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Introduction to Reactive Programming
Reactor 3 is a library built around the
Reactive Streams specification, bringing the
paradigm of Reactive Programming on the JVM.
In this course, you'll familiarize with the Reactor API. So let's make a quick introduction to the more general concepts in Reactive Streams and Reactive Programming.
Reactive Programming is a new paradigm in which you use declarative code (in a manner that is similar to functional programming) in order to build asynchronous processing pipelines. It is an event-based model where data is pushed to the consumer, as it becomes available: we deal with asynchronous sequences of events.
This is important in order to be more efficient with resources and increase an application's capacity to serve large number of clients, without the headache of writing low-level concurrent or and/or parallelized code.
By being built around the core pillars of being fully asynchronous and non-blocking,
Reactive Programming is an alternative to the more limited ways of doing asynchronous code
in the JDK: namely Callback based APIs and
It also facilitates composition, which in turn makes asynchronous code more readable and maintainable.
The Reactive Streams specification is an industry-driven effort to standardize Reactive Programming libraries on the JVM, and more importantly specify how they must behave so that they are interoperable. Implementors include Reactor 3 but also RxJava 2, Akka Streams, Vert.x and Ratpack.
It contains 4 very simple interfaces as well as a TCK, which shouldn't be overlooked since it is the rules of the specification that bring the most value to it.
From a user perspective however, it is fairly low-level. Reactor 3 aims at offering an
higher level API that can be leverage in a large breadth of situations, building it on top
of Reactive Streams
In reactive stream sequences, the source
Publisher produces data. But by default, it does
nothing until a
Subscriber has registered (subscribed), at which point it will push
data to it.
Reactor adds the concept of operators, which are chained together to describe what
processing to apply at each stage to the data. Applying an operator returns a new intermediate
Publisher (in fact it can be thought of as both a Subscriber to the operator upstream
and a Publisher for downstream). The final form of the data ends up in the final
that defines what to do from a user perspective.
Publisher<Integer> source = Flux.range(1, 10);
Flux<String> flux = Flux.just("A"); flux.map(s -> "foo" + s); flux.subscribe(System.out::println);