Spring Integration Java DSL: Line by line tutorial

Engineering | Artem Bilan | November 25, 2014 | ...

Dear Spring Community!

Just after the Spring Integration Java DSL 1.0 GA release announcement I want to introduce the Spring Integration Java DSL to you as a line by line tutorial based on the classic Cafe Demo integration sample. We describe here Spring Boot support, Spring Framework Java and Annotation configuration, the IntegrationFlow feature and pay tribute to Java 8 Lambda support which was an inspiration for the DSL style. Of course, it is all backed by the Spring Integration Core project.

For those, who are not interested in Java 8 yet, we provide similar tutorial without Lambdas: Spring Integration Java DSL (pre Java 8): Line by line tutorial.

But, before we launch into the description of the Cafe demonstration app here's a shorter example just to get started...

public class Start {

	public static void main(String[] args) throws InterruptedException {
		ConfigurableApplicationContext ctx = 
                                 SpringApplication.run(Start.class, args);

		List<String> strings = Arrays.asList("foo", "bar");


	public interface Upcase {

		@Gateway(requestChannel = "upcase.input")
		Collection<String> upcase(Collection<String> strings);


	public IntegrationFlow upcase() {
	     return f -> f
		 	.split()                                         // 1
			.<String, String>transform(String::toUpperCase)  // 2
			.aggregate();                                    // 3


We will leave the description of the infrastructure (annotations etc) to the main cafe flow description. Here, we want you to concentrate on the last @Bean, the IntegrationFlow as well as the gateway method which sends messages to that flow.

In the main method we send a collection of strings to the gateway and print the results to STDOUT. The flow first splits the collection into individual Strings (1); each string is then transformed to upper case (2) and finally we re-aggregate them back into a collection (3) Since that's the end of the flow, the framework returns the result of the aggregation back to the gateway and the new payload becomes the return value from the gateway method.

The equivalent XML configuration might be...

<int:gateway service interface="foo.Upcase" 

<int:splitter input-channel="upcase.input" output-channel="transform"/>

<int:transformer expression="payload.toUpperCase()"
    output-channel="aggregate" />

<int:aggregator input-channle="aggregate" />


<int:gateway service interface="foo.Upcase" 

<int:chain input-channel="upcase.input">
    <int:splitter />
    <int:transformer expression="payload.toUpperCase()" />
    <int:aggregator />

##Cafe Demo

The purpose of the Cafe Demo application is to demonstrate how Enterprise Integration Patterns (EIP) can be used to reflect the order-delivery scenario in a real life cafe. With this application, we handle several drink orders - hot and iced. After running the application we can see in the standard output (System.out.println) how cold drinks are prepared quicker than hot. However the delivery for the whole order is postponed until the hot drink is ready.

To reflect the domain model we have several classes: Order, OrderItem, Drink and Delivery. They all are mentioned in the integration scenario, but we won't analyze them here, because they are simple enough.

The source code for our application is placed only in a single class; significant lines are annotated with a number corresponding to the comments, which follow:

@SpringBootApplication               // 1
@IntegrationComponentScan            // 2
public class Application {

  public static void main(String[] args) throws Exception {
  	ConfigurableApplicationContext ctx =
  	              SpringApplication.run(Application.class, args);// 3

  	Cafe cafe = ctx.getBean(Cafe.class);                         // 4
  	for (int i = 1; i <= 100; i++) {                             // 5
       Order order = new Order(i);
       order.addItem(DrinkType.LATTE, 2, false); //hot
       order.addItem(DrinkType.MOCHA, 3, true);  //iced

  	System.out.println("Hit 'Enter' to terminate");              // 6

  @MessagingGateway                                              // 7
  public interface Cafe {

