During the design phases of an application, one of the questions we ask ourselves is: which programming language to use for the backend? Nowadays the choice is not easy. In fact, there are several programming languages, from the most classic and well known ones (for example Java), to the more “modern” ones such as Dart. Therefore, in many cases, both the functionality and computational capabilities required by the application and the skills of the developers involved in the project are analyzed. This last aspect, in many cases, heavily conditions the final choice.
Let’s assume for a moment that this constraint is looser and that, therefore, we can freely choose the programming language for our future application. Obviously, many programmers have their own preferences, but let’s not be constrained by that. Below we will look at two of the most widely used languages for backend development: Node.js and Python. We will see the main features of each of them and compare them.
Python is an open-source high-level programming language. It was first released in 1991 by Guido van Rossum. The latest version is Python 3.10.2, and it was released in January 2022.
Python runs primarily on Google’s App Engine. Also developed by Google, the App Engine allows you to develop web apps with Python and allows you to benefit from many libraries and tools that top Python developers use.
Node.js is designed as an event-driven environment, which allows asynchronous input/output. A certain process is called as soon as the respective event occurs, which means that no process blocks the thread. The event-driven architecture of Node.js is perfectly suited for the development of chat applications and web games.
However, this feature is not ideal for sequential and/or transactional procedures where an operation must occur if and only if all previous operations are successful. There are some patterns to overcome, if necessary, this drawback, but they complicate the writing and the understanding of the code.
Differently, Python is not natively event-oriented. It is possible, therefore, to use it to develop an asynchronous and event-driven application with the help of special modules and/or frameworks (e.g., asyncio, celery).
Both languages belong to the category of interpreted languages that are generally slower than compiled languages, such as Java. Despite this, both Python and Node.js show decent speed in code execution.
In particular, Node.js is faster than Python in many cases due to Google’s V8 engine and its event-driven non-blocking architecture. This aspect, combined with caching, allows you to process several requests at the same time speeding up the execution of the application. In contrast, Python is single-stream, which means that requests are processed much more slowly.
Applications that need to provide real-time functionality are usually developed in Node.js. However, if complex calculations or application logic are required, Python is preferred. For example, applications oriented towards data mining, machine learning and AI are often developed in Python.
The architecture of Node.js allows for native microservice-oriented development, where each module will run independently and communicate with others where necessary. This allows you to easily add more microservices and scale a web application both horizontally and vertically. Scaling horizontally means adding new nodes to the system, while scaling vertically requires increasing the resources of individual nodes.
To scale a Python application, you need to use multithreading. Although there are several libraries to do this, you must consider that the result is not a “true” multithreading. In fact, even if you define multiple threads, the Global Interpreter Lock (GIL) does not allow the Python interpreter to execute tasks simultaneously, but only one thread at a time. Python must necessarily use GIL, even though it negatively affects performance, because Python’s memory management is not thread-safe.
Node.js can be easily customized, extended and integrated with various tools. libraries and packages are managed by NPM – the Node Package Manager. It is one of the largest repositories of software libraries. NPM is fast, well-documented, and easy to learn to work with. Babel (a JS compiler) facilitates front-end development with older versions of Node or the browser. Jasmine., on the other hand, is useful for unit testing, while Log.io is used for project monitoring and troubleshooting. Finally, for data migration, process management, and module bundling, you can use Migrat, PM2, and Webpack. Web application development can be facilitated through the use of frameworks such as Express, Hapi, Meteor, Koa, Fastify, Nest, and Restify.
The pip package repository provides a plethora of modules to customize your Python project. Pip is fast, reliable and easy to use, so even developers find it easy to learn to work with. You can find modules for data analysis, libraries for interfacing to databases and much more. Among the most popular frameworks for developing web applications we remember Django and Flask that we have already analyzed in the article Django vs Flask: Python web frameworks comparison.
Python is full-stack, so it can be used for both backend and frontend development. Similar to Node.js, Python is cross-platform, so a Python program written on Mac will run on Linux. Both Mac and Linux have Python pre-installed, while on Windows you need to install the Python interpreter yourself. While Python is great for web and desktop development, it is rather weak for mobile computing. In the IoT and AI field, Python’s popularity is growing faster and faster in recent years.
In both cases, we recommend using virtualization environments such as Docker containers to isolate the development environment from that of the host operating system.
Due to its event-driven architecture, Node.js is a perfect fit for applications that have numerous concurrent requests, heavy client-side rendering, or frequent data exchange between client and server. Some examples include IoT solutions, chatbots, real-time messaging, streaming platforms, and complex Single Page Applications. However, Node.js is not the best option for developing applications that require a lot of CPU resources.
Python is suitable for developing projects of any size. It can be used for data science applications involving data analysis and visualization, speech and facial recognition systems, image processing software, neural networks, machine learning systems, 3D modeling, and games.
In this article, we have analyzed the two languages from different aspects. As mentioned at the beginning, the choice depends on various factors, including the functional and computational demands of the individual project. Personally, I prefer Python as it allows both full-stack development and data analysis, but that doesn’t mean that in some projects Node.js presents itself as the best alternative.
To summarize the main features of the two languages we report below an infographic.