Project sample built with these best-practices in mind.
### 1. Project Structure. Consistent & predictable There are many ways to structure the project, but the best structure is a structure that is consistent, straightforward, and has no surprises. - If looking at the project structure doesn't give you an idea of what the project is about, then the structure might be unclear. - If you have to open packages to understand what modules are located in them, then your structure is unclear. - If the frequency and location of the files feels random, then your project structure is bad. - If looking at the module's location and its name doesn't give you an idea of what's inside it, then your structure is very bad. Although the project structure, where we separate files by their type (e.g. api, crud, models, schemas) presented by [@tiangolo](https://github.com/tiangolo) is good for microservices or projects with fewer scopes, we couldn't fit it into our monolith with a lot of domains and modules. Structure that I found more scalable and evolvable is inspired by Netflix's [Dispatch](https://github.com/Netflix/dispatch) with some little modifications. ``` fastapi-project ├── alembic/ ├── src │ ├── auth │ │ ├── router.py │ │ ├── schemas.py # pydantic models │ │ ├── models.py # db models │ │ ├── dependencies.py │ │ ├── config.py # local configs │ │ ├── constants.py │ │ ├── exceptions.py │ │ ├── service.py │ │ └── utils.py │ ├── aws │ │ ├── client.py # client model for external service communication │ │ ├── schemas.py │ │ ├── config.py │ │ ├── constants.py │ │ ├── exceptions.py │ │ └── utils.py │ └── posts │ │ ├── router.py │ │ ├── schemas.py │ │ ├── models.py │ │ ├── dependencies.py │ │ ├── constants.py │ │ ├── exceptions.py │ │ ├── service.py │ │ └── utils.py │ ├── config.py # global configs │ ├── models.py # global models │ ├── exceptions.py # global exceptions │ ├── pagination.py # global module e.g. pagination │ ├── database.py # db connection related stuff │ └── main.py ├── tests/ │ ├── auth │ ├── aws │ └── posts ├── templates/ │ └── index.html ├── requirements │ ├── base.txt │ ├── dev.txt │ └── prod.txt ├── .env ├── .gitignore ├── logging.ini └── alembic.ini ``` 1. Store all domain directories inside `src` folder 1. `src/` - highest level of an app, contains common models, configs, and constants, etc. 2. `src/main.py` - root of the project, which inits the FastAPI app 2. Each package has its own router, schemas, models, etc. 1. `router.py` - is a core of each module with all the endpoints 2. `schemas.py` - for pydantic models 3. `models.py` - for db models 4. `service.py` - module specific business logic 5. `dependencies.py` - router dependencies 6. `constants.py` - module specific constants and error codes 7. `config.py` - e.g. env vars 8. `utils.py` - non-business logic functions, e.g. response normalization, data enrichment, etc. 9. `exceptions.py` - module specific exceptions, e.g. `PostNotFound`, `InvalidUserData` 3. When package requires services or dependencies or constants from other packages - import them with an explicit module name ```python from src.auth import constants as auth_constants from src.notifications import service as notification_service from src.posts.constants import ErrorCode as PostsErrorCode # in case we have Standard ErrorCode in constants module of each package ``` ### 2. Excessively use Pydantic for data validation Pydantic has a rich set of features to validate and transform data. In addition to regular features like required & non-required fields with default values, Pydantic has built-in comprehensive data processing tools like regex, enums for limited allowed options, length validation, email validation, etc. ```python3 from enum import Enum from pydantic import AnyUrl, BaseModel, EmailStr, Field, constr class MusicBand(str, Enum): AEROSMITH = "AEROSMITH" QUEEN = "QUEEN" ACDC = "AC/DC" class UserBase(BaseModel): first_name: str = Field(min_length=1, max_length=128) username: constr(regex="^[A-Za-z0-9-_]+$", to_lower=True, strip_whitespace=True) email: EmailStr age: int = Field(ge=18, default=None) # must be greater or equal to 18 favorite_band: MusicBand = None # only "AEROSMITH", "QUEEN", "AC/DC" values are allowed to be inputted website: AnyUrl = None ``` ### 3. Use dependencies for data validation vs DB Pydantic can only validate the values from client input. Use dependencies to validate data against database constraints like email already exists, user not found, etc. ```python3 # dependencies.py async def valid_post_id(post_id: UUID4) -> Mapping: