Compare Falcon and web2py
Falcon is a minimalist WSGI library for building web APIs, app backends and microservices. Compared to Django and Flask, it is fast, extensible, reliable, and encourages RESTful style. It doesn't come with batteries included and is ideal for applications that require a high level of customization. The fminimalist design allows developers to select the best strategies and 3rd-party packages from the Python ecosystem.
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 Falcon and web2py compare on various factors and features and which to choose when.
Type
The minimalist REST and app backend framework for Python with a focus on reliability, correctness, and performance at scale.
Type
Python full-stack for building web applications.
Used by
LinkedIn, Leadpages, Wargaming, and Rackspace.
Used by
Not in use at any large company.
100 job openings which list Falcon as a requirement.
8 job openings which list web2py as a requirement.
Very fast, even when compared to microframeworks like Flask. See the
benchmarks. *Caution* - The benchmarks are from the vendor itself.
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. Ideal for applications that require a high degree of customization and performance tuning.
Flexibility
Not as flexible as microframeworks, but doesn't always get in the way.
Ease of Learning
Has a slight learning curve but there are some great online resources, courses tutorials and YouTube videos.
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
Doesn't ship with one but developers can use the database library of their choice such as SQLAlchemy.
RDBMS Support
Ships with a Database Abstraction Layer (DAL) which supports MySQL, PostgreSQL, SQLite, and many other relational databases.
NoSQL Support
Doesn't come with built-in libraries but makes it easy for developers to use library of their choice such as MongoEngine to interact with MongoDB.
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
Does not ship with a web-based admin.
Admin Dashboard
Yes ships with a built-in admin panel.
REST Support
Encourages the REST architectural style by design. Resource classes implement HTTP method handlers that resolve requests and perform state transitions.
Security
No built-in protection but minimal attack surface for writing secure APIs
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
Doesn't ship with one but developers can use Jinja2 or Mako.
Web Forms
Built-in support. Read more
here.
Authentication
Not built-in but can be easily extended using authentication middlewares like
falcon-auth2.
Testing
Support using unittest and pytest. Read more
here.
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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