There’s a bunch of the most discussed questions on the Internet, and one of them certainly is: “Django vs Rails: which one to choose?”. Both of them are open source web frameworks that provide all the necessary functionality for developing web applications, which relies on MVC model (in Django, “view” part is placed in templates, though).
Although Django and Rails are both quite convenient platforms full of the possibilities (with the key difference is that Django is written in Python and Rails is written in Ruby), there’s still the dilemma. I am not pretending to give you a definite answer in this article which one is the best – rather, will try to help you find the best fit for your case.
Django is a framework for developing web application based on the Model View Controller (MVC) architectural design, which, however, has a different naming – Model View Template (MTV). It was released in 2005 and named after Belgian guitarist Django Reinhardt. As it was earlier stated on the Django website:
“Django started as an internal project at the Lawrence Journal-World newspaper in 2003. The web development team there often had to implement new features or even entire applications within hours. Therefore, Django was created to meet the fast deadlines of journalism websites, whilst at the same time keeping the development process clean and maintainable.”
The main philosophy of Python developers can be formulated as the following: “Explicit is better than implicit. Here you can take a close look at Django design philosophies. The most popular services built with Django are: Instagram, Pinterest, NASA website.
Ruby on Rails
Ruby on Rails is also a web framework for web application development based on MVC model. It was released slightly later than Django – in 2008. However, it has fastly become quite popular among developers. Such services as Fiverr, AirBnB, Github were built with Rails, and their number is growing. The main philosophy of Ruby says: “Convention over configuration.”
Yukihiro Matsumoto, the “father” of Ruby explained the idea he had got when he decided to create a new language:
“I want to solve problems I meet in the daily life by using computers, so I need to write programs. By using Ruby, I want to concentrate the things I do, not the magical rules of the language, like starting with public void something something something to say, “print hello world.” I just want to say, “print this!” I don’t want all the surrounding magic keywords. I just want to concentrate on the task. That’s the basic idea. So I have tried to make Ruby code concise and succinct”.
You can get to know more of Ruby on Rails doctrine here.
Keep an eye on the competition between Django and Rails on Twitter in real time:
Django vs Rails: Similarities
Both Django and Rails are based on the same architectural pattern that is called MVC. The latter implies that an application is divided into three parts, which separates business logic from data models and representations. The latter provides better scalability and abstraction possibilities.
Although both frameworks are designed in the same way, there are slight differences in naming, which may lead to a certain confusion when developers switch to Django from other MVC-based frameworks. The thing is that the Django vs Rails MVC issue implies different naming: what is called MVC in Rails is MTV (Model Template View) in Django.
Most developers are concerned about performance. It’s considered that Django framework is slightly faster than Rails. But still, this Django vs Rails difference in performance is so insignificant that one should consider it in the last instance.
There are many other things that influence performance. For instance, if we talk about creating a worth architecture, beginner developers should consider that Ruby is more oriented at the conventional part and skips most of the configuration things, which comes with certain restrictions. On the other hand, imperativity of Python may give more freedom but also take more time.
In case of synchronous web frameworks, scalability depends on increasing the number of application servers, not on speeding up a single application server. That’s why the comparison of Rails vs Django scalability should impact your choice of the web framework nearly in the last instance. And if you do get to think about scalability issues, think first which language you are most comfortable with, so you can create a scalable architecture.
Both Django and Rails are well-documented, so usually it’s an issue of few minutes for a beginner programmer either in Django or in Rails to find answers to most popular questions. Moreover, most frameworks have large supportive communities where developers can find help either online or offline.
Django vs Rails: Differences
The first thing that developers learn about Django is that it uses Python, which is certainly advantageous for those who are familiar with the language. However, as most developers and even non-developers (that dared to try it) say, Python is extremely understandable and easy to get into if to compare with other programming languages. It quite aligns with the Python’s design philosophies, one of which is: “Explicit is better than implicit.”
Ruby has its own intricacies. Whether it’s more difficult for learning than Python or not is the question that involves a lot of subjectivity, such as your background and purposes. For a complete newcomer, it might be trickier than Python to understand how things work.
In comparison to Django, Rails has more hidden features and provides more elegance, which might be preferable for experienced developers. Literally, it means less code to write, which also brings less transparency of what is going on under the hood.
In Django vs Rails productivity question, predictably, Django follows the same explicit approach. So developers should code most of the things that in Rails are done by default. Routing is performed explicitly by means of regular expressions, not to mention that variables and classes should be explicitly defined too.
In Rails, most things are done automatically and don’t need to be coded. When you create a controller, you don’t need to define any variables or import classes – it’s all done by the framework. Besides, routing to controllers in Rails is carried out by default (the information about it is available for editing in the configuration file).
Among developers, it’s a well-known thing that Python 3 is slightly slower than Ruby 2.5.
So if you get to make choice of Django vs Rails only with the regard of speed, the path should be obvious. But does it make sense to be guided by speed only?
Nowadays, speed isn’t a critical metric for increasing performance anymore. Development expertise and good hardware can solve most of the problems concerned with speed when it comes to choosing a language. Finally, if you take a look at Rails vs Django performance, you will find out that Django is slightly faster. So usually, there is no need to bother about differences in raw speed.
Ruby on Rails has tons of libraries (thanks to a somewhat more developed community than Django’s one), which you can easily look up by means of gem function. However, most of them are tailored to the needs of web development.
Python has almost the same amount of libraries for web development as Ruby but also it has such a relevant nowadays advantage as an extensive library collection for research, analytics, machine learning (ML).
Django vs Rails: conclusions
The right choice comes from analyzing your purposes and expertise. Such things as performance, productivity, and scalability are more dependent on how comfortable you are with a framework than on the specificity of any framework itself. And of course, the main thing to orient at comparing Django vs Rails is language.
So I suggest choosing the framework considering how familiar you are with the programming language and what are the requirements of the project. While in Ruby you can skip a lot of details that are successfully handled by the framework, Python is more imperative – requires more code, though. Moreover, it may be useful for some projects to have an access to ML libraries, which is also the case of Python.