1. Spring Cloud Stream Overview

1.1 Introducing Spring Cloud Stream

The Spring Cloud Stream project allows a user to develop and run messaging microservices using Spring Integration. Just add @EnableBinding and run your app as a Spring Boot app (single application context). You just need to connect to the physical broker for the bindings, which is automatic if the relevant binder implementation is available on the classpath. The sample uses Redis.

Here’s a sample source module (output channel only):

public class ModuleApplication {

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


public class TimerSource {

  private String format;

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


@EnableBinding is parameterized by one or more interfaces (in this case a single Source interface), which declares input and output channels. The interfaces Source, Sink and Processor are provided off the shelf, but you can define others. Here’s the definition of Source:

public interface Source {
  MessageChannel output();

The @Output annotation is used to identify output channels (messages leaving the module) and @Input is used to identify input channels (messages entering the module). It is optionally parameterized by a channel name - if the name is not provided the method name is used instead. An implementation of the interface is created for you and can be used in the application context by autowiring it, e.g. into a test case:

@SpringApplicationConfiguration(classes = ModuleApplication.class)
public class ModuleApplicationTests {

	private Source source

	public void contextLoads() {


In this case there is only one Source in the application context so there is no need to qualify it when it is autowired. If there is ambiguity, e.g. if you are composing one module from some others, you can use @Bindings qualifier to inject a specific channel set. The @Bindings qualifier takes a parameter which is the class that carries the @EnableBinding annotation (in this case the TimerSource).

1.1.1 Multiple Input or Output Channels

A module can have multiple input or output channels defined as @Input and @Output methods in an interface. Instead of just one channel named "input" or "output" you can add multiple MessageChannel methods annotated @Input or @Output and their names will be converted to external channel names on the broker. It is common to specify the channel names at runtime in order to have multiple modules communicate over a well known channel names. Channel names can be specified as properties that consist of the channel names prefixed with spring.cloud.stream.bindings (e.g. spring.cloud.stream.bindings.input or spring.cloud.stream.bindings.output). These properties can be specified though environment variables, the application YAML file or the other mechanism supported by Spring Boot.

Channel names can also have a channel type as a colon-separated prefix, and the semantics of the external bus channel changes accordingly. For example, you can have two MessageChannels called "output" and "foo" in a module with spring.cloud.stream.bindings.output=bar and spring.cloud.stream.bindings.foo=topic:foo, and the result is 2 external channels called "bar" and "topic:foo". The queue prefix for point to point semantics is also supported. Note, that in a future release only topic (pub/sub) semantics will be supported.

1.1.2 Inter-module communication

While Spring Cloud Stream makes it easy for individual modules to connect to messaging systems, the typical scenario for Spring Cloud Stream is the creation of multi-module pipelines, where modules are sending data to each other. This can be achieved by correlating the input and output destinations of adjacent modules, as in the following example.

Supposing that the design calls for the time-source module to send data to the log-sink module, we will use a common destination named foo for both modules. time-source will set spring.cloud.stream.bindings.output=foo and log-sink will set spring.cloud.stream.bindings.input=foo.

1.1.3 Advanced binding properties

The input and output channel names are the common properties to set in order to have Spring Cloud Stream applications communicate with each other as the channels are bound to an external message broker automatically. However, there are a number of scenarios when it is required to configure other attributes besides the channel name. This is done using the following naming scheme: spring.cloud.stream.bindings.<channelName>.<attributeName>=<attributeValue>. The destination attribute can also be used for configuring the external channel, as follows: spring.cloud.stream.bindings.input.destination=foo. This is equivalent to spring.cloud.stream.bindings.input=foo, but the latter can be used only when there are no other attributes to set on the binding. In other words, spring.cloud.stream.bindings.input.destination=foo,spring.cloud.stream.bindings.input.partitioned=true is a valid setup, whereas spring.cloud.stream.bindings.input=foo,spring.cloud.stream.bindings.input.partitioned=true is not valid.


