Flask vs Django: A Comparative Guide to Python Frameworks

The extent to which a framework anticipates your project's requirements will dictate the scope of its feature set. The more eventualities a framework caters to, the quicker it can help you bring your project to market.

However, such a comprehensive facility for rapid prototyping often comes with more than one cost. These can include:

A 'lighter' web framework poses few, if any, of these challenges. It will not usually impede the development of any desired extra features, will generally be optimized for further development (i.e. having established API routes and third-party extensions), and will tend to perform faster.

Notwithstanding, a 'minimalist' web framework of this type may present other issues:

Flask is our 'David', Django our 'Goliath'. Nonetheless, the outcome of the battle is anything but certain, because the two frameworks cater to two different use cases, where ease of implementation is played off against framework overhead.

Named after influential jazz guitarist Django Reinhardt, Django began life as a side-project by two developers working for the Lawrence Journal-World newspaper.

Django was originally conceived as yet another custom CMS in a period where such home-spun frameworks were more common practice, although this one was leveraging the increasingly popular Python language. It was released under a BSD license in 2005, and three years later fell within the remit of the newly-created Django Software Foundation.

Unlike Flask, Django is predicated around the model–view–controller (MVC) paradigm, wherein the output of an application is sandboxed into a 'view', separate from the internal data structure and machinations of the controller framework. This enables easy GUI development and provides standardized pathways for backend control panels and other useful framework instrumentalities.

In contrast to Flask, Django comes replete with its own ORM, negotiating data models via Python classes. The framework also contains a relational database capable of processing HTTP requests via web templates, known as 'views'.

Out of the box, Django comes with a flexible admin control panel, an authentication system that accommodates extensions, RSS feed generation, support for geographic information system (GIS) applications, ability to generate Google sitemaps, and default security measures against SQL injection, cross-site scripting, and several other common attack surfaces.

In terms of core functionality, the most direct contrast between Flask and Django is that the latter can run multiple applications within one framework implementation—a granular approach that can only be replicated by Flask via the creation of individual views.

Anecdotally, some developers and teams have used Flask in a transitional capacity, to gain solid understanding of the core concepts which the two systems share before migrating over to Django and allowing the meatier framework to pick up the greater brunt of the workload.

Though there is the consensus that Flask has a more shallow learning curve than Django, Flask's stripped-down feature set should not generally be misinterpreted. Some notable names, including Pinterest (see above), have migrated from Django to Flask in order to dump framework overhead, or to take advantage of specific functionality that both frameworks offer (natively or via extensions), but which can be more easily or efficiently isolated and targeted in the sparser Flask.

As usual, Flask leaves us to add this core functionality if we need it. The FlaskSecurity extension offers a complete suite of security tools and protocols, including role management, password hashing, session-based authentication, basic HTTP authentication, and JSON or AJAX support. It also optionally provides capabilities for login tracking, user registration, and token-based password recovery and account authentication.

However, many of these features entail the installation of secondary and tertiary libraries such as ItsDangerous and Passlib. Depending on the number of extra packages required on a per-feature basis, this is likely to increase framework overhead and narrow the performance gap between Flask and Django—which may have been a critical consideration at the outset. Notwithstanding, the dependency packages are in themselves quite granular, allowing for cherry-picking and optimization.

Predictably, Django includes most of this functionality by default. Out of the box, a Django installation caters against cross-site scripting (XSS), clickjacking, SQL injection, and cross-site request forgery (CSRF). It also accounts for host header validation.

Django is packaged with a native user authentication system that offers a base suite of standard extensible tools, such as: 

Third-party solutions are available for login-throttling, password strength checks, object-level permissions, and third-party authentication systems such as 0auth.

Endpoint testing and handling is available in both Flask and Django via Python's integrated unittest framework, and each can be configured or swapped out for secondary solutions as necessary.

Despite this, Django's integrated automated testing framework is a strong argument in its favor, particularly if you need to run dummy database transactions where the test data should be purged afterward—a configuration which is not trivial to accomplish in Flask. 

As we might expect by now, Flask relies on third-party libraries for testing and accomplishes this by exposing the Werkzeug test client to input as well as negotiating local transactions for the test.

As we have mentioned, some consider Flask a transitional or educational framework that will inevitably lead to Django. However, others have concluded that the reverse applies—that Flask, in the hands of a developer now familiar with the bottlenecks and exigencies of a Python web framework, provides all the necessary building blocks for a mature implementation, and that Django is the one doing the preliminary 'hand-holding'.

In response to the oft-leveled criticism that Flask requires bespoke solutions to challenges that come pre-solved in Django, we should note that user-generated GitHub repositories address such shortfalls in the majority of cases.

Where rapid prototyping is a priority for development teams that have no specialization in either framework, Django will get the project to market sooner, with a performance penalty that in most cases is trivial, except at the highest volumes of data turnover.

In this regard, however, much depends on the data architecture adopted. Because Django assumes a number of set pathways and workflows for your project, it can sometimes prove to be brittle if you need to depart from its models and paradigms for any reason.

The binding relationship Django maintains between business logic, views, and database operations can make it inflexible or cause performance issues at scale, unless these factors are anticipated at design time.

For a brand-new build centered around Create, Read, Update, Delete (CRUD) functionality, unburdened by the complexities of migration from previous projects, Django is an attractive choice, so long as the strictures of its ORM implementation suit your needs.

If your project requires a higher level of interactivity with other systems, and particularly if it must negotiate with legacy data architectures, the extra upfront development time entailed in using Flask is likely to bring long-term reward.

*All stats in this article current as of August 2019