2020/06/fastapi-vs-flask/ about blog-comments HOT 17 CLOSED

@deb17 There are extensions such as fastapi-sqlalchemy. But it's still new and not as feature rich as flask-sqlachemy.

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Very clear and concise intro to FastAPI for Flask users! FastAPI has some great documentation but I'd love if you included an example of the FastAPI documentation generation. Then you could show how to use the example key of schema_extra in class Config as well as tags to customize the generated documentation. It would fit very nicely with how you have introduced FastAPI's implementation of Pydantic schema. I really enjoyed using these features recently if you want some examples.

That would make this an even more perfect intro to FastAPI for friends and colleagues who use Flask!

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@cjakuc That sounds like a good idea. I'll add sections on those to the post.

If you've some interesting examples to share from your experience, I would love to hear them.

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I like the updates to the post! I had no idea about /redoc. I've only used FastAPI once now but really loved it and there's obviously still so much for me to learn, I'll share my example below.

Here is the deployed API with customized schema_extra and tags.

Here is a .py file in the GitHub repo that shows the definition of a customized schema_extra.

The list of tags/descriptions are created in main.py, passed into the app when it is instantiated (also in main.py), and called when creating the corresponding endpoints within the routers folder.

My work could definitely use some refactoring to better utilize the Pydantic BaseModel like you did in your nested objects example. That would make the schema_extra tweaks a little redundant but you could still do this to show an example when defining the UserGroup class:

class UserGroup(BaseModel):
    users: List[User]
    group: str

    class Config:
        schema_extra = {
            "example": {
                "users": [
                    {
                    "name": "xyz",
                    "age": 25
                    },
                    {
                    "name": "abc",
                    "age": 30
                    }
                ],
                "group": "Group A"
            }
        }

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Check out instant_api, for a FastAPI-like experience but within Flask.

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@alexmojaki Looks interesting. Thanks for sharing your project.

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I like fastAPI. I use it in one of my projects
FastAPI is good if you need just backend like API or something
I use it in my chatbot project that has no frontend part

but in another project I still prefer to use Flask
because if I make a web app I have to cope with logins or jwt, webforms, databases (orm) and some sort of admin panels

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@IDQDD Agree with you. It's important to choose the right tool for the right job. If a project has frontend templating needs, Flask/Django is a more suitable choice.

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Hi! It's really nice introduction to FastApi. In general FastApi looks like a good alternative for Flask. I wonder I you came across any limitations, shortcomings when working on production with FastApi?

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@kstrz For my usecase there were no limitations since my project involves only REST APIs and I was on "Python 3.7".

If your project involves a lot of jinja templating and server-side rendering of HTML templates, then FastAPI is not a good choice.

Also, FastAPI only supports python 3.6+, so if your project is written in older version of Python or has some legacy dependency, you might have to rewrite few portions. Making sure all dependencies work could be a trouble.

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Another similar library: https://github.com/zero-shubham/flask-ease

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Thank you for your overview about fastAPI

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Nice introduction to fastapi. I am thinking of using it as the backend for my vuejs projects. But what about database access? Does it have extensions like flask-sqlalchemy and flask-migrate? Or do we have to fall back on sqlalchemy and alembic?

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Great article, Amit!
Did you have any experience with streaming responses? In Flask, this is achieved by having your route method returning a generator that will be yielding response chunks. This is useful when the response data is being generated on the fly and large in size (e.g., my Flask REST API is building a video or audio clip and sending to client who asked to download it).
Thanks!

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@quantotto Look into: https://fastapi.tiangolo.com/advanced/custom-response/#streamingresponse

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Great post and very helpful to have the side by side examplse.
My understanding is that you need to defined all the FastApi function as "async" to really get performance benefits compared to Flask. If the functions are run on the main thread, then there is not really any peformance benefits.

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thx! Very usefull

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