- Already compressed objects are not fit for compression since they do not have compressible patterns.
Such objects do not produce efficient [`LZ compression`](https://en.wikipedia.org/wiki/LZ77_and_LZ78)
which is a fitness factor for a lossless data compression.

Pre-compressed input typically compresses in excess of 2GiB/s per core,
so performance impact should be minimal even if precompressed data is re-compressed.

- In this algorithm, we also make sure that we spread the drives out evenly. MinIO server expands ellipses passed as arguments. Here is a sample expansion to demonstrate the process.

	}

	return s, nil
}

// cleanup ".trash/" folder every 5m minutes with sufficient sleep cycles, between each
// deletes a dynamic sleeper is used with a factor of 10 ratio with max delay between
// deletes to be 2 seconds.
func (s *erasureSets) cleanupDeletedObjects(ctx context.Context) {
	timer := time.NewTimer(globalAPIConfig.getDeleteCleanupInterval())
	defer timer.Stop()

image: {{ .Values.image.repository }}:{{ .Values.image.tag }} imagePullPolicy: {{ .Values.image.pullPolicy }} command: [ "/bin/sh", "-ce", "/usr/bin/docker-entrypoint.sh minio server {{- range $i := until $poolCount }}{{ $factor := mul $i $nodeCount }}{{ $endIndex := add $factor $nodeCount }}{{ $beginIndex := mul $i $nodeCount }} {{ $scheme }}://{{ template `minio.fullname` $ }}-{{ `{` }}{{ $beginIndex }}...{{ sub $endIndex 1 }}{{ `}`}}.{{ template `minio.fullname` $ }}-svc.{{ $.Release.Namespace...