and cause properties. Usually, the raw data received from the broker is included in a header. For binders that support (and are configured with) a dead letter destination; a MessagePublishingErrorHandler is subscribed to the channel, and the raw data is forwarded to the dead letter destination.

On the producer side; for binders that support some kind of async result after publishing messages (e.g. RabbitMQ, Kafka), you can enable an error channel by setting the …producer.errorChannelEnabled to true. The payload of the ErrorMessage depends on the binder implementation but will be a MessagingException with the normal failedMessage property, as well as additional properties about the failure. Refer to the binder documentation for complete details.

Using @StreamListener for Automatic Content Type Handling

Complementary to its Spring Integration support, Spring Cloud Stream provides its own @StreamListener annotation, modeled after other Spring Messaging annotations (e.g. @MessageMapping, @JmsListener, @RabbitListener, etc.). The @StreamListener annotation provides a simpler model for handling inbound messages, especially when dealing with use cases that involve content type management and type coercion.

Spring Cloud Stream provides an extensible MessageConverter mechanism for handling data conversion by bound channels and for, in this case, dispatching to methods annotated with @StreamListener. The following is an example of an application which processes external Vote events:

@EnableBinding(Sink.class)
public class VoteHandler {

  @Autowired
  VotingService votingService;

  @StreamListener(Sink.INPUT)
  public void handle(Vote vote) {
    votingService.record(vote);
  }
}

The distinction between @StreamListener and a Spring Integration @ServiceActivator is seen when considering an inbound Message that has a String payload and a contentType header of application/json. In the case of @StreamListener, the MessageConverter mechanism will use the contentType header to parse the String payload into a Vote object.

As with other Spring Messaging methods, method arguments can be annotated with @Payload, @Headers and @Header.

For methods which return data, you must use the @SendTo annotation to specify the output binding destination for data returned by the method:

@EnableBinding(Processor.class)
public class TransformProcessor {

  @Autowired
  VotingService votingService;

  @StreamListener(Processor.INPUT)
  @SendTo(Processor.OUTPUT)
  public VoteResult handle(Vote vote) {
    return votingService.record(vote);
  }
}

Using @StreamListener for dispatching messages to multiple methods

Since version 1.2, Spring Cloud Stream supports dispatching messages to multiple @StreamListener methods registered on an input channel, based on a condition.

In order to be eligible to support conditional dispatching, a method must satisfy the follow conditions:

it must not return a value
it must be an individual message handling method (reactive API methods are not supported)

The condition is specified via a SpEL expression in the condition attribute of the annotation and is evaluated for each message. All the handlers that match the condition will be invoked in the same thread and no assumption must be made about the order in which the invocations take place.

An example of using @StreamListener with dispatching conditions can be seen below. In this example, all the messages bearing a header type with the value foo will be dispatched to the receiveFoo method, and all the messages bearing a header type with the value bar will be dispatched to the receiveBar method.

@EnableBinding(Sink.class)
@EnableAutoConfiguration
public static class TestPojoWithAnnotatedArguments {

    @StreamListener(target = Sink.INPUT, condition = "headers['type']=='foo'")
    public void receiveFoo(@Payload FooPojo fooPojo) {
       // handle the message
    }

    @StreamListener(target = Sink.INPUT, condition = "headers['type']=='bar'")
    public void receiveBar(@Payload BarPojo barPojo) {
       // handle the message
    }
}

Dispatching via @StreamListener conditions is only supported for handlers of individual messages, and not for reactive programming support (described below).

3.1.5. Reactive Programming Support

Spring Cloud Stream also supports the use of reactive APIs where incoming and outgoing data is handled as continuous data flows. Support for reactive APIs is available via the spring-cloud-stream-reactive, which needs to be added explicitly to your project.

The programming model with reactive APIs is declarative, where instead of specifying how each individual message should be handled, you can use operators that describe functional transformations from inbound to outbound data flows.

Spring Cloud Stream supports the following reactive APIs:

Reactor
RxJava 1.x

In the future, it is intended to support a more generic model based on Reactive Streams.

The reactive programming model is also using the @StreamListener annotation for setting up reactive handlers. The differences are that:

the @StreamListener annotation must not specify an input or output, as they are provided as arguments and return values from the method;
the arguments of the method must be annotated with @Input and @Output indicating which input or output will the incoming and respectively outgoing data flows connect to;
the return value of the method, if any, will be annotated with @Output, indicating the input where data shall be sent.

Reactive programming support requires Java 1.8.

As of Spring Cloud Stream 1.1.1 and later (starting with release train Brooklyn.SR2), reactive programming support requires the use of Reactor 3.0.4.RELEASE and higher. Earlier Reactor versions (including 3.0.1.RELEASE, 3.0.2.RELEASE and 3.0.3.RELEASE) are not supported. spring-cloud-stream-reactive will transitively retrieve the proper version, but it is possible for the project structure to manage the version of the io.projectreactor:reactor-core to an earlier release, especially when using Maven. This is the case for projects generated via Spring Initializr with Spring Boot 1.x, which will override the Reactor version to 2.0.8.RELEASE. In such cases you must ensure that the proper version of the artifact is released. This can be simply achieved by adding a direct dependency on io.projectreactor:reactor-core with a version of 3.0.4.RELEASE or later to your project.

The use of term reactive is currently referring to the reactive APIs being used and not to the execution model being reactive (i.e. the bound endpoints are still using a 'push' rather than 'pull' model). While some backpressure support is provided by the use of Reactor, we do intend on the long run to support entirely reactive pipelines by the use of native reactive clients for the connected middleware.

Reactor-based handlers

A Reactor based handler can have the following argument types:

For arguments annotated with @Input, it supports the Reactor type Flux. The parameterization of the inbound Flux follows the same rules as in the case of individual message handling: it can be the entire Message, a POJO which can be the Message payload, or a POJO which is the result of a transformation based on the Message content-type header. Multiple inputs are provided;
For arguments annotated with Output, it supports the type FluxSender which connects a Flux produced by the method with an output. Generally speaking, specifying outputs as arguments is only recommended when the method can have multiple outputs;

A Reactor based handler supports a return type of Flux, case in which it must be annotated with @Output. We recommend using the return value of the method when a single output flux is available.

Here is an example of a simple Reactor-based Processor.

@EnableBinding(Processor.class)
@EnableAutoConfiguration
public static class UppercaseTransformer {

  @StreamListener
  @Output(Processor.OUTPUT)
  public Flux<String> receive(@Input(Processor.INPUT) Flux<String> input) {
    return input.map(s -> s.toUpperCase());
  }
}

The same processor using output arguments looks like this:

@EnableBinding(Processor.class)
@EnableAutoConfiguration
public static class UppercaseTransformer {

  @StreamListener
  public void receive(@Input(Processor.INPUT) Flux<String> input,
     @Output(Processor.OUTPUT) FluxSender output) {
     output.send(input.map(s -> s.toUpperCase()));
  }
}

RxJava 1.x support

RxJava 1.x handlers follow the same rules as Reactor-based one, but will use Observable and ObservableSender arguments and return types.

So the first example above will become:

@EnableBinding(Processor.class)
@EnableAutoConfiguration
public static class UppercaseTransformer {

  @StreamListener
  @Output(Processor.OUTPUT)
  public Observable<String> receive(@Input(Processor.INPUT) Observable<String> input) {
    return input.map(s -> s.toUpperCase());
  }
}

The second example above will become:

@EnableBinding(Processor.class)
@EnableAutoConfiguration
public static class UppercaseTransformer {

  @StreamListener
  public void receive(@Input(Processor.INPUT) Observable<String> input,
     @Output(Processor.OUTPUT) ObservableSender output) {
     output.send(input.map(s -> s.toUpperCase()));
  }
}

Reactive Sources

Spring Cloud Stream reactive support also provides the ability for creating reactive sources through the StreamEmitter annotation. Using StreamEmitter annotation, a regular source may be converted to a reactive one. StreamEmitter is a method level annotation that marks a method to be an emitter to outputs declared via EnableBinding. It is not allowed to use the Input annotation along with StreamEmitter, as the methods marked with this annotation are not listening from any input, rather generating to an output. Following the same programming model used in StreamListener, StreamEmitter also allows flexible ways of using the Output annotation depending on whether the method has any arguments, return type etc.

Here are some examples of using StreamEmitter in various styles.

The following example will emit the "Hello World" message every millisecond and publish to a Flux. In this case, the resulting messages in Flux will be sent to the output channel of the Source.

@EnableBinding(Source.class)
@EnableAutoConfiguration
public static class HelloWorldEmitter {

  @StreamEmitter
  @Output(Source.OUTPUT)
  public Flux<String> emit() {
    return Flux.intervalMillis(1)
            .map(l -> "Hello World");
  }
}

Following is another flavor of the same sample as above. Instead of returning a Flux, this method uses a FluxSender to programmatically send Flux from a source.

@EnableBinding(Source.class)
@EnableAutoConfiguration
public static class HelloWorldEmitter {

  @StreamEmitter
  @Output(Source.OUTPUT)
  public void emit(FluxSender output) {
    output.send(Flux.intervalMillis(1)
            .map(l -> "Hello World"));
  }
}

Following is exactly same as the above snippet in functionality and style. However, instead of using an explicit Output annotation at the method level, it is used as the method parameter level.

@EnableBinding(Source.class)
@EnableAutoConfiguration
public static class HelloWorldEmitter {

  @StreamEmitter
  public void emit(@Output(Source.OUTPUT) FluxSender output) {
    output.send(Flux.intervalMillis(1)
            .map(l -> "Hello World"));
  }
}

Here is yet another flavor of writing reacting sources using the Reactive Streams Publisher API and the support for it in the Spring Integration Java DSL. The Publisher is still using Reactor Flux under the hood, but from an application perspective, that is transparent to the user and only needs Reactive Streams and Java DSL for Spring Integration.

@EnableBinding(Source.class)
@EnableAutoConfiguration
public static class HelloWorldEmitter {

  @StreamEmitter
  @Output(Source.OUTPUT)
  @Bean
  public Publisher<Message<String>> emit() {
    return IntegrationFlows.from(() ->
                new GenericMessage<>("Hello World"),
        e -> e.poller(p -> p.fixedDelay(1)))
        .toReactivePublisher();
  }
}

3.1.6. Aggregation

Spring Cloud Stream provides support for aggregating multiple applications together, connecting their input and output channels directly and avoiding the additional cost of exchanging messages via a broker. As of version 1.0 of Spring Cloud Stream, aggregation is supported only for the following types of applications:

sources - applications with a single output channel named output, typically having a single binding of the type org.springframework.cloud.stream.messaging.Source
sinks - applications with a single input channel named input, typically having a single binding of the type org.springframework.cloud.stream.messaging.Sink
processors - applications with a single input channel named input and a single output channel named output, typically having a single binding of the type org.springframework.cloud.stream.messaging.Processor.

They can be aggregated together by creating a sequence of interconnected applications, in which the output channel of an element in the sequence is connected to the input channel of the next element, if it exists. A sequence can start with either a source or a processor, it can contain an arbitrary number of processors and must end with either a processor or a sink.

Depending on the nature of the starting and ending element, the sequence may have one or more bindable channels, as follows:

if the sequence starts with a source and ends with a sink, all communication between the applications is direct and no channels will be bound
if the sequence starts with a processor, then its input channel will become the input channel of the aggregate and will be bound accordingly
if the sequence ends with a processor, then its output channel will become the output channel of the aggregate and will be bound accordingly

Aggregation is performed using the AggregateApplicationBuilder utility class, as in the following example. Let’s consider a project in which we have source, processor and a sink, which may be defined in the project, or may be contained in one of the project’s dependencies.

Each component (source, sink or processor) in an aggregate application must be provided in a separate package if the configuration classes use @SpringBootApplication. This is required to avoid cross-talk between applications, due to the classpath scanning performed by @SpringBootApplication on the configuration classes inside the same package. In the example below, it can be seen that the Source, Processor and Sink application classes are grouped in separate packages. A possible alternative is to provide the source, sink or processor configuration in a separate @Configuration class, avoid the use of @SpringBootApplication/@ComponentScan and use those for aggregation.

package com.app.mysink;

@SpringBootApplication
@EnableBinding(Sink.class)
public class SinkApplication {

	private static Logger logger = LoggerFactory.getLogger(SinkApplication.class);

	@ServiceActivator(inputChannel=Sink.INPUT)
	public void loggerSink(Object payload) {
		logger.info("Received: " + payload);
	}
}

package com.app.myprocessor;

// Imports omitted

@SpringBootApplication
@EnableBinding(Processor.class)
public class ProcessorApplication {

	@Transformer
	public String loggerSink(String payload) {
		return payload.toUpperCase();
	}
}

package com.app.mysource;

// Imports omitted

@SpringBootApplication
@EnableBinding(Source.class)
public class SourceApplication {

	@InboundChannelAdapter(value = Source.OUTPUT)
	public String timerMessageSource() {
		return new SimpleDateFormat().format(new Date());
	}
}

Each configuration can be used for running a separate component, but in this case they can be aggregated together as follows:

package com.app;

// Imports omitted

@SpringBootApplication
public class SampleAggregateApplication {

	public static void main(String[] args) {
		new AggregateApplicationBuilder()
			.from(SourceApplication.class).args("--fixedDelay=5000")
			.via(ProcessorApplication.class)
			.to(SinkApplication.class).args("--debug=true").run(args);
	}
}

The starting component of the sequence is provided as argument to the from() method. The ending component of the sequence is provided as argument to the to() method. Intermediate processors are provided as argument to the via() method. Multiple processors of the same type can be chained together (e.g. for pipelining transformations with different configurations). For each component, the builder can provide runtime arguments for Spring Boot configuration.

Configuring aggregate application

Spring Cloud Stream supports passing properties for the individual applications inside the aggregate application using 'namespace' as prefix.

The namespace can be set for applications as follows:

@SpringBootApplication
public class SampleAggregateApplication {

	public static void main(String[] args) {
		new AggregateApplicationBuilder()
			.from(SourceApplication.class).namespace("source").args("--fixedDelay=5000")
			.via(ProcessorApplication.class).namespace("processor1")
			.to(SinkApplication.class).namespace("sink").args("--debug=true").run(args);
	}
}

Once the 'namespace' is set for the individual applications, the application properties with the namespace as prefix can be passed to the aggregate application using any supported property source (commandline, environment properties etc.,)

For instance, to override the default fixedDelay and debug properties of 'source' and 'sink' applications:

java -jar target/MyAggregateApplication-0.0.1-SNAPSHOT.jar --source.fixedDelay=10000 --sink.debug=false

Configuring binding service properties for non self contained aggregate application

The non self-contained aggregate application is bound to external broker via either or both the inbound/outbound components (typically, message channels) of the aggregate application while the applications inside the aggregate application are directly bound. For example: a source application’s output and a processor application’s input are directly bound while the processor’s output channel is bound to an external destination at the broker. When passing the binding service properties for non-self contained aggregate application, it is required to pass the binding service properties to the aggregate application instead of setting them as 'args' to individual child application. For instance,

@SpringBootApplication
public class SampleAggregateApplication {

	public static void main(String[] args) {
		new AggregateApplicationBuilder()
			.from(SourceApplication.class).namespace("source").args("--fixedDelay=5000")
			.via(ProcessorApplication.class).namespace("processor1").args("--debug=true").run(args);
	}
}

The binding properties like --spring.cloud.stream.bindings.output.destination=processor-output need to be specified as one of the external configuration properties (cmdline arg etc.,).

4. Binders

Spring Cloud Stream provides a Binder abstraction for use in connecting to physical destinations at the external middleware. This section provides information about the main concepts behind the Binder SPI, its main components, and implementation-specific details.