post = await service.get_by_id(post_id) if not post: raise PostNotFound() return post # router.py @router.get("/posts/{post_id}", response_model=PostResponse) async def get_post_by_id(post: Mapping = Depends(valid_post_id)): return post @router.put("/posts/{post_id}", response_model=PostResponse) async def update_post( update_data: PostUpdate, post: Mapping = Depends(valid_post_id), ): updated_post: Mapping = await service.update(id=post["id"], data=update_data) return updated_post @router.get("/posts/{post_id}/reviews", response_model=list[ReviewsResponse]) async def get_post_reviews(post: Mapping = Depends(valid_post_id)): post_reviews: list[Mapping] = await reviews_service.get_by_post_id(post["id"]) return post_reviews ``` If we didn't put data validation to dependency, we would have to add post_id validation for every endpoint and write the same tests for each of them. ### 4. Chain dependencies Dependencies can use other dependencies and avoid code repetition for similar logic. ```python3 # dependencies.py from fastapi.security import OAuth2PasswordBearer from jose import JWTError, jwt async def valid_post_id(post_id: UUID4) -> Mapping: post = await service.get_by_id(post_id) if not post: raise PostNotFound() return post async def parse_jwt_data( token: str = Depends(OAuth2PasswordBearer(tokenUrl="/auth/token")) ) -> dict: try: payload = jwt.decode(token, "JWT_SECRET", algorithms=["HS256"]) except JWTError: raise InvalidCredentials() return {"user_id": payload["id"]} async def valid_owned_post( post: Mapping = Depends(valid_post_id), token_data: dict = Depends(parse_jwt_data), ) -> Mapping: if post["creator_id"] != token_data["user_id"]: raise UserNotOwner() return post # router.py @router.get("/users/{user_id}/posts/{post_id}", response_model=PostResponse) async def get_user_post(post: Mapping = Depends(valid_owned_post)): return post ``` ### 5. Decouple & Reuse dependencies. Dependency calls are cached. Dependencies can be reused multiple times, and they won't be recalculated - FastAPI caches dependency's result within a request's scope by default, i.e. if we have a dependency that calls service `get_post_by_id`, we won't be visiting DB each time we call this dependency - only the first function call. Knowing this, we can easily decouple dependencies onto multiple smaller functions that operate on a smaller domain and are easier to reuse in other routes. For example, in the code below we are using `parse_jwt_data` three times: 1. `valid_owned_post` 2. `valid_active_creator` 3. `get_user_post`, but `parse_jwt_data` is called only once, in the very first call. ```python3 # dependencies.py from fastapi import BackgroundTasks from fastapi.security import OAuth2PasswordBearer from jose import JWTError, jwt async def valid_post_id(post_id: UUID4) -> Mapping: post = await service.get_by_id(post_id) if not post: raise PostNotFound() return post async def parse_jwt_data( token: str = Depends(OAuth2PasswordBearer(tokenUrl="/auth/token")) ) -> dict: try: payload = jwt.decode(token, "JWT_SECRET", algorithms=["HS256"]) except JWTError: raise InvalidCredentials() return {"user_id": payload["id"]} async def valid_owned_post( post: Mapping = Depends(valid_post_id), token_data: dict = Depends(parse_jwt_data), ) -> Mapping: if post["creator_id"] != token_data["user_id"]: raise UserNotOwner() return post async def valid_active_creator( token_data: dict = Depends(parse_jwt_data), ): user = await users_service.get_by_id(token_data["user_id"]) if not user["is_active"]: raise UserIsBanned() if not user["is_creator"]: raise UserNotCreator() return user # router.py @router.get("/users/{user_id}/posts/{post_id}", response_model=PostResponse) async def get_user_post( worker: BackgroundTasks, post: Mapping = Depends(valid_owned_post), user: Mapping = Depends(valid_active_creator), ): """Get post that belong the active user.""" worker.add_task(notifications_service.send_email, user["id"]) return post ``` ### 6. Follow the REST Developing RESTful API makes it easier to reuse dependencies in routes like these: 1. `GET /courses/:course_id` 2. `GET /courses/:course_id/chapters/:chapter_id/lessons` 3. `GET /chapters/:chapter_id` The only caveat is to use the same variable names in the path: - If you have two endpoints `GET /profiles/:profile_id` and `GET /creators/:creator_id` that both validate whether the given `profile_id` exists, but `GET /creators/:creator_id` 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. ```python3 # src.profiles.dependencies async def valid_profile_id(profile_id: