*/
  Set<E> elementSet();

  /**
   * Returns a view of the contents of this multiset, grouped into {@code Multiset.Entry} instances,
   * each providing an element of the multiset and the count of that element. This set contains
   * exactly one entry for each distinct element in the multiset (thus it always has the same size
   * as the {@link #elementSet}). The order of the elements in the element set is unspecified.
   *

needed.  When you restart, it is immediate and non-disruptive to the applications. Each group of servers in the command-line is called a pool. There are 2 server pools in this example. New objects are placed in server pools in proportion to the amount of free space in each pool. Within each pool, the location of the erasure-set of drives is determined based on a deterministic hashing algorithm.

> **NOTE:** **Each pool you add must have the same erasure coding parity configuration as the original...

func hashDeterministicString(m map[string]string) uint64 {
	// Seed (random)
	crc := uint64(0xc2b40bbac11a7295)
	// Xor each value to make order independent
	for k, v := range m {
		// Separate key and value with an individual xor with a random number.
		// Add values of each, so they cannot be trivially collided.
		crc ^= (xxh3.HashString(k) ^ 0x4ee3bbaf7ab2506b) + (xxh3.HashString(v) ^ 0x8da4c8da66194257)
	}
	return crc

   * @return the value previously associated with the keys, or {@code null} if no such value existed
   */
  @CanIgnoreReturnValue
  @Nullable V remove(
      @CompatibleWith("R") @Nullable Object rowKey,
      @CompatibleWith("C") @Nullable Object columnKey);

  // Views

  /**
   * Returns a view of all mappings that have the given row key. For each row key / column key /

 *
 * <p>The time taken to compute multiple quantiles on the same dataset using {@link Scale#indexes
 * indexes} is generally less than the total time taken to compute each of them separately, and
 * sometimes much less. For example, on a large enough dataset, computing the 90th and 99th
 * percentiles together takes about 55% as long as computing them separately.
 *

As indicated, each of those components require a Java runtime to run.

The recommended way of invoking Gradle is to use the Gradle Wrapper.
The Wrapper then downloads the Gradle distribution (if necessary), starts the Launcher and runs the build, spawning and connecting to other processes such as the Daemon as required.

 * guaranteed to never be passed {@code null}, and must never return {@code null}.
 *
 * <h3>Common ways to use</h3>
 *
 * <p>Getting a converter:
 *
 * <ul>
 *   <li>Use a provided converter implementation, such as {@link Enums#stringConverter}, {@link
 *       com.google.common.primitives.Ints#stringConverter Ints.stringConverter} or the {@linkplain
 *       #reverse reverse} views of these.

   * @return the value previously associated with the keys, or {@code null} if no such value existed
   */
  @CanIgnoreReturnValue
  @Nullable V remove(
      @CompatibleWith("R") @Nullable Object rowKey,
      @CompatibleWith("C") @Nullable Object columnKey);

  // Views

  /**
   * Returns a view of all mappings that have the given row key. For each row key / column key /

 * will prefer pooled connections. Only pooled HTTP/2 connections are used for such de-duplication.
 *
 * It is possible to cancel the finding process by canceling its call.
 *
 * Implementations of this interface are not thread-safe. Each instance is thread-confined to the
 * thread executing the call.
 */
interface RoutePlanner {
  val address: Address

will be used to resolve bucket through DNS. For example if you have a bucket such as `mybucket`, the
client can use now `mybucket.domain.com` to directly resolve itself to the right cluster. `MINIO_PUBLIC_IPS`
points to the public IP address where each cluster might be accessible, this is unique for each cluster.

NOTE: `mybucket` only exists on one cluster either `cluster1` or `cluster2` this is random and