// 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),
	}

          "interval": "1m",
          "intervalFactor": 2,
          "legendFormat": "{{server}}",
          "refId": "A"
        }
      ],
      "title": "Avg. Active Workers",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "${DS_PROMETHEUS}"
      },
      "fieldConfig": {
        "defaults": {

// versionID, NOTE: versionID must be valid and should exist -
// and must not be a DeleteMarker or legacy object, if no
// versionID is specified 'null' versionID is updated instead.
//
// It is callers responsibility to set correct versionID, this
// function shouldn't be further extended to update immutable
// values such as ErasureInfo, ChecksumInfo.
//
// Metadata is only updated to new values, existing values

			// remember the pool index, we may sort the slice original index might be lost.
			pinfo := PoolObjInfo{
				Index: i,
			}
			// do not remove this check as it can lead to inconsistencies
			// for all callers of bucket replication.
			if !opts.MetadataChg {
				opts.VersionID = ""
			}
			pinfo.ObjInfo, pinfo.Err = pool.GetObjectInfo(ctx, bucket, object, opts)
			poolObjInfos[i] = pinfo
		}(i, pool, poolOpts[i])
	}

// * int is used as an index into arrays.
// * Deletion functions are safe to call on nullptr.
//
// Questions left to address:
// * Might at some point need a way for callers to provide their own Env.
// * Maybe add TF_TensorShape that encapsulates dimension info.
//
// Design decisions made:
// * Backing store for tensor memory has an associated deallocation

		// Test listing an object with uncommon delimiter and matching prefix
		{testBuckets[4], "file1/", "", "guidSplunk", 1000, resultCases[35], nil, true},
		// Test listing at prefix with expected prefix markers
		{testBuckets[5], "dir/", "", SlashSeparator, 1, resultCases[36], nil, true},
		// Test listing with prefix match
		{testBuckets[5], "foo/201910/11", "", "", 1000, resultCases[37], nil, true},

Я подготовил для вас Docker-образ, в который включён Gunicorn управляющий процессами (воркерами) Uvicorn, в соответствии с концепциями рассмотренными в предыдущей главе: [Рабочие процессы сервера (воркеры) - Gunicorn совместно с Uvicorn](server-workers.md){.internal-link target=_blank}.

Этот образ может быть полезен для ситуаций описанных тут: [Множество процессов внутри контейнера для особых случаев](#_11).

build:rbe_base --remote_timeout=3600
build:rbe_base --spawn_strategy=remote,worker,standalone,local
# Attempt to minimize the amount of data transfer between bazel and the remote
# workers:
build:rbe_base --remote_download_toplevel
test:rbe_base --test_env=USER=anon

# TODO(kanglan): Check if we want to merge rbe_linux into rbe_linux_cpu.
build:rbe_linux --config=rbe_base

					res["repl-pending-"+tgt] = fmt.Sprintf("%d versions, %d bytes", st.pendingCount, st.pendingSize)
				}
			}
		}
		if objDeleted {
			// we return errIgnoreFileContrib to signal this function's
			// callers to skip this object's contribution towards
			// usage.
			return sizeSummary{}, errIgnoreFileContrib
		}
		return sizeS, nil
	}, scanMode, weSleep)
	if err != nil {
		return dataUsageInfo, err
	}

				errFileVersionNotFound,
				errFileCorrupt,
			) {
				missingBlocks++
			}
		}

		// if missing metadata can be reconstructed, attempt to reconstruct.
		// additionally do not heal delete markers inline, let them be
		// healed upon regular heal process.
		if missingBlocks > 0 && missingBlocks < fi.Erasure.DataBlocks {
			globalMRFState.addPartialOp(PartialOperation{
				Bucket:    fi.Volume,