4.1. Producers and Consumers

Figure 5. Producers and Consumers

A producer is any component that sends messages to a channel. The channel can be bound to an external message broker via a Binder implementation for that broker. When invoking the bindProducer() method, the first parameter is the name of the destination within the broker, the second parameter is the local channel instance to which the producer will send messages, and the third parameter contains properties (such as a partition key expression) to be used within the adapter that is created for that channel.

A consumer is any component that receives messages from a channel. As with a producer, the consumer’s channel can be bound to an external message broker. When invoking the bindConsumer() method, the first parameter is the destination name, and a second parameter provides the name of a logical group of consumers. Each group that is represented by consumer bindings for a given destination receives a copy of each message that a producer sends to that destination (i.e., publish-subscribe semantics). If there are multiple consumer instances bound using the same group name, then messages will be load-balanced across those consumer instances so that each message sent by a producer is consumed by only a single consumer instance within each group (i.e., queueing semantics).

4.2. Binder SPI

The Binder SPI consists of a number of interfaces, out-of-the box utility classes and discovery strategies that provide a pluggable mechanism for connecting to external middleware.

The key point of the SPI is the Binder interface which is a strategy for connecting inputs and outputs to external middleware.

public interface Binder<T, C extends ConsumerProperties, P extends ProducerProperties> {
	Binding<T> bindConsumer(String name, String group, T inboundBindTarget, C consumerProperties);

	Binding<T> bindProducer(String name, T outboundBindTarget, P producerProperties);
}

The interface is parameterized, offering a number of extension points:

input and output bind targets - as of version 1.0, only MessageChannel is supported, but this is intended to be used as an extension point in the future;
extended consumer and producer properties - allowing specific Binder implementations to add supplemental properties which can be supported in a type-safe manner.

A typical binder implementation consists of the following

a class that implements the Binder interface;
a Spring @Configuration class that creates a bean of the type above along with the middleware connection infrastructure;
a META-INF/spring.binders file found on the classpath containing one or more binder definitions, e.g.

kafka:\
org.springframework.cloud.stream.binder.kafka.config.KafkaBinderConfiguration

4.3. Binder Detection

Spring Cloud Stream relies on implementations of the Binder SPI to perform the task of connecting channels to message brokers. Each Binder implementation typically connects to one type of messaging system.

4.3.1. Classpath Detection

By default, Spring Cloud Stream relies on Spring Boot’s auto-configuration to configure the binding process. If a single Binder implementation is found on the classpath, Spring Cloud Stream will use it automatically. For example, a Spring Cloud Stream project that aims to bind only to RabbitMQ can simply add the following dependency:

<dependency>
  <groupId>org.springframework.cloud</groupId>
  <artifactId>spring-cloud-stream-binder-rabbit</artifactId>
</dependency>

For the specific maven coordinates of other binder dependencies, please refer to the documentation of that binder implementation.

4.4. Multiple Binders on the Classpath

When multiple binders are present on the classpath, the application must indicate which binder is to be used for each channel binding. Each binder configuration contains a META-INF/spring.binders, which is a simple properties file:

rabbit:\
org.springframework.cloud.stream.binder.rabbit.config.RabbitServiceAutoConfiguration

Similar files exist for the other provided binder implementations (e.g., Kafka), and custom binder implementations are expected to provide them, as well. The key represents an identifying name for the binder implementation, whereas the value is a comma-separated list of configuration classes that each contain one and only one bean definition of type org.springframework.cloud.stream.binder.Binder.

Binder selection can either be performed globally, using the spring.cloud.stream.defaultBinder property (e.g., spring.cloud.stream.defaultBinder=rabbit) or individually, by configuring the binder on each channel binding. For instance, a processor application (that has channels with the names input and output for read/write respectively) which reads from Kafka and writes to RabbitMQ can specify the following configuration:

spring.cloud.stream.bindings.input.binder=kafka
spring.cloud.stream.bindings.output.binder=rabbit

4.5. Connecting to Multiple Systems

By default, binders share the application’s Spring Boot auto-configuration, so that one instance of each binder found on the classpath will be created. If your application should connect to more than one broker of the same type, you can specify multiple binder configurations, each with different environment settings.

Turning on explicit binder configuration will disable the default binder configuration process altogether. If you do this, all binders in use must be included in the configuration. Frameworks that intend to use Spring Cloud Stream transparently may create binder configurations that can be referenced by name, but will not affect the default binder configuration. In order to do so, a binder configuration may have its defaultCandidate flag set to false, e.g. spring.cloud.stream.binders.<configurationName>.defaultCandidate=false. This denotes a configuration that will exist independently of the default binder configuration process.

For example, this is the typical configuration for a processor application which connects to two RabbitMQ broker instances:

spring:
  cloud:
    stream:
      bindings:
        input:
          destination: foo
          binder: rabbit1
        output:
          destination: bar
          binder: rabbit2
      binders:
        rabbit1:
          type: rabbit
          environment:
            spring:
              rabbitmq:
                host: <host1>
        rabbit2:
          type: rabbit
          environment:
            spring:
              rabbitmq:
                host: <host2>

4.6. Binder configuration properties

The following properties are available when creating custom binder configurations. They must be prefixed with spring.cloud.stream.binders.<configurationName>.

type: The binder type. It typically references one of the binders found on the classpath, in particular a key in a META-INF/spring.binders file.

By default, it has the same value as the configuration name.
inheritEnvironment: Whether the configuration will inherit the environment of the application itself.

Default true.
environment: Root for a set of properties that can be used to customize the environment of the binder. When this is configured, the context in which the binder is being created is not a child of the application context. This allows for complete separation between the binder components and the application components.

Default empty.
defaultCandidate: Whether the binder configuration is a candidate for being considered a default binder, or can be used only when explicitly referenced. This allows adding binder configurations without interfering with the default processing.

Default true.

5. Configuration Options

Spring Cloud Stream supports general configuration options as well as configuration for bindings and binders. Some binders allow additional binding properties to support middleware-specific features.

Configuration options can be provided to Spring Cloud Stream applications via any mechanism supported by Spring Boot. This includes application arguments, environment variables, and YAML or .properties files.

5.1. Spring Cloud Stream Properties

spring.cloud.stream.instanceCount: The number of deployed instances of an application. Must be set for partitioning and if using Kafka.

Default: 1.
spring.cloud.stream.instanceIndex: The instance index of the application: a number from 0 to instanceCount-1. Used for partitioning and with Kafka. Automatically set in Cloud Foundry to match the application’s instance index.
spring.cloud.stream.dynamicDestinations: A list of destinations that can be bound dynamically (for example, in a dynamic routing scenario). If set, only listed destinations can be bound.

Default: empty (allowing any destination to be bound).
spring.cloud.stream.defaultBinder: The default binder to use, if multiple binders are configured. See Multiple Binders on the Classpath.

Default: empty.
spring.cloud.stream.overrideCloudConnectors: This property is only applicable when the cloud profile is active and Spring Cloud Connectors are provided with the application. If the property is false (the default), the binder will detect a suitable bound service (e.g. a RabbitMQ service bound in Cloud Foundry for the RabbitMQ binder) and will use it for creating connections (usually via Spring Cloud Connectors). When set to true, this property instructs binders to completely ignore the bound services and rely on Spring Boot properties (e.g. relying on the spring.rabbitmq.* properties provided in the environment for the RabbitMQ binder). The typical usage of this property is to be nested in a customized environment when connecting to multiple systems.

Default: false.

5.2. Binding Properties

Binding properties are supplied using the format spring.cloud.stream.bindings.<channelName>.<property>=<value>. The <channelName> represents the name of the channel being configured (e.g., output for a Source).

To avoid repetition, Spring Cloud Stream supports setting values for all channels, in the format spring.cloud.stream.default.<property>=<value>.

In what follows, we indicate where we have omitted the spring.cloud.stream.bindings.<channelName>. prefix and focus just on the property name, with the understanding that the prefix will be included at runtime.

5.2.1. Properties for Use of Spring Cloud Stream

The following binding properties are available for both input and output bindings and must be prefixed with spring.cloud.stream.bindings.<channelName>., e.g. spring.cloud.stream.bindings.input.destination=ticktock.

Default values can be set by using the prefix spring.cloud.stream.default, e.g. spring.cloud.stream.default.contentType=application/json.

destination: The target destination of a channel on the bound middleware (e.g., the RabbitMQ exchange or Kafka topic). If the channel is bound as a consumer, it could be bound to multiple destinations and the destination names can be specified as comma separated String values. If not set, the channel name is used instead. The default value of this property cannot be overridden.
group: The consumer group of the channel. Applies only to inbound bindings. See Consumer Groups.

Default: null (indicating an anonymous consumer).
contentType: The content type of the channel.

Default: null (so that no type coercion is performed).
binder: The binder used by this binding. See Multiple Binders on the Classpath for details.

Default: null (the default binder will be used, if one exists).

5.2.2. Consumer properties

The following binding properties are available for input bindings only and must be prefixed with spring.cloud.stream.bindings.<channelName>.consumer., e.g. spring.cloud.stream.bindings.input.consumer.concurrency=3.

Default values can be set by using the prefix spring.cloud.stream.default.consumer, e.g. spring.cloud.stream.default.consumer.headerMode=raw.

concurrency: The concurrency of the inbound consumer.

Default: 1.
partitioned: Whether the consumer receives data from a partitioned producer.

Default: false.
headerMode: When set to raw, disables header parsing on input. Effective only for messaging middleware that does not support message headers natively and requires header embedding. Useful when inbound data is coming from outside Spring Cloud Stream applications.

Default: embeddedHeaders.
maxAttempts: If processing fails, the number of attempts to process the message (including the first). Set to 1 to disable retry.

Default: 3.
backOffInitialInterval: The backoff initial interval on retry.

Default: 1000.
backOffMaxInterval: The maximum backoff interval.

Default: 10000.
backOffMultiplier: The backoff multiplier.

Default: 2.0.
instanceIndex: When set to a value greater than equal to zero, allows customizing the instance index of this consumer (if different from spring.cloud.stream.instanceIndex). When set to a negative value, it will default to spring.cloud.stream.instanceIndex.

Default: -1.
instanceCount: When set to a value greater than equal to zero, allows customizing the instance count of this consumer (if different from spring.cloud.stream.instanceCount). When set to a negative value, it will default to spring.cloud.stream.instanceCount.

Default: -1.

5.2.3. Producer Properties

The following binding properties are available for output bindings only and must be prefixed with spring.cloud.stream.bindings.<channelName>.producer., e.g. spring.cloud.stream.bindings.input.producer.partitionKeyExpression=payload.id.

Default values can be set by using the prefix spring.cloud.stream.default.producer, e.g. spring.cloud.stream.default.producer.partitionKeyExpression=payload.id.

partitionKeyExpression: A SpEL expression that determines how to partition outbound data. If set, or if partitionKeyExtractorClass is set, outbound data on this channel will be partitioned, and partitionCount must be set to a value greater than 1 to be effective. The two options are mutually exclusive. See Partitioning Support.

Default: null.
partitionKeyExtractorClass: A PartitionKeyExtractorStrategy implementation. If set, or if partitionKeyExpression is set, outbound data on this channel will be partitioned, and partitionCount must be set to a value greater than 1 to be effective. The two options are mutually exclusive. See Partitioning Support.

Default: null.
partitionSelectorClass: A PartitionSelectorStrategy implementation. Mutually exclusive with partitionSelectorExpression. If neither is set, the partition will be selected as the hashCode(key) % partitionCount, where key is computed via either partitionKeyExpression or partitionKeyExtractorClass.

Default: null.
partitionSelectorExpression: A SpEL expression for customizing partition selection. Mutually exclusive with partitionSelectorClass. If neither is set, the partition will be selected as the hashCode(key) % partitionCount, where key is computed via either partitionKeyExpression or partitionKeyExtractorClass.

Default: null.
partitionCount: The number of target partitions for the data, if partitioning is enabled. Must be set to a value greater than 1 if the producer is partitioned. On Kafka, interpreted as a hint; the larger of this and the partition count of the target topic is used instead.

Default: 1.
requiredGroups: A comma-separated list of groups to which the producer must ensure message delivery even if they start after it has been created (e.g., by pre-creating durable queues in RabbitMQ).
headerMode: When set to raw, disables header embedding on output. Effective only for messaging middleware that does not support message headers natively and requires header embedding. Useful when producing data for non-Spring Cloud Stream applications.

Default: embeddedHeaders.
useNativeEncoding: When set to true, the outbound message is serialized directly by client library, which must be configured correspondingly (e.g. setting an appropriate Kafka producer value serializer). When this configuration is being used, the outbound message marshalling is not based on the contentType of the binding. When native encoding is used, it is the responsibility of the consumer to use appropriate decoder (ex: Kafka consumer value de-serializer) to deserialize the inbound message. Also, when native encoding/decoding is used the headerMode property is ignored and headers will not be embedded into the message.

Default: false.
errorChannelEnabled: When set to true, if the binder supports async send results; send failures will be sent to an error channel for the destination. See Message Channel Binders and Error Channels for more information.

Default: false.

5.3. Using dynamically bound destinations

Besides the channels defined via @EnableBinding, Spring Cloud Stream allows applications to send messages to dynamically bound destinations. This is useful, for example, when the target destination needs to be determined at runtime. Applications can do so by using the BinderAwareChannelResolver bean, registered automatically by the @EnableBinding annotation.

The property 'spring.cloud.stream.dynamicDestinations' can be used for restricting the dynamic destination names to a set known beforehand (whitelisting). If the property is not set, any destination can be bound dynamicaly.

The BinderAwareChannelResolver can be used directly as in the following example, in which a REST controller uses a path variable to decide the target channel.

@EnableBinding
@Controller
public class SourceWithDynamicDestination {

	@Autowired
	private BinderAwareChannelResolver resolver;

	@RequestMapping(path = "/{target}", method = POST, consumes = "*/*")
	@ResponseStatus(HttpStatus.ACCEPTED)
	public void handleRequest(@RequestBody String body, @PathVariable("target") target,
	       @RequestHeader(HttpHeaders.CONTENT_TYPE) Object contentType) {
		sendMessage(body, target, contentType);
	}

	private void sendMessage(String body, String target, Object contentType) {
		resolver.resolveDestination(target).send(MessageBuilder.createMessage(body,
				new MessageHeaders(Collections.singletonMap(MessageHeaders.CONTENT_TYPE, contentType))));
	}
}

After starting the application on the default port 8080, when sending the following data:

curl -H "Content-Type: application/json" -X POST -d "customer-1" http://localhost:8080/customers

curl -H "Content-Type: application/json" -X POST -d "order-1" http://localhost:8080/orders

The destinations 'customers' and 'orders' are created in the broker (for example: exchange in case of Rabbit or topic in case of Kafka) with the names 'customers' and 'orders', and the data is published to the appropriate destinations.

The BinderAwareChannelResolver is a general purpose Spring Integration DestinationResolver and can be injected in other components. For example, in a router using a SpEL expression based on the target field of an incoming JSON message.

@EnableBinding
@Controller
public class SourceWithDynamicDestination {

	@Autowired
	private BinderAwareChannelResolver resolver;


	@RequestMapping(path = "/", method = POST, consumes = "application/json")
	@ResponseStatus(HttpStatus.ACCEPTED)
	public void handleRequest(@RequestBody String body, @RequestHeader(HttpHeaders.CONTENT_TYPE) Object contentType) {
		sendMessage(body, contentType);
	}

	private void sendMessage(Object body, Object contentType) {
		routerChannel().send(MessageBuilder.createMessage(body,
				new MessageHeaders(Collections.singletonMap(MessageHeaders.CONTENT_TYPE, contentType))));
	}

	@Bean(name = "routerChannel")
	public MessageChannel routerChannel() {
		return new DirectChannel();
	}

	@Bean
	@ServiceActivator(inputChannel = "routerChannel")
	public ExpressionEvaluatingRouter router() {
        ExpressionEvaluatingRouter router =
            new ExpressionEvaluatingRouter(new SpelExpressionParser().parseExpression("payload.target"));
		router.setDefaultOutputChannelName("default-output");
		router.setChannelResolver(resolver);
		return router;
	}
}

6. Content Type and Transformation

To allow you to propagate information about the content type of produced messages, Spring Cloud Stream attaches, by default, a contentType header to outbound messages. For middleware that does not directly support headers, Spring Cloud Stream provides its own mechanism of automatically wrapping outbound messages in an envelope of its own. For middleware that does support headers, Spring Cloud Stream applications may receive messages with a given content type from non-Spring Cloud Stream applications.