Spring Cloud Stream provides support for partitioning data between multiple instances of a given application. In a partitioned scenario, one or more producer modules will send data to one or more consumer modules, ensuring that data with common characteristics is processed by the same consumer instance. The physical communication medium (i.e. the broker topic or queue) is viewed as structured into multiple partitions. Regardless whether the broker type is naturally partitioned (e.g. Kafka) or not (e.g. Rabbit or Redis), Spring Cloud Stream provides a common abstraction for implementing partitioned processing use cases in a uniform fashion.

Setting up a partitioned processing scenario requires configuring both the data producing and the data consuming end.

Configuring output channels for partitioning

An output channel 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 seting spring.cloud.stream.bindings.output.partitionKeyExpression=payload.id,spring.cloud.stream.bindings.output.partitionCount=5 is a valid and typical configuration.

Based on this configuration, the 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 that is evaluated against the outbound message for extracting the partitioning key. If a SpEL expression is not sufficent for your needs, you can instead calculate the partition key value by setting the the property partitionKeyExtractorClass. This class must implement the interface org.springframework.cloud.stream.binder.PartitionKeyExtractorStrategy. While, in general, the SpEL expression is enough, more complex cases may use the custom implementation strategy.

Once the message key is calculated, the partition selection process will determine the target partition as a value between 0 and partitionCount. 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.

Additional properties can be configured for more advanced scenarios, as described in the following section.

Configuring input channels for partitioning

An input channel is configured to receive partitioned data by setting its partitioned binding property, as well as the instance index and instance count properties on the module, as follows: spring.cloud.stream.bindings.input.partitioned=true,spring.cloud.stream.instanceIndex=3,spring.cloud.stream.instanceCount=5. The instance count value represents the total number of similar modules between which the data needs to be partitioned, whereas instance index must be value unique across the multiple instances between 0 and instanceCount - 1. The instance index helps each module to identify the unique partition (or in the case of Kafka, the partition set) that they receive data from. It is important that both values are set correctly in order to ensure that all the data is consumed, as well as that the modules receive mutually exclusive datasets.

While setting up multiple instances for partitioned data processing may be complex in the standalone case, Spring Cloud Data Flow 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.

1.2 Binder selection

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. Spring Cloud Stream provides out of the box binders for Redis, Rabbit and Kafka.

1.2.1 Classpath detection

By default, Spring Cloud Stream relies on Spring Boot’s auto-configuration configure the binding process. If a single binder implementation is found on the classpath, Spring Cloud Stream will use it automatically. So, for example, a Spring Cloud Stream project that aims to connect to Rabbit MQ can simply add the following dependency to their application:


1.2.2 Multiple binders on the classpath

When multiple binders are present on the classpath, the application must indicate what binder has to be used for the channel. Each binder configuration contains a META-INF/spring.binders, which is in fact a property file:


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

Selecting the binder can be done globally by either using the spring.cloud.stream.defaultBinder property, e.g. spring.cloud.stream.defaultBinder=redis, or by individually configuring them on each channel.

For instance, a processor module that reads from Rabbit and writes to Redis can specify the following configuration: spring.cloud.stream.bindings.input.binder=rabbit,spring.cloud.stream.bindings.output.binder=redis.

1.2.3 Connecting to multiple systems

By default, binders share the Spring Boot autoconfiguration of the application module and create one instance of each binder found on the classpath. In scenarios where a module should connect to more than one broker of the same type, Spring Cloud Stream allows you to specify multiple binder configurations, with different environment settings. Please note that turning on explicit binder configuration will disable the default binder configuration process altogether, so all the binders in use must be included in the configuration.

For example, this is the typical configuration for a processor that connects to two rabbit instances:

          destination: foo
          binder: rabbit1
          destination: bar
          binder: rabbit2
          type: rabbit
                host: <host1>
          type: rabbit
                host: <host2>

1.3 Managed vs standalone

Code using the Spring Cloud Stream library can be deployed as a standalone application or be used as a Spring Cloud Data Flow module. In standalone mode your application will run happily as a service or in any PaaS (Cloud Foundry, Lattice, Heroku, Azure, etc.). Spring Cloud Data Flow helps orchestrating the communication between instances, so the aspects of module configuration that deal with module interconnection will be configured transparently.

1.3.1 Fat JAR

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