}
                });

    // To start, fill up the cache.
    // Each miss both increments the counter and causes the map to grow by one,
    // so until evictions begin, the size of the map is the greatest return
    // value seen so far
    while (cache.getUnchecked(nextRandomKey()) < maximumSize) {}

    requests.set(0);
    misses.set(0);
  }

  @Benchmark

/**
 * A decoded [mapping table] that can perform the [mapping step] of IDNA processing.
 *
 * This implementation is optimized for readability over efficiency.
 *
 * This implements non-transitional processing by preserving deviation characters.
 *
 * This implementation's STD3 rules are configured to `UseSTD3ASCIIRules=false`. This is
 * permissive and permits the `_` character.
 *

        }
    ]
}
{
    "title": "The Robots of Dawn",
    "authorInfo": {
        "name": "Isaac Asimov",
        "yearRange": [1920, 1992],
        "penName": "Paul French"
    },
    "genre": "Science fiction",
    "publicationHistory": [
        {
            "year": 1983,
            "publisher": "Phantasia Press",
            "type": "Hardcover",
            "pages": 336
        },
        {

  - Include taints by condition when determining if a node is unready/still starting
  - Fix `update-vendor.sh` to work on OSX and zsh
  - Add best-effort eviction for DaemonSet pods while scaling down non-empty nodes
  - Add build support for ARM64
  
  ### AliCloud
  - Add missing daemonsets and replicasets to ALI example cluster role
  
  ### Apache CloudStack

   * R*R)} of the population standard deviation of {@code y}, where {@code R} is the Pearson's
   * correlation coefficient (as given by {@link #pearsonsCorrelationCoefficient()}).
   *
   * <p>The corresponding root-mean-square error in {@code x} as a function of {@code y} is a
   * fraction {@code sqrt(1/(R*R) - 1)} of the population standard deviation of {@code x}. This fit

   * R*R)} of the population standard deviation of {@code y}, where {@code R} is the Pearson's
   * correlation coefficient (as given by {@link #pearsonsCorrelationCoefficient()}).
   *
   * <p>The corresponding root-mean-square error in {@code x} as a function of {@code y} is a
   * fraction {@code sqrt(1/(R*R) - 1)} of the population standard deviation of {@code x}. This fit

  /**
   * Returns the <a
   * href="http://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation">
   * corrected sample standard deviation</a> of the values. If this dataset is a sample drawn from a
   * population, this is an estimator of the population standard deviation of the population which
   * is less biased than {@link #populationStandardDeviation()} (the unbiased estimator depends on

  private static final int CRC32C_GENERATOR_FLIPPED = Integer.reverse(CRC32C_GENERATOR);

  public void testCrc32cByteTable() {
    // See Hacker's Delight 2nd Edition, Figure 14-7.
    int[] expected = new int[256];
    for (int i = 0; i < expected.length; i++) {
      int crc = i;
      for (int j = 7; j >= 0; j--) {
        int mask = -(crc & 1);

   * R*R)} of the population standard deviation of {@code y}, where {@code R} is the Pearson's
   * correlation coefficient (as given by {@link #pearsonsCorrelationCoefficient()}).
   *
   * <p>The corresponding root-mean-square error in {@code x} as a function of {@code y} is a
   * fraction {@code sqrt(1/(R*R) - 1)} of the population standard deviation of {@code x}. This fit

## 1. Prerequisites

* Familiarity with [Docker Compose](https://docs.docker.com/compose/overview/).
* Docker installed on your machine. Download the relevant installer from [here](https://www.docker.com/community-edition#/download).

## 2. Run Distributed MinIO on Docker Compose