```mermaid
flowchart LR
    subgraph global[global env]
        harry-1[harry v1]
    end
    subgraph stone-project[philosophers-stone project]
        stone(philosophers-stone) -->|requires| harry-1
    end
```

  - login: olexkram
    avatarUrl: https://avatars.githubusercontent.com/u/148793576?v=4
    url: https://github.com/olexkram
  - login: 0ne-stone
    avatarUrl: https://avatars.githubusercontent.com/u/62360849?u=746dd21c34e7e06eefb11b03e8bb01aaae3c2a4f&v=4
    url: https://github.com/0ne-stone
  - login: nayasinghania
    avatarUrl: https://avatars.githubusercontent.com/u/74111380?u=752e99a5e139389fdc0a0677122adc08438eb076&v=4

Then you could want to have **a single container** with a **process manager** starting **several worker processes** inside.

---

The main point is, **none** of these are **rules written in stone** that you have to blindly follow. You can use these ideas to **evaluate your own use case** and decide what is the best approach for your system, checking out how to manage the concepts of:

* Security - HTTPS
* Running on startup

* 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}.

///

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` { #using-the-jsonable-encoder }

            // Store new keys
            keyManager.storeSessionKey(encKeyId, newEncryptionKey, "AES");
            keyManager.storeSessionKey(decKeyId, newDecryptionKey, "AES");

            log.debug("Keys rotated successfully in SecureKeyManager");
        } else {
            // Securely wipe old keys
            secureWipeKeys();

            // Store new keys

And it will save the returned value in a <abbr title="A utility/system to store computed/generated values, to reuse them instead of computing them again.">"cache"</abbr> and pass it to all the "dependants" that need it in that specific request, instead of calling the dependency multiple times for the same request.

If you have a big application, you might end up accumulating **several tags**, and you would want to make sure you always use the **same tag** for related *path operations*.

In these cases, it could make sense to store the tags in an `Enum`.

**FastAPI** supports that the same way as with plain strings:

{* ../../docs_src/path_operation_configuration/tutorial002b.py hl[1,8:10,13,18] *}

{* ../../docs_src/extending_openapi/tutorial001.py hl[22:24] *}

### Cache the OpenAPI schema { #cache-the-openapi-schema }

You can use the property `.openapi_schema` as a "cache", to store your generated schema.

That way, your application won't have to generate the schema every time a user opens your API docs.

It will be generated only once, and then the same cached schema will be used for the next requests.

* Make sure you have well defined Pydantic models for your request bodies and responses.
* Configure any required permissions and roles using dependencies.
* Never store plaintext passwords, only password hashes.
* Implement and use well-known cryptographic tools, like Passlib and JWT tokens, etc.
* Add more granular permission controls with OAuth2 scopes where needed.
* ...etc.