```Python
contents = myfile.file.read()
```

/// note | `async` Technical Details

When you use the `async` methods, **FastAPI** runs the file methods in a threadpool and awaits for them.

///

/// note | Starlette Technical Details

```Python
contents = myfile.file.read()
```

/// note | Detalhes Técnicos do `async`

Quando você usa os métodos `async`, o **FastAPI** executa os métodos de arquivo em um threadpool e aguarda por eles.

///

/// note | Detalhes Técnicos do Starlette

```Python
contents = myfile.file.read()
```

/// note | Detalles Técnicos de `async`

Cuando usas los métodos `async`, **FastAPI** ejecuta los métodos del archivo en un threadpool y los espera.

///

/// note | Detalles Técnicos de Starlette

```Python
contents = myfile.file.read()
```

/// note | Technische Details zu `async`

Wenn Sie die `async`-Methoden verwenden, führt **FastAPI** die Datei-Methoden in einem <abbr title="Mehrere unabhängige Kindprozesse">Threadpool</abbr> aus und erwartet sie.

///

/// note | Technische Details zu Starlette

    // Fesen Client
    //

    /**
     * Gets the thread pool used by the client.
     *
     * @return the thread pool
     */
    @Override
    public ThreadPool threadPool() {
        return client.threadPool();
    }

    /**
     * Gets the admin client for cluster and index administration.
     *
     * @return the admin client
     */
    @Override

common:mkl_threadpool --define=build_with_mkl=true --define=enable_mkl=true
common:mkl_threadpool --define=tensorflow_mkldnn_contraction_kernel=0
common:mkl_threadpool --define=build_with_mkl_opensource=true
common:mkl_threadpool -c opt

# Config setting to build oneDNN with Compute Library for the Arm Architecture (ACL).
# with Eigen threadpool support

 *
 * {@snippet :
 * ListenableFuture<Boolean> adminIsLoggedIn =
 *     FluentFuture.from(usersDatabase.getAdminUser())
 *         .transform(User::getId, directExecutor())
 *         .transform(ActivityService::isLoggedIn, threadPool)
 *         .catching(RpcException.class, e -> false, directExecutor());
 * }
 *
 * <h3>Alternatives</h3>
 *
 * <h4>Frameworks</h4>
 *

    "@zlib//:__subpackages__",
    "@dlpack//:__subpackages__",
    "@arm_neon_2_x86_sse//:__subpackages__",
    "@cpuinfo//:__subpackages__",
    "@ruy//:__subpackages__",
    "@XNNPACK//:__subpackages__",
    "@pthreadpool//:__subpackages__",
    "@FXdiv//:__subpackages__",
    "@FP16//:__subpackages__",
    "@clog//:__subpackages__",
    "@flatbuffers//:__subpackages__",
    "@nccl_archive//:__subpackages__",

你大概可以跳過這段。

這裡是有關 FastAPI 內部技術細節。

如果你有相當多的技術背景（例如協程、執行緒、阻塞等），並且對 FastAPI 如何處理 `async def` 與常規 `def` 感到好奇，請繼續閱讀。

///

### 路徑操作函数

當你使用 `def` 而不是 `async def` 宣告*路徑操作函式*時，該函式會在外部的執行緒池（threadpool）中執行，然後等待結果，而不是直接呼叫（因為這樣會阻塞伺服器）。

如果你來自於其他不以這種方式運作的非同步框架，而且你習慣於使用普通的 `def` 定義僅進行簡單計算的*路徑操作函式*，目的是獲得微小的性能增益（大約 100 奈秒），請注意，在 FastAPI 中，效果會完全相反。在這些情況下，最好使用 `async def`除非你的*路徑操作函式*執行阻塞的 <abbr title="輸入/輸出：磁碟讀寫或網路通訊">I/O</abbr> 的程式碼。

    * Add optional `use_unbounded_threadpool` argument to `map`, to specify that the `map` should use an unbounded threadpool instead of the default pool that is based on the number of cores on the machine. This can improve throughput for map functions which perform IO or otherwise release the CPU.