UUID4) -> Mapping: profile = await service.get_by_id(profile_id) if not profile: raise ProfileNotFound() return profile # src.creators.dependencies async def valid_creator_id(profile: Mapping = Depends(valid_profile_id)) -> Mapping: if not profile["is_creator"]: raise ProfileNotCreator() return profile # src.profiles.router.py @router.get("/profiles/{profile_id}", response_model=ProfileResponse) async def get_user_profile_by_id(profile: Mapping = Depends(valid_profile_id)): """Get profile by id.""" return profile # src.creators.router.py @router.get("/creators/{profile_id}", response_model=ProfileResponse) async def get_user_profile_by_id( creator_profile: Mapping = Depends(valid_creator_id) ): """Get creator's profile by id.""" return creator_profile ``` Use /me endpoints for users resources (e.g. `GET /profiles/me`, `GET /users/me/posts`) 1. No need to validate that user id exists - it's already checked via auth method 2. No need to check whether the user id belongs to the requester ### 7. Don't make your routes async, if you have only blocking I/O operations Under the hood, FastAPI can [effectively handle](https://fastapi.tiangolo.com/async/#path-operation-functions) both async and sync I/O operations. - FastAPI runs `sync` routes in the [threadpool](https://en.wikipedia.org/wiki/Thread_pool) and blocking I/O operations won't stop the [event loop](https://docs.python.org/3/library/asyncio-eventloop.html) from executing the tasks. - Otherwise, if the route is defined `async` then it's called regularly via `await` and FastAPI trusts you to do only non-blocking I/O operations. The caveat is if you fail that trust and execute blocking operations within async routes, the event loop will not be able to run the next tasks until that blocking operation is done. ```python import asyncio import time @router.get("/terrible-ping") async def terrible_catastrophic_ping(): time.sleep(10) # I/O blocking operation for 10 seconds pong = service.get_pong() # I/O blocking operation to get pong from DB return {"pong": pong} @router.get("/good-ping") def good_ping(): time.sleep(10) # I/O blocking operation for 10 seconds, but in another thread pong = service.get_pong() # I/O blocking operation to get pong from DB, but in another thread return {"pong": pong} @router.get("/perfect-ping") async def perfect_ping(): await asyncio.sleep(10) # non-blocking I/O operation pong = await service.async_get_pong() # non-blocking I/O db call return {"pong": pong} ``` **What happens when we call:** 1. `GET /terrible-ping` 1. FastAPI server receives a request and starts handling it 2. Server's event loop and all the tasks in the queue will be waiting until `time.sleep()` is finished 1. Server thinks `time.sleep()` is not an I/O task, so it waits until it is finished 2. Server won't accept any new requests while waiting 3. Then, event loop and all the tasks in the queue will be waiting until `service.get_pong` is finished 1. Server thinks `service.get_pong()` is not an I/O task, so it waits until it is finished 2. Server won't accept any new requests while waiting 4. Server returns the response. 1. After a response, server starts accepting new requests 2. `GET /good-ping` 1. FastAPI server receives a request and starts handling it 2. FastAPI sends the whole route `good_ping` to the threadpool, where a worker thread will run the function 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) - Independently of main thread (i.e. our FastAPI app), worker thread will be waiting for `time.sleep` to finish and then for `service.get_pong` to finish - Sync operation blocks only the side thread, not the main one. 4. When `good_ping` finishes its work, server returns a response to the client 3. `GET /perfect-ping` 1. FastAPI server receives a request and starts handling it 2. FastAPI awaits `asyncio.sleep(10)` 3. Event loop selects next tasks from the queue and works on them (e.g. accept new request, call db) 4. When `asyncio.sleep(10)` is done, servers goes to the next lines and awaits `service.async_get_pong` 5. Event loop selects next tasks from the queue and works on them (e.g. accept new request, call db) 6. When `service.async_get_pong` is done, server returns a response to the client The second caveat is that operations that are non-blocking awaitables or are sent to the thread pool must be I/O intensive tasks (e.g. open file, db call, external API call). - Awaiting CPU-intensive tasks (e.g. heavy calculations, data processing, video transcoding) is worthless since the CPU has to work to finish the tasks, 