* href="http://llimllib.github.io/bloomfilter-tutorial/">tutorial</a> may help you understand how
 * they work.
 *
 * <p>The false positive probability ({@code FPP}) of a Bloom filter is defined as the probability
 * that {@linkplain #mightContain(Object)} will erroneously return {@code true} for an object that
 * has not actually been put in the {@code BloomFilter}.
 *

                  + count
                  + " trials.");
        }
      }
    }
  }

  /**
   * Test for avalanche. Avalanche means that output bits differ with roughly 1/2 probability on
   * different input keys. This test verifies that each possible 1-bit key delta achieves avalanche.
   *
   * <p>For more information: http://burtleburtle.net/bob/hash/avalanche.html
   */

     * determined experimentally to match our desired probability of false positives.
     */
    // NB: yes, this is surprisingly high, but that's what the experiments said was necessary
    // Raising this number slows the worst-case contains behavior, speeds up hashFloodingDetected,
    // and reduces the false-positive probability.
    static final int MAX_RUN_MULTIPLIER = 13;

    /**

   * that minimizes the need for remapping as {@code buckets} grows. That is, {@code
   * consistentHash(h, n)} equals:
   *
   * <ul>
   *   <li>{@code n - 1}, with approximate probability {@code 1/n}
   *   <li>{@code consistentHash(h, n - 1)}, otherwise (probability {@code 1 - 1/n})
   * </ul>
   *
   * <p>This method is suitable for the common use case of dividing work among buckets that meet the
   * following conditions:
   *

 * striped.get(key1) != striped.get(key2)}; the elements might nevertheless be mapped to the same
 * lock. The lower the number of stripes, the higher the probability of this happening.
 *
 * <p>There are three flavors of this class: {@code Striped<Lock>}, {@code Striped<Semaphore>}, and
 * {@code Striped<ReadWriteLock>}. For each type, two implementations are offered: {@linkplain

        .addEqualityGroup(ImmutableSet.of(1, 2, 1), ImmutableSet.of(2, 1, 1))
        .testEquals();
  }

  /**
   * The maximum allowed probability of falsely detecting a hash flooding attack if the input is
   * randomly generated.
   */
  private static final double HASH_FLOODING_FPP = 0.001;

   * saving space.
   */
  @VisibleForTesting static final double MAX_LOAD_FACTOR = 1.2;

  /**
   * Maximum allowed false positive probability of detecting a hash flooding attack given random
   * input.
   */
  @VisibleForTesting static final double HASH_FLOODING_FPP = 0.001;

  /**

              @SuppressWarnings("ThreadPriorityCheck") // doing our best to test for races
              protected Schedule getNextSchedule() throws Exception {
                // Explicitly yield to increase the probability of a pathological scheduling.
                Thread.yield();
                return new Schedule(0, SECONDS);
              }
            };
          }
        };
    service.useBarriers = false;

              @SuppressWarnings("ThreadPriorityCheck") // doing our best to test for races
              protected Schedule getNextSchedule() throws Exception {
                // Explicitly yield to increase the probability of a pathological scheduling.
                Thread.yield();
                return new Schedule(0, SECONDS);
              }
            };
          }
        };
    service.useBarriers = false;

// in case of SSE-S3. However, storing the ETag of an encrypted
// object in plaintext may reveal some information about the object.
// For example, two objects with the same ETag are identical with
// a very high probability.
//
// Therefore, an S3 implementation may encrypt an ETag before storing
// it. In this case, the stored ETag may not be a well-formed S3 ETag.
// For example, it can be larger due to a checksum added by authenticated