Compare Tornado and FastAPI
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.
Fast API is a high-performance web framework for building web applications with Python 3.6+ based on standard Python type hints. It is designed to be high performance and easy to learn.
Let's see how Tornado and FastAPI compare on various factors and features and which to choose when.
Type
Python web framework and asynchronous networking library, which does non-blocking I/O.
Type
Minimalistic framework based on starlette and pydantic for building fast web applications using async IO.
Used by 145,000 projects.
New addition to the Python web frameworks family but its popularity is on the rise.
Used by
FriendFeed, Turntable.fm, Fantamaster.it
Used by
Uber (internal use,) Explosion AI, Microsoft (internal use)
300 job openings which list Tornado as a requirement.
100 job openings which list Fast API as a requirement.
Flexibility
Tornado is simple and flexible.
Flexibility
Fast API is flexible to code and doesn't restrict users to a particular project or code layout.
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.
Ease of Learning
Easy to learn especially for people who are new to web development. However, it doesn't have a large number of online resources, courses and tutorials.
RDBMS Support
Through Plugins or Extensions
RDBMS Support
Through Plugins or Extensions
FastAPI doesn't come with built in ORM, however is compatible with
SQLAlchemy, Pydantic ORM mode.
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.
NoSQL Support
Fast API supports many NoSQL databases like MongoDb, ElasticSearch, Cassandra, CouchDB and ArangoDB. Read more
here.
Admin Dashboard
No built-in admin panel. No well-known 3rd party tools either.
Admin Dashboard
Yes, uses Swagger as an API documentation web user interface.
REST Support
No built-in support for REST API, but users can implement REST APIs manually.
REST Support
Yes, allows developers to build REST APIs quickly.
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.
Security
FastAPI provides several tools for many security schemes in the fastapi.security module. Not enough data.
Templating Library
Tornado uses
custom templating library out of the box.
Templating Library
FastAPI supports Jinja2 for templating and also supports aiofiles for serving static files
Web Forms
No built-in support.
Web Forms
Ships with it's own
Forms, with basic features.
Authentication
Provides user authentication and also supports 3rd party authentication and authorization systems like Google, Twitter, Facebook, etc.
Authentication
Fast API supports OAuth2, JWT and simple HTTP authentiation.
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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