var (
	memTotalMD     = NewGaugeMD(memTotal, "Total memory on the node")
	memUsedMD      = NewGaugeMD(memUsed, "Used memory on the node")
	memUsedPercMD  = NewGaugeMD(memUsedPerc, "Used memory percentage on the node")
	memFreeMD      = NewGaugeMD(memFree, "Free memory on the node")
	memBuffersMD   = NewGaugeMD(memBuffers, "Buffers memory on the node")
	memCacheMD     = NewGaugeMD(memCache, "Cache memory on the node")

   * 2^n}, its memory usage is only {@code O(n)}. When the power set is constructed, the input set
   * is merely copied. Only as the power set is iterated are the individual subsets created, and
   * these subsets themselves occupy only a small constant amount of memory.
   *
   * @param set the set of elements to construct a power set from

	rootAccess                  bool
	syncEvents                  bool
	objectMaxVersions           int64
}

const (
	cgroupV1MemLimitFile = "/sys/fs/cgroup/memory/memory.limit_in_bytes"
	cgroupV2MemLimitFile = "/sys/fs/cgroup/memory.max"
	cgroupMemNoLimit     = 9223372036854771712
)

func cgroupMemLimit() (limit uint64) {
	buf, err := os.ReadFile(cgroupV2MemLimitFile)
	if err != nil {

### Memory per Process

Now, when the program loads things in memory, for example, a machine learning model in a variable, or the contents of a large file in a variable, all that **consumes a bit of the memory (RAM)** of the server.

* Restarts
* Replication (the number of processes running)
* Memory
* Previous steps before starting

## Memory

If you run **a single process per container** you will have a more or less well-defined, stable, and limited amount of memory consumed by each of those containers (more than one if they are replicated).

      startupProbe:
        enabled: true
        failureThreshold: 600 # 10 minutes
      # Resources for the sidecar.
      resources:
        requests:
          cpu: 100m
          memory: 128Mi
        limits:
          cpu: 2000m
          memory: 1024Mi

      startupProbe:
        enabled: true
        failureThreshold: 600 # 10 minutes

      # Resources for the sidecar.
      resources:
        requests:
          cpu: 100m
          memory: 128Mi
        limits:
          cpu: 2000m
          memory: 1024Mi

					success = false
				} else {
					stopFn(oi, nil)
				}
				ri.trackCurrentBucketObject(r.Target.Bucket, oi, success)
				globalBatchJobsMetrics.save(job.ID, ri)
				// persist in-memory state to disk after every 10secs.
				batchLogIf(ctx, ri.updateAfter(ctx, api, 10*time.Second, job))

				if wait := globalBatchConfig.ReplicationWait(); wait > 0 {
					time.Sleep(wait)
				}
			}()
		}
		wk.Wait()

 * concurrently.
 */
class Dispatcher() {
  internal val lock: ReentrantLock = ReentrantLock()

  /**
   * The maximum number of requests to execute concurrently. Above this requests queue in memory,
   * waiting for the running calls to complete.
   *
   * If more than [maxRequests] requests are in flight when this is invoked, those requests will
   * remain in flight.
   */
  var maxRequests = 64

	}

	cache := store.lock()
	defer store.unlock()

	// We should only update the in-memory cache if there were no changes
	// to the in-memory cache since the disk loading began. If there
	// were changes to the in-memory cache we should wait for the next
	// cycle until we can safely update the in-memory cache.
	//
	// An in-memory cache must be replaced only if we know for sure that the