return null // Another edit is in progress.
    }

    if (entry != null && entry.lockingSourceCount != 0) {
      return null // We can't write this file because a reader is still reading it.
    }

    if (mostRecentTrimFailed || mostRecentRebuildFailed) {
      // The OS has become our enemy! If the trim job failed, it means we are storing more data than

## 上下文管理器

### 什么是“上下文管理器”

“上下文管理器”是您可以在`with`语句中使用的任何Python对象。

例如，<a href="https://docs.python.org/zh-cn/3/tutorial/inputoutput.html#reading-and-writing-files" class="external-link" target="_blank">您可以使用`with`读取文件</a>：

```Python
with open("./somefile.txt") as f:
    contents = f.read()
    print(contents)
```

   * body. This was misleading for tracing because it was too early.
   */
  open fun responseBodyStart(call: Call) {
  }

  /**
   * Invoked immediately after receiving a response body and completing reading it.
   *
   * Will only be invoked for requests having a response body e.g. won't be invoked for a web socket
   * upgrade.
   *

      if (this.errorCode == null) {
        this.errorCode = errorCode
        condition.signalAll()
      }
    }
  }

  /**
   * Returns true if read timeouts should be enforced while reading response headers or body bytes.
   * We always do timeouts in the HTTP server role. For clients, we only do timeouts after the
   * request is transmitted. This is only interesting for duplex calls where the request and

    // Give up because the cache cannot be written.
    try {
      editor?.abort()
    } catch (_: IOException) {
    }
  }

  /**
   * Initialize the cache. This will include reading the journal files from the storage and building
   * up the necessary in-memory cache information.
   *
   * The initialization time may vary depending on the journal file size and the current actual

## Recap

You have been reading here some of the main concepts that you would probably need to keep in mind when deciding how to deploy your application:

* Security - HTTPS
* Running on startup
* Restarts

### Create Pydantic *models* / schemas for reading / returning

Now create Pydantic *models* (schemas) that will be used when reading data, when returning it from the API.

For example, before creating an item, we don't know what will be the ID assigned to it, but when reading it (when returning it from the API) we will already know its ID.

### Creating the `Settings` only once with `lru_cache`

Reading a file from disk is normally a costly (slow) operation, so you probably want to do it only once and then re-use the same settings object, instead of reading it for each request.

But every time we do:

```Python
Settings()
```

Aún así, en ambas situaciones, es probable que **FastAPI** sea [aún más rápido](index.md#rendimiento){.Internal-link target=_blank} que (o al menos comparable) a tu framework anterior.

That's why in this example we have to declare it in the `response_model` parameter.

...but continue reading below to see how to overcome that.

## Return Type and Data Filtering

Let's continue from the previous example. We wanted to **annotate the function with one type** but return something that includes **more data**.