The Reactive Manifesto describes the four characteristics defining a reactive application: responsive, resilient, elastic, and message driven. Reactive Streams, one of the tools used to create reactive applications, is a small API for the JVM that defines the interfaces needed to connect a stream of data, with back-pressure, to the parts of a reactive application. And with the addition of back-pressure support in Spark Streaming in Spark 1.5, it is simpler than ever before to use these three technologies together.
Luc Bourlier explores communication with back-pressure, describes its implementation in Reactive Streams, and shows how it can be used to integrate Spark Streaming in reactive applications.
Luc Bourlier has been working on the JVM since 2002, first for IBM on the Debugger team of the Eclipse project, where he wrote the expression evaluation engine. After a few other Eclipse projects, Luc went to TomTom to recreate their data distribution platform for over-the-air services. He then joined Lightbend to work on the Eclipse plugin for Scala before switching to the Fast Data team to focus on deployment and interaction of streaming systems.
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