Spring Cloud Stream can handle messages based on this information in two ways:

Through its contentType settings on inbound and outbound channels
Through its argument mapping performed for methods annotated with @StreamListener

Spring Cloud Stream allows you to declaratively configure type conversion for inputs and outputs using the spring.cloud.stream.bindings.<channelName>.content-type property of a binding. Note that general type conversion may also be accomplished easily by using a transformer inside your application. Currently, Spring Cloud Stream natively supports the following type conversions commonly used in streams:

JSON to/from POJO
JSON to/from org.springframework.tuple.Tuple
Object to/from byte[] : Either the raw bytes serialized for remote transport, bytes emitted by an application, or converted to bytes using Java serialization(requires the object to be Serializable)
String to/from byte[]
Object to plain text (invokes the object’s toString() method)

Where JSON represents either a byte array or String payload containing JSON. Currently, Objects may be converted from a JSON byte array or String. Converting to JSON always produces a String.

If no content-type property is set on an outbound channel, Spring Cloud Stream will serialize the payload using a serializer based on the Kryo serialization framework. Deserializing messages at the destination requires the payload class to be present on the receiver’s classpath.

6.1. MIME types

content-type values are parsed as media types, e.g., application/json or text/plain;charset=UTF-8. MIME types are especially useful for indicating how to convert to String or byte[] content. Spring Cloud Stream also uses MIME type format to represent Java types, using the general type application/x-java-object with a type parameter. For example, application/x-java-object;type=java.util.Map or application/x-java-object;type=com.bar.Foo can be set as the content-type property of an input binding. In addition, Spring Cloud Stream provides custom MIME types, notably, application/x-spring-tuple to specify a Tuple.

6.2. MIME types and Java types

The type conversions Spring Cloud Stream provides out of the box are summarized in the following table: 'Source Payload' means the payload before conversion and 'Target Payload' means the 'payload' after conversion. The type conversion can occur either on the 'producer' side (output) or at the 'consumer' side (input).

Source Payload Target Payload content-type header (source message) content-type header (after conversion) Comments

Source Payload	Target Payload	`content-type` header (source message)	`content-type` header (after conversion)	Comments
POJO	JSON String	ignored	application/json
Tuple	JSON String	ignored	application/json	JSON is tailored for Tuple
POJO	String (toString())	ignored	text/plain, java.lang.String
POJO	byte[] (java.io serialized)	ignored	application/x-java-serialized-object
JSON byte[] or String	POJO	application/json (or none)	application/x-java-object
byte[] or String	Serializable	application/x-java-serialized-object	application/x-java-object
JSON byte[] or String	Tuple	application/json (or none)	application/x-spring-tuple
byte[]	String	any	text/plain, java.lang.String	will apply any Charset specified in the content-type header
String	byte[]	any	application/octet-stream	will apply any Charset specified in the content-type header

POJO

JSON String

ignored

application/json

Tuple

JSON String

ignored

application/json

JSON is tailored for Tuple

POJO

String (toString())

ignored

text/plain, java.lang.String

POJO

byte[] (java.io serialized)

ignored

application/x-java-serialized-object

JSON byte[] or String

POJO

application/json (or none)

application/x-java-object

byte[] or String

Serializable

application/x-java-serialized-object

application/x-java-object

JSON byte[] or String

Tuple

application/json (or none)

application/x-spring-tuple

byte[]

String

any

text/plain, java.lang.String

will apply any Charset specified in the content-type header

String

byte[]

any

application/octet-stream

will apply any Charset specified in the content-type header

Conversion applies to payloads that require type conversion. For example, if an application produces an XML string with outputType=application/json, the payload will not be converted from XML to JSON. This is because the payload send to the outbound channel is already a String so no conversion will be applied at runtime. It is also important to note that when using the default serialization mechanism, the payload class must be shared between the sending and receiving application, and compatible with the binary content. This can create issues when application code changes independently in the two applications, as the binary format and code may become incompatible.

While conversion is supported for both inbound and outbound channels, it is especially recommended to be used for the conversion of outbound messages. For the conversion of inbound messages, especially when the target is a POJO, the @StreamListener support will perform the conversion automatically.

6.3. Customizing message conversion

Besides the conversions that it supports out of the box, Spring Cloud Stream also supports registering your own message conversion implementations. This allows you to send and receive data in a variety of custom formats, including binary, and associate them with specific contentTypes. Spring Cloud Stream registers all the beans of type org.springframework.messaging.converter.MessageConverter as custom message converters along with the out of the box message converters.

If your message converter needs to work with a specific content-type and target class (for both input and output), then the message converter needs to extend org.springframework.messaging.converter.AbstractMessageConverter. For conversion when using @StreamListener, a message converter that implements org.springframework.messaging.converter.MessageConverter would suffice.

Here is an example of creating a message converter bean (with the content-type application/bar) inside a Spring Cloud Stream application:

@EnableBinding(Sink.class)
@SpringBootApplication
public static class SinkApplication {

  ...

  @Bean
  public MessageConverter customMessageConverter() {
    return new MyCustomMessageConverter();
  }

public class MyCustomMessageConverter extends AbstractMessageConverter {

	public MyCustomMessageConverter() {
		super(new MimeType("application", "bar"));
	}

	@Override
  protected boolean supports(Class<?> clazz) {
    return (Bar.class == clazz);
  }

	@Override
	protected Object convertFromInternal(Message<?> message, Class<?> targetClass, Object conversionHint) {
		Object payload = message.getPayload();
		return (payload instanceof Bar ? payload : new Bar((byte[]) payload));
	}
}

Spring Cloud Stream also provides support for Avro-based converters and schema evolution. See the specific section for details.

6.4. `@StreamListener` and Message Conversion

The @StreamListener annotation provides a convenient way for converting incoming messages without the need to specify the content type of an input channel. During the dispatching process to methods annotated with @StreamListener, a conversion will be applied automatically if the argument requires it.

For example, let’s consider a message with the String content {"greeting":"Hello, world"} and a content-type header of application/json is received on the input channel. Let us consider the following application that receives it:

public class GreetingMessage {

  String greeting;

  public String getGreeting() {
    return greeting;
  }

  public void setGreeting(String greeting) {
    this.greeting = greeting;
  }
}

@EnableBinding(Sink.class)
@EnableAutoConfiguration
public static class GreetingSink {

		@StreamListener(Sink.INPUT)
		public void receive(Greeting greeting) {
			// handle Greeting
		}
	}

The argument of the method will be populated automatically with the POJO containing the unmarshalled form of the JSON String.

7. Schema Evolution Support

Spring Cloud Stream provides support for schema evolution so that the data can be evolved over time and still work with older or newer producers and consumers and vice versa. Most serialization models, especially the ones that aim for portability across different platforms and languages, rely on a schema that describes how the data is serialized in the binary payload. In order to serialize the data and then to interpret it, both the sending and receiving sides must have access to a schema that describes the binary format. In certain cases, the schema can be inferred from the payload type on serialization or from the target type on deserialization. However, many applications benefit from having access to an explicit schema that describes the binary data format. A schema registry lets you store schema information in a textual format (typically JSON) and makes that information accessible to various applications that need it to receive and send data in binary format. A schema is referenceable as a tuple consisting of:

A subject that is the logical name of the schema
The schema version
The schema format, which describes the binary format of the data

This following sections goes through the details of various components involved in schema evolution process.

7.1. Schema Registry Client

The client-side abstraction for interacting with schema registry servers is the SchemaRegistryClient interface, which has the following structure:

public interface SchemaRegistryClient {

    SchemaRegistrationResponse register(String subject, String format, String schema);

    String fetch(SchemaReference schemaReference);

    String fetch(Integer id);

}

Spring Cloud Stream provides out-of-the-box implementations for interacting with its own schema server and for interacting with the Confluent Schema Registry.

A client for the Spring Cloud Stream schema registry can be configured by using the @EnableSchemaRegistryClient, as follows:

  @EnableBinding(Sink.class)
  @SpringBootApplication
  @EnableSchemaRegistryClient
  public static class AvroSinkApplication {
    ...
  }

The default converter is optimized to cache not only the schemas from the remote server but also the parse() and toString() methods, which are quite expensive. Because of this, it uses a DefaultSchemaRegistryClient that does not cache responses. If you intend to change the default behavior, you can use the client directly on your code and override it to the desired outcome. To do so, you have to add the property spring.cloud.stream.schemaRegistryClient.cached=true to your application properties.

7.1.1. Schema Registry Client Properties

The Schema Registry Client supports the following properties:

spring.cloud.stream.schemaRegistryClient.endpoint: The location of the schema-server. When setting this, use a full URL, including protocol (http or https) , port, and context path.
Default: localhost:8990/
spring.cloud.stream.schemaRegistryClient.cached: Whether the client should cache schema server responses. Normally set to false, as the caching happens in the message converter. Clients using the schema registry client should set this to true.
Default: true

7.2. Avro Schema Registry Client Message Converters

For applications that have a SchemaRegistryClient bean registered with the application context, Spring Cloud Stream auto configures an Apache Avro message converter for schema management. This eases schema evolution, as applications that receive messages can get easy access to a writer schema that can be reconciled with their own reader schema.

For outbound messages, if the content type of the channel is set to application/*+avro, the MessageConverter is activated, as shown in the following example:

spring.cloud.stream.bindings.output.contentType=application/*+avro

During the outbound conversion, the message converter tries to infer the schema of each outbound messages (based on its type) and register it to a subject (based on the payload type) by using the SchemaRegistryClient. If an identical schema is already found, then a reference to it is retrieved. If not, the schema is registered, and a new version number is provided. The message is sent with a contentType header by using the following scheme: application/[prefix].[subject].v[version]+avro, where prefix is configurable and subject is deduced from the payload type.

For example, a message of the type User might be sent as a binary payload with a content type of application/vnd.user.v2+avro, where user is the subject and 2 is the version number.

When receiving messages, the converter infers the schema reference from the header of the incoming message and tries to retrieve it. The schema is used as the writer schema in the deserialization process.

7.2.1. Avro Schema Registry Message Converter Properties

If you have enabled Avro based schema registry client by setting spring.cloud.stream.bindings.output.contentType=application/*+avro, you can customize the behavior of the registration by setting the following properties.

spring.cloud.stream.schema.avro.dynamicSchemaGenerationEnabled: Enable if you want the converter to use reflection to infer a Schema from a POJO.

Default: false
spring.cloud.stream.schema.avro.readerSchema: Avro compares schema versions by looking at a writer schema (origin payload) and a reader schema (your application payload). See the Avro documentation for more information. If set, this overrides any lookups at the schema server and uses the local schema as the reader schema. Default: null
spring.cloud.stream.schema.avro.schemaLocations: Registers any .avsc files listed in this property with the Schema Server.

Default: empty
spring.cloud.stream.schema.avro.prefix: The prefix to be used on the Content-Type header.

Default: vnd

7.3. Apache Avro Message Converters

Spring Cloud Stream provides support for schema-based message converters through its spring-cloud-stream-schema module. Currently, the only serialization format supported out of the box for schema-based message converters is Apache Avro, with more formats to be added in future versions.

The spring-cloud-stream-schema module contains two types of message converters that can be used for Apache Avro serialization:

Converters that use the class information of the serialized or deserialized objects or a schema with a location known at startup.
Converters that use a schema registry. They locate the schemas at runtime and dynamically register new schemas as domain objects evolve.

7.4. Converters with Schema Support

The AvroSchemaMessageConverter supports serializing and deserializing messages either by using a predefined schema or by using the schema information available in the class (either reflectively or contained in the SpecificRecord). If you provide a custom converter, then the default AvroSchemaMessageConverter bean is not created. The following example shows a custom converter:

To use custom converters, you can simply add it to the application context, optionally specifying one or more MimeTypes with which to associate it. The default MimeType is application/avro.

If the target type of the conversion is a GenericRecord, a schema must be set.

The following example shows how to configure a converter in a sink application by registering the Apache Avro MessageConverter without a predefined schema. In this example, note that the mime type value is avro/bytes, not the default application/avro.

@EnableBinding(Sink.class)
@SpringBootApplication
public static class SinkApplication {

  ...

  @Bean
  public MessageConverter userMessageConverter() {
      return new AvroSchemaMessageConverter(MimeType.valueOf("avro/bytes"));
  }
}

Conversely, the following application registers a converter with a predefined schema (found on the classpath):

@EnableBinding(Sink.class)
@SpringBootApplication
public static class SinkApplication {

  ...

  @Bean
  public MessageConverter userMessageConverter() {
      AvroSchemaMessageConverter converter = new AvroSchemaMessageConverter(MimeType.valueOf("avro/bytes"));
      converter.setSchemaLocation(new ClassPathResource("schemas/User.avro"));
      return converter;
  }
}

7.5. Schema Registry Server

Spring Cloud Stream provides a schema registry server implementation. To use it, you can add the spring-cloud-stream-schema-server artifact to your project and use the @EnableSchemaRegistryServer annotation, which adds the schema registry server REST controller to your application. This annotation is intended to be used with Spring Boot web applications, and the listening port of the server is controlled by the server.port property. The spring.cloud.stream.schema.server.path property can be used to control the root path of the schema server (especially when it is embedded in other applications). The spring.cloud.stream.schema.server.allowSchemaDeletion boolean property enables the deletion of a schema. By default, this is disabled.

The schema registry server uses a relational database to store the schemas. By default, it uses an embedded database. You can customize the schema storage by using the Spring Boot SQL database and JDBC configuration options.

The following example shows a Spring Boot application that enables the schema registry:

@SpringBootApplication
@EnableSchemaRegistryServer
public class SchemaRegistryServerApplication {
    public static void main(String[] args) {
        SpringApplication.run(SchemaRegistryServerApplication.class, args);
    }
}

7.5.1. Schema Registry Server API

The Schema Registry Server API consists of the following operations:

POST / — see “Registering a New Schema”
'GET /{subject}/{format}/{version}' — see “Retrieving an Existing Schema by Subject, Format, and Version”
GET /{subject}/{format} — see “Retrieving an Existing Schema by Subject and Format”
GET /schemas/{id} — see “Retrieving an Existing Schema by ID”
DELETE /{subject}/{format}/{version} — see “Deleting a Schema by Subject, Format, and Version”
DELETE /schemas/{id} — see “Deleting a Schema by ID”
DELETE /{subject} — see “Deleting a Schema by Subject”

Registering a New Schema

To register a new schema, send a POST request to the / endpoint.

The / accepts a JSON payload with the following fields:

subject: The schema subject
format: The schema format
definition: The schema definition

Its response is a schema object in JSON, with the following fields:

id: The schema ID
subject: The schema subject
format: The schema format
version: The schema version
definition: The schema definition

Retrieving an Existing Schema by Subject, Format, and Version

To retrieve an existing schema by subject, format, and version, send GET request to the /{subject}/{format}/{version} endpoint.

Its response is a schema object in JSON, with the following fields:

id: The schema ID
subject: The schema subject
format: The schema format
version: The schema version
definition: The schema definition

Retrieving an Existing Schema by Subject and Format

To retrieve an existing schema by subject and format, send a GET request to the /subject/format endpoint.

