- Node.js as backend technology
- Python as backend technology
- Node js vs Python performance
- Python vs Node.js: Syntax
- Python vs Node.js Scalability
- When should you use Node.js as backend technology?
- When should you use Python as backend technology?
- Choosing between Python and Node.js
- FAQs about Python vs. Node.JS
If you are looking to develop an app, there is a lot to consider, and one thing you need to get right is the programming language. And that’s why many companies face a dilemma when they have to choose between Node.js and Python as a backend technology for their app development project.
Node.js and Python are probably the most popular solutions for backend development. What is the difference between them? Which technology should you choose in 2022? In this blog post, we will take a look at Python vs Node JS to give you a better understanding and help you to decide on the right one for you.
Although Node.js is the more established of the two, Python has become exceptionally popular in recent times, and this means they are both regularly compared against one another. So, let’s get to the bottom of these two platforms.
Node.js as backend technology
With Node.js, programmers can develop backend and frontend with a single unified stack. This distinctive feature means that teams can use the same skill set, avoid re-hiring, and onboard new team members with ease.
If it is a real-time web project you are going to be working on, Node.js is a wise choice. It is used by many reputable organizations and corporations, including GoDaddy, LinkedIn, IBM, PayPal, Walmart, Netflix, and Microsoft.
Node was designed specifically for web development and is therefore focused on its key challenges. Key benefits include fast performance, flexible syntax, efficient code sharing, and frequent libraries updates. In terms of the features on Node.js, a single-threaded model is used with event looping. This sort of event mechanism enables the server to respond in a non-blocking mode. There is no buffering and it is one of the fastest in terms of code execution.
There are many different benefits associated with Node.js, including the following:
- The package manager on Node.js is also a range of open-source JS plugins. Node.js developers use NPM modules in more than 97 percent of web applications.
- You can edit numerous modules at the same time thanks to the package manager, which comes with free modules, and this is ideal for microservice architecture.
- When compared with Go, PHP, and Java, Node.js has been proven to be between two and three times faster, a study says.
- MongoDB for data management.
- Express.js for efficient backend development.
- It addresses all of the main web development challenges, as it has been made with web development in mind. Crucial advantages here include efficient code sharing, flexible syntax, regular library tools updates, and fast performance.
Since there is a package manager with free modules, developers can handle multiple modules at once, which is perfect for microservice architecture. PayPal and Netflix used Node.js to move from monolithic to microservices. The Node.js package manager is also a collection of open-source plugins. NPM collects more than 840,000 libraries.
Node.js uses callbacks to track completed tasks. As the project scales and the number of active processes grows, callbacks increase. At some point, developers risk getting lost in the callbacks and overlooking important bugs or technical debt.
Python as backend technology
Now that you have a good understanding of what Node.js is, we are going to take a look at Python so that you can understand what this platform is and how to use it effectively. Python is a multipurpose programming language. It is dynamic, high-level, and object-oriented. The dynamic typing and syntax, with interpreted nature, mean that it is an excellent language for scripting.
Many programming patterns are supported by Python, including procedural styles, functional programming, and object-oriented programming. In addition to this, it is an interpreted language, which means it is not able to convert into code that is computer-readable before runtime is run.
Python is thought to be the most popular programming language for machine learning. It is also the second most popular programming language for GitHub.
A universal programming language, Python is used for backend development, automation, scientific computing, big data, and AI development. It is also often used for complicated web projects that use automation and AI, rely on challenging calculations, and process large quantities of data.
There are many different features of Python. Python enables low-level modules inclusion to the Python interpreter, which means programmers can customize their tools with ease. plus, there are interfaces to all of the key commercial databases.
There are plenty of other benefits you can expect with Python as well, including the following:
- The majority of hosting providers offer native support for Python code, meaning integrating APIs and additional plugins is easy. You can use the likes of Heroku, Digital Option, TMDHosting, A2 Hosting, AWS, and several other platforms.
- There are a lot of AI in-built libraries as well, from Scikit-learn for data analysis to Tensorflow for neural networks.
- The syntax is very simple, so Python code is highly readable. It should not take you long at all to get the hang of straightforward tasks.
