As the stream is ongoing events, these events trigger asynchronously, and upon the triggering of each event, one callback method will be executed. The one who listens to the event is called as subscriber and the process as subscribing to the events. The stream under observation can be denoted as subject or observable.

The Reactive stream is not a very old concept. In 2013, engineers from Netflix, TypeSafe, and Pivotal came together and started working on it. The streams used for Akka and Scala were difficult to grasp for business logic developments. The Rx-based approach came in discussion between Roland Kuhn and Erik Meijer, which was centric to the asynchronous approach, and the specification came in the market by 2015.

The following are the features of the streams:

• Reactive streams help us exchange the stream data asynchronously
• Asynchronous data happens without forcing buffering of the data
• Designing of the stream is done in such a way that the queues bounded to the threads creates back pressure

It provides the abstraction, helping the developers concentrate on the business logic part without worrying about how to handle the streams. The handling of a single event stream is not a problem at all. Even at a time, we can handle multiple streams without any problem using the reactive imperative programming style. The declarative reactive programming approach is much suited in straightforward cases, where it provides abstraction, as well as to keep us close to the operative systems.

Most of the applications handle enormous live data whose size cannot be predetermined and needs special care. The resource must be used in such a way that it can be used parallel to one or more CPU cores simultaneously. Reactive streams exchanges stream data within asynchronous boundaries taking care that the receiver will not be forced to buffer the data. The reactive system aims for the following:

• Processing numerous elements
• The elements will be processed in sequence
• The elements can be exchanged between the components asynchronously
• The utmost care will be taken to make non-blocking backpressure

In enterprise applications, the developers need to handle complex events for asynchronous programming from functions or maybe from network calls. The developers, in such cases, need to deal with normal completions and failures due to errors. Such a situation increases the complexity within imperative reactive programming. The complexity and much ease are provided by functional reactive programming. The Reactive Stream handles backpressure. The backpressure is one of the very famous terms everyone uses when they come across reactive programming.