Python and Node.js are two very popular technologies in use among software developers worldwide.
One might wonder why the Python programming language would be compared to Node.js, which appears to be only a subset of what Python offers. The reason why is because Node.js has become one of the dominant runtime environments for server-side applications, and as a result, competes with Python on a number of use cases.
Python on the other hand has lived on the server since its inception from the 1990s, and has survived through 3 major releases. It is still extremely relevant today, having been adopted for more contemporary use cases like deep learning applications as well as more traditional use cases like web scraping and general backend infrastructure.
This article will explore Python vs Node.js and where they stand today in terms of their popularity and where they are used, before comparing the technologies with a range of key metrics.
Let’s firstly understand why the two technologies are in their strongest positions today than at any point in their histories.
Table of Contents
Python Programming Language
Python is a dynamically typed, interpreted, general purpose programming language. As such, it has been used for many types of applications ranging from proxy servers and web scrapers to web servers, game engines and data science analysis tools.
Python also comes with simple error handling – and since it is an interpreted language, it is well suited for rapid prototyping, as Python programs need not be rebuilt on every code change. The same can be said for Node.js, although the error handling mechanisms are different – as we’ll address later in the side-to-side comparisons.
Python has evolved over the years to stay relevant, which has been less than smooth sailing for the language. Python 3.0 was initially released 13 years ago in 2008, with it taking this long for the vast majority (97% in 2021) to migrate from version 2.
Breaking changes to syntax were made in 3.0, in addition to a major under-the-hood overhaul, that caused difficulties for many projects to migrate. To further highlight this turbulent upgrade, in 2017 only 47% of Python developers were on version 3.0 or above.
Nevertheless, Python has stayed relevant and is the favoured technology for many developers, most notably in web development frameworks, data science, machine learning and reinforcement learning applications.
It is important to note that Node.js is still single-threaded, so asynchronous functions should not be mistaken for concurrent execution. More on this further down.
If the reader is interested in true concurrent programming, Google’s Go programming language offers a modern and elegant solution for multi-threaded, parallel programming. Read Iglu’s accompanying piece to learn more: Google’s Go Language, and Why You Should Learn It.
Nonetheless, Node.js is a very fast, efficient and appreciated runtime environment.
Node.js gives developers direct access to the file system via the fs module that is included in the standard library. There is also great database support with Node.js, so data processing and file management comes naturally.
Such support bolsters Node.js’s proposition for command line applications and web development tooling – which projects like Webpack, Babel, and a range of frontend libraries like React Native and Vue.js rely on for their development pipelines.
I mention React Native here to emphasise that Node.js facilitates native app development – iOS, Android, and others – in addition to server-side and front-end web applications. Node.js therefore spans a vast range of apps, and at many stages of each of those app’s development pipelines.
Like Python, Node.js can be installed across all major operating systems, and is a free, cross platform and open source technology.
So where has this led to in 2022? We’ll now dive into some more technical comparisons of the two technologies.
Python vs Node.js: Side-to-Side Comparison
Likewise, we will also compare aspects of the Python runtime engine alongside the programming language itself. Node.js vs Python will now be put head to head on key metrics.
Speed and Performance
Performance is an extremely important metric for a development team, with bottlenecks of a programming language often deciding whether it is viable or not.
However, determining performance is not as clear-cut as simply looking at open source speed comparison graphs.
BenchmarksGame clearly highlights that Node.js offers massive performance gains over Python in most of their tests. However, these tests are performed against specific algorithms, which will likely not predict the speed of your own projects.
Saying this, Python is generally dramatically slower than Node.js at raw performance as a backend technology.
These data may be conflicting for those who know that machine learning applications are primarily implemented in Python – a domain where efficiency and GPU support are paramount to process millions (or billions) of operations for larger neural networks.
Python is adopted in this field due to many of the core modules used for neural networks and common mathematical operations being written in C, C++ or other more performant languages, with Python APIs being wrapped by such operations – which are compiled to machine language.
At the time of writing, 34% of the NumPy package is written in C. Doing so gives Python programs a performance boost, while developers only ever need to call Python APIs.
The Node.js V8 engine is written in C++, making it somewhat natural to connect these C-based add-ons.
Execution speed is only one metric when measuring overall performance though. There are a couple of other key features to consider here:
- Asynchronous processing support (even if single threaded).