  	@Gateway(requestChannel = "orders.input")                    // 8
  	void placeOrder(Order order);                                // 9


  private AtomicInteger hotDrinkCounter = new AtomicInteger();

  private AtomicInteger coldDrinkCounter = new AtomicInteger();  // 10

  @Bean(name = PollerMetadata.DEFAULT_POLLER)
  public PollerMetadata poller() {                               // 11
  	return Pollers.fixedDelay(1000).get();

  public IntegrationFlow orders() {                             // 12
  	return f -> f                                               // 13
  	  .split(Order.class, Order::getItems)                      // 14
  	  .channel(c -> c.executor(Executors.newCachedThreadPool()))// 15
  	  .<OrderItem, Boolean>route(OrderItem::isIced, mapping -> mapping // 16
  	    .subFlowMapping("true", sf -> sf                        // 17
  	      .channel(c -> c.queue(10))                            // 18
  	      .publishSubscribeChannel(c -> c                       // 19
  	        .subscribe(s ->                                     // 20
  	          s.handle(m -> sleepUninterruptibly(1, TimeUnit.SECONDS)))// 21
  	        .subscribe(sub -> sub                               // 22
  	          .<OrderItem, String>transform(item ->
  	              + " prepared cold drink #"
  	              + this.coldDrinkCounter.incrementAndGet()
  	              + " for order #" + item.getOrderNumber()
  	              + ": " + item)                                 // 23
  	          .handle(m -> System.out.println(m.getPayload())))))// 24
  	    .subFlowMapping("false", sf -> sf                        // 25
  	      .channel(c -> c.queue(10))
  	      .publishSubscribeChannel(c -> c
  	        .subscribe(s ->
  	          s.handle(m -> sleepUninterruptibly(5, TimeUnit.SECONDS)))// 26
  	        .subscribe(sub -> sub
  	          .<OrderItem, String>transform(item ->
  	              + " prepared hot drink #"
  	              + this.hotDrinkCounter.incrementAndGet()
  	              + " for order #" + item.getOrderNumber()
  	              + ": " + item)
  	          .handle(m -> System.out.println(m.getPayload()))))))
  	  .<OrderItem, Drink>transform(orderItem ->
  	    new Drink(orderItem.getOrderNumber(),
  	      orderItem.getShots()))                                // 27
  	  .aggregate(aggregator -> aggregator                       // 28
  	    .outputProcessor(group ->                               // 29
  	      new Delivery(group.getMessages()
  	        .map(message -> (Drink) message.getPayload())
  	        .collect(Collectors.toList())))                     // 30
  	    .correlationStrategy(m ->
  	      ((Drink) m.getPayload()).getOrderNumber()), null)     // 31
  	  .handle(CharacterStreamWritingMessageHandler.stdout());   // 32


Examining the code line by line...



This new meta-annotation from Spring Boot 1.2. Includes @Configuration and @EnableAutoConfiguration. Since we are in a Spring Integration application and Spring Boot has auto-configuration for it, the @EnableIntegration is automatically applied, to initialize the Spring Integration infrastructure including an environment for the Java DSL - DslIntegrationConfigurationInitializer, which is picked up by the IntegrationConfigurationBeanFactoryPostProcessor from /META-INF/spring.factories.



The Spring Integration analogue of @ComponentScan to scan components based on interfaces, (the Spring Framework's @ComponentScan only looks at classes). Spring Integration supports the discovery of interfaces annotated with @MessagingGateway (see #7 below).


ConfigurableApplicationContext ctx = SpringApplication.run(Application.class, args);

The main method of our class is designed to start the Spring Boot application using the configuration from this class and starts an ApplicationContext via Spring Boot. In addition, it delegates command line arguments to the Spring Boot. For example you can specify --debug to see logs for the boot auto-configuration report.


Cafe cafe = ctx.getBean(Cafe.class);

Since we already have an ApplicationContext we can start to interact with application. And Cafe is that entry point - in EIP terms a gateway. Gateways are simply interfaces and the application does not interact with the Messaging API; it simply deals with the domain (see #7 below).


for (int i = 1; i <= 100; i++) {

To demonstrate the cafe "work" we intiate 100 orders with two drinks - one hot and one iced. And send the Order to the Cafe gateway.


System.out.println("Hit 'Enter' to terminate");

Typically Spring Integration application are asynchronous, hence to avoid early exit from the main Thread we block the main method until some end-user interaction through the command line. Non daemon threads will keep the application open but System.read() provides us with a mechanism to close the application cleanly.



