Compare Flask and Tornado
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.
Tornado is a Python web framework and asynchronous networking library developed at FriendFeed. By using non-blocking network I/O, Tornado can scale to tens of thousands of open connections, making it ideal for long polling, WebSockets, and other applications that require a long-lived connection to each user. It is great for use cases that are I/O intensive (e.g., proxies) but not ideal for compute-intensive use cases.
Let's see how Flask and Tornado compare on various factors and features and which to choose when.
Type
Python microframework for building web applications.
Type
Python web framework and asynchronous networking library, which does non-blocking I/O.
Used by 397,000 projects.
Used by 145,000 projects.
Used by
Netflix, Zillow, Lyft.
Used by
FriendFeed, Turntable.fm, Fantamaster.it
1067 job openings which list Flask as a requirement.
300 job openings which list Tornado as a requirement.
Because it is minimal and doesn't have a lot of overhead, Flask is very performant. Extensions could impact performance negatively.
Flexibility
Very flexible and doesn't require users to use any particular project or code layout. (A structured approach is still recommended.)
Flexibility
Tornado is simple and 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
Tornado is not difficult to learn if the user is familiar with asynchronous and non-blocking I/O. The online resources, courses, and tutorials are not as plentiful compared to Flask or Django.
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
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 for NoSQL databases, but 3rd party libraries support asynchronous, non-blocking access.
Motor is one such library from the makers of MongoDB. However, a lot of client libraries like
DynamoDB appear to have been deprecated or not actively maintained.
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
No built-in admin panel. No well-known 3rd party tools either.
REST Support
No built-in support for REST API, but users can implement REST APIs manually.
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
security mechanisms such as secure cookies, XSRF, DNS Rebinding and etc. Has been used in production for many years so security is generally decent.
Templating Library
Flask uses
Jinja2 out of the box.
Templating Library
Tornado uses
custom templating library 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
No built-in support.
Authentication
Provides user authentication and also supports 3rd party authentication and authorization systems like Google, Twitter, Facebook, etc.
Testing
Built-in support using Python's
unittest framework.
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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