Node.js and Python are two established solutions for server-side application development. With either platform, your team can develop and manage web apps of any complexity and functionality. As an app owner, you might be wondering which tool to choose for your next project. The choice will not be easy, and to make the final decision you should be aware of the advantages and limitations of Node and Python, and, more importantly, understand cases and types of applications where Node.js could be better than Python, and vice versa.
Node.js is one of a few server-side solutions based on the concept of event-driven programming, which allows creating highly scalable servers without threading.
On top of that, Node supports multiple concurrent requests and operations via asynchronous calls and non-blocking I/O.
Python, founded in 1991 by the developer Guido Van Rossum, supports multiple programming paradigms, which makes it a great choice for a variety of business tasks.
With Python’s object-oriented programming and comprehensive standard library, which supports automatic memory management and dynamic features, you can build a wide variety of applications for servers and desktops.
Even though in Node.js, your code usually runs in a single thread, its event-based architecture and non-blocking I/O allow for maximising the usage of a single CPU and computer memory, making servers faster and more productive.
The event-driven architecture based on asynchronous calls allows Node.js servers process more concurrent requests than most conventional multi-threaded servers.
And with v10.5.0 came the Worker Threads, allowing you to delegate some work to separate threads as well, somewhat mitigating the problem of single-core usage. In addition, Node.js’s non-blocking I/O, which does not block program execution under I/O-heavy workloads, is the first thing that improves the runtime performance of Node.js applications, making Node one of the fastest server-side solutions around.
One of the most cited issues in web application development is the usage of different languages and environments at the back end and front end.
The same language at the front end and the back end means you need a smaller and more efficient team, which can communicate better and, as a result, deliver tasks much faster.
Even though Node.js is a single-threaded solution, its standard library provides modules that support high scalability.
Node clusters and workers are abstractions that can spawn additional Node.js processes depending on the workload of your web application.
Limited only by the number of CPUs, you can easily scale your Node applications to fully functional enterprise solutions.
And since Worker Threads became a thing, we can easily spawn multiple CPU-intensive threads that will not block the main event-loop.
Node’s event-driven architecture comes with certain limitations as far as CPU-intensive operations are concerned. Although Node is great in handling multiple concurrent requests, it exhibits significant performance bottlenecks in such operations as generating graphics or resizing images.
Luckily, a workaround in which you make a new task queue to manage CPU intensive requests exists, but it requires spawning additional workers or using Worker Threads and introducing new layers to your Node.js application.
Node.js philosophy leaves it too much freedom of choice for developers in what modules and tools to select. What follows is that developers can spend a considerable amount of time figuring out which module or library to choose for handling a development task. Developers need more planning, time, and involvement to find well-maintained modules and install and integrate them.
Unfortunately, some of these modules might be buggy and introduce unexpected behaviour to your Node applications. If your application uses many modules built by different developers and having a different level of maintenance and bug fixing, it is crucial to have an experienced developer in your team.
Python has a concise and expressive syntax that helps think more clearly when writing programs and makes it easier for others to maintain and enhance your applications.
Also, being dynamically typed and flexible, Python allows writing a code that is less verbose but more modular and extensible.
Python’s readability facilitates the coordination of teams working on large projects and helps concentrate on completing real tasks rather than scaffolding and tweaking your code base.
Python is also quite easy to learn, which allows experienced developers to quickly jump into your Python projects and bring their experience in other programming languages to the table.
Thanks to clean, concise and simple syntax, developing in Python is much faster than in Java or C.
Although it’s always difficult to provide an accurate assessment of the development speed, most experts agree that developing a Python application is about 5 to 10 times faster than developing the same application in Java.
The time savings are even greater if compared to C and C++. The speed advantages substantially outweigh performance downsides of Python that originate from its interpreted nature and dynamic typing.
Due to the language’s extremely stable architecture, the availability of web development frameworks (e.g Django) and third-party modules, applications developed in Python are no less reliable than those written in any other framework.
The rich standard/native library for server-side development is one of the main strengths of Python in comparison to Node.js.
In Python, however, many useful functions come natively. For example, Python supports deleting a directory, creating temp files, argument parsing, unit testing, logging, printf format strings and many more by default.
If Python’s standard library lacks the functionality you need, similarly to Node.js, Python has a developed ecosystem of modules to choose from.
Our brief overview demonstrates that neither Node nor Python is perfect. If so, how to make the right decision about which one to use?
The answer is straightforward: both environments are good for different types of apps and tasks.