import com.google.common.annotations.GwtCompatible;
import java.lang.ref.WeakReference;
import java.util.Locale;
import java.util.regex.Pattern;
import org.jspecify.annotations.Nullable;

/**
 * Methods factored out so that they can be emulated differently in GWT.
 *
 * @author Jesse Wilson
 */
@GwtCompatible
final class Platform {
  private static final PatternCompiler patternCompiler = loadPatternCompiler();

import com.google.common.annotations.GwtCompatible;
import java.lang.ref.WeakReference;
import java.util.Locale;
import java.util.regex.Pattern;
import org.jspecify.annotations.Nullable;

/**
 * Methods factored out so that they can be emulated differently in GWT.
 *
 * @author Jesse Wilson
 */
@GwtCompatible
final class Platform {
  private static final PatternCompiler patternCompiler = loadPatternCompiler();

## Une application avec des callbacks { #an-app-with-callbacks }

Voyons tout cela avec un exemple.

Imaginez que vous développiez une application qui permet de créer des factures.

Ces factures auront un `id`, un `title` (facultatif), un `customer` et un `total`.

L’utilisateur de votre API (une personne développeuse externe) créera une facture dans votre API avec une requête POST.

import com.google.common.annotations.J2ktIncompatible;
import java.util.Arrays;
import java.util.Map;
import java.util.Set;
import org.jspecify.annotations.Nullable;

/**
 * Methods factored out so that they can be emulated differently in GWT.
 *
 * @author Hayward Chan
 */
@GwtCompatible
final class Platform {
  /** Returns the platform preferred implementation of a map based on a hash table. */

import java.util.Arrays;
import java.util.LinkedHashMap;
import java.util.Map;
import java.util.Set;
import java.util.concurrent.ConcurrentHashMap;
import org.jspecify.annotations.Nullable;

/**
 * Methods factored out so that they can be emulated differently in GWT.
 *
 * @author Hayward Chan
 */
@GwtCompatible
final class Platform {

  /** Returns the platform preferred implementation of a map based on a hash table. */

        if (bucketLength > MAX_HASH_BUCKET_LENGTH) {
          return true;
        }
      }
    }
    return false;
  }

  /**
   * Closed addressing tends to perform well even with high load factors. Being conservative here
   * ensures that the table is still likely to be relatively sparse (hence it misses fast) while
   * saving space.
   */
  @VisibleForTesting static final double MAX_LOAD_FACTOR = 1.0;

        boolQuery.filter(roleQuery);
    }

    /**
     * Builds boost query functions to modify document scoring based on various factors.
     * This method adds field value factors, key matching boosts, and custom boost functions.
     *
     * @param queryContext the query context to add boost functions to
     */

 * <p>This class should not be assumed to be universally superior to {@code
 * java.util.LinkedHashMap}. Generally speaking, this class reduces object allocation and memory
 * consumption at the price of moderately increased constant factors of CPU. Only use this class
 * when there is a specific reason to prioritize memory over CPU.
 *
 * @author Louis Wasserman
 */
@J2ktIncompatible // no support for access-order mode in LinkedHashMap delegate

 *       anything about how the property is tested. To find out, read the code.
 *   <li>These tests are "conformance tests", and do not attempt to test throughput, latency,
 *       scalability or other performance factors (see the separate "jtreg" tests for a set intended
 *       to check these for the most central aspects of functionality.) So, most tests use the
 *       smallest sensible numbers of threads, collection sizes, etc needed to check basic

- *If total drives has many common divisors the algorithm chooses the minimum amounts of erasure sets possible for a erasure set size of any N*.  In the example with 1024 drives - 4, 8, 16 are GCD factors. With 16 drives we get a total of 64 possible sets, with 8 drives we get a total of 128 possible sets, with 4 drives we get a total of 256 possible sets. So algorithm automatically chooses 64 sets, which is *16* 64 = 1024* drives in total.