Written by Stanley Ulili✏️
Choosing a backend programming language is never an easy task. After all, different languages have their pros and cons that you need to consider to make sure it is the right tool for the application you’re trying to build.
Node.js and Python are some of the most popular choices for backend development. Both have very strong package ecosystems and communities and choosing between the two can be difficult.
In this article, we will analyze the pro and cons of both Node.js and Python, and see the scenarios where one would be better than the other so that you can make the best choice for your backend.
We will cover the following subjects:
- What is Node.js?
- What is Python?
- Comparing architectures
- Concurrency and parallelism
- Performance and speed
- Static typing
- Community and libraries
- Common use cases
Node.js is multi-paradigm and supports the following paradigms:
- Functional programming
Python is an interpreted, general-purpose programming language commonly used for scripting, backend development, machine learning, and data science, to mention a few. It supports multiple paradigms such as:
- Functional programming
It was designed and developed by Guido van Rossum, and was released in 1991 to mainstream success; Python has consistently ranked in the top 10 of the TIOBE Programming Community Index. Aside from that, big companies such as Google, Facebook, Dropbox, and Instagram use it for both their internal and external tools — even NASA has found applications for it.
A software architecture describes how major components in a system interact, relate, and are organized. Good architecture design can lead to systems that scale, perform well, and are maintainable.
In this section, we’ll take a bird's eye view of both Node.js and Python architectures.
Node.js is single-threaded, non-blocking, and implements an event-driven architecture. It has a single thread where all code you write and the libraries you use executes. It also makes use of other threads that the libuv C library provides to handle expensive or long-running tasks.
Node.js uses callbacks to signal the completion of long-running tasks, and once finished, they are added to a task queue before finally being added back to the main thread. This behavior is what makes Node.js non-blocking because expensive tasks don’t block the main thread; instead, they execute in separate libuv threads, and Node.js continues executing other parts of the source code.
Python is also a single-threaded language, largely because it implements a Global Interpreter Lock (GIL), a mechanism that allows only one thread to take hold of the Python interpreter and run Python code at a given time. Even if the Python program uses multiple threads, the GIL will switch among the threads at regular intervals to give each thread a chance to execute code — but they cannot execute in parallel by default. This behavior is what makes Python single-threaded.
Unlike Node.js, Python is not based on an event-driven architecture. However, you can still leverage it using the asyncio package, which allows you to write asynchronous code with the async/await syntax since it implements the event loop, futures, etc.
Even though the language's architectures are different, both languages are good choices and can support synchronous and asynchronous programming.
Another important aspect of choosing a backend is concurrency and parallelism. These terms tend to confuse people, so let's define them so that we can be on the same page:
- Concurrency: when two or more tasks execute in multiple threads, but not at the same time. Instead, execution switches between the tasks, and when the computer interrupts a task to switch to another one, it can continue executing the other task from the interruption point
- Parallelism: when multiple tasks execute in different threads at the same time
Concurrency and parallelism are invaluable when your application tasks are CPU-bound, such as in the following tasks:
- Processing images
- Doing complex calculations
- Video compression
If you want to see some additional CPU-bound task examples, see this article.
Now, if you want to improve the performance of these tasks, you can split them among different threads and execute them in parallel.
With that, let's now see how Node and Python deal with concurrency and parallelism.
Even though Node is single-threaded, you can write multi-threaded programs using the
The worker threads share the same memory and process ID as the main thread (parent), and threads communicate with each other through message passing. You can learn more about how to write multi-threaded programs in Node.js elsewhere on the blog.
In Python, you can achieve concurrency with the use of the threading module, which creates threads to execute parts of your code. However, this does not mean threads will execute in parallel. This is because of the GIL, which ensures that only one thread can execute Python code, and switches between them in regular intervals.
While concurrency is helpful to I/O-bound tasks, CPU-bound tasks benefit greatly from parallelism. To achieve parallelism, Python provides the multiprocessing module that creates a process on each core and lets you leverage a multi-core system to execute Python code in parallel.
