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If we define our functions from the above example as Spring beans,

}

we can compose these functions using the spring.cloud.function.definition property spring.cloud.function.definition=upper|reverse

Here | is a composition operator which results in an auto-configured bean implementing the composite function, along with related resources to let you seamlessly invoke the composite function.

Spring Cloud Function extends native Java Function composition to support composition with Supplier and Consumer.

This follows from concepts which are implicitly true:

As we shall see, Spring Cloud Stream Applications employ these concepts to great effect.

When using function composition, we have to consider compatible argument types.
Using native Java composition, we can do compose a Function<Integer,String> with a Function<String, Integer> into a Function<Integer, Integer> :

When running a Spring application, Spring Cloud Function uses Spring’s standard type conversion support to coerce function arguments as needed.
Given the following Function bean definitions, the function definition intToStr|doubleit works as expected, converting the String to an Integer.

}

In addition to converting primitives, Spring functions can convert between Message and POJO, JSON String and POJO, and more.
For example, the following functions can be composed in either order:

}

In some cases, function composition is a no-brainer. From the start, we have provided pre-packaged processors to perform simple transformations, or filtering using SpEL. The legacy architecture required a separate processor when using the prepackaged sources or sinks. A common complaint from users was “why do I need to deploy a separate application just to evaluate a SpEL expression?” To address this, we initially introduced a form of support for function composition in an earlier release. To use this feature with the prepackaged applications required forking them to modify the code or the build dependencies to provide the functions.

The current release provides function composition out of the box for all of the prepackaged sources. Specifically, a source can now be composed with prepackaged functions to perform any of the following locally:

As an example, we can compose the time source with a header enricher and filter with configuration properties and run it as a standalone Spring boot application:

This will publish the time, such as `07/16/20 16:43:48, every other second whenever the number of seconds is even, to the configured destination even.

Here we are using a prepackaged time source for RabbitMQ, binding the output to a topic exchange named even. The binder will create the exchange if it does not exist. The function definition extends the supplier to extract the seconds, convert it to an integer and store it in the seconds message header and then filter on the value of the header. Only even values pass the filter.

In 2018, we introduced a reference architecture for running file ingest with Spring Cloud Data Flow and Spring Batch. To do this, we forked the sftp source as sftp-dataflow, specifically to implement a prepackaged source that produces task launch requests. The task launch request is a simple value object, rendered as JSON, and consumed by the tasklauncher-sink. The sink acts as a client to Data Flow to launch a batch application per the request. We initially chose sftp since it is the most commonly used protocol for file processing. However, we realized that the same pattern can be applied to any source. We now can do this with function composition. Along with the standard sftp source , we can trigger a task launch from ftp, file, s3, and so one. Even the time source can be used to launch a task at regular intervals.

This somewhat contrived example produces task launch requests:

The payload, as JSON, is {"args":[],"deploymentProps":{},"name":"task-34"}

In reality, when users develop a Spring Cloud Stream pipeline, they are likely to select a source and sink from our prepackaged Spring Cloud Stream Applications. Processors are typically user-written code, implementing specific business logic. If you are writing a processor, or want to extend a source or sink, any of the functions are available to you. Since we publish the functions as separate artifacts, you can simply include them in your dependencies. You can either use declarative composition, as shown above, or you can inject them into your code and invoke them programmatically. Of course, you can easily integrate your own functions as well.