Its response is a list of schemas with each schema object in JSON, with the following fields:

id: The schema ID
subject: The schema subject
format: The schema format
version: The schema version
definition: The schema definition

Retrieving an Existing Schema by ID

To retrieve a schema by its ID, send a GET request to the /schemas/{id} endpoint.

Its response is a schema object in JSON, with the following fields:

id: The schema ID
subject: The schema subject
format: The schema format
version: The schema version
definition: The schema definition

Deleting a Schema by Subject, Format, and Version

To delete a schema identified by its subject, format, and version, send a DELETE request to the /{subject}/{format}/{version} endpoint.

Deleting a Schema by ID

To delete a schema by its ID, send a DELETE request to the /schemas/{id} endpoint.

Deleting a Schema by Subject

DELETE /{subject}

Delete existing schemas by their subject.

This note applies to users of Spring Cloud Stream 1.1.0.RELEASE only. Spring Cloud Stream 1.1.0.RELEASE used the table name, schema, for storing Schema objects. Schema is a keyword in a number of database implementations. To avoid any conflicts in the future, starting with 1.1.1.RELEASE, we have opted for the name SCHEMA_REPOSITORY for the storage table. Any Spring Cloud Stream 1.1.0.RELEASE users who upgrade should migrate their existing schemas to the new table before upgrading.

7.5.2. Using Confluent’s Schema Registry

The default configuration creates a DefaultSchemaRegistryClient bean. If you want to use the Confluent schema registry, you need to create a bean of type ConfluentSchemaRegistryClient, which supersedes the one configured by default by the framework. The following example shows how to create such a bean:

@Bean
public SchemaRegistryClient schemaRegistryClient(@Value("${spring.cloud.stream.schemaRegistryClient.endpoint}") String endpoint){
  ConfluentSchemaRegistryClient client = new ConfluentSchemaRegistryClient();
  client.setEndpoint(endpoint);
  return client;
}

The ConfluentSchemaRegistryClient is tested against Confluent platform version 4.0.0.

7.6. Schema Registration and Resolution

To better understand how Spring Cloud Stream registers and resolves new schemas and its use of Avro schema comparison features, we provide two separate subsections:

“Schema Registration Process (Serialization)”
“Schema Resolution Process (Deserialization)”

7.6.1. Schema Registration Process (Serialization)

The first part of the registration process is extracting a schema from the payload that is being sent over a channel. Avro types such as SpecificRecord or GenericRecord already contain a schema, which can be retrieved immediately from the instance. In the case of POJOs, a schema is inferred if the spring.cloud.stream.schema.avro.dynamicSchemaGenerationEnabled property is set to true (the default).

Figure 6. Schema Writer Resolution Process

Ones a schema is obtained, the converter loads its metadata (version) from the remote server. First, it queries a local cache. If no result is found, it submits the data to the server, which replies with versioning information. The converter always caches the results to avoid the overhead of querying the Schema Server for every new message that needs to be serialized.

Figure 7. Schema Registration Process

With the schema version information, the converter sets the contentType header of the message to carry the version information — for example: application/vnd.user.v1+avro.

7.6.2. Schema Resolution Process (Deserialization)

When reading messages that contain version information (that is, a contentType header with a scheme like the one described under “Schema Registration Process (Serialization)”), the converter queries the Schema server to fetch the writer schema of the message. Once it has found the correct schema of the incoming message, it retrieves the reader schema and, by using Avro’s schema resolution support, reads it into the reader definition (setting defaults and any missing properties).

Figure 8. Schema Reading Resolution Process

You should understand the difference between a writer schema (the application that wrote the message) and a reader schema (the receiving application). We suggest taking a moment to read the Avro terminology and understand the process. Spring Cloud Stream always fetches the writer schema to determine how to read a message. If you want to get Avro’s schema evolution support working, you need to make sure that a readerSchema was properly set for your application.

8. Inter-Application Communication

8.1. Connecting Multiple Application Instances

While Spring Cloud Stream makes it easy for individual Spring Boot applications to connect to messaging systems, the typical scenario for Spring Cloud Stream is the creation of multi-application pipelines, where microservice applications send data to each other. You can achieve this scenario by correlating the input and output destinations of adjacent applications.

Supposing that a design calls for the Time Source application to send data to the Log Sink application, you can use a common destination named ticktock for bindings within both applications.

Time Source (that has the channel name output) will set the following property:

spring.cloud.stream.bindings.output.destination=ticktock

Log Sink (that has the channel name input) will set the following property:

spring.cloud.stream.bindings.input.destination=ticktock

8.2. Instance Index and Instance Count

When scaling up Spring Cloud Stream applications, each instance can receive information about how many other instances of the same application exist and what its own instance index is. Spring Cloud Stream does this through the spring.cloud.stream.instanceCount and spring.cloud.stream.instanceIndex properties. For example, if there are three instances of a HDFS sink application, all three instances will have spring.cloud.stream.instanceCount set to 3, and the individual applications will have spring.cloud.stream.instanceIndex set to 0, 1, and 2, respectively.

When Spring Cloud Stream applications are deployed via Spring Cloud Data Flow, these properties are configured automatically; when Spring Cloud Stream applications are launched independently, these properties must be set correctly. By default, spring.cloud.stream.instanceCount is 1, and spring.cloud.stream.instanceIndex is 0.

In a scaled-up scenario, correct configuration of these two properties is important for addressing partitioning behavior (see below) in general, and the two properties are always required by certain binders (e.g., the Kafka binder) in order to ensure that data are split correctly across multiple consumer instances.

8.3. Partitioning

8.3.1. Configuring Output Bindings for Partitioning

An output binding is configured to send partitioned data by setting one and only one of its partitionKeyExpression or partitionKeyExtractorClass properties, as well as its partitionCount property. For example, the following is a valid and typical configuration:

spring.cloud.stream.bindings.output.producer.partitionKeyExpression=payload.id
spring.cloud.stream.bindings.output.producer.partitionCount=5

Based on the above example configuration, data will be sent to the target partition using the following logic.

A partition key’s value is calculated for each message sent to a partitioned output channel based on the partitionKeyExpression. The partitionKeyExpression is a SpEL expression which is evaluated against the outbound message for extracting the partitioning key.

If a SpEL expression is not sufficient for your needs, you can instead calculate the partition key value by setting the property partitionKeyExtractorClass to a class which implements the org.springframework.cloud.stream.binder.PartitionKeyExtractorStrategy interface. While the SpEL expression should usually suffice, more complex cases may use the custom implementation strategy. In that case, the property 'partitionKeyExtractorClass' can be set as follows:

spring.cloud.stream.bindings.output.producer.partitionKeyExtractorClass=com.example.MyKeyExtractor
spring.cloud.stream.bindings.output.producer.partitionCount=5

Once the message key is calculated, the partition selection process will determine the target partition as a value between 0 and partitionCount - 1. The default calculation, applicable in most scenarios, is based on the formula key.hashCode() % partitionCount. This can be customized on the binding, either by setting a SpEL expression to be evaluated against the 'key' (via the partitionSelectorExpression property) or by setting a org.springframework.cloud.stream.binder.PartitionSelectorStrategy implementation (via the partitionSelectorClass property).

The binding level properties for 'partitionSelectorExpression' and 'partitionSelectorClass' can be specified similar to the way 'partitionKeyExpression' and 'partitionKeyExtractorClass' properties are specified in the above examples. Additional properties can be configured for more advanced scenarios, as described in the following section.

Spring-managed custom `PartitionKeyExtractorClass` implementations

In the example above, a custom strategy such as MyKeyExtractor is instantiated by the Spring Cloud Stream directly. In some cases, it is necessary for such a custom strategy implementation to be created as a Spring bean, for being able to be managed by Spring, so that it can perform dependency injection, property binding, etc. This can be done by configuring it as a @Bean in the application context and using the fully qualified class name as the bean’s name, as in the following example.

@Bean(name="com.example.MyKeyExtractor")
public MyKeyExtractor extractor() {
    return new MyKeyExtractor();
}

As a Spring bean, the custom strategy benefits from the full lifecycle of a Spring bean. For example, if the implementation need access to the application context directly, it can make implement 'ApplicationContextAware'.

Configuring Input Bindings for Partitioning

An input binding (with the channel name input) is configured to receive partitioned data by setting its partitioned property, as well as the instanceIndex and instanceCount properties on the application itself, as in the following example:

spring.cloud.stream.bindings.input.consumer.partitioned=true
spring.cloud.stream.instanceIndex=3
spring.cloud.stream.instanceCount=5

The instanceCount value represents the total number of application instances between which the data need to be partitioned, and the instanceIndex must be a unique value across the multiple instances, between 0 and instanceCount - 1. The instance index helps each application instance to identify the unique partition (or, in the case of Kafka, the partition set) from which it receives data. It is important to set both values correctly in order to ensure that all of the data is consumed and that the application instances receive mutually exclusive datasets.

While a scenario which using multiple instances for partitioned data processing may be complex to set up in a standalone case, Spring Cloud Dataflow can simplify the process significantly by populating both the input and output values correctly as well as relying on the runtime infrastructure to provide information about the instance index and instance count.

9. Testing

Spring Cloud Stream provides support for testing your microservice applications without connecting to a messaging system. You can do that by using the TestSupportBinder provided by the spring-cloud-stream-test-support library, which can be added as a test dependency to the application:

   <dependency>
       <groupId>org.springframework.cloud</groupId>
       <artifactId>spring-cloud-stream-test-support</artifactId>
       <scope>test</scope>
   </dependency>

The TestSupportBinder uses the Spring Boot autoconfiguration mechanism to supersede the other binders found on the classpath. Therefore, when adding a binder as a dependency, make sure that the test scope is being used.

The TestSupportBinder allows users to interact with the bound channels and inspect what messages are sent and received by the application

For outbound message channels, the TestSupportBinder registers a single subscriber and retains the messages emitted by the application in a MessageCollector. They can be retrieved during tests and have assertions made against them.

The user can also send messages to inbound message channels, so that the consumer application can consume the messages. The following example shows how to test both input and output channels on a processor.

@RunWith(SpringRunner.class)
@SpringBootTest(webEnvironment= SpringBootTest.WebEnvironment.RANDOM_PORT)
public class ExampleTest {

  @Autowired
  private Processor processor;

  @Autowired
  private MessageCollector messageCollector;

  @Test
  @SuppressWarnings("unchecked")
  public void testWiring() {
    Message<String> message = new GenericMessage<>("hello");
    processor.input().send(message);
    Message<String> received = (Message<String>) messageCollector.forChannel(processor.output()).poll();
    assertThat(received.getPayload(), equalTo("hello world"));
  }


  @SpringBootApplication
  @EnableBinding(Processor.class)
  public static class MyProcessor {

    @Autowired
    private Processor channels;

    @Transformer(inputChannel = Processor.INPUT, outputChannel = Processor.OUTPUT)
    public String transform(String in) {
      return in + " world";
    }
  }
}

In the example above, we are creating an application that has an input and an output channel, bound through the Processor interface. The bound interface is injected into the test so we can have access to both channels. We are sending a message on the input channel and we are using the MessageCollector provided by Spring Cloud Stream’s test support to capture the message has been sent to the output channel as a result. Once we have received the message, we can validate that the component functions correctly.

9.1. Disabling the test binder autoconfiguration

The intent behind the test binder superseding all the other binders on the classpath is to make it easy to test your applications without making changes to your production dependencies. In some cases (e.g. integration tests) it is useful to use the actual production binders instead, and that requires disabling the test binder autoconfiguration. In order to do so, you can exclude the org.springframework.cloud.stream.test.binder.TestSupportBinderAutoConfiguration class using one of the Spring Boot autoconfiguration exclusion mechanisms, as in the following example.

	@SpringBootApplication(exclude = TestSupportBinderAutoConfiguration.class)
	@EnableBinding(Processor.class)
	public static class MyProcessor {

		@Transformer(inputChannel = Processor.INPUT, outputChannel = Processor.OUTPUT)
		public String transform(String in) {
			return in + " world";
		}
	}

When autoconfiguration is disabled, the test binder is available on the classpath, and its defaultCandidate property is set to false, so that it does not interfere with the regular user configuration. It can be referenced under the name test e.g.:

spring.cloud.stream.defaultBinder=test

10. Health Indicator

Spring Cloud Stream provides a health indicator for binders. It is registered under the name binders and can be enabled or disabled by setting the management.health.binders.enabled property.

By default management.health.binders.enabled is set to false. Setting management.health.binders.enabled to true enables the health indicator, allowing you to access the /health endpoint to retrieve the binder health indicators.

Health indicators are binder-specific and certain binder implementations may not necessarily provide a health indicator.

11. Metrics Emitter

Spring Cloud Stream provides a module called spring-cloud-stream-metrics that can be used to emit any available metric from Spring Boot metrics endpoint to a named channel. This module allow operators to collect metrics from stream applications without relying on polling their endpoints.

The module is activated when you set the destination name for metrics binding, e.g. spring.cloud.stream.bindings.applicationMetrics.destination=<DESTINATION_NAME>. applicationMetrics can be configured in a similar fashion to any other producer binding. The default contentType setting of applicationMetrics is application/json.

The following properties can be used for customizing the emission of metrics:

spring.cloud.stream.metrics.key: The name of the metric being emitted. Should be an unique value per application.
Default: ${spring.application.name:${vcap.application.name:${spring.config.name:application}}}
spring.cloud.stream.metrics.prefix: Prefix string to be prepended to the metrics key.

Default: ``
spring.cloud.stream.metrics.properties: Just like the includes option, it allows white listing application properties that will be added to the metrics payload

Default: null.

A detailed overview of the metrics export process can be found in the Spring Boot reference documentation. Spring Cloud Stream provides a metric exporter named application that can be configured via regular Spring Boot metrics configuration properties.

The exporter can be configured either by using the global Spring Boot configuration settings for exporters, or by using exporter-specific properties. For using the global configuration settings, the properties should be prefixed by spring.metric.export (e.g. spring.metric.export.includes=integration**). These configuration options will apply to all exporters (unless they have been configured differently). Alternatively, if it is intended to use configuration settings that are different from the other exporters (e.g. for restricting the number of metrics published), the Spring Cloud Stream provided metrics exporter can be configured using the prefix spring.metrics.export.triggers.application (e.g. spring.metrics.export.triggers.application.includes=integration**).

Due to Spring Boot’s relaxed binding the value of a property being included can be slightly different than the original value.

As a rule of thumb, the metric exporter will attempt to normalize all the properties in a consistent format using the dot notation (e.g. JAVA_HOME becomes java.home).

The goal of normalization is to make downstream consumers of those metrics capable of receiving property names consistently, regardless of how they are set on the monitored application (--spring.application.name or SPRING_APPLICATION_NAME would always yield spring.application.name).