- You can easily integrate Python with Kava, CORBA, ActiveX, COM, C++, and C.
- High-level dynamic data types are available. Dynamic type verification is also supported.
- Structural and functional programming methods are supported, as is OOP.
- Python offers programmers a range of built-in AI libraries (Scikit-learn, Pylearn2, and Tensorflow).
- Most hosting companies provide native support for Python code and allow also integration of APIs and plugins.
The backend speed comparison of Java vs Node.js vs Python has shown that Python is much slower in handling server-side operations.
Python is not a native Android or iOS programming language. It’s not a good option for hybrid development neither. You can adapt Python to mobile development with the help of frameworks and platforms, however it will impact the app’s performance.
Probably the biggest impact of GIL is that a single shared object for all threads forces all threads into a single core – even if none of them share any other data; thus, threading is effectively non-simultaneous; since it uses CPU bound threads (not the kind of concurrency you’d expect in a multi-core CPU). Essentially, this means that programs with threads that run Python code are at a disadvantage and can even run slower than programs without threads, depending on what they are doing.
Node js vs Python performance
A lot of people want to know how the two platforms are different in terms of speed. With Node.js, execution is a lot simpler and quicker thanks to the non-blocking architecture. Plus, you can execute codes outside of your web browser.
Speed can be impacted when using Python because of the innate architecture, which puts major restrictions on multithreading, so it is important to keep this in mind. Because the process will not run in parallel, the overall processing speed is slowed down. Although Python is easy to execute and learn, it is not fast enough for apps to recall data from the server.
Also, as Node.js processes the code outside of the browser, the applications use fewer resources, which contributes to performance improvement. It also lets you use technologies like TCP sockets that are not available in browsers. Consequently, the event-driven, non-blocking UI enables multiple requests to be processed simultaneously, speeding up code execution.
Last but not least, Node.js supports caching of individual modules, which significantly speeds up app loading and improves responsiveness.
Python are both interpretive languages, so they slower compared to compiled languages like Java. Python, unlike Node.js, is a single-flow language, which means that requests are processed quite slowly. Python is therefore not the ideal choice for projects where speed and efficiency are important or that require a lot of demanding computations.
However, newer Python versions (v3.5 and up) have introduced “asynchronicity”, so it should now be as fast as Node.js. And if programmers need expensive computation, they can write modules in C/C++ and export them to Python, since both Node. JS and Python were written in C.
Python vs Node.js: Syntax
The syntax is considered to be the biggest strength of Python. Developers need to write fewer lines of code in Python compared to Node.js. Python’s syntax is simpler and free of curly braces. This makes coding in Python easier, and debugging is easier too. The codes of Python are so easy to read that even a client with basic technical knowledge can understand them.
Node Package Manager is responsible for managing packages in Node.js. The NPM manages more than 1.3 million packages, making it the largest repository for packages in the world. It also shows how extensible it is. You can find all the available packages with a simple search.
The libraries and packages in Python are handled by pip, Python’s default package installer. The number of packages for Python exceeds 220 thousand across different categories like image processing, computation, data science, etc.
Python vs Node.js Scalability
Python does not provide proper support for scalability for several reasons. The runtime interpretation of code makes Python a slower programming language. Although python supports multithreading it is nullified by GIL that prevents multiple tasks from running simultaneously. This is due to an internal locking mechanism that prevents multiple tasks from running simultaneously.
However, programmers can overcome these constraints by using Python implementations with a better architecture, like CPython or Jython, and load balancing mechanisms.
Node.js inherently offers scalability as it is integrated with the runtime environment and has a clustering module that is capable of harnessing the full power of your machine. Node.js enables easier vertical and horizontal scaling of web apps by adding newer nodes for horizontal scaling and additional resources for vertical scaling.
Other areas to consider
From the various libraries available to error handling, there are some different areas to consider when determining the best platform for you, but ultimately, it is all about figuring out which option best aligns with your goals and needs for your project.
Node.js and Python are both used for the backend in web development. However, Node.js is only focused on the backend, while Python is a general-purpose language that can be used for many domains and tasks. Node.js is fast, lightweight, and offers comprehensive JS development. At the same time, Python offers a simple structure, extensive AI and ML libraries, and an active developer community.
