32. Inter-Application Communication

Spring Cloud Stream enables communication between applications. Inter-application communication is a complex issue spanning several concerns, as described in the following topics:

32.1 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.

Suppose a design calls for the Time Source application to send data to the Log Sink application. You could use a common destination named ticktock for bindings within both applications.

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


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


32.2 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 have spring.cloud.stream.instanceCount set to 3 , and the individual applications have spring.cloud.stream.instanceIndex set to 0 , 1 , and 2 , respectively.

When Spring Cloud Stream applications are deployed through 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 (for example, the Kafka binder) in order to ensure that data are split correctly across multiple consumer instances.

32.3 Partitioning

Partitioning in Spring Cloud Stream consists of two tasks:

32.3.1 Configuring Output Bindings for Partitioning

You can configure an output binding to send partitioned data by setting one and only one of its partitionKeyExpression or partitionKeyExtractorName properties, as well as its partitionCount property.

For example, the following is a valid and typical configuration:


Based on that example configuration, data is sent to the target partition by 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 sufficient for your needs, you can instead calculate the partition key value by providing an implementation of org.springframework.cloud.stream.binder.PartitionKeyExtractorStrategy and configuring it as a bean (by using the @Bean annotation). If you have more then one bean of type org.springframework.cloud.stream.binder.PartitionKeyExtractorStrategy available in the Application Context, you can further filter it by specifying its name with the partitionKeyExtractorName property, as shown in the following example:

. . .
public CustomPartitionKeyExtractorClass customPartitionKeyExtractor() {
    return new CustomPartitionKeyExtractorClass();

In previous versions of Spring Cloud Stream, you could specify the implementation of org.springframework.cloud.stream.binder.PartitionKeyExtractorStrategy by setting the spring.cloud.stream.bindings.output.producer.partitionKeyExtractorClass property. Since version 2.0, this property is deprecated, and support for it will be removed in a future version.

Once the message key is calculated, the partition selection process determines the target partition as a value between 0 and partitionCount - 1 . The default calculation, applicable in most scenarios, is based on the following formula: key.hashCode() % partitionCount . This can be customized on the binding, either by setting a SpEL expression to be evaluated against the 'key' (through the partitionSelectorExpression property) or by configuring an implementation of org.springframework.cloud.stream.binder.PartitionSelectorStrategy as a bean (by using the @Bean annotation). Similar to the PartitionKeyExtractorStrategy , you can further filter it by using the spring.cloud.stream.bindings.output.producer.partitionSelectorName property when more than one bean of this type is available in the Application Context, as shown in the following example:

. . .
public CustomPartitionSelectorClass customPartitionSelector() {
    return new CustomPartitionSelectorClass();

In previous versions of Spring Cloud Stream you could specify the implementation of org.springframework.cloud.stream.binder.PartitionSelectorStrategy by setting the spring.cloud.stream.bindings.output.producer.partitionSelectorClass property. Since version 2.0, this property is deprecated and support for it will be removed in a future version.

32.3.2 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 shown in the following example:


The instanceCount value represents the total number of application instances between which the data should be partitioned. The instanceIndex must be a unique value across the multiple instances, with a value between 0 and instanceCount - 1 . The instance index helps each application instance to identify the unique partition(s) from which it receives data. It is required by binders using technology that does not support partitioning natively. For example, with RabbitMQ, there is a queue for each partition, with the queue name containing the instance index. With Kafka, if autoRebalanceEnabled is true (default), Kafka takes care of distributing partitions across instances, and these properties are not required. If autoRebalanceEnabled is set to false, the instanceCount and instanceIndex are used by the binder to determine which partition(s) the instance subscribes to (you must have at least as many partitions as there are instances). The binder allocates the partitions instead of Kafka. This might be useful if you want messages for a particular partition to always go to the same instance. When a binder configuration requires them, 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 in 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 and by letting you rely on the runtime infrastructure to provide information about the instance index and instance count.