doluedu-fastapi-template/README.md

WIP: FastAPI Best Practices

Opinionated list of best practices and conventions we used at our startup.

For the last 1.5 years in production, we have been making good and bad decisions that impacted our developer experience dramatically. Some of them are worth sharing.

Contents

1. Project Structure. Consistent & predictable
2. Excessively use Pydantic for data validation
3. Use dependencies for data validation vs DB
4. Chain dependencies
5. Decouple & Reuse dependencies. Dependency calls are cached.
6. Follow the REST
7. Don't make your routes async, if you have only blocking I/O operations
8. Custom base model from day 0
9. Docs
10. Use Pydantic's BaseSettings for configs
11. SQLAlchemy: Set DB keys naming convention from day 0
12. Set DB table naming convention immediately from day 0
13. Set UUIDs within the app
14. Set tests client async from day 0
15. Set postgres identity from day 0
16. Use BackgroundTasks
17. Typing is important
18. Don't hope your clients will send small BLOBs. Save files in chunk
19. Be careful with dynamic pydantic fields
20. SQL-first, Pydantic-second
21. Validate file formats
22. Validate url source (if users are able to upload files)
23. root_validator to use multiple columns during validation
24. pre=True if data need to be pre-handled before validation
25. raise a ValueError in pydantic, if schema faces http client
26. remember fastapi response modeling
27. if must use sdk, but it's not async, use threadpools

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 it, then your structure is unclear.
• 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 bad.
• If the frequency and location of the files feels random, then your project structure is bad.

Although, the project structure, where we separate files by their type (e.g. api, crud, models, schemas) presented by @tiangolo is perfect 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 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 - module specific exceptions, e.g. PostNotFound, InvalidUserData
3. When package requires services or dependencies or constants from other packages - import them with explicit module name

from src.auth import constants as auth_constants
from src.notifictions 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 and default data, it has built-in comprehensive data processing params like regex, enums for limited allowed options, length validation, email validation, etc.

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 of client input. Use dependencies to validate data against database requirements like email already exists, user not found, etc.

# 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.

# 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)):
    """Get post that belong the user."""
    return post

5. Decouple & Reuse dependencies. Dependency calls are cached.

Dependencies can be reused multiple times, and they won't be recalculated - FastAPI caches their result by default, 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.

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. 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.

# 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()
    
    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.

# src.profiles.dependencies
async def valid_profile_id(profile_id: UUID4) -> Mapping:
    profile = await service.get_by_id(post_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("/profiles/{profile_id}", response_model=ProfileResponse)
async def get_user_profile_by_id(
     creator_profile: Mapping = Depends(valid_creator_id)
):
    """Get profile by id."""
    return creator_profile

Use /me endpoints for users own 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 both async and sync I/O operations.

• FastAPI calls sync routes in the threadpool and blocking I/O operations won't stop event loop from executing the tasks.
• Otherwise, if the route is defined as 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, event loop will not be able to run the next tasks until that blocking operation is done.

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 I/O blocking operation
    pong = await service.async_get_pong()  # non I/O blocking 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
   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 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).

• Awaiting CPU intensive tasks (e.g. heavy calculations, data processing, video transcoding) is worthless, since 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. 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
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 pydantic 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.

from datetime import datetime
from zoneinfo import ZoneInfo

import orjson
from fastapi.encoders import jsonable_encoder
from pydantic import BaseModel, root_validator


def orjson_dumps(v, *, default):
    # orjson.dumps returns bytes, to match standard json.dumps we need to decode
    return orjson.dumps(v, default=default).decode()


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 ORJSONModel(BaseModel):
    class Config:
        json_loads = orjson.loads
        json_dumps = orjson_dumps
        json_encoders = {datetime: convert_datetime_to_gmt}  # method for customer JSON encoding of datetime fields

    @root_validator()
    def set_null_microseconds(cls, data: dict) -> dict:
       """Drops microseconds in all the datetime field values."""
        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 = super().dict(**kwargs)

        return jsonable_encoder(default_dict)

In the example above we have decided to make a global base model which:

• uses orjson to serialize data
• 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.

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

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 to parse environment variables and process them with its validators.

from pydantic import AnyUrl, BaseSettings, PostgresDsn

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 from day 0

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. Set DB table naming convention immediately from day 0

13. Set UUIDs within the app

Setting them in database makes it harder to write integration tests.

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. asyn_asgi_testclient or httpx

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 connection (excuse me?) or aren't planning to write integration tests.

15. Set postgres identity from day 0

16. Use BackgroundTasks

They are stable enough for async (delayed) tasks

from fastapi import BackgroundTasks
from pydantic import UUID4

from src.notifications import service as notifications_service


# router.py
@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"}

17. Typing is important

FastAPI, Pydantic, and modern IDEs encourage to take use of type hints.

Without Type Hints

With Type Hints

18. Don't hope your clients will send small BLOBs. Save files in chunk.

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)

19. Be careful with dynamic pydantic fields

If you have a pydantic field that can accept multiple types, be sure validator explicitly knows the difference between those types.

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. Not so bad solution. Order field types properly: from the most strict ones to loose ones.

class Post(BaseModel):
   content: Video | Article

2. Not so bad solution. Validate input has only valid fields

from pydantic import BaseModel, root_validator

class Article(BaseModel):
   text: str | None
   extra: str | None
   
   @root_validator(pre=True)  # validate all values before pydantic
   def has_only_article_fields(cls, data: dict):
      """Silly and ugly solution to validate data has only article fields."""
      fields = set(data.keys())
      if fields != {"text", "extra"}:
         raise ValueError("invalid fields")

      return data
       

class Video(BaseModel):
   video_id: int
   text: str | None
   extra: str | None
   
   @root_validator(pre=True)
   def has_only_video_fields(cls, data: dict):
      """Silly and ugly solution to validate data has only article fields."""
      fields = set(data.keys())
      if fields != {"text", "extra", "video_id"}:
         raise ValueError("invalid fields")

      return data

   
class Post(BaseModel):
   content: Article | Video

3. Good solution. Use Pydantic's Smart Union (>v1.9)

from pydantic import BaseModel

class Post(BaseModel):
   content: Article | Video

   class Config:
      smart_union = True

20. SQL-first, Pydantic-second

21. Validate file formats

22. Validate url source (if users are able to upload files)

Bad users could send strange urls for user facing public objects.

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 | None, str, bool]:
        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 Post(BaseModel):
    thumbnail_url: CompanyMediaUrl

23. root_validator to use multiple columns during validation

from pydantic import BaseModel, root_validator


class Profile(BaseModel):
    username: str | None
    first_name: str
    last_name: str

    @root_validator()
    def set_username(cls, data: dict) -> dict:
        if not data.get("username"):
            data["username"] = f'{data["first_name"]}_{data["last_name"]}'
        
        return data

24. pre if data need to be pre-handled before validation

25. you can just raise a ValueError in pydantic schemas, if schemas faces http client

it wil return a nice response

26. don't forget that fastapi converts response Model to Dict then to Model then to JSON

it may lead to bugs like model can parse only raw data (e.g. forced data aggregation for raw data)

27. if must use sdk, but it's not async, use threadpools

28. use linters (black, isort, autoflake)

29. set logs from day 0