The annotation to mark a business interface to indicate it is a gateway between the end-application and integration layer. It is an analogue of <gateway /> component from Spring Integration XML configuration. Spring Integration creates a Proxy for this interface and populates it as a bean in the application context. The purpose of this Proxy is to wrap parameters in a Message<?> object and send it to the MessageChannel according to the provided options.


@Gateway(requestChannel = "orders.input")

The method level annotation to distinct business logic by methods as well as by the target integration flows. In this sample we use a requestChannel reference of orders.input, which is a MessageChannel bean name of our IntegrationFlow input channel (see below #13).


void placeOrder(Order order);

The interface method is a central point to interact from end-application with the integration layer. This method has a void return type. It means that our integration flow is one-way and we just send messages to the integration flow, but don't wait for a reply.


private AtomicInteger hotDrinkCounter = new AtomicInteger();
private AtomicInteger coldDrinkCounter = new AtomicInteger();

Two counters to gather the information how our cafe works with drinks.


@Bean(name = PollerMetadata.DEFAULT_POLLER)
public PollerMetadata poller() {

The default poller bean. It is a analogue of <poller default="true"> component from Spring Integration XML configuration. Required for endpoints where the inputChannel is a PollableChannel. In this case, it is necessary for the two Cafe queues - hot and iced (see below #18). Here we use the Pollers factory from the DSL project and use its method-chain fluent API to build the poller metadata. Note that Pollers can be used directly from an IntegrationFlow definition, if a specific poller (rather than the default poller) is needed for an endpoint.


public IntegrationFlow orders() {

The IntegrationFlow bean definition. It is the central component of the Spring Integration Java DSL, although it does not play any role at runtime, just during the bean registration phase. All other code below registers Spring Integration components (MessageChannel, MessageHandler, EventDrivenConsumer, MessageProducer, MessageSource etc.) in the IntegrationFlow object, which is parsed by the IntegrationFlowBeanPostProcessor to process those components and register them as beans in the application context as necessary (some elements, such as channels may already exist).


return f -> f

The IntegrationFlow is a Consumer functional interface, so we can minimize our code and concentrate just only on the integration scenario requirements. Its Lambda accepts IntegrationFlowDefinition as an argument. This class offers a comprehensive set of methods which can be composed to the chain. We call these EIP-methods, because they provide implementations for EI patterns and populate components from Spring Integration Core. During the bean registration phase, the IntegrationFlowBeanPostProcessor converts this inline (Lambda) IntegrationFlow to a StandardIntegrationFlow and processes its components. The same we can achieve using IntegrationFlows factory (e.g. IntegrationFlow.from("channelX"). ... .get()), but we find the Lambda definition more elegant. An IntegrationFlow definition using a Lambda populates DirectChannel as an inputChannel of the flow and it is registered in the application context as a bean with the name orders.input in this our sample (flow bean name + ".input"). That's why we use that name for the Cafe gateway.


.split(Order.class, Order::getItems)

Since our integration flow accepts message through the orders.input channel, we are ready to consume and process them. The first EIP-method in our scenario is .split(). We know that the message payload from orders.input channel is an Order domain object, so we can simply use its type here and use the Java 8 method-reference feature. The first parameter is a type of message payload we expect, and the second is a method reference to the getItems() method, which returns Collection<OrderItem>. So, this performs the split EI pattern, when we send each collection entry as a separate message to the next channel. In the background, the .split() method registers a MethodInvokingSplitter MessageHandler implementation and the EventDrivenConsumer for that MessageHandler, and wiring in the orders.input channel as the inputChannel.


.channel(c -> c.executor(Executors.newCachedThreadPool()))

The .channel() EIP-method allows the specification of concrete MessageChannels between endpoints, as it is done via output-channel/input-channel attributes pair with Spring Integration XML configuration. By default, endpoints in the DSL integration flow definition are wired with DirectChannels, which get the bean names based on the IntegrationFlow bean name and index in the flow chain. In this case we use another Lambda expression, which selects a specific MessageChannel implementation from its Channels factory and configures it with the fluent API. The current channel here is an ExecutorChannel, to allow to distribute messages from the splitter to separate Threads, to process them in parallel in the downstream flow.


