# Probando una Base de Datos { #testing-a-database }

Puedes estudiar sobre bases de datos, SQL y SQLModel en la <a href="https://sqlmodel.tiangolo.com/" class="external-link" target="_blank">documentación de SQLModel</a>. 🤓

Hay un mini <a href="https://sqlmodel.tiangolo.com/tutorial/fastapi/" class="external-link" target="_blank">tutorial sobre el uso de SQLModel con FastAPI</a>. ✨

  - 🧰 [SQLModel](https://sqlmodel.tiangolo.com) for the Python SQL database interactions (ORM).
  - 🔍 [Pydantic](https://docs.pydantic.dev), used by FastAPI, for the data validation and settings management.
  - 💾 [PostgreSQL](https://www.postgresql.org) as the SQL database.
- 🚀 [React](https://react.dev) for the frontend.
  - 💃 Using TypeScript, hooks, Vite, and other parts of a modern frontend stack.

* `Field(primary_key=True)` tells SQLModel that the `id` is the **primary key** in the SQL database (you can learn more about SQL primary keys in the SQLModel docs).

Being able to use asynchronous functions in your tests could be useful, for example, when you're querying your database asynchronously. Imagine you want to test sending requests to your FastAPI application and then verify that your backend successfully wrote the correct data in the database, while using an async database library.

Let's look at how we can make that work.

## pytest.mark.anyio { #pytest-mark-anyio }

                    "operationId": "read_items_items__get",
                    "parameters": [
                        {
                            "description": "Query string for the items to search in the database that have a good match",
                            "required": False,
                            "deprecated": True,
                            "schema": {
                                "anyOf": [

app = FastAPI()


@app.get("/items/")
async def read_items(
    q: Union[str, None] = Query(
        default=None,
        title="Query string",
        description="Query string for the items to search in the database that have a good match",
        min_length=3,
    ),
):
    results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
    if q:
        results.update({"q": q})

                    "operationId": "read_items_items__get",
                    "parameters": [
                        {
                            "description": "Query string for the items to search in the database that have a good match",
                            "required": False,
                            "schema": {
                                "anyOf": [
                                    {

) -> AsyncGenerator[_T, None]:
    # blocking __exit__ from running waiting on a free thread
    # can create race conditions/deadlocks if the context manager itself
    # has its own internal pool (e.g. a database connection pool)
    # to avoid this we let __exit__ run without a capacity limit
    # since we're creating a new limiter for each call, any non-zero limit
    # works (1 is arbitrary)
    exit_limiter = CapacityLimiter(1)

For example, you could want to **return a dictionary** or a database object, but **declare it as a Pydantic model**. This way the Pydantic model would do all the data documentation, validation, etc. for the object that you returned (e.g. a dictionary or database object).

      advanced/conditional-openapi.md: how-to/conditional-openapi.md
      advanced/extending-openapi.md: how-to/extending-openapi.md
      advanced/testing-database.md: how-to/testing-database.md
  mkdocstrings:
    handlers:
      python:
        options:
          extensions:
          - griffe_typingdoc
          show_root_heading: true
          show_if_no_docstring: true