501 lines
20 KiB
Markdown
501 lines
20 KiB
Markdown
## WIP: FastAPI Best Practices
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Opinionated list of best practices and conventions we have developed after 1.5 years in production.
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### 1. Project Structure. Group files by module domain, not file types.
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I didn't like the project structure presented by @tiangolo,
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where we separate files by their type (e.g. api, crud, models, schemas).
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Structure that I find more scalable and evolvable is inspired by Netflix's [Dispatch](https://github.com/Netflix/dispatch) with some little modifications.
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```
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fastapi-project
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├── alembic/
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├── src
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│ ├── auth
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│ │ ├── router.py
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│ │ ├── schemas.py # pydantic models
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│ │ ├── models.py # db models
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│ │ ├── dependencies.py
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│ │ ├── config.py # local configs
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│ │ ├── constants.py
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│ │ ├── exceptions.py
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│ │ ├── service.py
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│ │ └── utils.py
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│ ├── aws
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│ │ ├── client.py # client model for external service communication
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│ │ ├── schemas.py
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│ │ ├── config.py
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│ │ ├── constants.py
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│ │ ├── exceptions.py
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│ │ └── utils.py
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│ └── posts
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│ │ ├── router.py
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│ │ ├── schemas.py
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│ │ ├── models.py
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│ │ ├── dependencies.py
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│ │ ├── constants.py
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│ │ ├── exceptions.py
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│ │ ├── service.py
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│ │ └── utils.py
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│ ├── config.py # global configs
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│ ├── models.py # global models
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│ ├── exceptions.py # global exceptions
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│ ├── pagination.py # global module e.g. pagination
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│ ├── database.py # db connection related stuff
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│ └── main.py
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├── tests/
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│ ├── auth
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│ ├── aws
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│ └── posts
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├── templates/
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│ └── index.html
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├── requirements
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│ ├── base.txt
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│ ├── dev.txt
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│ └── prod.txt
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├── .env
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├── .gitignore
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├── logging.ini
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└── alembic.ini
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```
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1. Store all the module directories inside src folder
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1. `src/` - highest level of an app, contains common models, configs, and constants, etc.
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2. `src/main.py` - root of the project, which inits the FastAPI app
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2. Each package has its own router, schemas, models, etc.
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1. `router.py` - is a core of each module with all the endpoints
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2. `schemas.py` - for pydantic models
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3. `models.py` - for db models
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4. `service.py` - module specific business logic
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5. `dependencies.py` - router dependencies
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6. `constants.py` - module specific constants and error codes
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7. `config.py` - e.g. env vars
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8. `utils.py` - non-business logic functions, e.g. response normalization, data enrichment, etc.
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9. `exceptions` - module specific exceptions, e.g. `PostNotFound`, `InvalidUserData`
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3. When package requires services or dependencies or constants from other packages - import them with explicit module name
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```python
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from src.auth import constants as auth_constants
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from src.notifictions import service as notification_service
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from src.posts.constants import ErrorCode as PostsErrorCode # in case we have Standard ErrorCode in constants module of each package
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```
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### 2. Excessively use Pydantic
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Pydantic has a rich set of features to validate and transform data.
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In addition to regular features like required, non-required fields and default data,
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it has built-in comprehensive data processing params like regex, enums for limited allowed options, length validation, email validation, etc.
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```python3
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from enum import Enum
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from pydantic import AnyUrl, BaseModel, EmailStr, Field, constr
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class MusicBand(str, Enum):
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AEROSMITH = "AEROSMITH"
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QUEEN = "QUEEN"
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ACDC = "AC/DC"
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class UserBase(BaseModel):
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first_name: str = Field(min_length=1, max_length=128)
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username: constr(regex="^[A-Za-z0-9-_]+$", to_lower=True, strip_whitespace=True)
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email: EmailStr
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age: int = Field(ge=18, default=None) # must be greater or equal to 18
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favorite_band: MusicBand = None # only "AEROSMITH", "QUEEN", "AC/DC" values are allowed to be inputted
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website: AnyUrl = None
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```
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### 3. Use dependencies for data validation vs DB
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Pydantic can only validate the values of client input.
