Scale By the Bay Scale By the Bay

Retrofit your Java App with a Reactive Flow Pipeline

- PDT
functional

Mary Grygleski
IBM, Developer Advocate

Mary is a Senior Developer Advocate at IBM with the Liberty/Websphere team, focusing on Liberty, Microprofile, Jakarta EE, Java, Open Source, Cloud, and Distributed Systems. She transitioned from Unix/C to Java around 2000 and has never looked back since then. She considers herself as a polyglot and loves to continue learning new and better ways to solve real-life problems. She is an active tech community builder outside of her day job, and currently the president of the Chicago Java Users Group (CJUG), as well as a co-organizer for several IBM-sponsored meetup groups in the Greater Chicago area.

Fabio Tiriticco
Fontem Ventures B.V., Software Engineer

Fabio is a software engineer and community leader based in Amsterdam - he runs the 2000-member strong Reactive Amsterdam meetup and the annual Kubernetes Community Days conference. When not in front of a screen, he enjoys long-distance bike rides and playing his bass guitar.


Legacy applications that were developed in bygone days may appear to be close to unsalvageable. In reality, these applications are still running in production and carrying out the important day-to-day missions for their respective companies. After all, companies have spent a considerable amount of time and money on developing those applications, and despite the lack of perfection, these applications nonetheless keep their companies in operation. So does it make sense for an entire legacy application to be re-written? Keep in mind that the implementation of certain business functionality can be a daunting task for the busy developers. How about if we redesign the system, and identify pieces of the complex business functionality in the legacy system that can potentially be "recycled", and retrofit them into the new system that leverages on the power of the reactive data flow pipeline?

This presentation will be a lively discussion with hands-on coding to illustrate how to construct a reactive, event-driven data flow pipeline, which are composed of different library implementations of the Reactive Streams specification, such as Akka Streams, Eclipse Vert.x and RxJava. The sample use case will mimic a real-life example of data being collected from multiple distributed sources, which will then be fed to a legacy processor as "wrapped" by a reactive microservice for transformation, after which the resulting data will flow to a "sink" to be prepared for further processing. We will highlight the strength of the Reactive Streams in controlling backpressure during processing.