- Parallel or concurrent processing support (multi-threaded applications).
These not only affect the speed of a program but also whether it can scale. For this reason, scalability will be discussed next separately.
Python vs Node JS Performance Verdict
Node.js clearly is the more efficient runtime, but there are workarounds for Python and Node.js to speed up performance by implementing key functions in more efficient compiled languages (C/C++).
This has to be considered with a high level programming language such as Python and Node.js, giving teams the ease-of-use of simplified syntax and automatic memory management for key logic, while having the ability to wrap resource sensitive or otherwise-nonviable code in more suitable compilations.
Scalability is a complex problem that leans to many variables, ranging from language-specific features (asynchronous functions, multi-threading capabilities), how fast the program executes and the resources required to do so (as we discussed above), and how the language can adapt to a server-cluster that can scale up and down, with methods of keeping each server instance updated.
These are indeed many factors to consider. Many of these scaling methods did not exist when Python was initially released decades ago, so Node.js enjoyed the benefit of having these insights before its 2009 open-source release.
Both Python and Node.js programs run on a single thread, but Node.js offers more asynchronous support than Python does as a result of its event loop model (of which a clear technical explanation can be found here on the official website).
Similarly, both technologies now support asynchronous programming, which will be looked into in more detail next.
Asynchronous Programming with Node JS vs Python
Asynchronous functions allow a program to handle other processing while the asynchronous function is being executed.
Examples of such for Python / Node.js include database calls (that take longer the more complex the query), REST API calls, file management, image processing, and more – anything that can be achieved behind the scenes while the “main” program runs.
Programs can wait for asynchronous functions, commonly defined with the async keyword, to complete – with the await keyword allowing parent functions to halt execution until the function completes. So your program is flexible with which logic needs to wait, and which can continue to process.
The asyncio library is commonly used with Python for asynchronous support, whereas Node.js typically does not require this extra layer of abstraction.
For scaling processes, Node.js has the upper-hand with tools like PM2 that were designed specifically for scaling Node.js programs.
PM2 also supports Python processes, but overall support is far behind that of Node.js.
Node.js vs Python Scalability Verdict
Node.js in 2021 is ahead of the curve for scalability, but Node.js needs to perform well on scaling given that its use cases rely on such features more than Python’s.
Python continues to evolve with its coroutine support, making async programming possible for Python programs provided developers upgrade to version 3.5 or above.
Saying all this though, both technologies can be wrapped into containers and deployed to clusters using tools like Kubernetes, Ansible or cloud tools for horizontal scaling and for resource management.
Libraries and Dev Tools
With open source technologies such as the ones we’re discussing, the creation of tools surrounding the language often falls to passionate developers interested in streamlining their particular needs.
The success of NPM has prompted other tech giants to offer package hosting, such as GitHub Packages, in addition to private registries like Verdaccio (that also act as proxies to the public NPM archive).
Command line tools in the npm node package manager, and pip or conda for Python, are all of a high quality specific to their audience’s requirements, allowing developers to manage the libraries they require from their corresponding archives.
Another advantage of cloud-based editors is their access to backend infrastructure, provided by the service in question.
This is invaluable for machine learning developers with Python, for example, with services like Google Colab giving developers access to scalable GPUs and online editor via the popular Jupyter Notebook interface.
Node.js vs Python Libraries and Dev Tools Verdict
There is no clear winner in this category – both Node.js and Python have an extensive, robust and capable ecosystem of tools.
In addition to common support for IDEs, syntax highlighting, auto completion, virtual environments, etc, there are also platforms specific to a demographic such as with the Anaconda platform for Python.
Node.js package library support is also expanding: private package managers that act as proxies to the public NPM archive are good examples of this.
We can define extensibility in two ways when comparing Node.js and Python:
- The ability to extend the functionality of the language itself.
- The ability to create libraries and frameworks to enhance functionality.
This article has already addressed the first point well – we understand that both Python and Node.js can be expanded with packages or modules, which can be part-coded in lower level programming languages like C or C++.
In addition to this, the amount of libraries available via the aforementioned package managers give a huge amount of functionality to the developer without even writing a line of code – in this respect Python and Node.js are very extensible.