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Use dependencies to validate data against database requirements like email already exists, user not found, etc.
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```python3
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# dependencies.py
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async def valid_post_id(post_id: UUID4) -> Mapping:
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post = await service.get_by_id(post_id)
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if not post:
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raise PostNotFound()
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return post
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# router.py
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@router.get("/posts/{post_id}", response_model=PostResponse)
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async def get_post_by_id(post: Mapping = Depends(valid_post_id)):
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return post
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@router.put("/posts/{post_id}", response_model=PostResponse)
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async def update_post(
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update_data: PostUpdate,
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post: Mapping = Depends(valid_post_id),
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):
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updated_post: Mapping = await service.update(id=post["id"], data=update_data)
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return updated_post
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@router.get("/posts/{post_id}/reviews", response_model=list[ReviewsResponse])
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async def get_post_reviews(post: Mapping = Depends(valid_post_id)):
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post_reviews: list[Mapping] = await reviews_service.get_by_post_id(post["id"])
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return post_reviews
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```
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If we didn't put data validation to dependency, we would have to add post_id validation
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for every endpoint and write the same tests for each of them.
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### 5. Chain dependencies
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Dependencies can use other dependencies and avoid code repetition for similar logic.
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```python3
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# dependencies.py
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from fastapi.security import OAuth2PasswordBearer
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from jose import JWTError, jwt
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async def valid_post_id(post_id: UUID4) -> Mapping:
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post = await service.get_by_id(post_id)
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if not post:
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raise PostNotFound()
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return post
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async def parse_jwt_data(
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token: str = Depends(OAuth2PasswordBearer(tokenUrl="/auth/token"))
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) -> dict:
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try:
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payload = jwt.decode(token, "JWT_SECRET", algorithms=["HS256"])
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except JWTError:
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raise InvalidCredentials()
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return {"user_id": payload["id"]}
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async def valid_owned_post(
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post: Mapping = Depends(valid_post_id),
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token_data: dict = Depends(parse_jwt_data),
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) -> Mapping:
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if post["creator_id"] != token_data["user_id"]:
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raise UserNotOwner()
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return post
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# router.py
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@router.get("/users/{user_id}/posts/{post_id}", response_model=PostResponse)
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async def get_user_post(post: Mapping = Depends(valid_owned_post)):
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"""Get post that belong the user."""
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return post
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```
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### 6. Decouple & Reuse dependencies. Dependency calls are cached.
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Dependencies can be reused multiple times, and they won't be recalculated - FastAPI caches their result by default,
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e.g. if we have a dependency which calls service `get_post_by_id`, we won't be visiting DB each time we call this dependency - only the first function call.
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Knowing this, we can easily decouple dependencies onto multiple smaller functions that operate on a smaller scope and are easier to reuse in other routes.
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For example, in the code below we are using `parse_jwt_data` three times:
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1. `valid_owned_post`
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2. `valid_active_creator`
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3. `get_user_post`,
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but `parse_jwt_data` is called only once, in the very first call.
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```python3
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# dependencies.py
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from fastapi import BackgroundTasks
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from fastapi.security import OAuth2PasswordBearer
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from jose import JWTError, jwt
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async def valid_post_id(post_id: UUID4) -> Mapping:
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post = await service.get_by_id(post_id)
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if not post:
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raise PostNotFound()
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return post
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async def parse_jwt_data(
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token: str = Depends(OAuth2PasswordBearer(tokenUrl="/auth/token"))
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) -> dict:
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try:
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payload = jwt.decode(token, "JWT_SECRET", algorithms=["HS256"])
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except JWTError:
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raise InvalidCredentials()
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return {"user_id": payload["id"]}
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async def valid_owned_post(
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post: Mapping = Depends(valid_post_id),
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token_data: dict = Depends(parse_jwt_data),
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) -> Mapping:
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if post["creator_id"] != token_data["user_id"]:
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raise UserNotOwner()
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return post
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async def valid_active_creator(
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token_data: dict = Depends(parse_jwt_data),
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):
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user = await users_service.get_by_id(token_data["user_id"])
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if not user["is_active"]:
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raise UserIsBanned()
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return user
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# router.py
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@router.get("/users/{user_id}/posts/{post_id}", response_model=PostResponse)
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async def get_user_post(
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worker: BackgroundTasks,
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post: Mapping = Depends(valid_owned_post),
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user: Mapping = Depends(valid_active_creator),
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):
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"""Get post that belong the active user."""
