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

/// note | `async` Teknik Detaylar

`async` method’ları kullandığınızda, **FastAPI** dosya method’larını bir threadpool içinde çalıştırır ve bunları await eder.

///

/// note | Starlette Teknik Detaylar

@defaultMessage Spawns a new thread which is solely under lucenes control use ThreadPool#relativeTimeInMillis instead
org.apache.lucene.search.TimeLimitingCollector#getGlobalTimerThread()
org.apache.lucene.search.TimeLimitingCollector#getGlobalCounter()

@app.get("/items/")
def read_items():
    data = some_blocking_library.fetch_items()
    return data
```

In case of doubt, or by default, use regular `def` functions, those will be run in a threadpool so they don't block the event loop.

The same rules apply to dependencies.

Make sure blocking code is not run inside of `async` functions. The logic will work, but will damage the performance heavily.

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

    // 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

 *
 * {@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>
 *

///

### Функції операції шляху { #path-operation-functions }

Коли ви оголошуєте функцію операції шляху зі звичайним `def` замість `async def`, вона виконується у зовнішньому пулі потоків (threadpool), який потім «очікується», замість прямого виклику (оскільки прямий виклик блокував би сервер).

    "@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__",

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。

///

### 路徑操作函式 { #path-operation-functions }

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）。

    * 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.