.<OrderItem, Boolean>route(OrderItem::isIced, mapping -> mapping

The next EIP-method in our scenario is .route(), to send hot/iced order items to different Cafe kitchens. We again use here a method reference (isIced()) to get the routingKey from the incoming message. The second Lambda parameter represents a router mapping - something similar to <mapping> sub-element for the <router> component from Spring Integration XML configuration. However since we are using Java we can go a bit further with its Lambda support! The Spring Integration Java DSL introduced the subflow definition for routers in addition to traditional channel mapping. Each subflow is executed depending on the routing and, if the subflow produces a result, it is passed to the next element in the flow definition after the router.


.subFlowMapping("true", sf -> sf 

Specifies the integration flow for the current router's mappingKey. We have in this samples two subflows - hot and iced. The subflow is the same IntegrationFlow functional interface, therefore we can use its Lambda exactly the same as we do on the top level IntegrationFlow definition. The subflows don't have any runtime dependency with its parent, it's just a logical relationship.


.channel(c -> c.queue(10))

We already know that a Lambda definition for the IntegrationFlow starts from [FLOW_BEAN_NAME].input DirectChannel, so it may be a question "how does it work here if we specify .channel() again?". The DSL takes care of such a case and wires those two channels with a BridgeHandler and endpoint. In our sample, we use here a restricted QueueChannel to reflect the Cafe kitchen busy state from real life. And here is a place where we need that global poller for the next endpoint which is listening on this channel.


.publishSubscribeChannel(c -> c

The .publishSubscribeChannel() EIP-method is a variant of the .channel() for a MessageChannels.publishSubscribe(), but with the .subscribe() option when we can specify subflow as a subscriber to the channel. Right, subflow one more time! So, subflows can be specified to any depth. Independently of the presence .subscribe() subflows, the next endpoint in the parent flow is also a subscriber to this .publishSubscribeChannel(). Since we are in the .route() subflow already, the last subscriber is an implicit BridgeHandler which just pops the message to the top level - to a similar implicit BridgeHandler to pop message to the next .transform() endpoint in the main flow. And one more note about this current position of our flow: the previous EIP-method is .channel(c -> c.queue(10)) and this one is for MessageChannel too. So, they are again tied with an implicit BridgeHandler as well. In a real application we could avoid this .publishSubscribeChannel() just with the single .handle() for the Cafe kitchen, but our goal here to cover DSL features as much as possible. That's why we distribute the kitchen work to several subflows for the same PublishSubscribeChannel.


.subscribe(s ->

The .subscribe() method accepts an IntegrationFlow as parameter, which can be specified as Lambda to configure subscriber as subflow. We use here several subflow subscribers to avoid multi-line Lambdas and cover some DSL as we as Spring Integration capabilities.


s.handle(m -> sleepUninterruptibly(1, TimeUnit.SECONDS)))

Here we use a simple .handle() EIP-method just to block the current Thread for some timeout to demonstrate how quickly the Cafe kitchen prepares a drink. Here we use Google Guava Uninterruptibles.sleepUninterruptibly, to avoid using a try...catch block within the Lambda expression, although you can do that and your Lambda will be multi-line. Or you can move that code to a separate method and use it here as method reference.

Since we don't use any Executor on the .publishSubscribeChannel() all subscribers will beperformed sequentially on the same Thread; in our case it is one of TaskScheduler's Threads from poller on the previous QueueChannel. That's why this sleep blocks all downstream process and allows to demonstrate the busy state for that restricted to 10 QueueChannel.


.subscribe(sub -> sub

The next subflow subscriber which will be performed only after that sleep with 1 second for iced drink. We use here one more subflow because .handle() of previous one is one-way with the nature of the Lambda for MessageHandler. That's why, to go ahead with process of our whole flow, we have several subscribers: some of subflows finish after their work and don't return anything to the parent flow.


 .<OrderItem, String>transform(item ->
  	              + " prepared cold drink #"
  	              + this.coldDrinkCounter.incrementAndGet()
  	              + " for order #" + item.getOrderNumber()
  	              + ": " + item)         

The transformer in the current subscriber subflow is to convert the OrderItem to the friendly STDOUT message for the next .handle. Here we see the use of generics with the Lambda expression. This is implemented using the GenericTransformer functional interface.


