Compare Flask and Pyramid
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.
Pyramid is a web framework that is written in Python and is based on WSGI. It is minimalistic and inspired by Zope, Pylons, and Django. Before 2010, Pyramid was known as repoze.bfg.
Let's see how Flask and Pyramid compare on various factors and features and which to choose when.
Type
Python microframework for building web applications.
Type
Python "microframework" for building web applications.
Used by 397,000 projects.
Used by
Netflix, Zillow, Lyft.
Used by
Reddit, Yelp, Mozilla, SurveyMoney, NewCars.com.
1067 job openings which list Flask as a requirement.
80 job openings which list Pyramid as a requirement.
Because it is minimal and doesn't have a lot of overhead, Flask is very performant. Extensions could impact performance negatively.
Claims to have top-notch performance but virtue of being a microframework but
benchmarks prove it to be slower than even Django beyond simple use cases.
Flexibility
Very flexible and doesn't require users to use any particular project or code layout. (A structured approach is still recommended.)
Flexibility
Pyramid is flexible.
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
Being a microframework, the learning curve isn't very steep. However, it doesn't have the same amount of tutorials, courses, and resources available for leaning compared to Django and Flask
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
Through Plugins or Extensions
No built-in ORM framework. Leaves it up to developers to choose a library like
SQLAlchemy
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
Doesn't get in the way of using NoSQL, and they are supported just as well as a relational database. Read more
here.
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
Through Plugins or Extensions
REST Support
Because it is a minimal framework, users can build a REST API themselves easily, or use a 3rd party library like
Cornice.
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 CSRF, but
XSS vulnerabilities have been discovered in older versions.
Templating Library
Flask uses
Jinja2 out of the box.
Templating Library
Doesn't ship with a default templating language and leaves it up to developers to add one.
Jinja2 can be added to Pyramid. Another engine popular amongst Pyramid users is
Chameleon .
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
No built-in support. Prefers 3rd party
Deform library.
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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Comments (2)
Fedora G
Message from Brainix Reddit admin.
Umer Mansoor
Thanks for letting me know. Updated the list.