format.Erasure.Sets[i] = make([]string, setLen)
		for j := range setLen {
			format.Erasure.Sets[i][j] = mustGetUUID()
		}
	}
	return format
}

// Returns format Erasure version after reading `format.json`, returns
// successfully the version only if the backend is Erasure.
func formatGetBackendErasureVersion(b []byte) (string, error) {
	meta := &formatMetaV1{}
	if err := json.Unmarshal(b, meta); err != nil {

### Memória do servidor { #server-memory }

Por exemplo, se seu código carrega um modelo de Machine Learning com **1 GB de tamanho**, quando você executa um processo com sua API, ele consumirá pelo menos 1 GB de RAM. E se você iniciar **4 processos** (4 trabalhadores), cada um consumirá 1 GB de RAM. Então, no total, sua API consumirá **4 GB de RAM**.

		Name:      errorsTotal,
		Help:      "Objects with healing failed since server uptime",
		Type:      counterMetric,
	}
}

func getHealLastActivityTimeMD() MetricDescription {
	return MetricDescription{
		Namespace: healMetricNamespace,
		Subsystem: timeSubsystem,
		Name:      lastActivityTime,
		Help:      "Time elapsed (in nano seconds) since last self healing activity",
		Type:      gaugeMetric,
	}
}

///

### <a href="https://webargs.readthedocs.io/en/latest/" class="external-link" target="_blank">Webargs</a> { #webargs }

Another big feature required by APIs is <abbr title="reading and converting to Python data">parsing</abbr> data from incoming requests.

Webargs is a tool that was made to provide that on top of several frameworks, including Flask.

			c.Lock()
			defer c.Unlock()
			c.state = srState{}
			c.enabled = false
		}
		return err
	}

	// attempt to read just the version key in the state file to ensure we
	// are reading a compatible version.
	var ver struct {
		Version int `json:"version"`
	}
	err = json.Unmarshal(buf, &ver)
	if err != nil {
		return err
	}
	if ver.Version != srStateFormatVersion1 {

		opts.VersionID = oi.VersionID
	}
	tags := newLifecycleAuditEvent(src, lcEvent).Tags()
	if lcEvent.Action.DeleteRestored() {
		// delete locally restored copy of object or object version
		// from the source, while leaving metadata behind. The data on
		// transitioned tier lies untouched and still accessible
		opts.Transition.ExpireRestored = true
		_, err := objectAPI.DeleteObject(ctx, oi.Bucket, oi.Name, opts)
		if err == nil {

### Конкурентность + параллелизм: Веб + Машинное обучение { #concurrency-parallelism-web-machine-learning }

С **FastAPI** вы можете использовать преимущества конкурентности, что очень распространено в веб-разработке (это та же основная «фишка» NodeJS).

				} else if !errors.Is(err, errDriveIsRoot) {
					if log {
						printEndpointError(endpoint, err, true)
					}
				}
				return
			}
			if disk.IsLocal() {
				h := disk.Healing()
				if h != nil && !h.Finished {
					globalBackgroundHealState.pushHealLocalDisks(disk.Endpoint())
				}
			}
			s.erasureDisksMu.Lock()
			setIndex, diskIndex, err := findDiskIndex(s.format, format)

Si tu aplicación es **simple**, probablemente esto **no será un problema**, y puede que no necesites especificar límites de memoria estrictos. Pero si estás **usando mucha memoria** (por ejemplo, con modelos de **Machine Learning**), deberías verificar cuánta memoria estás consumiendo y ajustar el **número de contenedores** que se ejecutan en **cada máquina** (y tal vez agregar más máquinas a tu cluster).

If your application is **simple**, this will probably **not be a problem**, and you might not need to specify hard memory limits. But if you are **using a lot of memory** (for example with **machine learning** models), you should check how much memory you are consuming and adjust the **number of containers** that runs in **each machine** (and maybe add more machines to your cluster).