.handle(m -> System.out.println(m.getPayload())))))

The .handle() here just to demonstrate how to use Lambda expression to print the payload to STDOUT. It is a signal that our drink is ready. After that the final (implicit) subscriber to the PublishSubscribeChannel just sends the message with the OrderItem to the .transform() in the main flow.


.subFlowMapping("false", sf -> sf

The .subFlowMapping() for the hot drinks. Actually it is similar to the previous iced drinks subflow, but with specific hot business logic.


s.handle(m -> sleepUninterruptibly(5, TimeUnit.SECONDS)))

The sleepUninterruptibly for hot drinks. Right, we need more time to boil the water!


 .<OrderItem, Drink>transform(orderItem ->
  	    new Drink(orderItem.getOrderNumber(),

The main OrderItem to Drink transformer, which is performed when the .route() subflow returns its result after the Cafe kitchen subscribers have finished preparing the drink.


.aggregate(aggregator -> aggregator

The .aggregate() EIP-method provides similar options to configure an AggregatingMessageHandler and its endpoint, like we can do with the <aggregator> component when using Spring Integration XML configuration. Of course, with the Java DSL we have more power to configure the aggregator just in place, without any other extra beans. And Lambdas come to the rescue again! From the Cafe business logic perspective we compose the Delivery for the initial Order, since we .split() the original order to the OrderItems near the beginning.


.outputProcessor(group -> 

The .outputProcessor() of the AggregatorSpec allows us to emit a custom result after aggregator completes the group. It's an analogue of ref/method from the <aggregator> component or the @Aggregator annotation on a POJO method. Our goal here to compose a Delivery for all Drinks.


new Delivery(group.getMessages()
  	        .map(message -> (Drink) message.getPayload())

As you see we use here the Java 8 Stream feature for Collection. We iterate over messages from the released MessageGroup and convert (map) each of them to its Drink payload. The result of the Stream (.collect()) (a list of Drinks) is passed to the Delivery constructor. The Message with this new Delivery payload is sent to the next endpoint in our Cafe scenario.


.correlationStrategy(m ->
  	      ((Drink) m.getPayload()).getOrderNumber()), null)

The .correlationStrategy() Lambda demonstrates how we can customize an aggregator behaviour. Of course, we can rely here just only on a built-in SequenceDetails from Spring Integration, which is populated by default from .split() in the beginning of our flow to each split message, but the Lambda sample for the CorrelationStrategy is included for illustration. (With XML, we could have used a correlation-expression or a custom CorrelationStrategy). The second argument in this line for the .aggregate() EIP-method is for the endpointConfigurer to customize options like autoStartup, requiresReply, adviceChain etc. We use here null to show that we rely on the default options for the endpoint. Many of EIP-methods provide overloaded versions with and without endpointConfigurer, but .aggregate() requires an endpoint argument, to avoid an explicit cast for the AggregatorSpec Lambda argument.



It is the end of our flow - the Delivery is delivered to the client! We just print here the message payload to STDOUT using out-of-the-box CharacterStreamWritingMessageHandler from Spring Integration Core. This is a case to show how existing components from Spring Integration Core (and its modules) can be used from the Java DSL.

Well, we have finished describing the Cafe Demo sample based on the Spring Integration Java DSL. Compare it with XML sample to get more information regarding Spring Integration.

This is not an overall tutorial to the DSL stuff. We don't review here the endpointConfigurer options, Transformers factory, the IntegrationComponentSpec hierarchy, the NamespaceFactories, how we can specify several IntegrationFlow beans and wire them to a single application etc., see the Reference Manual for more information.

At least this line-by-line tutorial should show you Spring Integration Java DSL basics and its seamless fusion between Spring Framework Java & Annotation configuration, Spring Integration foundation and Java 8 Lambda support!

Also see the si4demo to see the evolution of Spring Integration including the Java DSL, as shown at the 2014 SpringOne/2GX Conference. (Video should be available soon).

As always, we look forward to your comments and feedback (StackOverflow (spring-integration tag), Spring JIRA, GitHub) and we very much welcome contributions!

P.S. Even if this tutorial is fully based on the Java 8 Lambda support, we don't want to miss pre Java 8 users, we are going to provide similar non-Lambda blog post. Stay tuned!

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