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. - Running CPU-intensive tasks in other threads also isn't effective, because of [GIL](https://realpython.com/python-gil/). In short, GIL allows only one thread to work at a time, which makes it useless for CPU tasks. - If you want to optimize CPU intensive tasks you should send them to workers in another process. **Related StackOverflow questions of confused users** 1. https://stackoverflow.com/questions/62976648/architecture-flask-vs-fastapi/70309597#70309597 - Here you can also check [my answer](https://stackoverflow.com/a/70309597/6927498) 2. https://stackoverflow.com/questions/65342833/fastapi-uploadfile-is-slow-compared-to-flask 3. https://stackoverflow.com/questions/71516140/fastapi-runs-api-calls-in-serial-instead-of-parallel-fashion ### 8. Custom base model from day 0. Having a controllable global base model allows us to customize all the models within the app. For example, we could have a standard datetime format or add a super method for all subclasses of the base model. ```python from datetime import datetime from typing import Any from zoneinfo import ZoneInfo from fastapi.encoders import jsonable_encoder from pydantic import BaseModel, ConfigDict, model_validator def convert_datetime_to_gmt(dt: datetime) -> str: if not dt.tzinfo: dt = dt.replace(tzinfo=ZoneInfo("UTC")) return dt.strftime("%Y-%m-%dT%H:%M:%S%z") class CustomModel(BaseModel): model_config = ConfigDict( json_encoders={datetime: convert_datetime_to_gmt}, populate_by_name=True, ) @model_validator(mode="before") @classmethod def set_null_microseconds(cls, data: dict[str, Any]) -> dict[str, Any]: datetime_fields = { k: v.replace(microsecond=0) for k, v in data.items() if isinstance(k, datetime) } return {**data, **datetime_fields} def serializable_dict(self, **kwargs): """Return a dict which contains only serializable fields.""" default_dict = self.model_dump() return jsonable_encoder(default_dict) ``` In the example above we have decided to make a global base model which: - drops microseconds to 0 in all date formats - serializes all datetime fields to standard format with explicit timezone ### 9. Docs 1. Unless your API is public, hide docs by default. Show it explicitly on the selected envs only. ```python from fastapi import FastAPI from starlette.config import Config config = Config(".env") # parse .env file for env variables ENVIRONMENT = config("ENVIRONMENT") # get current env name SHOW_DOCS_ENVIRONMENT = ("local", "staging") # explicit list of allowed envs app_configs = {"title": "My Cool API"} if ENVIRONMENT not in SHOW_DOCS_ENVIRONMENT: app_configs["openapi_url"] = None # set url for docs as null app = FastAPI(**app_configs) ``` 2. Help FastAPI to generate an easy-to-understand docs 1. Set `response_model`, `status_code`, `description`, etc. 2. If models and statuses vary, use `responses` route attribute to add docs for different responses ```python from fastapi import APIRouter, status router = APIRouter() @router.post( "/endpoints", response_model=DefaultResponseModel, # default response pydantic model status_code=status.HTTP_201_CREATED, # default status code description="Description of the well documented endpoint", tags=["Endpoint Category"], summary="Summary of the Endpoint", responses={ 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:  ### 10. Use Pydantic's BaseSettings for configs Pydantic gives a [powerful tool](https://docs.pydantic.dev/latest/concepts/pydantic_settings/) to parse environment variables and process them with its validators. ```python from pydantic import AnyUrl, PostgresDsn from pydantic_settings import BaseSettings # pydantic v2 class AppSettings(BaseSettings): class Config: env_file = ".env" env_file_encoding = "utf-8" env_prefix = "app_" DATABASE_URL: PostgresDsn IS_GOOD_ENV: bool = True ALLOWED_CORS_ORIGINS: set[AnyUrl] ``` ### 11. SQLAlchemy: Set DB keys naming convention Explicitly setting the indexes' namings according to your database's convention is preferable over sqlalchemy's. ```python from sqlalchemy import MetaData POSTGRES_INDEXES_NAMING_CONVENTION = { "ix": "%(column_0_label)s_idx", "uq": "%(table_name)s_%(column_0_name)s_key", "ck": "%(table_name)s_%(constraint_name)s_check", "fk": "%(table_name)s_%(column_0_name)s_fkey", "pk": "%(table_name)s_pkey", } metadata = MetaData(naming_convention=POSTGRES_INDEXES_NAMING_CONVENTION) ``` ### 12. Migrations. Alembic. 1. Migrations must be static and revertable. If your migrations depend on dynamically generated data, then make sure the only thing that is dynamic is the data itself, not its structure. 