Exploring Reactive Programming

Synchronous and asynchronous programming are not unfamiliar concepts to programmers.

The complexity of multi-threading in large-scale applications and network latency were two problems that asynchronous programming resolved. Engineers developed various tools and frameworks to simplify and improve the efficiency of asynchronous programming.

Concurrent message-driven programming became feasible at this time because of the creation of frameworks like Akka and Sodium. The Reactive Extension library is a new utility created by Microsoft engineers.

Programming with asynchronous data streams is known as reactive programming. Consider it as having many more advantages than asynchronous programming using Futures.

ReactiveX (reactivex.io) is a library for creating asynchronous and data stream apps. Reactive Extensions, or Rx, is another name for ReactiveX.

Rx has been translated into other languages, including Java, named RxJava.

Reactive programming in Java uses the functional features that Java already has to alter the data while extending the observer pattern to accommodate event-driven programming.

Observer Pattern

It is where a group of observers is maintained for an object (observable) and are informed when the object's state changes.

For example, consider a coffee shop, where customers are notified when their coffee is ready to be served and invited to come to get a cup. Customers will have their preferences and have paid for (subscribed to) the product. In this case, the state may be compared to the distinctive flavor of each coffee while the event is coffee. The subscriber would have signed up for a specific taste, and the shop will only produce an order if there are subscribers. Coffee won't be made while it waits for subscribers.

Similar to this, RxJava only allows observers (also known as subscribers) who have subscribed to an observable to push notifications.

What is Reactive Programming?

A reactive system should be message-driven, resilient, responsive, and elastic, according to the Reactive Manifesto (v2.0).

The methods and interfaces for reactive-stream specification are defined by Class Flow as of Java 9. RxJava or other packages offer the implementation.

According to the reactive-streams definition, these interfaces are compatible and applicable in distributed and concurrent asynchronous settings: Each of which is defined as a void "one-way" message.

Expressed, a Completable Future might be all that is required if Action 1 is an asynchronous call that returns a value. Action 2 subsequently executes an action based on the value returned by Action 1.

Reactive Streams might be a viable option if, for example, Action 1 returns a stream of data (an event listener sending data depending on a click action), and Action 2 is executed on each piece of data the stream delivers.

So, we can say that Reactive programming is a style where specific design patterns use APIs, libraries, and language characteristics to enable async reactive program execution.

Four principles of reactive programming

Four guiding principles form the foundation of reactive applications: 

• Responsiveness
• Resiliency
• Scalability
• Event-Driven or Message-Driven

Responsive, resilient, and scalable systems are built on a message-driven architecture. The goal is to create a responsive application. Responsive applications are scalable and resilient. Without scalability and resilience, responsiveness is impossible to achieve.

Let's take a high-level look at each principle to understand why they all need to be used in tandem to create high-quality software in the present.

Responsiveness

What is meant by an application as responsive?

A responsive system's overall framework is a message-driven architecture. In order to guarantee a consistently positive user experience, a responsive system is quick to respond to all users — in sunny conditions and inclement weather. The two characteristics of a reactive application, resilience, and scalability, determine responsiveness and a positive user experience under various circumstances. These can include the failure of an external system or a surge in traffic.

Importance of responsiveness in a message-driven architecture

The world moves at its own pace. As we can see, a message-driven architecture gives us an asynchronous boundary that frees us from space and time constraints.

Resiliency

Most applications are created and designed with clear skies, but mistakes can and do happen. There seem to be reports of significant application failures or coordinated hacker system intrusions every few days that cause downtime, data loss, and reputational damage.

A resilient system follows the sound design and architectural principles to guarantee responsiveness in blue and grey skies. The twenty-tens (the 2010s) interconnected applications are all about application-level composition, connectivity, and security.

As a result of web services and other network protocols, applications are now built from various other applications. An application created today might be dependent on 10, 20, or even more external services than are allowed by its own trusted firewall. It might also support many external clients, including users and other systems.

Scalability

When developing consistently responsive applications, resilience and scalability go hand in hand. A scalable system is easily upgraded on demand to guarantee responsiveness under various load conditions. Anyone who conducts business online knows that the traffic peaks coincide with when we sell the most goods. A significant increase in traffic typically indicates that we're doing something right unless the spike results from a deliberate cyberattack. When traffic is heavy, people want to tip us.

Deal with a sudden increase in traffic or a gradual but noticeable one

Decide on the paradigm first, then decide on the languages and toolkits supporting it. Developers all too frequently choose a language and framework purely out of convenience. Tooling decisions should be approached as we would any significant investment because they are difficult to reverse once made. Making technical selection choices based on principles and analysis puts us far ahead of the competition.

Event-Driven or Message-Driven

Reactive applications are built on an event-driven architecture. Message-driven, actor-based or a hybrid of the two are all possible in an event-driven application. An event-driven system relies on events that one or more observers watch. Because the caller is not required to block while waiting for the invoked routine to respond, this is distinct from imperative programming. Events are observed (or listened for) rather than directed to a specific address, which has implications that we'll discuss in more detail.

The message-passing architecture is extended by actor-based concurrency, in which messages are sent to actors as recipients. Messages may pass between threads or be delivered to the mailbox of a different actor on a separate physical server. As a result, actors can be dispersed across the network and still communicate with one another as if they were all using the same JVM, enabling elasticity—scaling out on demand. The primary distinction between messages and events is that the former is directed, whereas the latter takes place. Events can be observed by 0–N observers, while messages have a fixed destination.

Conclusion

All of the aforementioned only scratch the surface of creating applications today. It explains why reactive learning programming isn't just a fad but a paradigm for all new software developers. The only way to satisfy user expectations is to prioritize scalability and resilience to achieve responsiveness, regardless of the language or toolkit we select. With each year that goes by, this will become more crucial.

To know more about Reactive Programming, visit the Cogent Infotech website.

FAQs

1. Why is reactive programming Java necessary?

Reactive programming is based on a data stream that we can watch, respond to, and even apply back pressure to. It results in non-blocking execution and, as a result, more significant scaling with fewer execution threads.‍

2. Is the architecture of reactive programming identical to that of the Future?

Future and Reactive Extensions (Rx) are comparable. While Rx returns a stream that may be subscribed to, Future might return an independent element. On several systems, the same set of standards is supported.

3. Is event-driven and reactive programming the same thing?

Event-driven programming is process-oriented, and reactive programming is data-oriented.