How are an iPaas and Reactive Programming related?
The relationship between Integration Platform as a Service (iPaaS) and reactive programming lies in how iPaaS platforms can leverage reactive programming principles and techniques to enhance their capabilities and address specific integration challenges. While reactive programming is not a mandatory component of iPaaS, it is a smart and strategic move to improve the efficiency, responsiveness, and scalability of integration processes within an iPaaS environment. An IpaaS solution is designed to facilitate the integration of various applications, systems, and data sources within an organization. The future of an iPaaS enabled with reactive programming holds great promise for organizations looking to streamline their integration processes. These platforms will evolve to become more agile, responsive, and capable of handling the complex and real-time demands of modern digital ecosystems. As technology continues to advance, iPaaS will play a crucial role in facilitating seamless and efficient data exchange between systems, services, and emerging technologies.
With such a promising future, our in house developers couldn’t resist the opportunity of delving into it and witnessing, first hand, the impact of reactive programming on our own hero iPaaS solution, Aekyam . We aim to share the process and findings in this blog post.
What is Reactive Programming?
Reactive programming is a programming paradigm focused on handling asynchronous data streams and events. It uses observable data streams and observers to model and process real-time or time-sensitive information. It emphasizes non-blocking and asynchronous operations, making it suitable for responsive applications. Developers can transform and compose data streams using operators, simplifying complex event-driven scenarios. Reactive programming is commonly used in scenarios requiring real-time updates, such as web applications and IoT systems. Reactive systems are more flexible, loosely-coupled and scalable. This makes them easier to develop and modify.
How is Reactive Paradigm different from the Traditions approach?
Consider a conventional web application backend that lacks the implementation of reactive programming principles.
There are, of course, a few limitations of the traditional approach such as :-
- Thread Occupancy: Once a thread is assigned to a request, it remains unavailable until that specific request completes its processing.
- Thread Congestion: When all available threads are occupied, any new request to the server must wait until at least one thread is free.
- Performance Degradation: When all threads are busy, performance lags as memory resources are utilized by all the occupied threads.
The choice between these approaches depends on the specific integration requirements and the nature of the systems being integrated.
Publisher-Subscriber Model :-
The Publisher-Subscriber Model in reactive programming seamlessly handles asynchronous events from diverse sources, promoting real-time integration. It ensures a flexible, scalable architecture, supports event-driven designs for responsiveness, simplifies error handling, and aids monitoring and event routing, making it indispensable for iPaaS platforms. Unlike the Observer Pattern, subscribers don’t need to know about the publisher’s existence, enhancing decoupling.
Reactive Programming enhances the Publisher-Subscriber pattern by simplifying asynchronous and event-driven implementation with non-blocking, efficient scaling, and streamlined data delivery, including ‘onComplete()’ for successful retrieval, ‘onError()’ for exceptions, and ‘onNext()’ for new elements in the sequence.
- Publisher: The publisher, acting as the data source, delivers an unbounded sequence of elements, like those retrieved from a database or API call, to the subscriber based on their demands and requirements, through a single method in the publisher interface.
- Subscriber: Also known as the consumer, it’s responsible for determining when and how many elements it can receive from the publisher. The subscriber interface encompasses four abstract methods that outline its role in the process.stract methods.
- Subscription: The subscription represents a unique relationship between the subscriber and the publisher, establishing the necessary connection for data flow.
- Processor: The processor, as its name suggests, represents a processing stage positioned between the subscriber and the publisher. It must adhere to the contracts of both interfaces, acting as an intermediary in the data processing flow.
- Backpressure: The Reactive Approach handles data flow challenges when producers outpace consumers by implementing backpressure, enabling consumers to regulate the data rate they receive to avoid overwhelming them and maintain a balanced data flow in a pipeline of reactive components from a data source to an HTTP socket.
Acuver experiments on Performance :-
We carried out an experiment to evaluate performance by comparing the Reactive and Non-Reactive applications. Two Spring Boot services were created, each with its own endpoint. One of the services utilized Reactive Spring WebFlux, while the other relied on traditional Spring Boot. A Node.js application was also developed to asynchronously call these service endpoints.
In the first phase of the experiment, the Reactive service was executed. The endpoint was called with multiple requests, ranging from 10 to 10,000. The average time taken to complete each individual request, as well as the overall time required for all requests, was recorded. Subsequently, the same procedure was repeated for the non-reactive service. The obtained data allowed for the generation of graphs that depicted the average time taken to fulfil each request.
- Average time to complete each request graphs: Reactive and Non-Reactive showed similar processing times for low concurrent requests, but significantly differed with high concurrent requests, highlighting the value of the reactive approach for applications serving many users simultaneously.
- Time to complete all the requests graphs: Again, both Reactive and Non-Reactive had similar processing times with fewer concurrent requests, but showed substantial differences with higher concurrent requests.
Our conclusion was that a reactive approach is useful for applications used by a large number of users at once. It saves memory by completing all processes in lesser time, so upcoming requests can be utilized without delay and failures.
Conclusion :-
Reactive programming offers distinctive benefits in the following scenarios:
- High-Concurrency Web Applications: When developing a web application that is expected to have frequent usage by numerous users simultaneously, the traditional approach may engage all threads in the web server, resulting in a lag. In such cases, utilizing reactive programming can enhance the application’s ability to handle concurrent requests and maintain responsiveness.
- Data Streaming Applications: Reactive programming is well-suited for applications that involve continuous data streaming. In these scenarios, data flows in constantly, and the reactive approach allows for efficient handling and processing of incoming data streams.
- Big Data and Microservices Applications: Reactive programming efficiently manages data flow in big data applications and benefits microservices with stream-based inter-service communication.
In conclusion, reactive programming empowers developers to create more responsive, scalable, and efficient applications. Its focus on handling asynchronous events, managing data streams, and optimizing resource utilization makes it a valuable paradigm in today’s software development landscape. By embracing reactive principles and tools, developers can build systems that are better equipped to handle the challenges of modern, real-time computing, providing enhanced user experiences and improved system performance.
Author: Bhaskar Singh