2. Generate migrations with descriptive names & slugs. Slug is required and should explain the changes. 3. Set human-readable file template for new migrations. We use `*date*_*slug*.py` pattern, e.g. `2022-08-24_post_content_idx.py` ``` # alembic.ini file_template = %%(year)d-%%(month).2d-%%(day).2d_%%(slug)s ``` ### 13. Set DB naming convention Being consistent with names is important. Some rules we followed: 1. lower_case_snake 2. singular form (e.g. `post`, `post_like`, `user_playlist`) 3. group similar tables with module prefix, e.g. `payment_account`, `payment_bill`, `post`, `post_like` 4. stay consistent across tables, but concrete namings are ok, e.g. 1. use `profile_id` in all tables, but if some of them need only profiles that are creators, use `creator_id` 2. use `post_id` for all abstract tables like `post_like`, `post_view`, but use concrete naming in relevant modules like `course_id` in `chapters.course_id` 5. `_at` suffix for datetime 6. `_date` suffix for date ### 14. Set tests client async from day 0 Writing integration tests with DB will most likely lead to messed up event loop errors in the future. Set the async test client immediately, e.g. [async_asgi_testclient](https://github.com/vinissimus/async-asgi-testclient) or [httpx](https://github.com/encode/starlette/issues/652) ```python import pytest from async_asgi_testclient import TestClient from src.main import app # inited FastAPI app @pytest.fixture async def client(): host, port = "127.0.0.1", "5555" scope = {"client": (host, port)} async with TestClient( app, scope=scope, headers={"X-User-Fingerprint": "Test"} ) as client: yield client @pytest.mark.asyncio async def test_create_post(client: TestClient): resp = await client.post("/posts") assert resp.status_code == 201 ``` Unless you have sync db connections (excuse me?) or aren't planning to write integration tests. ### 15. BackgroundTasks > asyncio.create_task BackgroundTasks can [effectively run](https://github.com/encode/starlette/blob/31164e346b9bd1ce17d968e1301c3bb2c23bb418/starlette/background.py#L25) both blocking and non-blocking I/O operations the same way FastAPI handles blocking routes (`sync` tasks are run in a threadpool, while `async` tasks are awaited later) - Don't lie to the worker and don't mark blocking I/O operations as `async` - Don't use it for heavy CPU intensive tasks. ```python from fastapi import APIRouter, BackgroundTasks from pydantic import UUID4 from src.notifications import service as notifications_service router = APIRouter() @router.post("/users/{user_id}/email") async def send_user_email(worker: BackgroundTasks, user_id: UUID4): """Send email to user""" worker.add_task(notifications_service.send_email, user_id) # send email after responding client return {"status": "ok"} ``` ### 16. Typing is important FastAPI, Pydantic, and modern IDEs encourage to take use of type hints. **Without Type Hints**
**With Type Hints**
### 17. Save files in chunks.
Don't hope your clients will send small files.
```python
import aiofiles
from fastapi import UploadFile
DEFAULT_CHUNK_SIZE = 1024 * 1024 * 50 # 50 megabytes
async def save_video(video_file: UploadFile):
async with aiofiles.open("/file/path/name.mp4", "wb") as f:
while chunk := await video_file.read(DEFAULT_CHUNK_SIZE):
await f.write(chunk)
```
### 18. Be careful with dynamic pydantic fields (Pydantic v1)
If you have a pydantic field that can accept a union of types, be sure the validator explicitly knows the difference between those types.
```python
from pydantic import BaseModel
class Article(BaseModel):
text: str | None
extra: str | None
class Video(BaseModel):
video_id: int
text: str | None
extra: str | None
class Post(BaseModel):
content: Article | Video
post = Post(content={"video_id": 1, "text": "text"})
print(type(post.content))
# OUTPUT: Article
# Article is very inclusive and all fields are optional, allowing any dict to become valid
```
**Solutions:**
1. Validate input has only allowed valid fields and raise error if unknowns are provided
```python
from pydantic import BaseModel, Extra
class Article(BaseModel):
text: str | None
extra: str | None
class Config:
extra = Extra.forbid
class Video(BaseModel):
video_id: int
text: str | None
extra: str | None
class Config:
extra = Extra.forbid
class Post(BaseModel):
content: Article | Video
```
2. Use Pydantic's Smart Union (>v1.9, <2.0) if fields are simple
It's a good solution if the fields are simple like `int` or `bool`,
but it doesn't work for complex fields like classes.