Each process has its own interpreter and GIL, but it does have a few caveats, though. For one, the processes have limited communication in comparison to worker threads, and for another, starting a process tends to be more expensive than starting a thread.
Python’s threading module pales in comparison to the Node.js
worker_thread module, which can achieve concurrency and parallelism easily. Node.js wins because it supports concurrency and parallelism without requiring a workaround, as Python does.
A faster backend can reduce your server response times, which in turn boosts page speed. A good page speed can help your web application rank well on Google, and give your users a good experience.
The speed of a programming language tends to go together with how the source code is executed. Let's explore how Node.js and Python compare during execution and how it affects each of their execution speeds.
Node is known for executing code fast, and most of it can be boiled down to a couple of reasons.
Secondly, Node.js is non-blocking and built on an event-driven architecture. It has asynchronous methods for almost every I/O method operation in Node.js. Since Node.js is single-threaded, if an operation takes a long time, it does not block the main thread. Instead, it executes it in parallel, giving room to other parts of your code to execute.
Python’s execution speed is much slower than Node’s. There are a few factors that affect Python's speed. For starters, Python automatically compiles the source code into byte code, which is a low-level format that only the Python Virtual Machine (PVM) interprets. This has performance implications because the CPU does not directly execute the byte code — instead, the PVM interprets the code, which slows the execution time.
As a solution to this problem, Python has alternative implementations such as PyPy, which claims to be 4.5 times faster than the default Python implementation through the use of just-in-time (JIT). If speed is something your Python application desperately needs, you should consider using PyPy.
With that being said, although Python is slower than Node.js, its speed is still good enough for a lot of projects, and that’s why it’s still popular.
Node.js is the winner because it executes as fast as it is compiled down to machine code, while Python is interpreted with the PVM, a process that tends to slow down execution.
When an application gets traction, the following happens:
- Client requests increase, due to a higher number of users
- The amount of data that needs processing increases
- New features are introduced
The ability for the application to grow and adjust due to an increase in demand without losing performance is known as scaling.
Node.js provides a native cluster module that allows you to scale your application without extra effort. The module creates a separate process, or worker, on each core in a multi-core system. Each worker has an instance of your application, and the cluster module has an inbuilt load balancer that distributes incoming requests to all workers using the round-robin algorithm.
Node.js also scales well because it uses fewer threads to handle client requests. As a result, it spends most of its resources serving clients instead of dealing with the overhead of thread lifecycles that can be expensive.
Python does not have the native equivalent of Node.js’s cluster module. The closest is the multiprocessing module that can create processes on each core, but it lacks some of the functionality for clusters. To do cluster computing, you can use third-party packages such as:
The Python wiki has a comprehensive list of Python cluster computing packages.
The Node.js cluster module allows Node apps to scale more easily in comparison to Python. However, it's important to acknowledge that most people these days are using Docker for scaling.
With Docker, you can create multiple containers where each container contains an instance of your application. You can create as many containers as there are cores available on your system, and put a load balancer in each to distribute the requests. So, whether you go with Python or Node.js, you can use Docker to make scaling easier.
Not every programming language can efficiently solve each problem you have, and sometimes you need to extend a programming language with another one that can excel at the task at hand.
Let’s explore the extensibility of Node.js and Python.
You can extend Node.js with C/C++ through the use of addons. For example, a C++ addon allows you to write a C++ program and then load it in your Node.js program using the
require method. With this ability, you can take advantage of C++ libraries, speed, or threads.
To implement the addons, you can use:
You can also extend Node.js with Rust; check out this tutorial to learn how to do it.
Python also has good language extension capabilities. You can extend it with C or C++, and this allows you to invoke C/C++ libraries within Python, or invoke Python code in C/C++.
You can also use alternative Python implementations to extend Python with the following:
- Jython: makes integration with Java much easier
- IronPython: allows Python to integrate smoothly with Microsoft's .NET framework
Both have good support for extending them with other languages.