When should you use Node.js as backend technology?
Node.js is perfect for developing small web applications and websites. However, since there are no explicit coding standards, companies should not use this framework for large projects. The event-based nature of Node.js makes it a perfect ingredient for applications that need to handle concurrent requests in real-time, including frequent data transfers from client to server.
Compared to Python, Node.js offers more performance and speed. It is an ideal solution for developing chat applications, Collaboration systems, Complicated single-page apps, IoT implementations, Streaming platforms. It can be leveraged in high-load applications development and creation of e-commerce platforms that depend on processing speed.
Node.js major use cases
eBay – A global eCommerce store for B2C and C2C selling activities around the globe. eBay has leveraged the ability of Node.JS to manage data-intensive applications and high-traffic websites.
LinkedIn – the global platform for professionals to communicate with each other leverages Node.JS to manage the ever-growing users’ database. Connections and many messages are well handled by the high-speed feature of Node.js.
PayPal leverages Node.js because it allows the company to serve over 200 million users in virtually all currencies without any slowdown.
Uber relies on Node.js because it scales gracefully and can efficiently handle a large number of requests.
Many companies that have frequent interactions between client-side application and cloud servers, such as Medium, Trello, have chosen Node.js because of its efficient processing.
When should you use Python as backend technology?
Python offers web frameworks, a library of open-source data analysis tools, and testing tools. This programming language is an accessible language for junior coders as the development community offers many insightful resources. Python is also taught in universities and used to work with such user-friendly devices, as the Raspberry Pi.
Python is suitable for a wide range of tasks, including web applications, numerical computation, machine learning, and network programming. It is an excellent programming language for a wide range of tasks. Python is great for creating MVPs and prototypes. Python is suitable for both small and large projects and, more importantly, it is an important part of the data science industry, where most tools are based on Python.
Comparing Python to Node.js, Python excels in consistency, stability, and ease of use. Python is ideal for creation of scientific applications and Big Data solutions. Python is used in such domains as Data Visualization, Machine Learning, Image Processing, Voice, and Facial Recognition.
Python uses cases
Python has a lot of talent mainly because of its straightforward syntax. This is one of the reasons why giants like Facebook use Python extensively to develop their technologies. Python is also used as the default language for many popular data science courses.
Instagram uses Python for its backend service. Google has been using Python for years. Spotify leverages this programming language for their data analytics and backend services. Netflix uses Python because its developers are a big fan of this programming language.
Choosing between Python and Node.js
Python and Node.js are very powerful options for programmers to create web applications. The decision to use either solution depends on two key factors: the purpose of the project and the skills of the development team. Both offer advantages, but these should never outweigh the requirements of your project.
It is very difficult to choose between Node.js and Python, as both technologies offer top-notch features and benefits. It’s crucial to understand which benefits or features of a backend technology you are looking for.
So, we have provided an insight into Python vs Node.js. We hope that our article will help you to get a better understanding of the two backend solutions. It is not a case of one being better than the other. It is all about considering the projects you are going to be working on and which platform is going to be most suitable for your requirements. We hope our article clears some of your doubts about Node.js and Python and brings you one step closer to choosing the right technology for your project.
Use the features and benefits information we have provided above to help you figure out which offer makes the most sense for you. Do not hesitate to get in touch with us: we will assist you in choosing the right backend technology for your project. At Elitex, we have a solid team of experienced backend developers who are proficient in both Node.js and Python.
FAQs about Python vs. Node.JS
Which is better: Node.JS or Python?
It’s hard to say which is better, as both have their pros and cons and both are used for different types of projects. You need to choose Node. JS or Python depending on the requirements of your app project.
Which is faster: Python or Node.JS?
Node.js is faster than Python because it is built on Chrome’s powerful V8 engine. For this reason you should choose Node.js for real-time mobile applications.
When should you use Node.JS?
Since Node.js offers high performance and fast processing, it’s an excellent technology for real-time messaging applications, heavy load apps, e-commerce websites, and many other apps that require fast processing.
When should you use Python?
Python can be useful for a variety of projects, such as scientific applications, Big Data solutions, government projects, and all those projects where stability and consistency are top priorities.
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