Most programmers prefer a backend technology for their web app development projects. This preference is majorly dependent on their training or expertise in a particular technology. While this might be acceptable, it’s often not the correct strategy to get the wanted results for companies.

In general, both Node JS and Python are broadly utilized server-side technologies even though both of them were revealed on separate timelines and produced for diverse purposes.

Python has been around since 1991 and was designed as a server-side programming language that could be absolutely deployed for mobile and web applications.

In distinction, Node.js was founded in 2009 as a JavaScript runtime environment that reformed the usage of JavaScript on the server-side.

Node.js

Node.js is event-driven, it’s scalable and causes extraordinary performances to the project.

Its asynchronous nature implements it to manage concurrent requests without blocking the I/O operations.

Most development companies prefer to hire Node.js developers.

Python

On the other side, Python is a full-fledged, high-level, and object-oriented programming language.

Since it’s been available for almost 30 years, it has a ton of libraries, APIs, and ancillary tools ready.

Python is favorable to numerous programming paradigms, and it’s broadly applied for business applications.

Node.js vs Python: Which one is more popular?

The prevalence of backend technology plays a decisive part in whether it’ll be practiced for your project. First off from the software development companies viewpoint, the development team has to determine which language has a notable degree of expertise available in the market.

And then from the POV of developers, a conclusion has to be given whether they are ready to pick up languages that are predominantly practiced by various companies.

Looking at the reputation of websites, SimilarWeb reported that Node.js was recognized to be the most practiced tool in the first 10,000, 100,000, 1,000,000, and the ‘entire’ sections of websites listed on their popularity.

As per the Stack Overflow 2019 survey, Python was the most ‘preferred’ programming language while Node.js was the most chosen tool among expert programmers.

Scalability in Node.js vs Python

Node.js gives you enough versatility to circumvent a core. This way, alternatively of producing a core and then scaling everything around it, you build a set of microservices and modules.

When your application is scaled, these microservices and modules can dynamically operate their own processes. Hence, you can scale the app horizontally by attaching nodes to the current ones and you can extend it vertically by combining resources.

Python has problems with threads. It’s modeled on the Global Interpreter Lock, which does not permit it to perform various threads at once. This indicates that you cannot run different processes before the sequentially historical process is completed.

Learning trajectory & Syntax ease in Node.js vs Python

The learning curve for Node.js is not that abrupt if you are previously accustomed to Javascript. Some people think that its installation and documentation are a bit complicated because of Node.js’ event-driven programming theory. This concept is the fundamental reason why Node.js is scalable and effective.

Python’s USP has been its predominance and clear syntax. It is an accepted aspect that the length of code is comparatively smaller in Python when opposed to Node.js or other programming languages and runtime environments. Writing code in Python is just incredibly easy.

Extensibility in Node.js vs Python

If you’re practicing its older versions, Node.js can be matched with Babel for more regular front-end development. You can likewise practice Jasmine for unit-based testing or Log.io to handle the project. Webpack and PM2 can assist you with module bundling whereas Express is one of the popular frameworks applied with Node.js.

Sublime Text is usually practiced with Python to write codes. Robot Framework assists in automated testing. And then there are modern Nodejs frameworks similar to Django and Web2Py, which attach a boatload of characteristics.

Performance & Speed in Node.js vs Python

With Node.js' non-blocking architecture, its execution process is easier and quicker. On top of this, there is yet one more perk that delivers Node.js a solid contender. That is it executes the code outside of the web browser.

Python’s inherent architecture prevents multithreading and is a dilemma in this regard as well. It slows down the processing rate as processes don’t move parallelly. While Python’s syntax is straightforward to learn and complete, it is not agile enough for applications that generally recall data from the web server.

Community in Node.js vs Python

You may look at Node.js as a comparatively newer form of technology and might consider its community to be inadequate. However, you’d be astonished to learn that the Node.js community is considerably large and globally engaged with experienced Node.js Developers. It’s super straightforward to identify talent in the Node.js circles.

Python, being the more traditional language of the two, normally has a greater community. From junior to senior-level givers, Python doesn’t have a shortage of expertise. One of the most important advantages of such a sizable community is the readiness to discover developers. And active support from these developers ends in sudden solutions and overall enhancement of the language.

Error Handling Abilities in Node.js vs Python

Node.js’ parallel-process running peculiarity can make obtaining defects and failures in the code a tiresome process. Python’s more uncomplicated syntax and absence of parallel processes come in helpful while considering the code for bugs and glitches.

Conclusion

Deciding between Node.js and Python can be complicated particularly when you want to develop a complete product and not just a team project. The characteristics and benefits of both parties can be terrifying and can cloud your decision.

This is why the perfect way to deal with this difficulty is by focusing on the end-use of your output. List out all the characteristics of your product, and how you intend to manage them. And the language or runtime environment that sustains more aptness should be your pick.