if err != nil {
		decomLogIf(ctx, fmt.Errorf("invalid workers value err: %v, defaulting to %d", err, len(pool.sets)))
		workerSize = len(pool.sets)
	}

	// Each decom worker needs one List() goroutine/worker
	// add that many extra workers.
	workerSize += len(pool.sets)

	wk, err := workers.New(workerSize)
	if err != nil {
		return err
	}

	var vc *versioning.Versioning

	}

	workerSize, err := strconv.Atoi(env.Get("_MINIO_BATCH_KEYROTATION_WORKERS", strconv.Itoa(runtime.GOMAXPROCS(0)/2)))
	if err != nil {
		return err
	}

	wk, err := workers.New(workerSize)
	if err != nil {
		// invalid worker size.
		return err
	}

	ctx, cancel := context.WithCancel(ctx)

	results := make(chan itemOrErr[ObjectInfo], 100)

        subprojectName.contains("native") ||
            // Include precondition-tester here so we understand that tests do run on macOS intel as well
            subprojectName in listOf("file-watching", "snapshots", "workers", "logging", "precondition-tester")
    } else {
        true

可以通过多种方法来实现这一目标，我将在接下来的章节中向您详细介绍具体策略，例如在谈论 Docker 和容器时。

要考虑的主要限制是必须有一个**单个**组件来处理**公共IP**中的**端口**。 然后它必须有一种方法将通信**传输**到复制的**进程/worker**。

以下是一些可能的组合和策略：

* **Gunicorn** 管理 **Uvicorn workers**
     * Gunicorn 将是监听 **IP** 和 **端口** 的 **进程管理器**，复制将通过 **多个 Uvicorn 工作进程** 进行
* **Uvicorn** 管理 **Uvicorn workers**
     * 一个 Uvicorn **进程管理器** 将监听 **IP** 和 **端口**，并且它将启动 **多个 Uvicorn 工作进程**
* **Kubernetes** 和其他分布式 **容器系统**

func (m *rateMeasurement) getExpMovingAvgBytesPerSecond() float64 {
	m.lock.Lock()
	defer m.lock.Unlock()
	return m.expMovingAvg
}

// ActiveWorkerStat is stat for active replication workers
type ActiveWorkerStat struct {
	Curr int     `json:"curr"`
	Avg  float32 `json:"avg"`
	Max  int     `json:"max"`
	hist metrics.Histogram
}

func newActiveWorkerStat(r metrics.Registry) *ActiveWorkerStat {

                "%windows.java25.openjdk.64bit%",
            )
        val expectedInstallationPaths = (if (os == Os.WINDOWS) windowsPaths else linuxPaths).joinToString(",")
        return listOf(
            "-Dorg.gradle.workers.max=%maxParallelForks%",
            "-PmaxParallelForks=%maxParallelForks% $PLUGINS_PORTAL_URL_OVERRIDE -Dscan.value.tcPipeline=master -s",
            "%additional.gradle.parameters%",

 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package gradlebuild.incubation.action

import org.gradle.api.logging.Logger
import org.gradle.workers.WorkAction
import org.slf4j.LoggerFactory


abstract class IncubatingApiReportAggregationWorkAction : WorkAction<IncubatingApiReportAggregationParameter> {

    companion object {

  "durations" : [ {
    "testProject" : "nativeMonolithic",
    "linux" : 1215
  }, {
    "testProject" : "nativeMonolithicOverlapping",
    "linux" : 1324
  } ]
}, {
  "scenario" : "org.gradle.performance.regression.nativeplatform.RealWorldNativePluginPerformanceTest.build with 12 parallel workers",
  "durations" : [ {
    "testProject" : "nativeMonolithic",

    listOf(
        // We pass the 'maxParallelForks' setting as 'workers.max' to limit the maximum number of executers even
        // if multiple test tasks run in parallel. We also pass it to the Gradle build as a maximum (maxParallelForks)
        // for each test task, such that we are independent of whatever default value is defined in the build itself.
        "-Dorg.gradle.workers.max=$maxParallelForks",
        "-PmaxParallelForks=$maxParallelForks",

load, number of objects in the object store as well as storage speed. In addition, any bandwidth limits set via `mc admin bucket remote add` could also contribute to replication speed. The number of workers used for replication defaults to 100. Based on network bandwidth and system load, the number of workers used in replication can be configured using `mc admin config set alias api` to set the `replication_workers`. The prometheus metrics exposed by MinIO can be used to plan resource allocation and bandwidth...