Compare Flask and web2py
Flask is a Python web framework for building web applications. It is based on Werkzeug and Jinja 2. It is a minimalist, 'no batteries included' framework. Yet it can be scaled extensively and support complex applications and use cases by adding required functionality as needed. It follows the philosophy that if something needs to be initialized, it should be initialized by the developer.
web2py was originally designed as a teaching tool, but it gained adoption outside of the academic world. It is a full-stack framework containing all the components needed to build fully functional web applications using the Model View Controller (MVC) pattern. Inspired by the Ruby on Rails and Django. It is not very popular right now but was ranked amongst top Python web frameworks in 2011.
Let's see how Flask and web2py compare on various factors and features and which to choose when.
Type
Python microframework for building web applications.
Type
Python full-stack for building web applications.
Used by 397,000 projects.
Used by
Netflix, Zillow, Lyft.
Used by
Not in use at any large company.
1067 job openings which list Flask as a requirement.
8 job openings which list web2py as a requirement.
Because it is minimal and doesn't have a lot of overhead, Flask is very performant. Extensions could impact performance negatively.
Web2py takes a unique approach where models and controllers are executed in a single global environment, which is initialized at each HTTP request. While there are pros to this approach, such as developers never having to worry about cleaning up or avoid conflict between requests, the major disadvantage is that the code is models is executed with every request which carries a performance penalty.
Flexibility
Very flexible and doesn't require users to use any particular project or code layout. (A structured approach is still recommended.)
Flexibility
Not as flexible as microframeworks, but doesn't always get in the way.
Ease of Learning
Flask is simple and its core features are not difficult to learn. There are also plenty of online resources available to aid in learning.
Ease of Learning
Limited online tutorials and resources, and many are several years old. The best resource for learning is web2py author's own
"web2py Complete Reference Manual", which seems to be written in 2013.
RDBMS Support
Through Plugins or Extensions
Flask doesn't come with a built-in ORM framework. Developers can use one of many open source libraries or extensions. Such as
Flask-SQLAlchemy,
Flask-Pony, etc.
RDBMS Support
Ships with a Database Abstraction Layer (DAL) which supports MySQL, PostgreSQL, SQLite, and many other relational databases.
NoSQL Support
NoSQL databases are supported through open source libraries or extensions. To use MongoDB with Flask,
Flask-PyMong is a popular choice. CouchDB, Cassandra, and DynamoDB are also supported via libraries.
Verdict Flask is a great choice if you want to develop for a NoSQL database.
NoSQL Support
No built-in support. Very limited support for NoSQL databases. Currently, it only supports Google Datastore on the Google App Engine.
Admin Dashboard
Through Plugins or Extensions
No built-in admin panel, but you can use the
Flask-Admin extension. It supports a number of backends like SQLAlchemy, MongoEngine, Peewee etc.
Admin Dashboard
Yes ships with a built-in admin panel.
Security
Despite being a minimalist Framework, Flask does an excellent job of addressing common security concerns like CSRF, XSS, JSON security and
more out of the box. 3rd party extensions like
Flask-Security can be used for common security measures. However, it requires that developers evaluate these extensions carefully for security risks and apply timely updates manually when vulnerabilities are discovered.
Security
Built-in protection against input injections, XSS, and common vulnerabilities. Read more
here. It has known security vulnerabilities. Please see list
here.
Templating Library
Flask uses
Jinja2 out of the box.
Web Forms
No built-in support but there is
Flask-WTF extention. For SQLAlchemy support, that is, to create forms based on models, there is
WTForms-Alchemy
Web Forms
Built-in support. Read more
here.
Testing
Built-in support using Python's
unittest framework.
How is performance rating determined?
Performance rating is determined using reputable online benchmarks listed below.
Where is job data coming from?
Job data is collected from Indeed, Google Jobs and Stack Overflow jobs.
How is popularity calculated?
Popularity is calculated using a formula which looks at weighted score on the following publicly available data points:
- Popularity per Google Trends
- Number of GitHub Users
- Number of GitHub Stars
How is this calculated?
Ease of learning is calculated using the following data:
- Number of features and depth of tool.
- Number of online resources: articles, blogs, tutorials and YouTube videos.
- Number of courses
- Freshness of online material
For example, a microframework may not have a lot of online resources but still get a high-rating because it's minamalistic and easy to learn just by following official documentation.
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