## Decision

1. The Gradle distribution will never include a Java runtime.
   Instead, Gradle will leverage Java toolchains for the Daemon and worker processes.
2. The Gradle Daemon, Worker processes, and Tooling API client will continue to support running on different Java versions during a single Gradle invocation.
   The exact versions supported will be determined by the Gradle version.

    <!-- These packages are duplicated in workers and daemon-server-worker, don't require a package-info.java in each place -->
    <suppress checks="JavadocPackage"
              files=".*[/\\]platforms[/\\]core-execution[/\\]daemon-server-worker[/\\]src[/\\]main[/\\]java[/\\]org[/\\]gradle[/\\]workers[/\\][^/\\]+"/>

    <!-- Don't require api docs for projects only used internally -->

Y si necesitas tener múltiples workers, puedes simplemente utilizar la opción de línea de comandos `--workers`.

/// note | Detalles Técnicos

La imagen de Docker se creó cuando Uvicorn no soportaba gestionar y reiniciar workers muertos, por lo que era necesario usar Gunicorn con Uvicorn, lo que añadía bastante complejidad, solo para que Gunicorn gestionara y reiniciara los worker processes de Uvicorn.

	sync.RWMutex                 // mutex for Cache
	mostRecentStatsMu sync.Mutex // mutex for mostRecentStats

	wlock sync.RWMutex // mutex for active workers

	movingAvgTicker *time.Ticker // Ticker for calculating moving averages
	wTimer          *time.Ticker // ticker for calculating active workers
	qTimer          *time.Ticker // ticker for calculating queue stats
}

func (r *ReplicationStats) trackEWMA() {
	for {
		select {

		t.Run(fmt.Sprintf("TestApplyNewerNoncurrentVersionsLimit-%d", i), func(t *testing.T) {
			workers := []chan expiryOp{make(chan expiryOp)}
			es.workers.Store(&workers)
			workerReady := make(chan struct{})
			var wg sync.WaitGroup
			wg.Add(1)
			var gotExpired []ObjectToDelete
			go expiryWorker(&wg, workerReady, workers[0], &gotExpired)
			<-workerReady

			item := scannerItem{
				Path:        obj,

	recordsRead int                       // number of records read in current slice
	input       chan *queueItem           // input for workers
	queue       chan *queueItem           // output from workers in order
	err         error                     // global error state, only touched by Reader.Read
	bufferPool  bpool.Pool[[]byte]        // pool of []byte objects for input

In vielen Fällen möchten Sie jedoch mehrere Workerprozesse gleichzeitig ausführen.

### Mehrere Prozesse – Worker { #multiple-processes-workers }

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**

            <option value="$PROJECT_DIR$/platforms/core-execution/scoped-persistent-cache" />
            <option value="$PROJECT_DIR$/platforms/core-execution/snapshots" />
            <option value="$PROJECT_DIR$/platforms/core-execution/worker-main" />
            <option value="$PROJECT_DIR$/platforms/core-execution/workers" />

	recordsRead  int                    // number of records read in current slice
	input        chan *queueItem        // input for workers
	queue        chan *queueItem        // output from workers in order
	err          error                  // global error state, only touched by Reader.Read
	bufferPool   bpool.Pool[[]byte]     // pool of []byte objects for input