.coverage
coverage.xml
.netlify
test.db
log.txt
Pipfile.lock
env3.*
env
docs_build
site_build
venv
docs.zip
archive.zip

# vim temporary files
*~
.*.sw?
.cache

# macOS

        run: bash scripts/test.sh
        env:
          COVERAGE_FILE: coverage/.coverage.${{ runner.os }}-py${{ matrix.python-version }}
          CONTEXT: ${{ runner.os }}-py${{ matrix.python-version }}
      - name: Store coverage files
        uses: actions/upload-artifact@v3
        with:
          name: coverage
          path: coverage

  coverage-combine:
    needs: [test]
    runs-on: ubuntu-latest
    steps:

* `tax: float = 10.5` has a default of `10.5`.
* `tags: List[str] = []` has a default of an empty list: `[]`.

but you might want to omit them from the result if they were not actually stored.

For example, if you have models with many optional attributes in a NoSQL database, but you don't want to send very long JSON responses full of default values.

### Use the `response_model_exclude_unset` parameter

然后再用它生成一个只含已设置（在请求中所发送）数据，且省略了默认值的 `dict`：

```Python hl_lines="34"
{!../../../docs_src/body_updates/tutorial002.py!}
```

### 使用 Pydantic 的 `update` 参数

接下来，用 `.copy()` 为已有模型创建调用 `update` 参数的副本，该参数为包含更新数据的 `dict`。

例如，`stored_item_model.copy(update=update_data)`：

```Python hl_lines="35"
{!../../../docs_src/body_updates/tutorial002.py!}
```

### 更新部分数据小结

简而言之，更新部分数据应：

* 使用 `PATCH` 而不是 `PUT` （可选，也可以用 `PUT`）；
* 提取存储的数据；

It's relatively tightly coupled with relational databases (like MySQL or PostgreSQL), so, having a NoSQL database (like Couchbase, MongoDB, Cassandra, etc) as the main store engine is not very easy.

def test_upload_file_is_closed(tmp_path: Path):
    path = tmp_path / "test.txt"
    path.write_bytes(b"<file content>")
    app = FastAPI()

    testing_file_store: List[UploadFile] = []

    @app.post("/uploadfile/")
    def create_upload_file(file: UploadFile):
        testing_file_store.append(file)
        return {"filename": file.filename}

    client = TestClient(app)
    with path.open("rb") as file:

Using `UploadFile` has several advantages over `bytes`:

* You don't have to use `File()` in the default value of the parameter.
* It uses a "spooled" file:
    * A file stored in memory up to a maximum size limit, and after passing this limit it will be stored in disk.
* This means that it will work well for large files like images, videos, large binaries, etc. without consuming all the memory.
* You can get metadata from the uploaded file.

# JSON Compatible Encoder

There are some cases where you might need to convert a data type (like a Pydantic model) to something compatible with JSON (like a `dict`, `list`, etc).

For example, if you need to store it in a database.

For that, **FastAPI** provides a `jsonable_encoder()` function.

## Using the `jsonable_encoder`

Let's imagine that you have a database `fake_db` that only receives JSON compatible data.

* The **input model** needs to be able to have a password.
* The **output model** should not have a password.
* The **database model** would probably need to have a hashed password.

!!! danger
    Never store user's plaintext passwords. Always store a "secure hash" that you can then verify.

    If you don't know, you will learn what a "password hash" is in the [security chapters](security/simple-oauth2.md#password-hashing){.internal-link target=_blank}.

    ```

### Использование параметра `update` в Pydantic

Теперь можно создать копию существующей модели, используя `.copy()`, и передать параметр `update` с `dict`, содержащим данные для обновления.

Например, `stored_item_model.copy(update=update_data)`:

=== "Python 3.10+"

    ```Python hl_lines="33"
    {!> ../../../docs_src/body_updates/tutorial002_py310.py!}
    ```

=== "Python 3.9+"

    ```Python hl_lines="35"