/// check | Periksa

Dengan deklarasi tipe data Python, **FastAPI** melakukan validasi data.

Perhatikan kesalahan tersebut juga menjelaskan validasi apa yang tidak sesuai.

Validasi ini sangat membantu ketika mengembangkan dan men-*debug* kode yang berhubungan dengan API anda.

///

## Dokumentasi

```Python
q: Union[str, None] = Query(default=None, max_length=50)
```

Cela va valider les données, montrer une erreur claire si ces dernières ne sont pas valides, et documenter le paramètre dans le schéma `OpenAPI` de cette *path operation*.

## Rajouter plus de validation

Vous pouvez aussi rajouter un second paramètre `min_length` :

            // validate()
            Method validateMethod = WebApiUtil.class.getMethod("validate");
            assertTrue("validate should be static", java.lang.reflect.Modifier.isStatic(validateMethod.getModifiers()));
            assertTrue("validate should be public", java.lang.reflect.Modifier.isPublic(validateMethod.getModifiers()));

	n = copy(p, r.data)
	r.eof = true
	return n, nil
}

// Wrapper for calling testMakeBucket for both Erasure and FS.
func TestMakeBucket(t *testing.T) {
	ExecObjectLayerTest(t, testMakeBucket)
}

// Tests validate bucket creation.
func testMakeBucket(obj ObjectLayer, instanceType string, t TestErrHandler) {
	err := obj.MakeBucket(context.Background(), "bucket-unknown", MakeBucketOptions{})
	if err != nil {

        assertEquals(Integer.MAX_VALUE, validator.safeAdd(Integer.MAX_VALUE, 0));
        assertEquals(Integer.MIN_VALUE, validator.safeAdd(Integer.MIN_VALUE, 0));
        assertEquals(0, validator.safeMultiply(0, Integer.MAX_VALUE));
        assertEquals(1, validator.safeMultiply(1, 1));

        // Test exact buffer boundaries
        byte[] buffer = new byte[100];

    * Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data.
* Validate **complex structures**:
    * Use of hierarchical Pydantic models, Python `typing`’s `List` and `Dict`, etc.
    * And validators allow complex data schemas to be clearly and easily defined, checked and documented as JSON Schema.

    * Doordat pydantic's datastructuren enkel instanties zijn van klassen, die je definieert, werkt automatische aanvulling, linting, mypy en je intuïtie allemaal goed met je gevalideerde data.
* Valideer **complexe structuren**:
    * Gebruik van hiërarchische Pydantic modellen, Python `typing`'s `List` en `Dict`, enz.

     * @param form The search form.
     * @return The HTML response.
     */
    @Execute
    @Secured({ ROLE, ROLE + VIEW })
    public HtmlResponse index(final SearchForm form) {
        validate(form, messages -> {}, this::asDictIndexHtml);
        kuromojiPager.clear();
        return asHtml(path_AdminDictKuromoji_AdminDictKuromojiJsp).renderWith(data -> {
            searchPaging(data, form);
        });

		// Part with size larger than 5Mb.
		{bucketNames[0], objectNames[0], uploadIDs[0], 5, string(validPart), validPartMD5, int64(len(validPart))},
		{bucketNames[0], objectNames[0], uploadIDs[0], 6, string(validPart), validPartMD5, int64(len(validPart))},
		{bucketNames[0], objectNames[0], uploadIDs[0], 7, string(validPart), validPartMD5, int64(len(validPart))},
	}
	sha256sum := ""
	var opts ObjectOptions

    * Swagger UI.
    * ReDoc.

---

Coming back to the previous code example, **FastAPI** will:

* Validate that there is an `item_id` in the path for `GET` and `PUT` requests.
* Validate that the `item_id` is of type `int` for `GET` and `PUT` requests.
    * If it is not, the client will see a useful, clear error.