Imperative programming vs Reactive Programming
Imperative programming vs Reactive Programming
Imperative programming - Something happens, in other words an event occurs, and your code is notified of that event. For example, a user clicked a button and where you handle the event in your code, you decide what that action should mean to your system. You might save records to a DB, call another service, send an email, or a combination of all of these. The important bit here, is that the event is directly coupled to specific actions taking place.
Reactive programming enables you to respond to events that occur, often in the form of streams. Multiple concerns can subscribe to the same event and let the event have it’s effect in it’s domain, regardless of what happens in other domains. In other words, it allows for loosely coupled code that can easily be extended with more functionality. It’s possible that various big down-stream systems coded in different stacks are affected by an event, or even a whole bunch of serverless functions executing somewhere in the cloud.
The Dichotomy of Data Flow: Imperative vs. Reactive Programming
In the landscape of software development, two prominent programming paradigms stand in contrast: the traditional Imperative Programming and the more recent Reactive Programming. While one dictates a step-by-step execution of commands, the other focuses on reacting to the flow of data.
Imperative Programming, the long-established and widely understood model, operates on a sequential and command-based approach. In this paradigm, developers explicitly write code that details the precise sequence of operations the program must perform to achieve a desired state. This approach is akin to providing a detailed recipe, where each step must be followed in a specific order. Think of it as a one-way street of commands; for instance, if you assign a = b + c, the value of a is calculated at that moment and remains unchanged even if b or c are modified later.
On the other hand, Reactive Programming represents a paradigm shift, centering on data streams and the propagation of change. In this model, the program is designed to react automatically to changes in data. Instead of a static assignment, a reactive approach would establish a relationship where a is always dependent on b and c, so any alteration in their values would automatically trigger a recalculation and update for a. This is particularly effective for applications with real-time data, user interfaces, and event-driven systems.
The Forerunner: The Observer Pattern
While Imperative Programming stands as the direct opposite, an older and foundational pattern that paved the way for Reactive Programming is the Observer pattern. This behavioral design pattern establishes a one-to-many dependency between objects. When the state of one object (the “subject” or “observable”) changes, all its dependents (“observers”) are notified and updated automatically.
The Observer Pattern is a core concept within Reactive Programming. However, Reactive Programming significantly expands on this by providing more advanced tools and constructs to manage complex, asynchronous data streams. While the Observer Pattern typically deals with simple notifications between objects, Reactive Programming introduces powerful operators to filter, transform, and combine these streams of data, making it a more robust and scalable solution for modern, data-intensive applications.