maxPartsCount             = 10000
	maxPartSize               = 1024 * 1024 * 1024 * 5
	minPartSize               = 1024 * 1024 * 128 // chosen by us to be optimal for HDDs
)

// optimalPartInfo - calculate the optimal part info for a given
// object size.
//
// NOTE: Assumption here is that for any object to be uploaded to any S3 compatible
// object storage it will have the following parameters as constants.

alias kv-pairify='yq ".configuration[]" | jq ".[]" | jq -r ".name + \"=\" + .value"'
```

Let's take for example a set of 12 compute nodes with an aggregate memory of _1.2TiB_, we need to do following settings for optimal results. Add the following optimal entries for _core-site.xml_ to configure _s3a_ with **MinIO**. Most important options here are

```
cat ${HADOOP_CONF_DIR}/core-site.xml | kv-pairify | grep "mapred"

final class SingletonImmutableSet<E> extends ImmutableSet<E> {
  // We deliberately avoid caching the asList and hashCode here, to ensure that with
  // compressed oops, a SingletonImmutableSet packs all the way down to the optimal 16 bytes.

  final transient E element;

  SingletonImmutableSet(E element) {
    this.element = Preconditions.checkNotNull(element);
  }

  @Override
  public int size() {
    return 1;
  }

  // n: expected insertions
  // b: m/n, bits per insertion
  // p: expected false positive probability
  //
  // 1) Optimal k = b * ln2
  // 2) p = (1 - e ^ (-kn/m))^k
  // 3) For optimal k: p = 2 ^ (-k) ~= 0.6185^b
  // 4) For optimal k: m = -nlnp / ((ln2) ^ 2)

  /**
   * Computes the optimal number of hash functions (k) for a given false positive probability (p).
   *

# MinIO Server Limits Per Tenant

For optimal production setup MinIO recommends Linux kernel version 4.x and later.

## Erasure Code (Multiple Drives / Servers)

| Item                                                            | Specification |
|:----------------------------------------------------------------|:--------------|
| Maximum number of servers per cluster                           | no-limit      |

type endpointSet struct {
	argPatterns []ellipses.ArgPattern
	endpoints   []string   // Endpoints saved from previous GetEndpoints().
	setIndexes  [][]uint64 // All the sets.
}

// Supported set sizes this is used to find the optimal
// single set size.
var setSizes = []uint64{2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}

// getDivisibleSize - returns a greatest common divisor of
// all the ellipses sizes.

list_quorum                     (string)    set the acceptable quorum expected for list operations e.g. "optimal", "reduced", "disk", "strict", "auto" (default: 'strict')
replication_priority            (string)    set replication priority (default: 'auto')

 * amortized since some operations can trigger a hash table resize.
 *
 * <p>Unlike {@code java.util.HashSet}, iteration is only proportional to the actual {@code size()},
 * which is optimal, and <i>not</i> the size of the internal hashtable, which could be much larger
 * than {@code size()}. Furthermore, this structure only depends on a fixed number of arrays; {@code

    tester.testAllPublicInstanceMethods(BloomFilter.create(Funnels.unencodedCharsFunnel(), 100));
    tester.testAllPublicStaticMethods(BloomFilter.class);
  }

  /** Tests that we never get an optimal hashes number of zero. */
  public void testOptimalHashes() {
    for (int n = 1; n < 1000; n++) {
      for (double p = 0.1; p > 1e-10; p /= 10) {

---

Für dieses Beispiel verwenden wir jedoch ein sehr einfaches HTML-Dokument mit etwas JavaScript, alles in einem langen String.

Das ist natürlich nicht optimal und man würde das nicht in der Produktion machen.

In der Produktion hätten Sie eine der oben genannten Optionen.