Without Smart Union
```python
from pydantic import BaseModel
class Post(BaseModel):
field_1: bool | int
field_2: int | str
content: Article | Video
p = Post(field_1=1, field_2="1", content={"video_id": 1})
print(p.field_1)
# OUTPUT: True
print(type(p.field_2))
# OUTPUT: int
print(type(p.content))
# OUTPUT: Article
```
With Smart Union
```python
class Post(BaseModel):
field_1: bool | int
field_2: int | str
content: Article | Video
class Config:
smart_union = True
p = Post(field_1=1, field_2="1", content={"video_id": 1})
print(p.field_1)
# OUTPUT: 1
print(type(p.field_2))
# OUTPUT: str
print(type(p.content))
# OUTPUT: Article, because smart_union doesn't work for complex fields like classes
```
3. Fast Workaround
Order field types properly: from the most strict ones to loose ones.
```python
class Post(BaseModel):
content: Video | Article
```
### 19. SQL-first, Pydantic-second
- Usually, database handles data processing much faster and cleaner than CPython will ever do.
- It's preferable to do all the complex joins and simple data manipulations with SQL.
- It's preferable to aggregate JSONs in DB for responses with nested objects.
```python
# src.posts.service
from typing import Mapping
from pydantic import UUID4
from sqlalchemy import desc, func, select, text
from sqlalchemy.sql.functions import coalesce
from src.database import database, posts, profiles, post_review, products
async def get_posts(
creator_id: UUID4, *, limit: int = 10, offset: int = 0
) -> list[Mapping]:
select_query = (
select(
(
posts.c.id,
posts.c.type,
posts.c.slug,
posts.c.title,
func.json_build_object(
text("'id', profiles.id"),
text("'first_name', profiles.first_name"),
text("'last_name', profiles.last_name"),
text("'username', profiles.username"),
).label("creator"),
)
)
.select_from(posts.join(profiles, posts.c.owner_id == profiles.c.id))
.where(posts.c.owner_id == creator_id)
.limit(limit)
.offset(offset)
.group_by(
posts.c.id,
posts.c.type,
posts.c.slug,
posts.c.title,
profiles.c.id,
profiles.c.first_name,
profiles.c.last_name,
profiles.c.username,
profiles.c.avatar,
)
.order_by(
desc(coalesce(posts.c.updated_at, posts.c.published_at, posts.c.created_at))
)
)
return await database.fetch_all(select_query)
# src.posts.schemas
import orjson
from enum import Enum
from pydantic import BaseModel, UUID4, validator
class PostType(str, Enum):
ARTICLE = "ARTICLE"
COURSE = "COURSE"
class Creator(BaseModel):
id: UUID4
first_name: str
last_name: str
username: str
class Post(BaseModel):
id: UUID4
type: PostType
slug: str
title: str
creator: Creator
@validator("creator", pre=True) # before default validation
def parse_json(cls, creator: str | dict | Creator) -> dict | Creator:
if isinstance(creator, str): # i.e. json
return orjson.loads(creator)
return creator
# src.posts.router
from fastapi import APIRouter, Depends
router = APIRouter()
@router.get("/creators/{creator_id}/posts", response_model=list[Post])
async def get_creator_posts(creator: Mapping = Depends(valid_creator_id)):
posts = await service.get_posts(creator["id"])
return posts
```
If an aggregated data form DB is a simple JSON, then take a look at Pydantic's `Json` field type,
which will load raw JSON first.
```python
from pydantic import BaseModel, Json
class A(BaseModel):
numbers: Json[list[int]]
dicts: Json[dict[str, int]]
valid_a = A(numbers="[1, 2, 3]", dicts='{"key": 1000}') # becomes A(numbers=[1,2,3], dicts={"key": 1000})
invalid_a = A(numbers='["a", "b", "c"]', dicts='{"key": "str instead of int"}') # raises ValueError
```
### 20. Validate hosts, if users can send publicly available URLs
For example, we have a specific endpoint which:
1. accepts media file from the user,
2. generates unique url for this file,
3. returns url to user,
1. which they will use in other endpoints like `PUT /profiles/me`, `POST /posts`
2. these endpoints accept files only from whitelisted hosts
4. uploads file to AWS with this name and matching URL.