Below is a sample of the data published to the channel in JSON format by the following command:

java -jar time-source.jar \
    --spring.cloud.stream.bindings.applicationMetrics.destination=someMetrics \
    --spring.cloud.stream.metrics.properties=spring.application** \
    --spring.metrics.export.includes=integration.channel.input**,integration.channel.output**

The resulting JSON is:

{
   "name":"time-source",
   "metrics":[
      {
         "name":"integration.channel.output.errorRate.mean",
         "value":0.0,
         "timestamp":"2017-04-11T16:56:35.790Z"
      },
      {
         "name":"integration.channel.output.errorRate.max",
         "value":0.0,
         "timestamp":"2017-04-11T16:56:35.790Z"
      },
      {
         "name":"integration.channel.output.errorRate.min",
         "value":0.0,
         "timestamp":"2017-04-11T16:56:35.790Z"
      },
      {
         "name":"integration.channel.output.errorRate.stdev",
         "value":0.0,
         "timestamp":"2017-04-11T16:56:35.790Z"
      },
      {
         "name":"integration.channel.output.errorRate.count",
         "value":0.0,
         "timestamp":"2017-04-11T16:56:35.790Z"
      },
      {
         "name":"integration.channel.output.sendCount",
         "value":6.0,
         "timestamp":"2017-04-11T16:56:35.790Z"
      },
      {
         "name":"integration.channel.output.sendRate.mean",
         "value":0.994885872292989,
         "timestamp":"2017-04-11T16:56:35.790Z"
      },
      {
         "name":"integration.channel.output.sendRate.max",
         "value":1.006247080013156,
         "timestamp":"2017-04-11T16:56:35.790Z"
      },
      {
         "name":"integration.channel.output.sendRate.min",
         "value":1.0012035220116378,
         "timestamp":"2017-04-11T16:56:35.790Z"
      },
      {
         "name":"integration.channel.output.sendRate.stdev",
         "value":6.505181111084848E-4,
         "timestamp":"2017-04-11T16:56:35.790Z"
      },
      {
         "name":"integration.channel.output.sendRate.count",
         "value":6.0,
         "timestamp":"2017-04-11T16:56:35.790Z"
      }
   ],
   "createdTime":"2017-04-11T20:56:35.790Z",
   "properties":{
      "spring.application.name":"time-source",
      "spring.application.index":"0"
   }
}

12. Samples

For Spring Cloud Stream samples, please refer to the spring-cloud-stream-samples repository on GitHub.

13. Getting Started

To get started with creating Spring Cloud Stream applications, visit the Spring Initializr and create a new Maven project named "GreetingSource". Select Spring Boot {supported-spring-boot-version} in the dropdown. In the Search for dependencies text box type Stream Rabbit or Stream Kafka depending on what binder you want to use.

Next, create a new class, GreetingSource, in the same package as the GreetingSourceApplication class. Give it the following code:

import org.springframework.cloud.stream.annotation.EnableBinding;
import org.springframework.cloud.stream.messaging.Source;
import org.springframework.integration.annotation.InboundChannelAdapter;

@EnableBinding(Source.class)
public class GreetingSource {

    @InboundChannelAdapter(Source.OUTPUT)
    public String greet() {
        return "hello world " + System.currentTimeMillis();
    }
}

The @EnableBinding annotation is what triggers the creation of Spring Integration infrastructure components. Specifically, it will create a Kafka connection factory, a Kafka outbound channel adapter, and the message channel defined inside the Source interface:

public interface Source {

  String OUTPUT = "output";

  @Output(Source.OUTPUT)
  MessageChannel output();

}

The auto-configuration also creates a default poller, so that the greet() method will be invoked once per second. The standard Spring Integration @InboundChannelAdapter annotation sends a message to the source’s output channel, using the return value as the payload of the message.

To test-drive this setup, run a Kafka message broker. An easy way to do this is to use a Docker image:

# On OS X
$ docker run -p 2181:2181 -p 9092:9092 --env ADVERTISED_HOST=`docker-machine ip \`docker-machine active\`` --env ADVERTISED_PORT=9092 spotify/kafka

# On Linux
$ docker run -p 2181:2181 -p 9092:9092 --env ADVERTISED_HOST=localhost --env ADVERTISED_PORT=9092 spotify/kafka

Build the application:

./mvnw clean package

The consumer application is coded in a similar manner. Go back to Initializr and create another project, named LoggingSink. Then create a new class, LoggingSink, in the same package as the class LoggingSinkApplication and with the following code:

import org.springframework.cloud.stream.annotation.EnableBinding;
import org.springframework.cloud.stream.annotation.StreamListener;
import org.springframework.cloud.stream.messaging.Sink;

@EnableBinding(Sink.class)
public class LoggingSink {

    @StreamListener(Sink.INPUT)
    public void log(String message) {
        System.out.println(message);
    }
}

Build the application:

./mvnw clean package

To connect the GreetingSource application to the LoggingSink application, each application must share the same destination name. Starting up both applications as shown below, you will see the consumer application printing "hello world" and a timestamp to the console:

cd GreetingSource
java -jar target/GreetingSource-0.0.1-SNAPSHOT.jar --spring.cloud.stream.bindings.output.destination=mydest

cd LoggingSink
java -jar target/LoggingSink-0.0.1-SNAPSHOT.jar --server.port=8090 --spring.cloud.stream.bindings.input.destination=mydest

(The different server port prevents collisions of the HTTP port used to service the Spring Boot Actuator endpoints in the two applications.)

The output of the LoggingSink application will look something like the following:

[           main] s.b.c.e.t.TomcatEmbeddedServletContainer : Tomcat started on port(s): 8090 (http)
[           main] com.example.LoggingSinkApplication       : Started LoggingSinkApplication in 6.828 seconds (JVM running for 7.371)
hello world 1458595076731
hello world 1458595077732
hello world 1458595078733
hello world 1458595079734
hello world 1458595080735

13.1. Deploying Stream applications on CloudFoundry

On CloudFoundry services are usually exposed via a special environment variable called VCAP_SERVICES.

When configuring your binder connections, you can use the values from an environment variable as explained on the dataflow cloudfoundry server docs.

Binder Implementations

14. Apache Kafka Binder

14.1. Usage

For using the Apache Kafka binder, you just need to add it to your Spring Cloud Stream application, using the following Maven coordinates:

<dependency>
  <groupId>org.springframework.cloud</groupId>
  <artifactId>spring-cloud-stream-binder-kafka</artifactId>
</dependency>

Alternatively, you can also use the Spring Cloud Stream Kafka Starter.

<dependency>
  <groupId>org.springframework.cloud</groupId>
  <artifactId>spring-cloud-starter-stream-kafka</artifactId>
</dependency>

14.2. Apache Kafka Binder Overview

A simplified diagram of how the Apache Kafka binder operates can be seen below.

Figure 9. Kafka Binder

The Apache Kafka Binder implementation maps each destination to an Apache Kafka topic. The consumer group maps directly to the same Apache Kafka concept. Partitioning also maps directly to Apache Kafka partitions as well.

14.3. Configuration Options

This section contains the configuration options used by the Apache Kafka binder.

For common configuration options and properties pertaining to binder, refer to the core documentation.

14.3.1. Kafka Binder Properties

spring.cloud.stream.kafka.binder.brokers: A list of brokers to which the Kafka binder will connect.

Default: localhost.
spring.cloud.stream.kafka.binder.defaultBrokerPort: brokers allows hosts specified with or without port information (e.g., host1,host2:port2). This sets the default port when no port is configured in the broker list.

Default: 9092.
spring.cloud.stream.kafka.binder.zkNodes: A list of ZooKeeper nodes to which the Kafka binder can connect.

Default: localhost.
spring.cloud.stream.kafka.binder.defaultZkPort: zkNodes allows hosts specified with or without port information (e.g., host1,host2:port2). This sets the default port when no port is configured in the node list.

Default: 2181.
spring.cloud.stream.kafka.binder.configuration: Key/Value map of client properties (both producers and consumer) passed to all clients created by the binder. Due to the fact that these properties will be used by both producers and consumers, usage should be restricted to common properties, especially security settings.

Default: Empty map.
spring.cloud.stream.kafka.binder.headers: The list of custom headers that will be transported by the binder.

Default: empty.
spring.cloud.stream.kafka.binder.healthTimeout: The time to wait to get partition information in seconds; default 60. Health will report as down if this timer expires.

Default: 10.
spring.cloud.stream.kafka.binder.offsetUpdateTimeWindow: The frequency, in milliseconds, with which offsets are saved. Ignored if 0.

Default: 10000.
spring.cloud.stream.kafka.binder.offsetUpdateCount: The frequency, in number of updates, which which consumed offsets are persisted. Ignored if 0. Mutually exclusive with offsetUpdateTimeWindow.

Default: 0.
spring.cloud.stream.kafka.binder.requiredAcks: The number of required acks on the broker.

Default: 1.
spring.cloud.stream.kafka.binder.minPartitionCount: Effective only if autoCreateTopics or autoAddPartitions is set. The global minimum number of partitions that the binder will configure on topics on which it produces/consumes data. It can be superseded by the partitionCount setting of the producer or by the value of instanceCount * concurrency settings of the producer (if either is larger).

Default: 1.
spring.cloud.stream.kafka.binder.replicationFactor: The replication factor of auto-created topics if autoCreateTopics is active.

Default: 1.
spring.cloud.stream.kafka.binder.autoCreateTopics: If set to true, the binder will create new topics automatically. If set to false, the binder will rely on the topics being already configured. In the latter case, if the topics do not exist, the binder will fail to start. Of note, this setting is independent of the auto.topic.create.enable setting of the broker and it does not influence it: if the server is set to auto-create topics, they may be created as part of the metadata retrieval request, with default broker settings.

Default: true.
spring.cloud.stream.kafka.binder.autoAddPartitions: If set to true, the binder will create add new partitions if required. If set to false, the binder will rely on the partition size of the topic being already configured. If the partition count of the target topic is smaller than the expected value, the binder will fail to start.

Default: false.
spring.cloud.stream.kafka.binder.socketBufferSize: Size (in bytes) of the socket buffer to be used by the Kafka consumers.

Default: 2097152.

14.3.2. Kafka Consumer Properties

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

autoRebalanceEnabled: When true, topic partitions will be automatically rebalanced between the members of a consumer group. When false, each consumer will be assigned a fixed set of partitions based on spring.cloud.stream.instanceCount and spring.cloud.stream.instanceIndex. This requires both spring.cloud.stream.instanceCount and spring.cloud.stream.instanceIndex properties to be set appropriately on each launched instance. The property spring.cloud.stream.instanceCount must typically be greater than 1 in this case.

Default: true.
autoCommitOffset: Whether to autocommit offsets when a message has been processed. If set to false, a header with the key kafka_acknowledgment of the type org.springframework.kafka.support.Acknowledgment header will be present in the inbound message. Applications may use this header for acknowledging messages. See the examples section for details. When this property is set to false, Kafka binder will set the ack mode to org.springframework.kafka.listener.AbstractMessageListenerContainer.AckMode.MANUAL.

Default: true.
autoCommitOnError: Effective only if autoCommitOffset is set to true. If set to false it suppresses auto-commits for messages that result in errors, and will commit only for successful messages, allows a stream to automatically replay from the last successfully processed message, in case of persistent failures. If set to true, it will always auto-commit (if auto-commit is enabled). If not set (default), it effectively has the same value as enableDlq, auto-committing erroneous messages if they are sent to a DLQ, and not committing them otherwise.

Default: not set.
recoveryInterval: The interval between connection recovery attempts, in milliseconds.

Default: 5000.
startOffset: The starting offset for new groups. Allowed values: earliest, latest. If the consumer group is set explicitly for the consumer 'binding' (via spring.cloud.stream.bindings.<channelName>.group), then 'startOffset' is set to earliest; otherwise it is set to latest for the anonymous consumer group.

Default: null (equivalent to earliest).
enableDlq: When set to true, it will send enable DLQ behavior for the consumer. By default, messages that result in errors will be forwarded to a topic named error.<destination>.<group>. The DLQ topic name can be configurable via the property dlqName. This provides an alternative option to the more common Kafka replay scenario for the case when the number of errors is relatively small and replaying the entire original topic may be too cumbersome.

Default: false.
configuration: Map with a key/value pair containing generic Kafka consumer properties.

Default: Empty map.
dlqName: The name of the DLQ topic to receive the error messages.

Default: null (If not specified, messages that result in errors will be forwarded to a topic named error.<destination>.<group>).

14.3.3. Kafka Producer Properties

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

bufferSize: Upper limit, in bytes, of how much data the Kafka producer will attempt to batch before sending.

Default: 16384.
sync: Whether the producer is synchronous.

Default: false.
batchTimeout: How long the producer will wait before sending in order to allow more messages to accumulate in the same batch. (Normally the producer does not wait at all, and simply sends all the messages that accumulated while the previous send was in progress.) A non-zero value may increase throughput at the expense of latency.

Default: 0.
messageKeyExpression: A SpEL expression evaluated against the outgoing message used to populate the key of the produced Kafka message. For example headers.key or payload.myKey.

Default: none.
configuration: Map with a key/value pair containing generic Kafka producer properties.

Default: Empty map.

The Kafka binder will use the partitionCount setting of the producer as a hint to create a topic with the given partition count (in conjunction with the minPartitionCount, the maximum of the two being the value being used). Exercise caution when configuring both minPartitionCount for a binder and partitionCount for an application, as the larger value will be used. If a topic already exists with a smaller partition count and autoAddPartitions is disabled (the default), then the binder will fail to start. If a topic already exists with a smaller partition count and autoAddPartitions is enabled, new partitions will be added. If a topic already exists with a larger number of partitions than the maximum of (minPartitionCount and partitionCount), the existing partition count will be used.

14.3.4. Usage examples

In this section, we illustrate the use of the above properties for specific scenarios.

Example: Setting `autoCommitOffset` false and relying on manual acking.

This example illustrates how one may manually acknowledge offsets in a consumer application.

This example requires that spring.cloud.stream.kafka.bindings.input.consumer.autoCommitOffset is set to false. Use the corresponding input channel name for your example.

@SpringBootApplication
@EnableBinding(Sink.class)
public class ManuallyAcknowdledgingConsumer {

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

 @StreamListener(Sink.INPUT)
 public void process(Message<?> message) {
     Acknowledgment acknowledgment = message.getHeaders().get(KafkaHeaders.ACKNOWLEDGMENT, Acknowledgment.class);
     if (acknowledgment != null) {
         System.out.println("Acknowledgment provided");
         acknowledgment.acknowledge();
     }
 }
}

Example: security configuration

Apache Kafka 0.9 supports secure connections between client and brokers. To take advantage of this feature, follow the guidelines in the Apache Kafka Documentation as well as the Kafka 0.9 security guidelines from the Confluent documentation. Use the spring.cloud.stream.kafka.binder.configuration option to set security properties for all clients created by the binder.

For example, for setting security.protocol to SASL_SSL, set:

spring.cloud.stream.kafka.binder.configuration.security.protocol=SASL_SSL

All the other security properties can be set in a similar manner.

When using Kerberos, follow the instructions in the reference documentation for creating and referencing the JAAS configuration.

Spring Cloud Stream supports passing JAAS configuration information to the application using a JAAS configuration file and using Spring Boot properties.

Using JAAS configuration files

The JAAS, and (optionally) krb5 file locations can be set for Spring Cloud Stream applications by using system properties. Here is an example of launching a Spring Cloud Stream application with SASL and Kerberos using a JAAS configuration file:

 java -Djava.security.auth.login.config=/path.to/kafka_client_jaas.conf -jar log.jar \
   --spring.cloud.stream.kafka.binder.brokers=secure.server:9092 \
   --spring.cloud.stream.kafka.binder.zkNodes=secure.zookeeper:2181 \
   --spring.cloud.stream.bindings.input.destination=stream.ticktock \
   --spring.cloud.stream.kafka.binder.configuration.security.protocol=SASL_PLAINTEXT

Using Spring Boot properties

As an alternative to having a JAAS configuration file, Spring Cloud Stream provides a mechanism for setting up the JAAS configuration for Spring Cloud Stream applications using Spring Boot properties.

The following properties can be used for configuring the login context of the Kafka client.

spring.cloud.stream.kafka.binder.jaas.loginModule: The login module name. Not necessary to be set in normal cases.

Default: com.sun.security.auth.module.Krb5LoginModule.
spring.cloud.stream.kafka.binder.jaas.controlFlag: The control flag of the login module.

Default: required.
spring.cloud.stream.kafka.binder.jaas.options: Map with a key/value pair containing the login module options.

Default: Empty map.

