mirror of https://github.com/tiangolo/fastapi.git
120 lines
5.3 KiB
Markdown
120 lines
5.3 KiB
Markdown
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## FastAPI features
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**FastAPI** gives you the following:
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* Automatic API documentation with the open standard: <a href="https://github.com/OAI/OpenAPI-Specification" target="_blank"><strong>OpenAPI</strong></a>.
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* Automatic data model documentation with <a href="http://json-schema.org/" target="_blank"><strong>JSON Schema</strong></a> (as OpenAPI itself is based on JSON Schema).
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* Interactive API documentation and exploration web user interface with <a href="https://github.com/swagger-api/swagger-ui" target="_blank"><strong>Swagger UI</strong></a>.
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* Alternative API documentation with <a href="https://github.com/Rebilly/ReDoc" target="_blank"><strong>ReDoc</strong></a>.
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* All based on standard **Python 3.6 type** declarations (thanks to Pydantic). No new syntax to learn:
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```Python
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from typing import List, Dict
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from datetime import date
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from pydantic import BaseModel
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# Declare a variable as an str
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# and get editor support inside the function
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def main(user_id: str):
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return user_id
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# A Pydantic model
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class User(BaseModel):
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id: int
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name: str
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joined: date
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```
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That can then be used like:
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```Python
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my_user: User = User(id=3, name="John Doe", joined="2018-07-19")
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second_user_data = {
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"id": 4,
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"name": "Mary",
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"joined": "2018-11-30",
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}
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# **second_user_data means:
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# pass the keys and values of the dict
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# directly as key-value arguments
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# equivalent to:
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# id=4, name="Mary", joined="2018-11-30"
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my_second_user: User = User(**second_user_data)
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```
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* Sensible **defaults** for everything, with optional configurations everywhere.
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* Validation for many **data types**, including:
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* JSON objects (`dict`).
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* JSON array (`list`) defining item types.
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* String (`str`) fields, defining min and max lengths.
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* Numbers (`int`, `float`) with min and max values, etc.
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* Security and authentication included: all the security schemes defined in OpenAPI, including:
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* HTTP Basic.
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* **OAuth2** (also with **JWT tokens**)
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* API keys, etc.
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* Plus the security features from Starlette (including session cookies).
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* All built as reusable tools and components that are easy to integrate with your systems, data stores, databases, etc.
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* Extremely easy, but extremely powerful <abbr title='also known as "components", "resources", "services", "providers"'><strong>Dependency Injection</strong></abbr> system:
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* Even dependencies can have dependencies, creating a hierarchy or **"graph" of dependencies**.
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* All **automatically handled** by the framework.
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* All the dependencies can **augment the endpoint** parameters and constraints.
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* **Automatic validation** even for parameters from dependencies.
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* Support for complex user authentication systems, **database connections**, etc.
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* **No compromise** with databases, frontends, etc. But easy integration with all.
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* **Unlimited "plug-ins"**:
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* Or in other way, no need for them, import and use the code you need.
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* Any integration is designed to be so simple to use (with dependencies) that you can create a "plug-in" for your application in 2 lines of code using the same structure and syntax as for your endpoints.
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* Fully compatible with (and based on) **Starlette**.
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* Any additional Starlette code you have, will also work.
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* Fully compatible with (and based on) **Pydantic**.
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* Any additional Pydantic code you have will also work.
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* Including external libraries also based on Pydantic, as <abbr title="Object-Relational Mapper">ORM</abbr>s, <abbr title="Object-Document Mapper">ODM</abbr>s for databases.
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* 100% test coverage (* not yet, in a couple days).
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* 100% type annotated code base.
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## Starlette features
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Plus **Starlette**'s features (FastAPI is just Starlette on steroids):
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* Seriously impressive performance. It is <a href="https://github.com/encode/starlette#performance" target="_blank">one of the fastest Python frameworks available, on par with **NodeJS** and **Go**</a>.
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* **WebSocket** support.
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* **GraphQL** support.
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* In-process background tasks.
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* Startup and shutdown events.
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* Test client built on `requests`.
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* **CORS**, GZip, Static Files, Streaming responses.
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* **Session and Cookie** support.
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* 100% test coverage.
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* 100% type annotated codebase.
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## Pydantic features
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Plus **Pydantic**'s features:
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* **No brainfuck**:
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* No new schema definition micro-language to learn.
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* If you know Python types you know how to use Pydantic.
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* Plays nicely with your **IDE/linter/brain**:
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* Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data.
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* **Fast**:
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* in <a href="https://pydantic-docs.helpmanual.io/#benchmarks-tag" target="_blank">benchmarks</a> Pydantic is faster than all other tested libraries.
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* Validate **complex structures**:
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* Use of hierarchical Pydantic models, Python `typing`’s `List` and `Dict`, etc.
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* And validators allow complex data schemas to be clearly and easily defined, checked and documented as JSON Schema.
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* You can have deeply **nested JSON** objects and have them all validated and annotated.
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* **Extendible**:
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* Pydantic allows custom data types to be defined or you can extend validation with methods on a model decorated with the validator decorator.
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