Now, extensibility is further bolstered with the impressive amount of frameworks, more commonly termed libraries, that are designed for specific requirements. For Python, we have popular web development frameworks like Django, Flask, Pyramid, and Web2Py – all of which are designed to accommodate web services and backend development.
On the flip side, we have advanced frameworks like TensorFlow or PyTorch for the machine learning use case, which give Python additional abilities – communicating closer to silicon in order to process huge neural networks – or the Pygame library for game development – with Python indeed running a GUI for these games.
Node.js also shines in extensibility when the runtime provides command line tools for development pipelines. These have come in the form of tools like Webpack and Babel, which automate the bundling (a.k.a. compilation) of web apps and backwards compatibility respectively.
Generators are also common with Node.js, such as express-generator that initialises a web server with the necessary boilerplate code.
Node.js vs Python Extensibility Verdict
As a slightly biased Node.js developer, I have had more exposure to the tools that Node offers for web app development, in addition to how it facilitates advanced pipelines like React Native and Expo’s mobile development pipeline – Node.js is extremely extensible and impressive in this respect.
But so is Python, as we have seen, with its capability to generate GUIs, work with optimised machine code, and its large host of frameworks for web development and other specialised use cases.
Python error handling is of the language’s most favoured characteristics, with its expandable and modular exception handling within try-catch blocks. With more advanced libraries like Robot Framework providing automation tools, bugs can be found and corrected in a controlled environment.
But it has to be said that the more complex a program is, the more bugs there will be found.Since asynchronous functions are used much more in Node.js vs Python, we can expect more complexity and therefore more errors.
To add to Node.js’s bug-free challenge, it supports undefined variables (variables with no value assigned), in addition to null variables.
Either of these cases is perfectly valid in Node.js – which prompted TypeScript to include strict null checks (disallow null values) as a configuration option.
Node.js vs Python Error Handling Verdict
Node.js is harder to code bug-free than Python is, but Node.js’s event driven architecture and focus on asynchronous programming makes it more likely for bugs to occur, giving an unfair advantage to Python for the potential amount of bugs to be found.
Use Cases / Universality
By now we have ascertained that Python and Node.js are mostly used in very different circumstances, with some cross-over in the backend development space.
Python is heavily used in data analysis, AI, cloud and IoT devices. Given its extensibility, robust tooling and simple syntax discussed above, one can see why Python has become so dominant in these categories.
The ability to leverage all the extensibility Python offers does require a high degree of skill however, with the developer requiring knowledge in lower level languages, compilation and package orchestration in addition to fluency of the Python programming language.
Python is indeed beginner friendly, but requires dedication to master. With a well-rounded programming background however, Python has large universality potential.
A similar story can be said for Node.js. Like Python, Node.js is found in the cloud, on local development machines, and even on hobbyist gadgets like the Raspberry Pi.
Node.js applications comprise web services, command line tools and development tools that pertain to bundling, compilation, and boilerplate generation.
Node.js also facilitates real-time web applications and data streaming with fantastic web socket support, an area Node.js excels in compared to Python.
Node.js vs Python Error Universality Verdict
Both technologies have excelled in Universality in recent times, with no real strong competition from other programming languages to derail their momentums.
With future technologies like the metaverse and AR experiences on the agenda of big tech companies, both Node.js and Python appear well-poised to fuel these innovations, the prior facilitating real-time experiences and the latter used for engineering increasingly sophisticated AI solutions to make these experiences as intelligent as possible.
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Node.js vs Python: Summary
Admittedly, the choice between Python and Node.js is not as straightforward for all types of projects.
There are times where event driven Node.js is the obvious choice such as in large-scale web applications that have a high degree of server communication – chat bots, real time monitoring, REST APIs, GraphQL for more cutting edge solutions, are all areas where Node.js will be the better technology.
Python does have very successful web frameworks being used today, and is still the preferred choice for data processing over Node.js – web crawling, web scraping and general automation tasks have existed with Python since its early days.
But Python is maintaining its relevance through the huge growth of machine learning, reinforcement learning and natural language processing applications; its simple, expressive syntax and overall support for data science are keeping Python a very competitive and relevant language, even through its lack of multi-threading and slower runtime.
Needless to say, both technologies are on an upwards trajectory and are both strong primary programming languages to learn for developers.