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).

### Examples of Replication Tools and Strategies

				if v.N > 0 {
					di.Metrics.LastMinute[k] = v.asTimedAction()
				}
			}
			for k, v := range info.Metrics.APICalls {
				di.Metrics.APICalls[k] = v
			}
			if info.Total > 0 {
				di.Utilization = float64(info.Used / info.Total * 100)
			}
			disksInfo[index] = di
			return nil
		}, index)
	}

	g.Wait()
	return disksInfo
}

// Get an aggregated storage info across all disks.

	// in the 10 node shard would coordinate between other 9 shards to get the
	// rest of the `99*9` requests.
	//
	// This essentially splits the workload properly and also allows for network
	// utilization to be optimal, instead of blindly throttling the way we are
	// doing below. However the changes that are needed here are a bit involved,
	// further discussion advised. Remove this comment and remove the worker model

   ([#123492](https://github.com/kubernetes/kubernetes/pull/123492), [@carlory](https://github.com/carlory))
- Added metric name along with the utilization information when running `kubectl get hpa`. ([#122804](https://github.com/kubernetes/kubernetes/pull/122804), [@sreeram-venkitesh](https://github.com/sreeram-venkitesh))

Vous devez vous assurer qu'il est unique pour chaque opération.

```Python hl_lines="6"
{!../../docs_src/path_operation_advanced_configuration/tutorial001.py!}
```

### Utilisation du nom *path operation function* comme operationId

///

**FastAPI** est basé entièrement sur **Pydantic**.

Vous verrez bien plus d'exemples de son utilisation dans [Tutoriel - Guide utilisateur](tutorial/index.md){.internal-link target=_blank}.

## Les annotations de type dans **FastAPI**

**FastAPI** utilise ces annotations pour faire différentes choses.

Cette combinaison de Flask, Flask-apispec avec Marshmallow et Webargs était ma stack backend préférée jusqu'à la création de **FastAPI**.

Son utilisation a conduit à la création de plusieurs générateurs Flask full-stack. Ce sont les principales stacks que
j'ai (ainsi que plusieurs équipes externes) utilisées jusqu'à présent :

    Detection using Deep Neural Networks.
*   New (experimental)
    [Java API](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/java).
*   Add new Android image stylization demo based on "A Learned Representation
    For Artistic Style", and add YOLO object detector support.

## Breaking Changes to the API