Compare CherryPy and Falcon
CherryPy allows developers to build web applications in much the same way they would build any other object-oriented Python program. CherryPy has built-in tools for sessions, static files, cookies, file uploads, caching, encoding, authorization, compression, and many more.
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.
Let's see how CherryPy and Falcon compare on various factors and features and which to choose when.
Type
A minimalist, object-oriented and popular web framework for Python.
Type
The minimalist REST and app backend framework for Python with a focus on reliability, correctness, and performance at scale.
Used by
Used in production services at Netflix and Hulu
Used by
LinkedIn, Leadpages, Wargaming, and Rackspace.
16 job openings which list CherryPy in the job description.
100 job openings which list Falcon as a requirement.
Very
fast. Extensions could impact performance adversely.
Very fast, even when compared to microframeworks like Flask. See the
benchmarks. *Caution* - The benchmarks are from the vendor itself.
Flexibility
Very flexible and simple. Doesn't force anything on developers.
Flexibility
Very flexible. Ideal for applications that require a high degree of customization and performance tuning.
Ease of Learning
Straightforward and easy to learn. Being around for almost 20 years, it has a good amount of tutorials online.
Ease of Learning
Has a slight learning curve but there are some great online resources, courses tutorials and YouTube videos.
RDBMS Support
Through Plugins or Extensions
No built-in ORM framework. Leaves it up to developers to choose their own such as SQLAlchemy.
RDBMS Support
Doesn't ship with one but developers can use the database library of their choice such as SQLAlchemy.
NoSQL Support
No built-in support for NoSQL databases but developers can use libraries to talk to MongoDB, DynamoDB etc.
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.
Admin Dashboard
Through Plugins or Extensions
No built-in admin panel.
Admin Dashboard
Through Plugins or Extensions
Does not ship with a web-based admin.
REST Support
No built-in support but can be implemented easily. Read
this guide to implement.
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. Must be handled by developers themselves or by using 3rd party extensions.
Security
No built-in protection but minimal attack surface for writing secure APIs
Templating Library
Doesn't come with a built-in templating language or package. Developers can use Jinja2, Mako. See full list
here
Templating Library
Doesn't ship with one but developers can use Jinja2 or Mako.
Web Forms
No built-in support. Developers can use formencode or any other solution.
Authentication
Built-in implementation of HTTP Basic Access Authentication.
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.
If you found this useful, please help us grow by sharing this article with your followers using the sharing icons. Every share or call out will help. Thank you.
Similar Comparisons
