/**
   * This counter prevents an ABA issue where a thread may successfully schedule the worker, the
   * worker runs and exhausts the queue, another thread enqueues a task and fails to schedule the
   * worker, and then the first thread's call to delegate.execute() returns. Without this counter,
   * it would observe the QUEUING state and set it to QUEUED, and the worker would never be
   * scheduled again for future submissions.
   */
  @GuardedBy("queue")

## 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.

	retry:
		// If the channel reaches above half capacity
		// we spawn more workers. The workers spawned
		// from this main worker routine will exit
		// once the channel drops below half capacity
		// and when it's been at least 30 seconds since
		// we launched a new worker.
		if mainWorker && len(h.logCh) > cap(h.logCh)/2 {
			nWorkers := h.workers.Load()
			if nWorkers < h.maxWorkers {

	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,

Olası kombinasyonlar ve stratejiler:

* `--workers` ile **Uvicorn**
    * Bir Uvicorn **process manager** **IP** ve **port** üzerinde dinler ve **birden fazla Uvicorn worker process** başlatır.
* **Kubernetes** ve diğer dağıtık **container sistemleri**

在這種情境中，你大概會希望「每個容器只有一個（Uvicorn）行程」，因為你已在叢集層級處理了複本。

所以這種情況下，你「不會」想在容器中使用多個 workers（例如用 `--workers` 命令列選項）。你會想每個容器只執行「一個 Uvicorn 行程」（但可能有多個容器）。

在容器內再放一個行程管理器（如同多 workers 的情況）只會增加「不必要的複雜度」，而你很可能已用叢集系統處理好了。

### 多行程容器與特殊情境 { #containers-with-multiple-processes-and-special-cases }

當然，有些「特殊情況」你可能會想在「一個容器內」執行數個「Uvicorn worker 行程」。

在這些情況中，你可以用 `--workers` 命令列選項來設定要啟動的 workers 數量：

```{ .dockerfile .annotate }
FROM python:3.14

      int64_t init_timeout_in_ms, int retries,
      bool clear_existing_contexts = false) = 0;

  // Initializes context for the local worker and no contexts will be created
  // for remote workers. Currently this only works for resetting context.
  // TODO(b/289445025): Consider removing this when we find a proper fix.
  virtual absl::Status InitializeLocalOnlyContext(const ServerDef& server_def,

Dans cet exemple, il y a un **processus gestionnaire** qui démarre et contrôle deux **processus workers**.

Ce processus gestionnaire serait probablement celui qui écoute sur le **port** de l'IP. Et il transmettrait toute la communication aux processus 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