If we don't whitelist URL hosts, then bad users will have a chance to upload dangerous links.
```python
from pydantic import AnyUrl, BaseModel
ALLOWED_MEDIA_URLS = {"mysite.com", "mysite.org"}
class CompanyMediaUrl(AnyUrl):
@classmethod
def validate_host(cls, parts: dict) -> tuple[str, str, str, bool]: # pydantic v1
"""Extend pydantic's AnyUrl validation to whitelist URL hosts."""
host, tld, host_type, rebuild = super().validate_host(parts)
if host not in ALLOWED_MEDIA_URLS:
raise ValueError(
"Forbidden host url. Upload files only to internal services."
)
return host, tld, host_type, rebuild
class Profile(BaseModel):
avatar_url: CompanyMediaUrl # only whitelisted urls for avatar
```
### 21. Raise a ValueError in custom pydantic validators, if schema directly faces the client
It will return a nice detailed response to users.
```python
# src.profiles.schemas
from pydantic import BaseModel, validator
class ProfileCreate(BaseModel):
username: str
@validator("username") # pydantic v1
def validate_bad_words(cls, username: str):
if username == "me":
raise ValueError("bad username, choose another")
return username
# src.profiles.routes
from fastapi import APIRouter
router = APIRouter()
@router.post("/profiles")
async def get_creator_posts(profile_data: ProfileCreate):
pass
```
**Response Example:**
### 22. FastAPI converts Pydantic objects to dict, then to Pydantic object, then to JSON
If you think you can return Pydantic object that matches your route's `response_model` to make some optimizations,
then it's wrong.
FastAPI firstly converts that pydantic object to dict with its `jsonable_encoder`, then validates
data with your `response_model`, and only then serializes your object to JSON.
```python
from fastapi import FastAPI
from pydantic import BaseModel, root_validator
app = FastAPI()
class ProfileResponse(BaseModel):
@root_validator
def debug_usage(cls, data: dict):
print("created pydantic model")
return data
def dict(self, *args, **kwargs):
print("called dict")
return super().dict(*args, **kwargs)
@app.get("/", response_model=ProfileResponse)
async def root():
return ProfileResponse()
```
**Logs Output:**
```
[INFO] [2022-08-28 12:00:00.000000] created pydantic model
[INFO] [2022-08-28 12:00:00.000010] called dict
[INFO] [2022-08-28 12:00:00.000020] created pydantic model
[INFO] [2022-08-28 12:00:00.000030] called dict
```
### 23. If you must use sync SDK, then run it in a thread pool.
If you must use a library to interact with external services, and it's not `async`,
then make the HTTP calls in an external worker thread.
For a simple example, we could use our well-known `run_in_threadpool` from starlette.
```python
from fastapi import FastAPI
from fastapi.concurrency import run_in_threadpool
from my_sync_library import SyncAPIClient
app = FastAPI()
@app.get("/")
async def call_my_sync_library():
my_data = await service.get_my_data()
client = SyncAPIClient()
await run_in_threadpool(client.make_request, data=my_data)
```
### 24. Use linters (black, ruff)
With linters, you can forget about formatting the code and focus on writing the business logic.
Black is the uncompromising code formatter that eliminates so many small decisions you have to make during development.
Ruff is "blazingly-fast" new linter that replaces autoflake and isort, and supports more than 600 lint rules.
It's a popular good practice to use pre-commit hooks, but just using the script was ok for us.
```shell
#!/bin/sh -e
set -x
ruff --fix
black src tests
```
### Bonus Section
Some very kind people shared their own experience and best practices that are definitely worth reading.
Check them out at [issues](https://github.com/zhanymkanov/fastapi-best-practices/issues) section of the project.
For instance, [lowercase00](https://github.com/zhanymkanov/fastapi-best-practices/issues/4)
has described in details their best practices working with permissions & auth, class-based services & views,
task queues, custom response serializers, configuration with dynaconf, etc.
If you have something to share about your experience working with FastAPI, whether it's good or bad,
you are very welcome to create a new issue. It is our pleasure to read it.