An Introduction to reactive programming

Introduction

The rise of distributed programming and the ubiquity of multi-core processors have renewed interest in reactive programming. It is this reality that led most of the open source framework teams to invest in making reactive programming more accessible. Choosing between reactive programming vs. traditional blocking style or imperative development is a nuanced decision. Both have their place, but understanding when, where, why and “for what goal” is essential to achieving a positive result. Otherwise, it can be frustrating, even downright dangerous.With the advent of frameworks like spring,akka, vert.x, the choice is yours.

Reactive Programming Current State :

Reactive programming isn’t a new thing. Microsoft’s reactive extensions, JVM options like Scala/Akka, RxJava, and Vert.x have been around for awhile. New vendors are getting involved. Companies like Kaazing, Netflix, Pivotal, Red Hat, Twitter, and Lightbend (Typesafe) collaborated around a specification called Reactive Streams. These companies collectively formed an agreement on low-level reactive concepts that are now being used in JDK 9. Higher level frameworks like Akka, RxJava, Reactor, Ratpack, and Spring Framework 5.0 are using these ideas too.

However, mainstream IT has survived without the widespread usage of reactive programming for a long time.why now?

There are multiple reasons but main focus revolves around the following three.

The Raise of Microservices :

The increasing adoption of highly distributed, event-driven architectures often leads to applications exhibiting complex function/service interdependence and sequencing. As that increases, throughput can start to hit a ceiling with synchronous, blocking systems – causing overall system failure. The need to consume events with high concurrency then becomes very important in these scenarios: streaming or real-time data, for example, often demands fast messaging ingestion.

Resource availability at the high end :

Reactive applications aim for maximum service availability, achieved by decoupling network latency from request processing.
It’s the only way a service can remain available when attempting to handle extreme request volumes.

Serverless Computing :

Microservices are now the norm but recent efforts in resource mutualization, tooling, and cloud computing raise a new question: Is the next frontier function-oriented? Function-as-a-Service,
or “serverless”, pushes the data distribution even further, increasing network latency. Reactive systems become even more important here.

Use the Right Tools for the Job

In summary, make the reactive choice with care. Netflix, when working on Zuul 2, made similar observations, noting how the performance and the predictability of resource consumption helped them autoscale efficiently. It also made the system considerably more difficult to operate and debug.

As the good Dr. David Syer tells us:
“For the right problem, the effects are dramatic. For the wrong problem, the effects might go into reverse (you actually make things worse). Also remember, even if you pick the right problem, there is no such thing as a free lunch, and Reactive doesn’t solve the problems for you, it just gives you a toolbox that you can use to implement solutions.”

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