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worker.add_task(notifications_service.send_email, user["id"])
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return post
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```
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### 7. Follow the REST
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Developing RESTful API makes it easier to reuse dependencies in routes like these:
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1. `GET /courses/:course_id`
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2. `GET /courses/:course_id/chapters/:chapter_id/lessons`
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3. `GET /chapters/:chapter_id`
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The only caveat is to use the same variable names in the path:
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- If you have two endpoints `GET /profiles/:profile_id` and `GET /creators/:creator_id`
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that both validate whether the given profile_id exists, but `GET /creators/:creator_id`
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also checks if the profile is creator, then it's better to rename `creator_id` path variable to `profile_id` and chain those two dependencies.
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```python3
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# src.profiles.dependencies
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async def valid_profile_id(profile_id: UUID4) -> Mapping:
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profile = await service.get_by_id(post_id)
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if not profile:
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raise ProfileNotFound()
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return profile
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# src.creators.dependencies
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async def valid_creator_id(profile: Mapping = Depends(valid_profile_id)) -> Mapping:
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if not profile["is_creator"]:
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raise ProfileNotCreator()
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return profile
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# src.profiles.router.py
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@router.get("/profiles/{profile_id}", response_model=ProfileResponse)
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async def get_user_profile_by_id(profile: Mapping = Depends(valid_profile_id)):
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"""Get profile by id."""
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return profile
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# src.creators.router.py
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@router.get("/profiles/{profile_id}", response_model=ProfileResponse)
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async def get_user_profile_by_id(
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creator_profile: Mapping = Depends(valid_creator_id)
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):
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"""Get profile by id."""
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return creator_profile
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```
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Use /me endpoints for users own resources (e.g. `GET /profiles/me`, `GET /users/me/posts`)
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1. No need to validate that user id exists - it's already checked via auth method
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2. No need to check whether the user id belongs to the requester
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### 8. Don't make your routes async, if you have only blocking I/O operations
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Under the hood, FastAPI can [effectively handle](https://fastapi.tiangolo.com/async/#path-operation-functions) both async and sync I/O operations.
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- FastAPI calls sync routes in the [threadpool](https://en.wikipedia.org/wiki/Thread_pool)
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and blocking I/O operations won't stop [event loop](https://docs.python.org/3/library/asyncio-eventloop.html)
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from executing the tasks.
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- Otherwise, if the route is defined as `async` then it's called regularly via `await`
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and FastAPI trusts you to do only non-blocking I/O operations.
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The caveat is if you fail that trust and execute blocking operations within async routes,
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event loop will not be able to run the next tasks until that blocking operation is done.
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```python
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import asyncio
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import time
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@router.get("/terrible-ping")
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async def terrible_catastrophic_ping():
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time.sleep(10) # I/O blocking operation for 10 seconds
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pong = service.get_pong() # I/O blocking operation to get pong from DB
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return {"pong": pong}
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@router.get("/good-ping")
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def good_ping():
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time.sleep(10) # I/O blocking operation for 10 seconds, but in another thread
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pong = service.get_pong() # I/O blocking operation to get pong from DB, but in another thread
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return {"pong": pong}
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@router.get("/perfect-ping")
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async def perfect_ping():
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await asyncio.sleep(10) # non I/O blocking operation
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pong = await service.async_get_pong() # non I/O blocking db call
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return {"pong": pong}
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```
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**What happens when we call:**
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1. `GET /terrible-ping`
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1. FastAPI server receives a request and starts handling it
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2. Server's event loop and all the tasks in the queue will be waiting until `time.sleep()` is finished
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1. Server thinks `time.sleep()` is not an I/O task, so it waits until it is finished
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2. Server won't accept any new requests while waiting
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3. Then, event loop and all the tasks in the queue will be waiting until `service.get_pong` is finished
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1. Server thinks `service.get_pong()` is not an I/O task, so it waits until it is finished
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2. Server won't accept any new requests while waiting
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4. Server returns the response.
