Compare Django and Bottle
Django is Python web framework that encourages rapid development. It is based model-template-view (MTV) design pattern. It follows a "batteries included" philosophy and ships with many tools that are needed by application developers such as ORM framework, admin panel, directory structure and more.
Bottle is a fast, simple and lightweight WSGI micro web-framework for Python. It is distributed as a single file module and has no dependencies other than the Python Standard Library.
Let's see how Django and Bottle compare on various factors and features and which to choose when.
Type
Python all-inclusive, megaframework for building web application.
Type
Python microframework for building web applications.
Used by 367,000 projects.
Used by
Instagram, Pinterest, Coursera, Udemy.
2074 job openings which list Django as a requirement.
74 job openings which list Bottle as a requirement.
Not as fast as compared to bare-bones Flask or other microframeworks, but for many real-world use cases, the difference is negligible.
Very
fast. Extensions could impact performance adversely.
Flexibility
Django expects things to be done in a certain way unlike microframeworks (e.g. Flask) which have no opinion on how developers structure things. However, It does this without compromising on flexibility. Django has been used to build a variety of things from content management systems to social networks to scientific computing platforms.
Flexibility
Very flexible and extremely simple. Doesn't force anything on developers.
Ease of Learning
Has a learning curve especially for those who are not familiar with other web frameworks. But there are some great online resources, courses tutorials and YouTube videos.
Ease of Learning
Straightforward and easy to learn. It has a good amount of tutorials online.
RDBMS Support
Django ships with a built-in
ORM framework for developers to start using out of the box.
Verdict ORM is one of the best features of Django, loved by developers.
RDBMS Support
Through Plugins or Extensions
No built-in ORM framework. Leaves it up to developers to choose a library like
SQLAlchemy or extensions such as
Macaron.
NoSQL Support
NoSQL databases are not officially supported by Django. There are open source projects like
PynamoDB or
Django MongoDB Engine,
Django non-rel to support NoSQL. Some of these extensions support specific Django versions and don't
interplay well with Django ORM.
Verdict Using NoSQL database with Django is not recommended.
NoSQL Support
No built-in support but 3rd party Python libraries like
PyMongo or
Bottle-Mongo can be used to talk to NoSQL databases.
Admin Dashboard
Django ships with a web-based admin site that has a friendly UI. It allows you to quickly perform CRUD operations against your models from your browser to test things out.
Admin Dashboard
Through Plugins or Extensions
No built-in admin panel.
REST Support
While not built-in, REST development is supported via the popular and active
Django REST Framework project. It provides support for API versioning,
Browsable API for interacting with APIs through web browser, authentication (OAuth1 and OAuth2) and serialization support for both ORM and non-ORM sources.
REST Support
No built-in support but can be implemented easily.
Security
Built-in protection against several common attack vectors like CSRF, XSS, and SQL injection. When vulnerabilities are discovered, the Django team has an excellent
security policy and
fixes are released quickly.
Security
No built-in protection. Bottle is a minimalist framework. Must be handled by developers themselves or by using 3rd party extensions.
Web Forms
Ships with built-in
ModelForms which provides complete support for web forms including input validation, CSRF, XSS, and SQL injection.
Web Forms
No built-in support.
Authentication
Built-in
authentication, authorization, account management and support for sessions.
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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