Node.js and Python are both dynamically typed languages, which allow you to program quickly without the need to define types for the code you write. However, as your codebase grows, the need for static typing arises to help you catch bugs early on, and document your code for future reference. Even though Python and Node.js are dynamically typed, they both provide static typing tools that you can leverage in your codebase if need be.
When you use TypeScript, you save your source code in a
.ts extension instead of a
Unlike Node.js, Python does not need a separate language for types. Instead, it comes with type hints that you can use in your project. However, Python does not perform static typing analysis on its own; instead, you use a tool like mypy for static type checking. See this article if you want to learn how to do static type checking in Python.
The advantage of Python’s type hinting approach is that you don’t have to use a different file extension for your source code and compile it to a Python file extension. But the drawback is that newer type hints are introduced with each new Python release, each of which roughly takes a year. On the other hand, TypeScript has a release schedule of 3-4 months.
Node.js wins because of TypeScript, which evolves much faster than Python. But still, it’s also good to acknowledge Python's ability to add types without the need for another language.
A community plays a huge role in software development. A programming language with a large community tends to have:
- More libraries and tools for development
- More content for learning
- Easier-to-find support
- Easier-to-find developers for hire
Node.js and Python equally have strong communities, but let’s take a closer look at each of them.
Node.js has a strong and active community that has built over one million open-source packages, all of which are available to you on npm.
The following are some packages you are likely to come across:
- Express: a web framework for building web applications
- Axios: for making API requests
- Lodash: a utility library
To discover more packages, see the curated awesome-nodejs repository on GitHub.
Packages aside, Node.js has a plethora of high-quality written content, and video tutorials spread across many platforms, including this blog. This makes learning Node.js much easier, and when you're stuck on a task, there is a higher chance that someone has already asked that question before you on a Q&A platform like Stack Overflow.
In addition, Node.js also has a lot of international conferences where you can learn more about Node.js and meet other people, as well as online communities focused on Node.js.
Python also has an active community, with over 370k packages and 3.4 million releases on the Python Package Index. You can download them to your project using pip, a package installer that pulls packages from the Python Package Index.
The following are some of the popular packages:
See the awesome-python GitHub repo for a comprehensive list.
Like Node.js, Python has plenty of video and written content paired with active online communities, and conferences such as the Python Conference (PyCon), which is held in over 40 countries.
They both win here because both Node and Python have high-quality content, active communities, and a lot of packages for development use.
Python and Node.js each have their strengths and weaknesses, which we’ve covered in depth here. Some tasks better suit Python due to the packages and community around it, and some tasks are more suitable to Node.js, due to the architecture of the language and other factors.
Due to Node.js's non-blocking and event-driven architecture, it tends to be commonly used for:
- CPU bound operations: due to good multi-threading support
- I/O operations: due to non-blocking and event-driven architecture
- Real-time applications: using a library like socket.io
On the other hand, the scientific community heavily embraces Python, and as a result, there are many packages for machine learning, data analysis, and many more, such as:
If your application is more focused on data analysis or uses tools that scientists use, Python is an excellent choice.
Both are good. It mostly depends on what you want to use them for. Node.js is good for real-time applications, while Python is good for applications that require data analysis and visualization.
We have come to the end of this article. We have looked at the differences between Python and Node.js, and I hope you have learned that there is no such thing as a perfect tool. Still, these languages are trying hard to fix their limitations, either with inbuilt or third-party tools.
Your choice of backend language heavily depends on the kind of application you want to build, and I hope this guide has helped you make a good decision for your backend.
Deploying a Node-based web app or website is the easy part. Making sure your Node instance continues to serve resources to your app is where things get tougher. If you’re interested in ensuring requests to the backend or third party services are successful, try LogRocket.
LogRocket is like a DVR for web and mobile apps, recording literally everything that happens while a user interacts with your app. Instead of guessing why problems happen, you can aggregate and report on problematic network requests to quickly understand the root cause.