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1. After a response, server starts accepting new requests
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2. `GET /good-ping`
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1. FastAPI server receives a request and starts handling it
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2. FastAPI sends the whole route `good_ping` to the threadpool, where a worker thread will run the function
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3. While `good_ping` is being executed, event loop selects next tasks from the queue and works on them (e.g. accept new request, call db)
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- Independently of main thread (i.e. our FastAPI app),
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worker thread will be waiting for `time.sleep` to finish and then for `service.get_pong` to finish
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4. When `good_ping` finishes its work, server returns a response to the client
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3. `GET /perfect-ping`
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1. FastAPI server receives a request and starts handling it
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2. FastAPI awaits `asyncio.sleep(10)`
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3. Event loop selects next tasks from the queue and works on them (e.g. accept new request, call db)
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4. When `asyncio.sleep(10)` is done, servers goes to the next lines and awaits `service.async_get_pong`
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5. Event loop selects next tasks from the queue and works on them (e.g. accept new request, call db)
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6. When `service.async_get_pong` is done, server returns a response to the client
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The caveat is that operations that are non-blocking awaitables or sent to thread pool must be I/O intensive tasks (e.g. open file, db call, external API call).
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- Awaiting CPU intensive tasks (e.g. heavy calculations, data processing, video transcoding) is worthless, since CPU has to work to finish the tasks,
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while I/O operations are external and server does nothing while waiting for that operations to finish, thus it can go to the next tasks.
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- Running CPU intensive tasks in other threads also isn't effective, because of [GIL](https://realpython.com/python-gil/).
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In short, GIL allows only one thread to work at a time, which makes it useless for CPU tasks.
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- If you want to optimize CPU intensive tasks you should send them to workers in another process.
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**Related StackOverflow questions of confused users**
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1. https://stackoverflow.com/questions/62976648/architecture-flask-vs-fastapi/70309597#70309597
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- Here you can also check [my answer](https://stackoverflow.com/a/70309597/6927498)
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2. https://stackoverflow.com/questions/65342833/fastapi-uploadfile-is-slow-compared-to-flask
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3. https://stackoverflow.com/questions/71516140/fastapi-runs-api-calls-in-serial-instead-of-parallel-fashion
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### 9. Custom base model from day 0.
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Having a controllable global pydantic base model allows us to customize all the models within the app.
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For example, we could have a standard datetime format or add a super method for all subclasses of the base model.
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```python
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from datetime import datetime
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from zoneinfo import ZoneInfo
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import orjson
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from fastapi.encoders import jsonable_encoder
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from pydantic import BaseModel, root_validator
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def orjson_dumps(v, *, default):
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# orjson.dumps returns bytes, to match standard json.dumps we need to decode
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return orjson.dumps(v, default=default).decode()
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def convert_datetime_to_gmt(dt: datetime) -> str:
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if not dt.tzinfo:
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dt = dt.replace(tzinfo=ZoneInfo("UTC"))
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return dt.strftime("%Y-%m-%dT%H:%M:%S%z")
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class ORJSONModel(BaseModel):
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class Config:
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json_loads = orjson.loads
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json_dumps = orjson_dumps
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json_encoders = {datetime: convert_datetime_to_gmt} # method for customer JSON encoding of datetime fields
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@root_validator()
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def set_null_microseconds(cls, data: dict) -> dict:
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"""Drops microseconds in all the datetime field values."""