Here is an example of launching a Spring Cloud Stream application with SASL and Kerberos using Spring Boot configuration properties:

 java --spring.cloud.stream.kafka.binder.brokers=secure.server:9092 \
   --spring.cloud.stream.kafka.binder.zkNodes=secure.zookeeper:2181 \
   --spring.cloud.stream.bindings.input.destination=stream.ticktock \
   --spring.cloud.stream.kafka.binder.autoCreateTopics=false \
   --spring.cloud.stream.kafka.binder.configuration.security.protocol=SASL_PLAINTEXT \
   --spring.cloud.stream.kafka.binder.jaas.options.useKeyTab=true \
   --spring.cloud.stream.kafka.binder.jaas.options.storeKey=true \
   --spring.cloud.stream.kafka.binder.jaas.options.keyTab=/etc/security/keytabs/kafka_client.keytab \
   --spring.cloud.stream.kafka.binder.jaas.options.principal=kafka-client-1@EXAMPLE.COM

This represents the equivalent of the following JAAS file:

KafkaClient {
    com.sun.security.auth.module.Krb5LoginModule required
    useKeyTab=true
    storeKey=true
    keyTab="/etc/security/keytabs/kafka_client.keytab"
    principal="[email protected]";
};

If the topics required already exist on the broker, or will be created by an administrator, autocreation can be turned off and only client JAAS properties need to be sent. As an alternative to setting spring.cloud.stream.kafka.binder.autoCreateTopics you can simply remove the broker dependency from the application. See Excluding Kafka broker jar from the classpath of the binder based application for details.

Do not mix JAAS configuration files and Spring Boot properties in the same application. If the -Djava.security.auth.login.config system property is already present, Spring Cloud Stream will ignore the Spring Boot properties.

Exercise caution when using the autoCreateTopics and autoAddPartitions if using Kerberos. Usually applications may use principals that do not have administrative rights in Kafka and Zookeeper, and relying on Spring Cloud Stream to create/modify topics may fail. In secure environments, we strongly recommend creating topics and managing ACLs administratively using Kafka tooling.

Using the binder with Apache Kafka 0.10

The default Kafka support in Spring Cloud Stream Kafka binder is for Kafka version 0.10.1.1. The binder also supports connecting to other 0.10 based versions and 0.9 clients. In order to do this, when you create the project that contains your application, include spring-cloud-starter-stream-kafka as you normally would do for the default binder. Then add these dependencies at the top of the <dependencies> section in the pom.xml file to override the dependencies.

Here is an example for downgrading your application to 0.10.0.1. Since it is still on the 0.10 line, the default spring-kafka and spring-integration-kafka versions can be retained.

<dependency>
  <groupId>org.apache.kafka</groupId>
  <artifactId>kafka_2.11</artifactId>
  <version>0.10.0.1</version>
  <exclusions>
    <exclusion>
      <groupId>org.slf4j</groupId>
      <artifactId>slf4j-log4j12</artifactId>
    </exclusion>
  </exclusions>
</dependency>
<dependency>
  <groupId>org.apache.kafka</groupId>
  <artifactId>kafka-clients</artifactId>
  <version>0.10.0.1</version>
</dependency>

Here is another example of using 0.9.0.1 version.

<dependency>
  <groupId>org.springframework.kafka</groupId>
  <artifactId>spring-kafka</artifactId>
  <version>1.0.5.RELEASE</version>
</dependency>
<dependency>
  <groupId>org.springframework.integration</groupId>
  <artifactId>spring-integration-kafka</artifactId>
  <version>2.0.1.RELEASE</version>
</dependency>
<dependency>
  <groupId>org.apache.kafka</groupId>
  <artifactId>kafka_2.11</artifactId>
  <version>0.9.0.1</version>
  <exclusions>
    <exclusion>
      <groupId>org.slf4j</groupId>
      <artifactId>slf4j-log4j12</artifactId>
    </exclusion>
  </exclusions>
</dependency>
<dependency>
  <groupId>org.apache.kafka</groupId>
  <artifactId>kafka-clients</artifactId>
  <version>0.9.0.1</version>
</dependency>

The versions above are provided only for the sake of the example. For best results, we recommend using the most recent 0.10-compatible versions of the projects.

Excluding Kafka broker jar from the classpath of the binder based application

The Apache Kafka Binder uses the administrative utilities which are part of the Apache Kafka server library to create and reconfigure topics. If the inclusion of the Apache Kafka server library and its dependencies is not necessary at runtime because the application will rely on the topics being configured administratively, the Kafka binder allows for Apache Kafka server dependency to be excluded from the application.

If you use non default versions for Kafka dependencies as advised above, all you have to do is not to include the kafka broker dependency. If you use the default Kafka version, then ensure that you exclude the kafka broker jar from the spring-cloud-starter-stream-kafka dependency as following.

<dependency>
  <groupId>org.springframework.cloud</groupId>
  <artifactId>spring-cloud-starter-stream-kafka</artifactId>
  <exclusions>
    <exclusion>
      <groupId>org.apache.kafka</groupId>
      <artifactId>kafka_2.11</artifactId>
    </exclusion>
  </exclusions>
</dependency>

If you exclude the Apache Kafka server dependency and the topic is not present on the server, then the Apache Kafka broker will create the topic if auto topic creation is enabled on the server. Please keep in mind that if you are relying on this, then the Kafka server will use the default number of partitions and replication factors. On the other hand, if auto topic creation is disabled on the server, then care must be taken before running the application to create the topic with the desired number of partitions.

If you want to have full control over how partitions are allocated, then leave the default settings as they are, i.e. do not exclude the kafka broker jar and ensure that spring.cloud.stream.kafka.binder.autoCreateTopics is set to true, which is the default.

14.4. Kafka Streams Binding Capabilities of Spring Cloud Stream

Spring Cloud Stream Kafka support also includes a binder specifically designed for Kafka Streams binding. Using this binder, applications can be written that leverage the Kafka Streams API. For more information on Kafka Streams, see Kafka Streams API Developer Manual

Kafka Streams support in Spring Cloud Stream is based on the foundations provided by the Spring Kafka project. For details on that support, see Kafaka Streams Support in Spring Kafka.

Here are the maven coordinates for the Spring Cloud Stream KStream binder artifact.

<dependency>
  <groupId>org.springframework.cloud</groupId>
  <artifactId>spring-cloud-stream-binder-kstream</artifactId>
</dependency>

In addition to leveraging the Spring Cloud Stream programming model which is based on Spring Boot, one of the main other benefits that the KStream binder provides is the fact that it avoids the boilerplate configuration that one needs to write when using the Kafka Streams API directly. High level streams DSL provided through the Kafka Streams API can be used through Spring Cloud Stream in the current support.

14.4.1. Usage example of high level streams DSL

This application will listen from a Kafka topic and write the word count for each unique word that it sees in a 5 seconds time window.

@SpringBootApplication
@EnableBinding(KStreamProcessor.class)
public class WordCountProcessorApplication {

	@StreamListener("input")
	@SendTo("output")
	public KStream<?, String> process(KStream<?, String> input) {
		return input
				.flatMapValues(value -> Arrays.asList(value.toLowerCase().split("\\W+")))
				.map((key, word) -> new KeyValue<>(word, word))
				.groupByKey(Serdes.String(), Serdes.String())
				.count(TimeWindows.of(5000), "store-name")
				.toStream()
				.map((w, c) -> new KeyValue<>(null, "Count for " + w.key() + ": " + c));
	}

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

If you build it as Spring Boot runnable fat jar, you can run the above example in the following way:

java -jar uber.jar  --spring.cloud.stream.bindings.input.destination=words --spring.cloud.stream.bindings.output.destination=counts

This means that the application will listen from the incoming Kafka topic words and write to the output topic counts.

Spring Cloud Stream will ensure that the messages from both the incoming and outgoing topics are bound as KStream objects. As one may observe, the developer can exclusively focus on the business aspects of the code, i.e. writing the logic required in the processor rather than setting up the streams specific configuration required by the Kafka Streams infrastructure. All those boilerplate is handled by Spring Cloud Stream behind the scenes.

14.4.2. Support for interactive queries

If access to the KafkaStreams is needed for interactive queries, the internal KafkaStreams instance can be accessed via KStreamBuilderFactoryBean.getKafkaStreams(). You can autowire the KStreamBuilderFactoryBean instance provided by the KStream binder. Then you can get KafkaStreams instance from it and retrieve the underlying store, execute queries on it, etc.

14.4.3. Kafka Streams properties

configuration

Map with a key/value pair containing properties pertaining to Kafka Streams API. This property must be prefixed with spring.cloud.stream.kstream.binder..

Following are some examples of using this property.

spring.cloud.stream.kstream.binder.configuration.key.serde=org.apache.kafka.common.serialization.Serdes$StringSerde
spring.cloud.stream.kstream.binder.configuration.value.serde=org.apache.kafka.common.serialization.Serdes$StringSerde
spring.cloud.stream.kstream.binder.configuration.commit.interval.ms=1000

For more information about all the properties that may go into streams configuration, see StreamsConfig JavaDocs.

There can also be binding specific properties.

For instance, you can use a different Serde for your input or output destination.

spring.cloud.stream.kstream.bindings.output.producer.keySerde=org.apache.kafka.common.serialization.Serdes$IntegerSerde
spring.cloud.stream.kstream.bindings.output.producer.valueSerde=org.apache.kafka.common.serialization.Serdes$LongSerde

timewindow.length

Many streaming applications written using Kafka Streams involve windowning operations. If you specify this property, there is a org.apache.kafka.streams.kstream.TimeWindows bean automatically provided that can be autowired in applications. This property must be prefixed with spring.cloud.stream.kstream.. A bean of type org.apache.kafka.streams.kstream.TimeWindows is created only if this property is provided.

Following is an example of using this property.
Values are provided in milliseconds.

spring.cloud.stream.kstream.timeWindow.length=5000

timewindow.advanceBy

This property goes hand in hand with timewindow.length and has no effect on its own. If you provide this property, the generated org.apache.kafka.streams.kstream.TimeWindows bean will automatically conatin this information. This property must be prefixed with spring.cloud.stream.kstream..

Following is an example of using this property.
Values are provided in milliseconds.

spring.cloud.stream.kstream.timeWindow.advanceBy=1000

14.5. Error Channels

Starting with version 1.3, the binder unconditionally sends exceptions to an error channel for each consumer destination, and can be configured to send async producer send failures to an error channel too. See Message Channel Binders and Error Channels for more information.

The payload of the ErrorMessage for a send failure is a KafkaSendFailureException with properties:

failedMessage - the spring-messaging Message<?> that failed to be sent.
record - the raw ProducerRecord that was created from the failedMessage

There is no automatic handling of these exceptions (such as sending to a Dead-Letter queue); you can consume these exceptions with your own Spring Integration flow.

14.6. Kafka Metrics

Kafka binder module exposes the following metrics:

spring.cloud.stream.binder.kafka.someGroup.someTopic.lag - this metric indicates how many messages have not been yet consumed from given binder’s topic by given consumer group. For example if the value of the metric spring.cloud.stream.binder.kafka.myGroup.myTopic.lag is 1000, then consumer group myGroup has 1000 messages to waiting to be consumed from topic myTopic. This metric is particularly useful to provide auto-scaling feedback to PaaS platform of your choice.

14.7. Dead-Letter Topic Processing

Because it can’t be anticipated how users would want to dispose of dead-lettered messages, the framework 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 following spring-boot application is an example of how to route those messages back to the original topic, but moves them to a third "parking lot" topic after three attempts. The application is simply another spring-cloud-stream application that reads from the dead-letter topic. It terminates when no messages are received for 5 seconds.

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

There are several considerations.

Consider only running the rerouting when the main application is not running. Otherwise, the retries for transient errors will be used up very quickly.
Alternatively, use a two-stage approach - use this application to route to a third topic, and another to route from there back to the main topic.
Since this technique uses a message header to keep track of retries, it won’t work with headerMode=raw. In that case, consider adding some data to the payload (that can be ignored by the main application).
x-retries has to be added to the headers property spring.cloud.stream.kafka.binder.headers=x-retries on both this, and the main application so that the header is transported between the applications.
Since kafka is publish/subscribe, replayed messages will be sent to each consumer group, even those that successfully processed a message the first time around.

application.properties

spring.cloud.stream.bindings.input.group=so8400replay
spring.cloud.stream.bindings.input.destination=error.so8400out.so8400

spring.cloud.stream.bindings.output.destination=so8400out
spring.cloud.stream.bindings.output.producer.partitioned=true

spring.cloud.stream.bindings.parkingLot.destination=so8400in.parkingLot
spring.cloud.stream.bindings.parkingLot.producer.partitioned=true

spring.cloud.stream.kafka.binder.configuration.auto.offset.reset=earliest

spring.cloud.stream.kafka.binder.headers=x-retries

Application

@SpringBootApplication
@EnableBinding(TwoOutputProcessor.class)
public class ReRouteDlqKApplication implements CommandLineRunner {

    private static final String X_RETRIES_HEADER = "x-retries";

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

    private final AtomicInteger processed = new AtomicInteger();

    @Autowired
    private MessageChannel parkingLot;

    @StreamListener(Processor.INPUT)
    @SendTo(Processor.OUTPUT)
    public Message<?> reRoute(Message<?> failed) {
        processed.incrementAndGet();
        Integer retries = failed.getHeaders().get(X_RETRIES_HEADER, Integer.class);
        if (retries == null) {
            System.out.println("First retry for " + failed);
            return MessageBuilder.fromMessage(failed)
                    .setHeader(X_RETRIES_HEADER, new Integer(1))
                    .setHeader(BinderHeaders.PARTITION_OVERRIDE,
                            failed.getHeaders().get(KafkaHeaders.RECEIVED_PARTITION_ID))
                    .build();
        }
        else if (retries.intValue() < 3) {
            System.out.println("Another retry for " + failed);
            return MessageBuilder.fromMessage(failed)
                    .setHeader(X_RETRIES_HEADER, new Integer(retries.intValue() + 1))
                    .setHeader(BinderHeaders.PARTITION_OVERRIDE,
                            failed.getHeaders().get(KafkaHeaders.RECEIVED_PARTITION_ID))
                    .build();
        }
        else {
            System.out.println("Retries exhausted for " + failed);
            parkingLot.send(MessageBuilder.fromMessage(failed)
                    .setHeader(BinderHeaders.PARTITION_OVERRIDE,
                            failed.getHeaders().get(KafkaHeaders.RECEIVED_PARTITION_ID))
                    .build());
        }
        return null;
    }

    @Override
    public void run(String... args) throws Exception {
        while (true) {
            int count = this.processed.get();
            Thread.sleep(5000);
            if (count == this.processed.get()) {
                System.out.println("Idle, terminating");
                return;
            }
        }
    }

    public interface TwoOutputProcessor extends Processor {

        @Output("parkingLot")
        MessageChannel parkingLot();

    }

}

15. RabbitMQ Binder

15.1. Usage

For using the RabbitMQ binder, you just need to add it to your Spring Cloud Stream application, using the following Maven coordinates:

<dependency>
  <groupId>org.springframework.cloud</groupId>
  <artifactId>spring-cloud-stream-binder-rabbit</artifactId>
</dependency>

Alternatively, you can also use the Spring Cloud Stream RabbitMQ Starter.

<dependency>
  <groupId>org.springframework.cloud</groupId>
  <artifactId>spring-cloud-starter-stream-rabbit</artifactId>
</dependency>

15.2. RabbitMQ Binder Overview

A simplified diagram of how the RabbitMQ binder operates can be seen below.

Figure 10. RabbitMQ Binder

The RabbitMQ Binder implementation maps each destination to a TopicExchange. For each consumer group, a Queue will be bound to that TopicExchange. Each consumer instance have a corresponding RabbitMQ Consumer instance for its group’s Queue. For partitioned producers/consumers the queues are suffixed with the partition index and use the partition index as routing key.

