Continuous Delivery of Event Streaming Pipelines with Spring Cloud Data Flow

Event stream developers need to continuously update pipelines as applications change, and understanding common stream topologies is essential.

Spring Cloud Data Flow supports several generic event‑stream topologies and allows continuous deployment of stream applications. The previous part showed how to set up a local development environment and manage pipelines using Kafka Streams applications.

Named destinations let you specify the exact Kafka topic for sources or sinks. For example, the DSL http | filter > :user-click-events publishes filtered HTTP click events to the user-click-events topic, and :user-click-events > transform | jdbc consumes that topic, applies business logic, and stores the result in a relational database.

Parallel pipelines can be built by forking a stream, creating separate Kafka topics such as mainstream.http , mainstream.filter , and mainstream.transform . You can then tap these topics to create additional pipelines, e.g., unfiltered-http-events = :mainstream.http > jdbc or filtered-http-events = :mainstream.filter > mongodb .

Partitioned streams route payloads to specific downstream instances based on a key, useful when processing must be scoped to a particular partition.

Function composition allows you to attach custom Java Function beans to existing sources or sinks. The article demonstrates a @Bean public Function sendAsUserClicks() that converts string payloads to longs, which can be bound to the http‑ingest source.

When a stream requires multiple input or output bindings, the DSL uses the double‑pipe operator ( || ) to express custom bindings. An example combines a Kafka Streams join application with a logging app: join-user-clicks-and-regions || log-user-clicks-per-region .

Continuous deployment is supported natively: the application registry can hold multiple versions, and you can update a single component without redeploying the whole pipeline. Sample commands show how to register sources, create streams, deploy them, update a processor expression, and roll back to a previous version.

Finally, the article provides a complete end‑to‑end example that ingests user‑region and user‑click events via HTTP, joins them with a Kafka Streams application, and logs per‑region click counts, illustrating real‑time aggregation and the ability to destroy streams when finished.