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datetime_fields = {
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k: v.replace(microsecond=0)
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for k, v in data.items()
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if isinstance(k, datetime)
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}
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return {**data, **datetime_fields}
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def serializable_dict(self, **kwargs):
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"""Return a dict which contains only serializable fields."""
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default_dict = super().dict(**kwargs)
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return jsonable_encoder(default_dict)
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```
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In the example above we have decided to make a global base model which:
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- uses [orjson](https://github.com/ijl/orjson) to serialize data
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- drops microseconds to 0 in all date formats
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- serializes all datetime fields to standard format with explicit timezone
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### 10. Docs
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1. Unless your API is public, hide docs by default. Show it explicitly on the selected envs only.
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```python
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from fastapi import FastAPI
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from starlette.config import Config
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config = Config(".env") # parse .env file for env variables
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ENVIRONMENT = config("ENVIRONMENT") # get current env name
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SHOW_DOCS_ENVIRONMENT = ("local", "staging") # explicit list of allowed envs
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app_configs = {"title": "My Cool API"}
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if ENVIRONMENT not in SHOW_DOCS_ENVIRONMENT:
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app_configs["openapi_url"] = None # set url for docs as null
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app = FastAPI(**app_configs)
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```
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2. Help FastAPI to generate an easy-to-understand docs
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1. Set `response_model`, `status_code`, `description`, etc.
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2. If models and statuses vary, use `responses` route attribute to add docs for different responses
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```python
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from fastapi import APIRouter, status
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router = APIRouter()
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@router.post(
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"/endpoints",
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response_model=DefaultResponseModel, # default response pydantic model
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status_code=status.HTTP_201_CREATED, # default status code
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description="Description of the well documented endpoint",
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tags=["Endpoint Category"],
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summary="Summary of the Endpoint",
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responses={
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status.HTTP_200_OK: {
|
|
"model": OkResponse, # custom pydantic model for 200 response
|
|
"description": "Ok Response",
|
|
},
|
|
status.HTTP_201_CREATED: {
|
|
"model": CreatedResponse, # custom pydantic model for 201 response
|
|
"description": "Creates something from user request ",
|
|
},
|
|
status.HTTP_202_ACCEPTED: {
|
|
"model": AcceptedResponse, # custom pydantic model for 202 response
|
|
"description": "Accepts request and handles it later",
|
|
},
|
|
},
|
|
)
|
|
async def documented_route():
|
|
pass
|
|
```
|
|
Will generate docs like this:
|
|
|
|
|
|
### 11. Use Starlette's Config object, instead of 3rd party ones - it's decent enough
|
|
### 12. Set DB keys naming convention immediately, from day 0
|
|
### 13. Set DB table naming convention immediately, from day 0
|
|
### 14. Set uuids within the app
|
|
it's easier to test
|
|
### 15. Set tests client async from day 0
|
|
1. Unless you aren't planning to add integrational tests with db
|
|
2. If you do, then do it. Problems with event loop will appear once you want to prepare objects
|
|
### 16. Set postgres identity from day 0
|
|
### 17. take use of background workers - they are stable enough
|
|
good for both async and sync routes
|
|
### 18. take use of response model, response status, responses
|
|
### 19. typing is important - use it everywhere
|
|
### 20. save files in chunk
|
|
### 21. use smart union or add explicit invalidation
|
|
### 22. do a lot of logic in db, use pydantic to parse it (show the way we evolved creator field)
|
|
### 23. validate file formats
|
|
### 24. validate url source (if users are able to send files)
|
|
### 25. root_validator if multiple columns
|
|
### 26. pre if data need to be pre-handled before validation
|
|
### 27. you can just raise a ValueError in pydantic schemas, if that's it faces user request.
|
|
it will return a nice response
|
|
### 28. don't forget that fastapi converts response Model to Dict then to Model then to JSON
|
|
### 29. if no async lib, and poor documentation, then use starlette's run_in_threadpool or asgiref
|
|
### 30. use linters (black, isort, autoflake)
|
|
### 31. set logs from day 0
|