CharStreams.copy(reader, sb);
        }
        return sb.toString();
      }
    },
    // It really seems like this should be faster than TO_BYTE_ARRAY_NEW_STRING.  But it just isn't
    // my best guess is that the jdk authors have spent more time optimizing that callpath than this

## Other middlewares { #other-middlewares }

There are many other ASGI middlewares.

For example:

In früheren Versionen von Python hätten Sie Threads oder <a href="https://www.gevent.org/" class="external-link" target="_blank">Gevent</a> verwenden können. Der Code ist jedoch viel komplexer zu verstehen, zu debuggen und nachzuvollziehen.

In früheren Versionen von NodeJS / Browser JavaScript hätten Sie „Callbacks“ verwendet. Was zur "Callback-Hölle" führt.

## Coroutinen

        .putShort((short) 0x0000)
        .putShort((short) 0x0000);
    assertEquals(hashCode, hasher.hash().asLong());
  }

  public void testHashFloatIsStable() {
    // Just a spot check.  Better than nothing.
    Hasher hasher = HASH_FN.newHasher();
    hasher.putFloat(0x01000101f).putFloat(0f);
    assertEquals(0x49f9d18ee8ae1b28L, hasher.hash().asLong());

    hasher = HASH_FN.newHasher();

   * This implementation requires O(|N|) time. Classes extending this one may manually keep track of
   * the number of edges as the graph is updated, and override this method for better performance.
   */
  protected long edgeCount() {
    long degreeSum = 0L;
    for (N node : nodes()) {
      degreeSum += degree(node);
    }

# Decommissioning

Decommissiong is a mechanism in MinIO to drain older pools (usually with old hardware) and migrate the content from such pools to a newer pools (usually better hardware). Decommissioning spreads the data across all pools - for example, if you decommission `pool1`, all the data from `pool1` spreads across `pool2` and `pool3`.

## Features

## Storage usage

The selection of varying data and parity drives has a direct impact on the drive space usage. With storage class, you can optimize for high
redundancy or better drive space utilization.

To get an idea of how various combinations of data and parity drives affect the storage usage, let’s take an example of a 100 MiB file stored

   * moved to index "distance", and the element at index {@code i} ends up at index {@code (distance
   * + i) mod array.length}. This is equivalent to {@code Collections.rotate(Floats.asList(array),
   * distance)}, but is considerably faster and avoids allocation and garbage collection.
   *
   * <p>The provided "distance" may be negative, which will rotate left.
   *
   * @since 32.0.0
   */
  public static void rotate(float[] array, int distance) {

import org.jspecify.annotations.Nullable;

/**
 * A {@link PrimitiveSink} that can compute a hash code after reading the input. Each hasher should
 * translate all multibyte values ({@link #putInt(int)}, {@link #putLong(long)}, etc) to bytes in
 * little-endian order.
 *
 * <p><b>Warning:</b> The result of calling any methods after calling {@link #hash} is undefined.
 *

    // GWT's ConcurrentHashMap is a wrapper around HashMap, but it rejects null keys, which matches
    // the behaviour of the non-GWT implementation of newConcurrentHashSet().
    // On the other hand HashSet might be better for code size if apps aren't
    // already using Collections.newSetFromMap and ConcurrentHashMap.
    return Collections.newSetFromMap(new ConcurrentHashMap<E, Boolean>());
  }