On the other hand, if you have 2 servers and you are using **100% of their CPU and RAM**, at some point one process will ask for more memory, and the server will have to use the disk as "memory" (which can be thousands of times slower), or even **crash**. Or one process might need to do some computation and would have to wait until the CPU is free again.

participant dep as Dep with yield
participant operation as Path Operation
participant tasks as Background tasks

    Note over client,tasks: Can raise exception for dependency, handled after response is sent
    Note over client,operation: Can raise HTTPException and can change the response
    client ->> dep: Start request
    Note over dep: Run code up to yield
    opt raise
        dep -->> handler: Raise HTTPException

Funciona también como una referencia futura, para que puedas volver y ver exactamente lo que necesitas.

## Ejecuta el código

Todos los bloques de código se pueden copiar y usar directamente (en realidad son archivos Python probados).

* **PGAdmin** PostgreSQL database admin tool'u, PHPMyAdmin ve MySQL ile kolayca değiştirilebilir.
* **Flower** ile Celery job'larını monitörleme.
* **Traefik** ile backend ve frontend arasında yük dengeleme, böylece her ikisini de aynı domain altında, path ile ayrılmış, ancak farklı kapsayıcılar tarafından sunulabilirsiniz.

El framework **FastAPI** está creado para satisfacer eso. El auto-completado funciona en todas partes.

No vas a tener que volver a la documentación seguido.

Así es como tu editor te puede ayudar:

* en <a href="https://code.visualstudio.com/" class="external-link" target="_blank">Visual Studio Code</a>:

try:
    import email_validator

    assert email_validator  # make autoflake ignore the unused import
    from pydantic import EmailStr
except ImportError:  # pragma: no cover

    class EmailStr(str):  # type: ignore
        @classmethod
        def __get_validators__(cls) -> Iterable[Callable[..., Any]]:
            yield cls.validate

        @classmethod

And as you can have parallelism and asynchronicity at the same time, you get higher performance than most of the tested NodeJS frameworks and on par with Go, which is a compiled language closer to C <a href="https://www.techempower.com/benchmarks/#section=data-r17&hw=ph&test=query&l=zijmkf-1" class="external-link" target="_blank">(all thanks to Starlette)</a>.

### Is concurrency better than parallelism?

    * 基于 **Nginx** 的 Docker 服务器（优化了 Vue-router 配置）
    * Docker 多阶段构建，无需保存或提交编译的代码
    * 在构建时运行前端测试（可禁用）
    * 尽量模块化，开箱即用，但仍可使用 Vue CLI 重新生成或创建所需项目，或复用所需内容
* 使用 **PGAdmin** 管理 PostgreSQL 数据库，可轻松替换为 PHPMyAdmin 或 MySQL
* 使用 **Flower** 监控 Celery 任务
* 使用 **Traefik** 处理前后端负载平衡，可把前后端放在同一个域下，按路径分隔，但在不同容器中提供服务
* Traefik 集成，包括自动生成 Let's Encrypt **HTTPS** 凭证
* GitLab **CI**（持续集成），包括前后端测试

## 全栈 FastAPI + Couchbase

    Because of that, versions of FastAPI previous to 0.99.0 still used versions of OpenAPI lower than 3.1.0.

### Pydantic and FastAPI `examples`

When you add `examples` inside a Pydantic model, using `schema_extra` or `Field(examples=["something"])` that example is added to the **JSON Schema** for that Pydantic model.

    try:
        from pydantic_core.core_schema import (
            with_info_plain_validator_function as with_info_plain_validator_function,
        )
    except ImportError:  # pragma: no cover
        from pydantic_core.core_schema import (
            general_plain_validator_function as with_info_plain_validator_function,  # noqa: F401
        )

    Required = PydanticUndefined