CompactLinkedHashMap<K, V> create() {
    return new CompactLinkedHashMap<>();
  }

  /**
   * Creates a {@code CompactLinkedHashMap} instance, with a high enough "initial capacity" that it
   * <i>should</i> hold {@code expectedSize} elements without rebuilding internal data structures.
   *
   * @param expectedSize the number of elements you expect to add to the returned set

        .build();
    try (Response response = client.newCall(request).execute()) {
      return response.body().string();
    }
  }

  String bowlingJson(String player1, String player2) {
    return "{'winCondition':'HIGH_SCORE',"
        + "'name':'Bowling',"
        + "'round':4,"
        + "'lastSaved':1367702411696,"
        + "'dateStarted':1367702378785,"
        + "'players':["

      CompactLinkedHashMap<K, V> create() {
    return new CompactLinkedHashMap<>();
  }

  /**
   * Creates a {@code CompactLinkedHashMap} instance, with a high enough "initial capacity" that it
   * <i>should</i> hold {@code expectedSize} elements without rebuilding internal data structures.
   *
   * @param expectedSize the number of elements you expect to add to the returned set

   * Returns a hash function implementing FarmHash's Fingerprint64, an open-source algorithm.
   *
   * <p>This is designed for generating persistent fingerprints of strings. It isn't
   * cryptographically secure, but it produces a high-quality hash with fewer collisions than some
   * alternatives we've used in the past.
   *
   * <p>FarmHash fingerprints are encoded by {@link HashCode#asBytes} in little-endian order. This

        .withFeatures(features)
        .suppressing(parentBuilder.getSuppressedTests())
        .createTestSuite();
  }

  /**
   * Returns an array of four bogus elements that will always be too high or too low for the
   * display. This includes two values for each extreme.
   *
   * <p>This method (dangerously) assume that the strings {@code "!! a"} and {@code "~~ z"} will

        .withFeatures(features)
        .suppressing(parentBuilder.getSuppressedTests())
        .createTestSuite();
  }

  /**
   * Returns an array of four bogus elements that will always be too high or too low for the
   * display. This includes two values for each extreme.
   *
   * <p>This method (dangerously) assume that the strings {@code "!! a"} and {@code "~~ z"} will

      assertFalse(segment.isLocked());
      Map<?, ?> table = segmentTable(segment);
      // cleanup and then check count after we have a strong reference to all entries
      segment.cleanUp();
      // under high memory pressure keys/values may be nulled out but not yet enqueued
      assertThat(table.size()).isAtMost(segment.count);
      for (Entry<?, ?> entry : table.entrySet()) {
        assertNotNull(entry.getKey());

     * wrapper around j.u.HashSet, which has built-in flooding protection. MAX_RUN_MULTIPLIER was
     * 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.

      assertFalse(segment.isLocked());
      Map<?, ?> table = segmentTable(segment);
      // cleanup and then check count after we have a strong reference to all entries
      segment.cleanUp();
      // under high memory pressure keys/values may be nulled out but not yet enqueued
      assertThat(table.size()).isAtMost(segment.count);
      for (Entry<?, ?> entry : table.entrySet()) {
        assertNotNull(entry.getKey());

   * and accesses that were performed on the map. The queue is drained on writes and when it exceeds
   * its capacity threshold.
   *
   * The Least Recently Used page replacement algorithm was chosen due to its simplicity, high hit
   * rate, and ability to be implemented with O(1) time complexity. The initial LRU implementation
   * operates per-segment rather than globally for increased implementation simplicity. We expect