In vielen Fällen möchten Sie jedoch mehrere Workerprozesse gleichzeitig ausführen.

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

但在许多情况下，您会希望同时运行多个工作进程。

### 多进程 - Workers { #multiple-processes-workers }

如果您的客户端数量多于单个进程可以处理的数量（例如，如果虚拟机不是太大），并且服务器的 CPU 中有 **多个核心**，那么您可以让 **多个进程** 同时运行同一个应用程序，并在它们之间分发所有请求。

当您运行同一 API 程序的**多个进程**时，它们通常称为 **workers**。

### 工作进程和端口 { #worker-processes-and-ports }

还记得文档 [关于 HTTPS](https.md) 中只有一个进程可以侦听服务器中的端口和 IP 地址的一种组合吗？

现在仍然是对的。

	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

	if err != nil {
		rebalanceLogIf(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
	}

// EagerService to maintain consistency between client and worker. The
// context_id is initialized with a dummy value and is later set when the worker
// is initialized (either locally or remotely). The context_id can change during
// the process lifetime although this should cause the worker to be
// reinitialized (e.g. cleared caches) as well.

// newBatchJobPool creates a pool of job manifest workers of specified size
func newBatchJobPool(ctx context.Context, o ObjectLayer, workers int) *BatchJobPool {
	jpool := &BatchJobPool{
		ctx:          ctx,
		objLayer:     o,
		jobCh:        make(chan *BatchJobRequest, 10000),
		workerKillCh: make(chan struct{}, workers),
		jobCancelers: make(map[string]context.CancelFunc),
	}

	oi, ok := oiCache[fmt.Sprintf("%s-%s", toDel.ObjectName, toDel.VersionID)]
	return oi, ok
}

func batchObjsForDelete(ctx context.Context, r *BatchJobExpire, ri *batchJobInfo, job BatchJobRequest, api ObjectLayer, wk *workers.Workers, expireCh <-chan []expireObjInfo) {
	vc, _ := globalBucketVersioningSys.Get(r.Bucket)
	retryAttempts := job.Expire.Retry.Attempts
	if retryAttempts <= 0 {
		retryAttempts = batchExpireJobDefaultRetries
	}

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

WORKDIR /code

COPY ./requirements.txt /code/requirements.txt

RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt

COPY ./app /code/app

# (1)!
CMD ["fastapi", "run", "app/main.py", "--port", "80", "--workers", "4"]
```

	}

	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)

* der Task
* der Hintergrundtask
* die Taskfunktion

* das Template
* die Template-Engine

* die Typannotation
* der Typhinweis

* der Serverworker
* der Uvicorn-Worker
* der Gunicorn-Worker
* der Workerprozess
* die Workerklasse
* die Workload

* das Deployment
* deployen

* das SDK
* das Software Development Kit

* der `APIRouter`
* die `requirements.txt`