pluginAuthnServiceSuccAvgRttMsMinuteMD   = NewCounterMD(pluginAuthnServiceSuccAvgRttMsMinute, "When plugin authentication is configured, returns average round-trip-time of successful requests in the last full minute")

     *
     * @return number of active requests
     */
    public int getActiveRequests() {
        return activeRequests.get();
    }

    /**
     * Get average response time
     *
     * @return average response time in milliseconds
     */
    public long getAverageResponseTime() {
        return avgResponseTime.get();
    }

    /**
     * Get 95th percentile response time

</blockquote>

## Recherche

## Tips for Success

- **Small PRs**: Easier to review, faster to merge. Split large changes logically.
- **Clear Commits**: Use `git rebase -i` to refine history before submitting.
- **Engage Early**: Discuss complex changes in issues or Slack (https://slack.min.io) before coding.
- **Be Responsive**: Address reviewer feedback promptly to keep PRs moving.
- **Learn from Reviews**: Use feedback to improve future contributions.

        // Verify O(1) performance - should be reasonably fast (allowing for JVM overhead)
        assertTrue(avgAllocTimeNs < 10000, "Average allocation should be under 10000ns (O(1) performance)");
        assertTrue(avgReleaseTimeNs < 10000, "Average release should be under 10000ns (O(1) performance)");
        assertEquals(threadCount * operationsPerThread, allocations.get());

 * [okhttp-stats](https://github.com/flipkart-incubator/okhttp-stats): Get stats like average network speed.
 * [okhttp-system-keystore](https://github.com/charleskorn/okhttp-system-keystore): Use trusted certificates from the operating system keystore (Keychain on macOS, Certificate Store on Windows).

Avec **FastAPI** vous aurez toutes les fonctionnalités de **Pydantic**  (comme FastAPI est basé sur Pydantic pour toutes les manipulations de données):

* **Pas de prise de tête**:
    * Pas de nouveau langage de définition de schéma à apprendre.
    * Si vous connaissez le typage en python vous savez comment utiliser Pydantic.

  // We could make this a Map<E, EndpointPair<N>>. It would make incidentNodes(edge) slightly
  // faster, but also make Networks consume 5 to 20+% (increasing with average degree) more memory.
  final MapIteratorCache<E, N> edgeToReferenceNode; // referenceNode == source if directed

  /** Constructs a graph with the properties specified in {@code builder}. */

  // We could make this a Map<E, EndpointPair<N>>. It would make incidentNodes(edge) slightly
  // faster, but also make Networks consume 5 to 20+% (increasing with average degree) more memory.
  final MapIteratorCache<E, N> edgeToReferenceNode; // referenceNode == source if directed

  /** Constructs a graph with the properties specified in {@code builder}. */

		Name:      avgLinkLatency,
		Help:      "Average replication link latency in milliseconds",
		Type:      gaugeMetric,
	}
}

func getClusterReplAvgQueuedOperationsMD() MetricDescription {
	return MetricDescription{
		Namespace: nodeMetricNamespace,
		Subsystem: replicationSubsystem,
		Name:      avgInQueueCount,
		Help:      "Average number of objects queued for replication since server uptime",