Revision of FASTAPI and Data Modeling Python

1

+

FastAPI, a modern Python web framework, is often used with database modeling to create robust APIs. Here's how it's typically done:

2

+

3

+

1. Database Choice:

4

+

FastAPI doesn't enforce a specific database. You can choose SQL (like PostgreSQL, MySQL, SQLite) or NoSQL databases.

5

+

For SQL databases, SQLAlchemy is a common choice as an ORM (Object-Relational Mapper).

6

+

SQLModel, created by the same author as FastAPI, is designed to work seamlessly with it.

7

+

2. Data Models:

8

+

Pydantic: FastAPI uses Pydantic for data validation and serialization. You define data models using Pydantic, which are then used to validate incoming requests and structure responses.

9

+

SQLModel: SQLModel extends Pydantic to define database tables directly as Python classes. This allows you to define both data structures and database schemas in one place.

10

+

SQLAlchemy: If using SQLAlchemy, you define models that map to database tables.

11

+

3. Database Interaction:

12

+

SQLAlchemy: With SQLAlchemy, you create an engine that connects to your database. Then you define sessions to interact with the database.

13

+

SQLModel: SQLModel simplifies this process by providing its own engine and session management.

14

+

You typically create a dependency that yields a database session for each request to ensure proper database interaction.

15

+

4. ORM vs. Raw SQL:

16

+

ORMs like SQLAlchemy and SQLModel allow you to interact with databases using Python objects instead of writing raw SQL queries, making code more readable and maintainable.

17

+

You can still use raw SQL if needed.

18

+

5. FastAPI Integration:

19

+

FastAPI endpoints use Pydantic models to define request and response structures.

20

+

You can inject database sessions as dependencies into your endpoints.

21

+

FastAPI automatically generates API documentation based on your Pydantic models.

22

+

6. Example with SQLModel:

23

+

```Python

24

+

25

+

from typing import Optional

26

+

from fastapi import FastAPI, Depends

27

+

from sqlmodel import Field, Session, SQLModel, create_engine, select

28

+

29

+

class Hero(SQLModel, table=True):

30

+

id: Optional[int] = Field(default=None, primary_key=True)

31

+

name: str

32

+

secret_name: str

33

+

age: Optional[int] = Field(default=None, index=True)

34

+

35

+

sqlite_file_name = "database.db"

36

+

sqlite_url = f"sqlite:///{sqlite_file_name}"

37

+

connect_args = {"check_same_thread": False}

38

+

engine = create_engine(sqlite_url, connect_args=connect_args)

39

+

40

+

def create_db_and_tables():

41

+

SQLModel.metadata.create_all(engine)

42

+

43

+

def get_session():

44

+

with Session(engine) as session:

45

+

yield session

46

+

47

+

SessionDep = Depends(get_session)

48

+

app = FastAPI()

49

+

50

+

@app.on_event("startup")

51

+

def on_startup():

52

+

create_db_and_tables()

53

+

54

+

@app.get("/heroes/")

55

+

def read_heroes(session: SessionDep):

56

+

heroes = session.exec(select(Hero)).all()

57

+

return heroes

58

+

```

59

+

60

+

7. Key Considerations:

61

+

Dependencies: Use FastAPI's dependency injection to manage database sessions effectively.

62

+

Error Handling: FastAPI automatically handles validation errors based on Pydantic models.

63

+

Documentation: FastAPI generates interactive API documentation.

64

+

Testing: Write tests for your database interactions.

65

+

FastAPI's integration with Pydantic and ORMs simplifies database modeling, making it easier to build efficient and maintainable APIs.

kristofer / FASTAPI and Data Modeling Python

kristofer 已修改 1747839889. 還原成這個修訂版本

		@@ -0,0 +1,65 @@
1	+	FastAPI, a modern Python web framework, is often used with database modeling to create robust APIs. Here's how it's typically done:
2	+
3	+	1. Database Choice:
4	+	FastAPI doesn't enforce a specific database. You can choose SQL (like PostgreSQL, MySQL, SQLite) or NoSQL databases.
5	+	For SQL databases, SQLAlchemy is a common choice as an ORM (Object-Relational Mapper).
6	+	SQLModel, created by the same author as FastAPI, is designed to work seamlessly with it.
7	+	2. Data Models:
8	+	Pydantic: FastAPI uses Pydantic for data validation and serialization. You define data models using Pydantic, which are then used to validate incoming requests and structure responses.
9	+	SQLModel: SQLModel extends Pydantic to define database tables directly as Python classes. This allows you to define both data structures and database schemas in one place.
10	+	SQLAlchemy: If using SQLAlchemy, you define models that map to database tables.
11	+	3. Database Interaction:
12	+	SQLAlchemy: With SQLAlchemy, you create an engine that connects to your database. Then you define sessions to interact with the database.
13	+	SQLModel: SQLModel simplifies this process by providing its own engine and session management.
14	+	You typically create a dependency that yields a database session for each request to ensure proper database interaction.
15	+	4. ORM vs. Raw SQL:
16	+	ORMs like SQLAlchemy and SQLModel allow you to interact with databases using Python objects instead of writing raw SQL queries, making code more readable and maintainable.
17	+	You can still use raw SQL if needed.
18	+	5. FastAPI Integration:
19	+	FastAPI endpoints use Pydantic models to define request and response structures.
20	+	You can inject database sessions as dependencies into your endpoints.
21	+	FastAPI automatically generates API documentation based on your Pydantic models.
22	+	6. Example with SQLModel:
23	+	```Python
24	+
25	+	from typing import Optional
26	+	from fastapi import FastAPI, Depends
27	+	from sqlmodel import Field, Session, SQLModel, create_engine, select
28	+
29	+	class Hero(SQLModel, table=True):
30	+	id: Optional[int] = Field(default=None, primary_key=True)
31	+	name: str
32	+	secret_name: str
33	+	age: Optional[int] = Field(default=None, index=True)
34	+
35	+	sqlite_file_name = "database.db"
36	+	sqlite_url = f"sqlite:///{sqlite_file_name}"
37	+	connect_args = {"check_same_thread": False}
38	+	engine = create_engine(sqlite_url, connect_args=connect_args)
39	+
40	+	def create_db_and_tables():
41	+	SQLModel.metadata.create_all(engine)
42	+
43	+	def get_session():
44	+	with Session(engine) as session:
45	+	yield session
46	+
47	+	SessionDep = Depends(get_session)
48	+	app = FastAPI()
49	+
50	+	@app.on_event("startup")
51	+	def on_startup():
52	+	create_db_and_tables()
53	+
54	+	@app.get("/heroes/")
55	+	def read_heroes(session: SessionDep):
56	+	heroes = session.exec(select(Hero)).all()
57	+	return heroes
58	+	```
59	+
60	+	7. Key Considerations:
61	+	Dependencies: Use FastAPI's dependency injection to manage database sessions effectively.
62	+	Error Handling: FastAPI automatically handles validation errors based on Pydantic models.
63	+	Documentation: FastAPI generates interactive API documentation.
64	+	Testing: Write tests for your database interactions.
65	+	FastAPI's integration with Pydantic and ORMs simplifies database modeling, making it easier to build efficient and maintainable APIs.