labels.endTime=Heure de fin
labels.hq=hq
labels.inputs=Entrées
labels.jobLogging=Journalisation
labels.jobName=Nom
labels.jobStatus=Statut
labels.labelTypeIds=Étiquettes
labels.lang=Langue
labels.outputs=Sorties
labels.pos=Partie du discours
labels.purgeJobLogDay=Supprimer les anciens journaux de tâches
labels.purgeUserInfoDay=Supprimer les anciens journaux utilisateur
labels.reading=Lecture
labels.roleTypeIds=ID de rôle

import com.google.common.collect.testing.TestStringSortedMapGenerator;
import java.util.List;
import java.util.Map.Entry;
import java.util.SortedMap;
import org.jspecify.annotations.NullMarked;

/**
 * Generators of sorted maps and derived collections.
 *
 * @author Kevin Bourrillion
 * @author Jesse Wilson
 * @author Jared Levy
 * @author Hayward Chan
 * @author Chris Povirk
 * @author Louis Wasserman
 */
@GwtCompatible

 * parallel execution efficiency.
 *
 * <p>The algorithm calculates a weight for each project as:
 * weight = 1 + max(downstream_project_weights)
 *
 * <p>Projects are then sorted by weight in descending order, ensuring that
 * projects with longer dependency chains are built first. When projects have
 * the same weight, they are ordered by project ID for deterministic results.
 *
 * <p><b>Example:</b>

		setCounts = append(setCounts, setCount)
	}

	// Not necessarily needed but it ensures to the readers
	// eyes that we prefer a sorted setCount slice for the
	// subsequent function to figure out the right common
	// divisor, it avoids loops.
	slices.Sort(setCounts)

	return setCounts
}

// getSetIndexes returns list of indexes which provides the set size

Dans la liste des concepts de déploiement ci-dessus, l'utilisation de workers aide principalement à la partie réplication, et un peu aux redémarrages, mais vous devez toujours vous occuper des autres :

* **Sécurité - HTTPS**
* **Exécution au démarrage**
* ***Redémarrages***

@GwtIncompatible
@J2ktIncompatible
@NullUnmarked
enum QuantilesAlgorithm {

  /**
   * Sorts the dataset, and picks values from it. When computing multiple quantiles, we sort once
   * and pick multiple values.
   */
  SORTING {

    @Override
    double singleQuantile(int index, int scale, double[] dataset) {
      Arrays.sort(dataset);
      return singleQuantileFromSorted(index, scale, dataset);
    }

    @Override

            val allMinorLatests = allVersions.map { version ->
                VersionNumber.parse(version)
            }.sorted().groupBy { version ->
                VersionNumber.version(version.major, version.minor)
            }.map { (_, versions) ->
                versions.last()
            }

        } else {
            plan.steps(project)
                    .filter(step ->
                            step.phase != null && step.executions().findAny().isPresent())
                    .sorted(Comparator.comparingInt(plan.sortedNodes()::indexOf))
                    .forEach(step -> {
                        writer.accept("\t-----------------------------------------------------------------------");

            minimum: 0
            exclusiveMinimum: false
            maximum: 100
            exclusiveMaximum: false
            default: 20
            example: 20
        - name: sort
          in: query
          description: Sorted field name
          required: false
          schema:
            type: string
            example: score
        - name: fields.label
          in: query

   * // returns {"foo", "quux"}
   * }
   *
   * <p>This {@code Collector} uses O(k) memory and takes expected time O(n) (worst-case O(n log
   * k)), as opposed to e.g. {@code Stream.sorted(comparator).limit(k)}, which currently takes O(n
   * log n) time and O(n) space.
   *
   * @throws IllegalArgumentException if {@code k < 0}
   * @since 33.2.0 (available since 22.0 in guava-jre)
   */