An interesting detail is that the percentage of the **CPU used** by each process can **vary** a lot over time, but the **memory (RAM)** normally stays more or less **stable**.

If you have an API that does a comparable amount of computations every time and you have a lot of clients, then the **CPU utilization** will probably *also be stable* (instead of constantly going up and down quickly).

Pydantic is a library to define data validation, serialization and documentation (using JSON Schema) based on Python type hints.

That makes it extremely intuitive.

It is comparable to Marshmallow. Although it's faster than Marshmallow in benchmarks. And as it is based on the same Python type hints, the editor support is great.

/// check | **FastAPI** uses it to

}

// RoundTripper provides an interface for type roundtrip serialization.
type RoundTripper interface {
	msgp.Unmarshaler
	msgp.Marshaler
	msgp.Sizer

	comparable
}

// SingleHandler is a type safe handler for single roundtrip requests.
type SingleHandler[Req, Resp RoundTripper] struct {
	id            HandlerID
	sharedResp    bool
	callReuseReq  bool

            .expectedValuesPerKey(1);
    builder.put("key", "value");
    assertThat(builder.build().entries()).containsExactly(Maps.immutableEntry("key", "value"));
  }

  static class HashHostileComparable implements Comparable<HashHostileComparable> {
    final String string;

    public HashHostileComparable(String string) {
      this.string = string;
    }

    @Override
    public int hashCode() {

Pydantic est une bibliothèque permettant de définir la validation, la sérialisation et la documentation des données (à l'aide de JSON Schema) en se basant sur les Python type hints.

Cela le rend extrêmement intuitif.

Il est comparable à Marshmallow. Bien qu'il soit plus rapide que Marshmallow dans les benchmarks. Et comme il est
basé sur les mêmes type hints Python, le support de l'éditeur est grand.

/// check | **FastAPI** l'utilise pour

Pydantic es un paquete para definir validación de datos, serialización y documentación (usando JSON Schema) basándose en las anotaciones de tipos de Python.

Eso lo hace extremadamente intuitivo.

Es comparable a Marshmallow. Aunque es más rápido que Marshmallow en benchmarks. Y como está basado en las mismas anotaciones de tipos de Python, el soporte del editor es estupendo.

/// check | **FastAPI** lo usa para

Un detalle interesante es que el porcentaje de **CPU utilizado** por cada proceso puede **variar** mucho con el tiempo, pero la **memoria (RAM)** normalmente permanece más o menos **estable**.

Si tienes una API que hace una cantidad comparable de cálculos cada vez y tienes muchos clientes, entonces la **utilización de CPU** probablemente *también sea estable* (en lugar de constantemente subir y bajar rápidamente).

    assertEquals(Integer.class, f.resolveType(Function.class.getTypeParameters()[1]).getType());
    assertEquals(Long.class, f.resolveType(Iterable.class.getTypeParameters()[0]).getType());
  }

  public <E extends Comparable<Iterable<String>> & Iterable<Integer>>
      void testResolveType_fromTypeVariable_onlyDirectBoundsAreUsed() {
    TypeToken<?> e = TypeToken.of(new TypeCapture<E>() {}.capture());

    assertThat(map).hasSize(100_000);
    assertThat(map).containsExactlyEntriesIn(expected).inOrder();
  }

  private static class ClassWithTerribleHashCode implements Comparable<ClassWithTerribleHashCode> {
    private final int value;

    ClassWithTerribleHashCode(int value) {
      this.value = value;
    }

    @Override

		// gets rejected even when we skip certificate verification. This helps
		// clients detect malformed certificates during testing instead of e.g.
		// a self-signed certificate that works while a comparable certificate
		// issued by a trusted CA fails due to the MinIO server being less strict
		// w.r.t. key usage verification.
		//
		// Basically, MinIO is more consistent (from a client perspective) when