Using the autoBindDlq option, you can optionally configure the binder to create and configure dead-letter queues (DLQs) (and a dead-letter exchange DLX). The dead letter queue has the name of the destination, appended with .dlq. If retry is enabled (maxAttempts > 1) failed messages will be delivered to the DLQ. If retry is disabled (maxAttempts = 1), you should set requeueRejected to false (default) so that a failed message will be routed to the DLQ, instead of being requeued. In addition, republishToDlq causes the binder to publish a failed message to the DLQ (instead of rejecting it); this enables additional information to be added to the message in headers, such as the stack trace in the x-exception-stacktrace header. This option does not need retry enabled; you can republish a failed message after just one attempt. Starting with version 1.2, you can configure the delivery mode of republished messages; see property republishDeliveryMode.

Setting requeueRejected to true will cause the message to be requeued and redelivered continually, which is likely not what you want unless the failure issue is transient. In general, it’s better to enable retry within the binder by setting maxAttempts to greater than one, or set republishToDlq to true.

See RabbitMQ Binder Properties for more information about these properties.

The framework does not provide any standard mechanism to consume dead-letter messages (or to re-route them back to the primary queue). Some options are described in Dead-Letter Queue Processing.

When multiple RabbitMQ binders are used in a Spring Cloud Stream application, it is important to disable 'RabbitAutoConfiguration' to avoid the same configuration from RabbitAutoConfiguration being applied to the two binders.

Starting with version 1.3, the RabbitMessageChannelBinder creates an internal ConnectionFactory copy for the non-transactional producers to avoid dead locks on consumers when shared, cached connections are blocked because of Memory Alarm on Broker.

15.3. Configuration Options

This section contains settings specific to the RabbitMQ Binder and bound channels.

For general binding configuration options and properties, please refer to the Spring Cloud Stream core documentation.

15.3.1. RabbitMQ Binder Properties

By default, the RabbitMQ binder uses Spring Boot’s ConnectionFactory, and it therefore supports all Spring Boot configuration options for RabbitMQ. (For reference, consult the Spring Boot documentation.) RabbitMQ configuration options use the spring.rabbitmq prefix.

In addition to Spring Boot options, the RabbitMQ binder supports the following properties:

spring.cloud.stream.rabbit.binder.adminAddresses: A comma-separated list of RabbitMQ management plugin URLs. Only used when nodes contains more than one entry. Each entry in this list must have a corresponding entry in spring.rabbitmq.addresses. Only needed if you are using a RabbitMQ cluster and wish to consume from the node that hosts the queue. See Queue Affinity and the LocalizedQueueConnectionFactory for more information.

Default: empty.
spring.cloud.stream.rabbit.binder.nodes: A comma-separated list of RabbitMQ node names. When more than one entry, used to locate the server address where a queue is located. Each entry in this list must have a corresponding entry in spring.rabbitmq.addresses. Only needed if you are using a RabbitMQ cluster and wish to consume from the node that hosts the queue. See Queue Affinity and the LocalizedQueueConnectionFactory for more information.

Default: empty.
spring.cloud.stream.rabbit.binder.compressionLevel: Compression level for compressed bindings. See java.util.zip.Deflater.

Default: 1 (BEST_LEVEL).

15.3.2. RabbitMQ Consumer Properties

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

acknowledgeMode: The acknowledge mode.

Default: AUTO.
autoBindDlq: Whether to automatically declare the DLQ and bind it to the binder DLX.

Default: false.
bindingRoutingKey: The routing key with which to bind the queue to the exchange (if bindQueue is true). for partitioned destinations -<instanceIndex> will be appended.

Default: #.
bindQueue: Whether to bind the queue to the destination exchange; set to false if you have set up your own infrastructure and have previously created/bound the queue.

Default: true.
deadLetterQueueName: name of the DLQ

Default: prefix+destination.dlq
deadLetterExchange: a DLX to assign to the queue; if autoBindDlq is true

Default: 'prefix+DLX'
deadLetterRoutingKey: a dead letter routing key to assign to the queue; if autoBindDlq is true

Default: destination
declareExchange: Whether to declare the exchange for the destination.

Default: true.
delayedExchange: Whether to declare the exchange as a Delayed Message Exchange - requires the delayed message exchange plugin on the broker. The x-delayed-type argument is set to the exchangeType.

Default: false.
dlqDeadLetterExchange: if a DLQ is declared, a DLX to assign to that queue

Default: none
dlqDeadLetterRoutingKey: if a DLQ is declared, a dead letter routing key to assign to that queue; default none

Default: none
dlqExpires: how long before an unused dead letter queue is deleted (ms)

Default: no expiration
dlqLazy: Declare the dead letter queue with the x-queue-mode=lazy argument. See Lazy Queues. Consider using a policy instead of this setting because using a policy allows changing the setting without deleting the queue.

Default: false.
dlqMaxLength: maximum number of messages in the dead letter queue

Default: no limit
dlqMaxLengthBytes: maximum number of total bytes in the dead letter queue from all messages

Default: no limit
dlqMaxPriority: maximum priority of messages in the dead letter queue (0-255)

Default: none
dlqTtl: default time to live to apply to the dead letter queue when declared (ms)

Default: no limit
durableSubscription: Whether subscription should be durable. Only effective if group is also set.

Default: true.
exchangeAutoDelete: If declareExchange is true, whether the exchange should be auto-delete (removed after the last queue is removed).

Default: true.
exchangeDurable: If declareExchange is true, whether the exchange should be durable (survives broker restart).

Default: true.
exchangeType: The exchange type; direct, fanout or topic for non-partitioned destinations; direct or topic for partitioned destinations.

Default: topic.
exclusive: Create an exclusive consumer; concurrency should be 1 when this is true; often used when strict ordering is required but enabling a hot standby instance to take over after a failure. See recoveryInterval, which controls how often a standby instance will attempt to consume.

Default: false.
expires: how long before an unused queue is deleted (ms)

Default: no expiration
failedDeclarationRetryInterval: The interval (ms) between attempts to consume from a queue if it is missing.

Default: 5000
headerPatterns: Patterns for headers to be mapped from inbound messages.

Default: ['*'] (all headers).
lazy: Declare the queue with the x-queue-mode=lazy argument. See Lazy Queues. Consider using a policy instead of this setting because using a policy allows changing the setting without deleting the queue.

Default: false.
maxConcurrency: the maximum number of consumers

Default: 1.
maxLength: maximum number of messages in the queue

Default: no limit
maxLengthBytes: maximum number of total bytes in the queue from all messages

Default: no limit
maxPriority: maximum priority of messages in the queue (0-255)
Default: none
missingQueuesFatal: If the queue cannot be found, treat the condition as fatal and stop the listener container. Defaults to false so that the container keeps trying to consume from the queue, for example when using a cluster and the node hosting a non HA queue is down.
Default: false
prefetch: Prefetch count.

Default: 1.
prefix: A prefix to be added to the name of the destination and queues.

Default: "".
queueDeclarationRetries: The number of times to retry consuming from a queue if it is missing. Only relevant if missingQueuesFatal is true; otherwise the container keeps retrying indefinitely.
Default: 3
queueNameGroupOnly: When true, consume from a queue with a name equal to the group; otherwise the queue name is destination.group. This is useful, for example, when using Spring Cloud Stream to consume from an existing RabbitMQ queue.

Default: false.
recoveryInterval: The interval between connection recovery attempts, in milliseconds.

Default: 5000.
requeueRejected: Whether delivery failures should be requeued when retry is disabled or republishToDlq is false.

Default: false.
republishDeliveryMode: When republishToDlq is true, specify the delivery mode of the republished message.

Default: DeliveryMode.PERSISTENT
republishToDlq: By default, messages which fail after retries are exhausted are rejected. If a dead-letter queue (DLQ) is configured, RabbitMQ will route the failed message (unchanged) to the DLQ. If set to true, the binder will republish failed messages to the DLQ with additional headers, including the exception message and stack trace from the cause of the final failure.

Default: false
transacted: Whether to use transacted channels.

Default: false.
ttl: default time to live to apply to the queue when declared (ms)

Default: no limit
txSize: The number of deliveries between acks.

Default: 1.

15.3.3. Rabbit Producer Properties

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

autoBindDlq: Whether to automatically declare the DLQ and bind it to the binder DLX.

Default: false.
batchingEnabled: Whether to enable message batching by producers.

Default: false.
batchSize: The number of messages to buffer when batching is enabled.

Default: 100.
batchBufferLimit: Default: 10000.
batchTimeout: Default: 5000.
bindingRoutingKey: The routing key with which to bind the queue to the exchange (if bindQueue is true). Only applies to non-partitioned destinations. Only applies if requiredGroups are provided and then only to those groups.

Default: #.
bindQueue: Whether to bind the queue to the destination exchange; set to false if you have set up your own infrastructure and have previously created/bound the queue. Only applies if requiredGroups are provided and then only to those groups.

Default: true.
compress: Whether data should be compressed when sent.

Default: false.
deadLetterQueueName: name of the DLQ Only applies if requiredGroups are provided and then only to those groups.

Default: prefix+destination.dlq
deadLetterExchange: a DLX to assign to the queue; if autoBindDlq is true Only applies if requiredGroups are provided and then only to those groups.

Default: 'prefix+DLX'
deadLetterRoutingKey: a dead letter routing key to assign to the queue; if autoBindDlq is true Only applies if requiredGroups are provided and then only to those groups.

Default: destination
declareExchange: Whether to declare the exchange for the destination.

Default: true.
delay: A SpEL expression to evaluate the delay to apply to the message (x-delay header) - has no effect if the exchange is not a delayed message exchange.

Default: No x-delay header is set.
delayedExchange: Whether to declare the exchange as a Delayed Message Exchange - requires the delayed message exchange plugin on the broker. The x-delayed-type argument is set to the exchangeType.

Default: false.
deliveryMode: Delivery mode.

Default: PERSISTENT.
dlqDeadLetterExchange: if a DLQ is declared, a DLX to assign to that queue Only applies if requiredGroups are provided and then only to those groups.

Default: none
dlqDeadLetterRoutingKey: if a DLQ is declared, a dead letter routing key to assign to that queue; default none Only applies if requiredGroups are provided and then only to those groups.

Default: none
dlqExpires: how long before an unused dead letter queue is deleted (ms) Only applies if requiredGroups are provided and then only to those groups.

Default: no expiration
dlqLazy: Declare the dead letter queue with the x-queue-mode=lazy argument. See Lazy Queues. Consider using a policy instead of this setting because using a policy allows changing the setting without deleting the queue. Only applies if requiredGroups are provided and then only to those groups.
dlqMaxLength: maximum number of messages in the dead letter queue Only applies if requiredGroups are provided and then only to those groups.

Default: no limit
dlqMaxLengthBytes: maximum number of total bytes in the dead letter queue from all messages Only applies if requiredGroups are provided and then only to those groups.

Default: no limit
dlqMaxPriority: maximum priority of messages in the dead letter queue (0-255) Only applies if requiredGroups are provided and then only to those groups.

Default: none
dlqTtl: default time to live to apply to the dead letter queue when declared (ms) Only applies if requiredGroups are provided and then only to those groups.

Default: no limit
exchangeAutoDelete: If declareExchange is true, whether the exchange should be auto-delete (removed after the last queue is removed).

Default: true.
exchangeDurable: If declareExchange is true, whether the exchange should be durable (survives broker restart).

Default: true.
exchangeType: The exchange type; direct, fanout or topic for non-partitioned destinations; direct or topic for partitioned destinations.

Default: topic.
expires: how long before an unused queue is deleted (ms) Only applies if requiredGroups are provided and then only to those groups.

Default: no expiration
headerPatterns: Patterns for headers to be mapped to outbound messages.

Default: ['*'] (all headers).
lazy: Declare the queue with the x-queue-mode=lazy argument. See Lazy Queues. Consider using a policy instead of this setting because using a policy allows changing the setting without deleting the queue. Only applies if requiredGroups are provided and then only to those groups.

Default: false.
maxLength: maximum number of messages in the queue Only applies if requiredGroups are provided and then only to those groups.

Default: no limit
maxLengthBytes: maximum number of total bytes in the queue from all messages Only applies if requiredGroups are provided and then only to those groups.

Default: no limit
maxPriority: maximum priority of messages in the queue (0-255) Only applies if requiredGroups are provided and then only to those groups.
Default: none
prefix: A prefix to be added to the name of the destination exchange.

Default: "".
queueNameGroupOnly: When true, consume from a queue with a name equal to the group; otherwise the queue name is destination.group. This is useful, for example, when using Spring Cloud Stream to consume from an existing RabbitMQ queue. Only applies if requiredGroups are provided and then only to those groups.

Default: false.
routingKeyExpression: A SpEL expression to determine the routing key to use when publishing messages. For a fixed routing key, use a literal expression, e.g. routingKeyExpression='my.routingKey' in a properties file, or routingKeyExpression: '''my.routingKey''' in a YAML file.

Default: destination or destination-<partition> for partitioned destinations.
transacted: Whether to use transacted channels.

Default: false.
ttl: default time to live to apply to the queue when declared (ms) Only applies if requiredGroups are provided and then only to those groups.

Default: no limit

In the case of RabbitMQ, content type headers can be set by external applications. Spring Cloud Stream supports them as part of an extended internal protocol used for any type of transport (including transports, such as Kafka, that do not normally support headers).

15.4. Retry With the RabbitMQ Binder

15.4.1. Overview

When retry is enabled within the binder, the listener container thread is suspended for any back off periods that are configured. This might be important when strict ordering is required with a single consumer but for other use cases it prevents other messages from being processed on that thread. An alternative to using binder retry is to set up dead lettering with time to live on the dead-letter queue (DLQ), as well as dead-letter configuration on the DLQ itself. See RabbitMQ Binder Properties for more information about the properties discussed here. Example configuration to enable this feature:

Set autoBindDlq to true - the binder will create a DLQ; you can optionally specify a name in deadLetterQueueName
Set dlqTtl to the back off time you want to wait between redeliveries
Set the dlqDeadLetterExchange to the default exchange - expired messages from the DLQ will be routed to the original queue since the default deadLetterRoutingKey is the queue name (destination.group)

To force a message to be dead-lettered, either throw an AmqpRejectAndDontRequeueException, or set requeueRejected to true and throw any exception.

The loop will continue without end, which is fine for transient problems but you may want to give up after some number of attempts. Fortunately, RabbitMQ provides the x-death header which allows you to determine how many cycles have occurred.

To acknowledge a message after giving up, throw an ImmediateAcknowledgeAmqpException.

15.4.2. Putting it All Together

---
spring.cloud.stream.bindings.input.destination=myDestination
spring.cloud.stream.bindings.input.group=consumerGroup
#disable binder retries
spring.cloud.stream.bindings.input.consumer.max-attempts=1
#dlx/dlq setup
spring.cloud.stream.rabbit.bindings.input.consumer.auto-bind-dlq=true
spring.cloud.stream.rabbit.bindings.input.consumer.dlq-ttl=5000
spring.cloud.stream.rabbit.bindings.input.consumer.dlq-dead-letter-exchange=
---

This configuration creates an exchange myDestination with queue myDestination.consumerGroup bound to a topic exchange with a wildcard routing key #. It creates a DLQ bound to a direct exchange DLX with routing key myDestination.consumerGroup. When messages are rejected, they are routed to the DLQ. After 5 seconds, the message expires and is routed to the original queue using the queue name as the routing key.

Spring Boot application

@SpringBootApplication
@EnableBinding(Sink.class)
public class XDeathApplication {

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

    @StreamListener(Sink.INPUT)
    public void listen(String in, @Header(name = "x-death", required = false) Map<?,?> death) {
        if (death != null && death.get("count").equals(3L)) {
            // giving up - don't send to DLX
            throw new ImmediateAcknowledgeAmqpException("Failed after 4 attempts");
        }
        throw new AmqpRejectAndDontRequeueException("failed");
    }

}

Notice that the count property in the x-death header is a Long.

