* longer, since an iteration across all row keys occurs.
   *
   * <p>Note that this implementation is not synchronized. If multiple threads access this table
   * concurrently and one of the threads modifies the table, it must be synchronized externally.
   *
   * <p>The table is serializable if {@code backingMap}, {@code factory}, the maps generated by

	return writeAllMetadataWithRevert(ctx, disks, origbucket, bucket, prefix, files, quorum, true)
}

// writeUniqueFileInfo - writes unique `xl.meta` content for each disk concurrently.
func writeUniqueFileInfo(ctx context.Context, disks []StorageAPI, origbucket, bucket, prefix string, files []FileInfo, quorum int) ([]StorageAPI, error) {

  // Populated by the system.
  // Read-only.
  // More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#concurrency-control-and-consistency
  // +optional
  optional string resourceVersion = 2;

  // continue may be set if the user set a limit on the number of items returned, and indicates that

				stopFn := globalBatchJobsMetrics.trace(batchJobMetricReplication, job.ID, attempts)
				success := true
				if err := r.ReplicateFromSource(ctx, api, core, oi, retry); err != nil {
					// object must be deleted concurrently, allow these failures but do not count them
					if isErrVersionNotFound(err) || isErrObjectNotFound(err) {
						return
					}
					stopFn(oi, err)
					batchLogIf(ctx, err)
					success = false

		// Initiate ticker after body has been read.
		ticker := time.NewTicker(time.Second * 10)
		defer ticker.Stop()

		for {
			select {
			case <-ticker.C:
				// The done() might have been called
				// concurrently, check for it before we
				// write the filler byte.
				select {
				case err := <-doneCh:
					if err != nil {
						write([]byte{1})
						write([]byte(err.Error()))
					} else {

### Conclusão dos hambúrgueres

Nesse cenário dos "hambúrgueres com seu _crush_ :heart_eyes:", como tem muita espera, faz mais sentido ter um sistema concorrente.

Esse é o caso da maioria das aplicações web.

Geralmente são muitos usuários, e seu servidor está esperando pelas suas conexões não tão boas para enviar as requisições.

* Others...

## Replication - Processes and Memory

With a FastAPI application, using a server program like the `fastapi` command that runs Uvicorn, running it once in **one process** can serve multiple clients concurrently.

But in many cases, you will want to run several worker processes at the same time.

### Multiple Processes - Workers

        classification.

*   `tf.data`:

    *   Added support for cross-trainer data caching in tf.data service. This
        saves computation resources when concurrent training jobs train from the
        same dataset. See
        (https://www.tensorflow.org/api_docs/python/tf/data/experimental/service#sharing_tfdata_service_with_concurrent_trainers)
        for more details.

  public final Optional<@NonNull E> last() {
    // Iterables#getLast was inlined here so we don't have to throw/catch a NSEE

    // TODO(kevinb): Support a concurrently modified collection?
    Iterable<E> iterable = getDelegate();
    if (iterable instanceof List) {
      List<E> list = (List<E>) iterable;
      if (list.isEmpty()) {
        return Optional.absent();
      }

	values := make(url.Values)
	values.Set(peerRESTSize, strconv.Itoa(opts.objectSize))
	values.Set(peerRESTConcurrent, strconv.Itoa(opts.concurrency))
	values.Set(peerRESTDuration, opts.duration.String())
	values.Set(peerRESTStorageClass, opts.storageClass)
	values.Set(peerRESTBucket, opts.bucketName)