Given the simplicity of Flask, it seemed like a good match for building APIs. The next thing to find was a "Django REST Framework" for Flask.

/// check | Inspired **FastAPI** to

Be a micro-framework. Making it easy to mix and match the tools and parts needed.

Have a simple and easy to use routing system.

///

### [Requests](https://requests.readthedocs.io) { #requests }

STXVD2X VS, (RA+RB) #endif #define MASK_PTR R8 #define MASKV V0 #define INV V1 // The following macros are used for // the stitched implementation within // counterCryptASM. // Load the initial GCM counter value // in V30 and set up the counter increment // in V31 #define SETUP_COUNTER \ P8_LXVB16X(COUNTER, R0, V30); \ VSPLTISB $1, V28; \ VXOR V31, V31, V31; \ VSLDOI $1, V31, V28, V31 // These macros set up the initial value // for a single encryption, or 4 or 8 // stitched encryptions implemented...

With **FastAPI** you get all of **Pydantic**'s features (as FastAPI is based on Pydantic for all the data handling):

* **No brainfuck**:
    * No new schema definition micro-language to learn.
    * If you know Python types you know how to use Pydantic.

Com **FastAPI** você terá todos os recursos do **Pydantic** (já que FastAPI utiliza o Pydantic para todo o controle dos dados):

* **Sem pegadinhas**:
    * Sem novas definições de esquema de micro-linguagem para aprender.
    * Se você conhece os tipos do Python, você sabe como usar o Pydantic.

        assertTrue(queryFieldConfig.searchFieldSet.contains("field3"));
    }

    /**
     * Test performance improvement of Set-based lookup over array-based lookup.
     * This is a micro-benchmark to demonstrate the O(1) vs O(n) improvement.
     */
    @Test
    public void test_lookupPerformance_SetFasterThanArray() {
        // Create a large array of fields
        int fieldCount = 1000;

这也意味着在很多情况下，你可以将从请求中获得的相同对象**直接传到数据库**，因为所有的验证都是自动的。

反之亦然，在很多情况下，你也可以将从数据库中获取的对象**直接传到客户端**。

通过 **FastAPI** 你可以获得所有 **Pydantic**（FastAPI 基于 Pydantic 做了所有的数据处理）：

* **不烧脑**：
    * 没有新的模式定义 micro-language 需要学习。
    * 如果你知道 Python types，你就知道如何使用 Pydantic。
* 和你 **<abbr title="Integrated Development Environment - 集成开发环境: 类似于代码编辑器">IDE</abbr>/<dfn title="一个检查代码错误的程序">linter</dfn>/brain** 适配:

STXVD2X VS, (RA+RB) #endif #define MASK_PTR R8 #define MASKV V0 #define INV V1 // The following macros are used for // the stitched implementation within // counterCryptASM. // Load the initial GCM counter value // in V30 and set up the counter increment // in V31 #define SETUP_COUNTER \ P8_LXVB16X(COUNTER, R0, V30); \ VSPLTISB $1, V28; \ VXOR V31, V31, V31; \ VSLDOI $1, V31, V28, V31 // These macros set up the initial value // for a single encryption, or 4 or 8 // stitched encryptions implemented...

這也意味著在很多情況下，你可以把從請求中獲得的物件**直接傳到資料庫**，因為所有資料都會自動進行驗證。

反之亦然，在很多情況下，你也可以把從資料庫中獲取的物件**直接傳給客戶端**。

通過 **FastAPI** 你可以獲得所有 **Pydantic** 的特性（FastAPI 基於 Pydantic 做了所有的資料處理）：

* **更簡單**：
    * 不需要學習新的 micro-language 來定義結構。
    * 如果你知道 Python 型別，你就知道如何使用 Pydantic。
* 和你的 **<abbr title="Integrated Development Environment - 整合開發環境: 類似於程式碼編輯器">IDE</abbr>/<dfn title="檢查程式碼錯誤的程式">linter</dfn>/brain** 都能好好配合：

    try {
      // We get the result, even if collectOneValue is a no-op, so that we can fail fast.
      // We use getUninterruptibly over getDone as a micro-optimization, we know the future is done.
      collectOneValue(index, getUninterruptibly(future));
    } catch (ExecutionException e) {
      handleException(e.getCause());

pkg debug/elf, const R_RISCV_TPREL_S R_RISCV
pkg debug/elf, method (R_RISCV) GoString() string
pkg debug/elf, method (R_RISCV) String() string
pkg debug/elf, type R_RISCV int
pkg debug/macho, const CpuArm64 = 16777228
pkg debug/macho, const CpuArm64 Cpu
pkg debug/pe, const IMAGE_DIRECTORY_ENTRY_ARCHITECTURE = 7
pkg debug/pe, const IMAGE_DIRECTORY_ENTRY_ARCHITECTURE ideal-int
pkg debug/pe, const IMAGE_DIRECTORY_ENTRY_BASERELOC = 5