15.5. Error Channels

With rabbitmq, there are two types of send failures:

returned messages
negatively acknowledged Publisher Confirms

The latter is rare; quoting the RabbitMQ documentation "[A nack] will only be delivered if an internal error occurs in the Erlang process responsible for a queue.".

As well as enabling producer error channels as described in Message Channel Binders and Error Channels, the RabbitMQ binder will only send messages to the channels if the connection factory is appropriately configured:

ccf.setPublisherConfirms(true);
ccf.setPublisherReturns(true);

When using spring boot configuration for the connection factory, set properties:

spring.rabbitmq.publisher-confirms
spring.rabbitmq.publisher-returns

The payload of the ErrorMessage for a returned message is a ReturnedAmqpMessageException with properties:

failedMessage - the spring-messaging Message<?> that failed to be sent.
amqpMessage - the raw spring-amqp Message
replyCode - an integer value indicating the reason for the failure (e.g. 312 - No route)
replyText - a text value indicating the reason for the failure e.g. NO_ROUTE.
exchange - the exchange to which the message was published.
routingKey - the routing key used when the message was published.

For negatively acknowledged confirms, the payload is a NackedAmqpMessageException with properties:

failedMessage - the spring-messaging Message<?> that failed to be sent.
nackReason - a reason (if available; you may need to examine the broker logs for more information).

There is no automatic handling of these exceptions (such as sending to a Dead-Letter queue); you can consume these exceptions with your own Spring Integration flow.

15.6. Dead-Letter Queue Processing

Because it can’t be anticipated how users would want to dispose of dead-lettered messages, the framework 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 queue. However, if the problem is a permanent issue, that could cause an infinite loop. The following spring-boot application is an example of how to route those messages back to the original queue, but moves them to a third "parking lot" queue after three attempts. The second example utilizes the RabbitMQ Delayed Message Exchange to introduce a delay to the requeued message. In this example, the delay increases for each attempt. These examples use a @RabbitListener to receive messages from the DLQ, you could also use RabbitTemplate.receive() in a batch process.

The examples assume the original destination is so8400in and the consumer group is so8400.

15.6.1. Non-Partitioned Destinations

The first two examples are when the destination is not partitioned.

@SpringBootApplication
public class ReRouteDlqApplication {

    private static final String ORIGINAL_QUEUE = "so8400in.so8400";

    private static final String DLQ = ORIGINAL_QUEUE + ".dlq";

    private static final String PARKING_LOT = ORIGINAL_QUEUE + ".parkingLot";

    private static final String X_RETRIES_HEADER = "x-retries";

    public static void main(String[] args) throws Exception {
        ConfigurableApplicationContext context = SpringApplication.run(ReRouteDlqApplication.class, args);
        System.out.println("Hit enter to terminate");
        System.in.read();
        context.close();
    }

    @Autowired
    private RabbitTemplate rabbitTemplate;

    @RabbitListener(queues = DLQ)
    public void rePublish(Message failedMessage) {
        Integer retriesHeader = (Integer) failedMessage.getMessageProperties().getHeaders().get(X_RETRIES_HEADER);
        if (retriesHeader == null) {
            retriesHeader = Integer.valueOf(0);
        }
        if (retriesHeader < 3) {
            failedMessage.getMessageProperties().getHeaders().put(X_RETRIES_HEADER, retriesHeader + 1);
            this.rabbitTemplate.send(ORIGINAL_QUEUE, failedMessage);
        }
        else {
            this.rabbitTemplate.send(PARKING_LOT, failedMessage);
        }
    }

    @Bean
    public Queue parkingLot() {
        return new Queue(PARKING_LOT);
    }

}

@SpringBootApplication
public class ReRouteDlqApplication {

    private static final String ORIGINAL_QUEUE = "so8400in.so8400";

    private static final String DLQ = ORIGINAL_QUEUE + ".dlq";

    private static final String PARKING_LOT = ORIGINAL_QUEUE + ".parkingLot";

    private static final String X_RETRIES_HEADER = "x-retries";

    private static final String DELAY_EXCHANGE = "dlqReRouter";

    public static void main(String[] args) throws Exception {
        ConfigurableApplicationContext context = SpringApplication.run(ReRouteDlqApplication.class, args);
        System.out.println("Hit enter to terminate");
        System.in.read();
        context.close();
    }

    @Autowired
    private RabbitTemplate rabbitTemplate;

    @RabbitListener(queues = DLQ)
    public void rePublish(Message failedMessage) {
        Map<String, Object> headers = failedMessage.getMessageProperties().getHeaders();
        Integer retriesHeader = (Integer) headers.get(X_RETRIES_HEADER);
        if (retriesHeader == null) {
            retriesHeader = Integer.valueOf(0);
        }
        if (retriesHeader < 3) {
            headers.put(X_RETRIES_HEADER, retriesHeader + 1);
            headers.put("x-delay", 5000 * retriesHeader);
            this.rabbitTemplate.send(DELAY_EXCHANGE, ORIGINAL_QUEUE, failedMessage);
        }
        else {
            this.rabbitTemplate.send(PARKING_LOT, failedMessage);
        }
    }

    @Bean
    public DirectExchange delayExchange() {
        DirectExchange exchange = new DirectExchange(DELAY_EXCHANGE);
        exchange.setDelayed(true);
        return exchange;
    }

    @Bean
    public Binding bindOriginalToDelay() {
        return BindingBuilder.bind(new Queue(ORIGINAL_QUEUE)).to(delayExchange()).with(ORIGINAL_QUEUE);
    }

    @Bean
    public Queue parkingLot() {
        return new Queue(PARKING_LOT);
    }

}

15.6.2. Partitioned Destinations

With partitioned destinations, there is one DLQ for all partitions and we determine the original queue from the headers.

republishToDlq=false

When republishToDlq is false, RabbitMQ publishes the message to the DLX/DLQ with an x-death header containing information about the original destination.

@SpringBootApplication
public class ReRouteDlqApplication {

	private static final String ORIGINAL_QUEUE = "so8400in.so8400";

	private static final String DLQ = ORIGINAL_QUEUE + ".dlq";

	private static final String PARKING_LOT = ORIGINAL_QUEUE + ".parkingLot";

	private static final String X_DEATH_HEADER = "x-death";

	private static final String X_RETRIES_HEADER = "x-retries";

	public static void main(String[] args) throws Exception {
		ConfigurableApplicationContext context = SpringApplication.run(ReRouteDlqApplication.class, args);
		System.out.println("Hit enter to terminate");
		System.in.read();
		context.close();
	}

	@Autowired
	private RabbitTemplate rabbitTemplate;

	@SuppressWarnings("unchecked")
	@RabbitListener(queues = DLQ)
	public void rePublish(Message failedMessage) {
		Map<String, Object> headers = failedMessage.getMessageProperties().getHeaders();
		Integer retriesHeader = (Integer) headers.get(X_RETRIES_HEADER);
		if (retriesHeader == null) {
			retriesHeader = Integer.valueOf(0);
		}
		if (retriesHeader < 3) {
			headers.put(X_RETRIES_HEADER, retriesHeader + 1);
			List<Map<String, ?>> xDeath = (List<Map<String, ?>>) headers.get(X_DEATH_HEADER);
			String exchange = (String) xDeath.get(0).get("exchange");
			List<String> routingKeys = (List<String>) xDeath.get(0).get("routing-keys");
			this.rabbitTemplate.send(exchange, routingKeys.get(0), failedMessage);
		}
		else {
			this.rabbitTemplate.send(PARKING_LOT, failedMessage);
		}
	}

	@Bean
	public Queue parkingLot() {
		return new Queue(PARKING_LOT);
	}

}

republishToDlq=true

When republishToDlq is true, the republishing recoverer adds the original exchange and routing key to headers.

@SpringBootApplication
public class ReRouteDlqApplication {

	private static final String ORIGINAL_QUEUE = "so8400in.so8400";

	private static final String DLQ = ORIGINAL_QUEUE + ".dlq";

	private static final String PARKING_LOT = ORIGINAL_QUEUE + ".parkingLot";

	private static final String X_RETRIES_HEADER = "x-retries";

	private static final String X_ORIGINAL_EXCHANGE_HEADER = RepublishMessageRecoverer.X_ORIGINAL_EXCHANGE;

	private static final String X_ORIGINAL_ROUTING_KEY_HEADER = RepublishMessageRecoverer.X_ORIGINAL_ROUTING_KEY;

	public static void main(String[] args) throws Exception {
		ConfigurableApplicationContext context = SpringApplication.run(ReRouteDlqApplication.class, args);
		System.out.println("Hit enter to terminate");
		System.in.read();
		context.close();
	}

	@Autowired
	private RabbitTemplate rabbitTemplate;

	@RabbitListener(queues = DLQ)
	public void rePublish(Message failedMessage) {
		Map<String, Object> headers = failedMessage.getMessageProperties().getHeaders();
		Integer retriesHeader = (Integer) headers.get(X_RETRIES_HEADER);
		if (retriesHeader == null) {
			retriesHeader = Integer.valueOf(0);
		}
		if (retriesHeader < 3) {
			headers.put(X_RETRIES_HEADER, retriesHeader + 1);
			String exchange = (String) headers.get(X_ORIGINAL_EXCHANGE_HEADER);
			String originalRoutingKey = (String) headers.get(X_ORIGINAL_ROUTING_KEY_HEADER);
			this.rabbitTemplate.send(exchange, originalRoutingKey, failedMessage);
		}
		else {
			this.rabbitTemplate.send(PARKING_LOT, failedMessage);
		}
	}

	@Bean
	public Queue parkingLot() {
		return new Queue(PARKING_LOT);
	}

}

Appendices

Appendix A: Building

A.1. Basic Compile and Test

To build the source you will need to install JDK 1.7.

The build uses the Maven wrapper so you don’t have to install a specific version of Maven. To enable the tests for Redis, Rabbit, and Kafka bindings you should have those servers running before building. See below for more information on running the servers.

The main build command is

$ ./mvnw clean install

You can also add '-DskipTests' if you like, to avoid running the tests.

You can also install Maven (>=3.3.3) yourself and run the mvn command in place of ./mvnw in the examples below. If you do that you also might need to add -P spring if your local Maven settings do not contain repository declarations for spring pre-release artifacts.

Be aware that you might need to increase the amount of memory available to Maven by setting a MAVEN_OPTS environment variable with a value like -Xmx512m -XX:MaxPermSize=128m. We try to cover this in the .mvn configuration, so if you find you have to do it to make a build succeed, please raise a ticket to get the settings added to source control.

The projects that require middleware generally include a docker-compose.yml, so consider using Docker Compose to run the middeware servers in Docker containers. See the README in the scripts demo repository for specific instructions about the common cases of mongo, rabbit and redis.

A.2. Documentation

There is a "full" profile that will generate documentation.

A.3. Working with the code

If you don’t have an IDE preference we would recommend that you use Spring Tools Suite or Eclipse when working with the code. We use the m2eclipe eclipse plugin for maven support. Other IDEs and tools should also work without issue.

A.3.1. Importing into eclipse with m2eclipse

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.

Alternatively you can copy the repository settings from .settings.xml into your own ~/.m2/settings.xml.

A.3.2. Importing into eclipse without m2eclipse

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

$ ./mvnw eclipse:eclipse

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.

A.4. Sign the Contributor License Agreement

Before we accept a non-trivial patch or pull request we will need you to sign the contributor’s agreement. Signing the contributor’s agreement does not grant anyone commit rights to the main repository, but it does mean that we can accept your contributions, and you will get an author credit if we do. Active contributors might be asked to join the core team, and given the ability to merge pull requests.

A.5. Code Conventions and Housekeeping

None of these is essential for a pull request, but they will all help. They can also be added after the original pull request but before a merge.

Use the Spring Framework code format conventions. If you use Eclipse you can import formatter settings using the eclipse-code-formatter.xml file from the Spring Cloud Build project. If using IntelliJ, you can use the Eclipse Code Formatter Plugin to import the same file.
Make sure all new .java files to have a simple Javadoc class comment with at least an @author tag identifying you, and preferably at least a paragraph on what the class is for.
Add the ASF license header comment to all new .java files (copy from existing files in the project)
Add yourself as an @author to the .java files that you modify substantially (more than cosmetic changes).
Add some Javadocs and, if you change the namespace, some XSD doc elements.
A few unit tests would help a lot as well — someone has to do it.
If no-one else is using your branch, please rebase it against the current master (or other target branch in the main project).
When writing a commit message please follow these conventions, if you are fixing an existing issue please add Fixes gh-XXXX at the end of the commit message (where XXXX is the issue number).

Spring Cloud Stream Reference Guide

Spring Cloud Stream Core

1. Introducing Spring Cloud Stream

2. Main Concepts

2.1. Application Model

2.1.1. Fat JAR

2.2. The Binder Abstraction

2.3. Persistent Publish-Subscribe Support

2.4. Consumer Groups

2.4.1. Durability

2.5. Partitioning Support

3. Programming Model

3.1. Declaring and Binding Channels

3.1.1. Triggering Binding Via @EnableBinding

3.1.2. @Input and @Output

Customizing Channel Names

Source, Sink, and Processor

3.1.3. Accessing Bound Channels

Injecting the Bound Interfaces

Injecting Channels Directly

3.1.4. Producing and Consuming Messages

Native Spring Integration Support

Spring Integration Error Channel Support

Message Channel Binders and Error Channels

Using @StreamListener for Automatic Content Type Handling

Using @StreamListener for dispatching messages to multiple methods

3.1.5. Reactive Programming Support

Reactor-based handlers

RxJava 1.x support

Reactive Sources

3.1.6. Aggregation

Configuring aggregate application

Configuring binding service properties for non self contained aggregate application

4. Binders

4.1. Producers and Consumers

4.2. Binder SPI

4.3. Binder Detection

4.3.1. Classpath Detection

4.4. Multiple Binders on the Classpath

4.5. Connecting to Multiple Systems

4.6. Binder configuration properties

5. Configuration Options

5.1. Spring Cloud Stream Properties

5.2. Binding Properties

5.2.1. Properties for Use of Spring Cloud Stream

5.2.2. Consumer properties

5.2.3. Producer Properties

5.3. Using dynamically bound destinations

6. Content Type and Transformation

6.1. MIME types

6.2. MIME types and Java types

6.3. Customizing message conversion

6.4. @StreamListener and Message Conversion

7. Schema Evolution Support

7.1. Schema Registry Client

7.1.1. Schema Registry Client Properties

7.2. Avro Schema Registry Client Message Converters

7.2.1. Avro Schema Registry Message Converter Properties

7.3. Apache Avro Message Converters

7.4. Converters with Schema Support

7.5. Schema Registry Server

7.5.1. Schema Registry Server API

Registering a New Schema

Retrieving an Existing Schema by Subject, Format, and Version

Retrieving an Existing Schema by Subject and Format

Retrieving an Existing Schema by ID

Deleting a Schema by Subject, Format, and Version

Deleting a Schema by ID

Deleting a Schema by Subject

7.5.2. Using Confluent’s Schema Registry

7.6. Schema Registration and Resolution

7.6.1. Schema Registration Process (Serialization)

7.6.2. Schema Resolution Process (Deserialization)

8. Inter-Application Communication

8.1. Connecting Multiple Application Instances

8.2. Instance Index and Instance Count

8.3. Partitioning

8.3.1. Configuring Output Bindings for Partitioning

Spring-managed custom PartitionKeyExtractorClass implementations

Configuring Input Bindings for Partitioning

3.1.1. Triggering Binding Via `@EnableBinding`

3.1.2. `@Input` and `@Output`

`Source`, `Sink`, and `Processor`

6.4. `@StreamListener` and Message Conversion

Spring-managed custom `PartitionKeyExtractorClass` implementations

Example: Setting `autoCommitOffset` false and relying on manual acking.