Software design architecture for big data processing and big data applications with Node.Js

High Volume Transaction Processing (HVTP) software design with Node.js

Node.js backend applications use Javascript, which is, by design, a single-threaded concurrency model based on an event loop. This could be considered a disadvantage since other languages ​​like Java can be multithreaded. However, being multithreaded does not necessarily resolve the concurrency problems because Java shares the same application state between different threads, which can be catastrophic if it is not well managed.

The event loop does not allow the application state to be shared because it is single-threaded and has an event-managed queue model, which gives us an advantage to scale applications without experiencing the headache of a multithreading model.

Today, Node.js is considered to be a key tool for developing any type of web application, and it can easily replace Java or .Net in the backend without any loss of performance. In addition to the fact that it works better than a blocking event model, it also performs better than applications written in other languages. Node.js can be a key platform in software design architecture for big data processing and big data applications.

Naturally, Node.js is able to help developers be more productive and make applications perform better, but there are some techniques that can be adopted to give even more power to Node.js applications, especially if you are aiming to create your next big data platform. Developers can modify the default Node.js configuration to take advantage of multi-core processors. We can create separate processes to handle the high load of requisitions in addition to taking advantage of clustering. Developers can also put a reverse proxy in front of the Node.js server to protect their applications’ code from direct exposure to the Internet and allow them to balance the load of access between different applications.

With these few changes to our Node.js APIs, we have managed to guarantee application performance, even when our services have thousands of concurrent accesses.

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