* numbers of keys and values without rehashing.
   *
   * @param expectedKeys the expected number of distinct keys
   * @param expectedValuesPerKey the expected average number of values per key
   * @throws IllegalArgumentException if {@code expectedKeys} or {@code expectedValuesPerKey} is
   *     negative
   */
  public static <K extends @Nullable Object, V extends @Nullable Object>

   * numbers of keys and values without rehashing.
   *
   * @param expectedKeys the expected number of distinct keys
   * @param expectedValuesPerKey the expected average number of values per key
   * @throws IllegalArgumentException if {@code expectedKeys} or {@code expectedValuesPerKey} is
   *     negative
   */
  public static <K extends @Nullable Object, V extends @Nullable Object>

 * add(x)} operations <i>do not</i> create objects for the garbage collector to deal with, and for
 * every element added, the garbage collector will have to traverse {@code 1.5} references on
 * average, in the marking phase, not {@code 5.0} as in {@code java.util.HashSet}.
 *
 * <p>If there are no removals, then {@link #iterator iteration} order is the same as insertion
 * order. Any removal invalidates any ordering guarantees.

   * Creates a {@code RateLimiter} with the specified stable throughput, given as "permits per
   * second" (commonly referred to as <i>QPS</i>, queries per second).
   *
   * <p>The returned {@code RateLimiter} ensures that on average no more than {@code
   * permitsPerSecond} are issued during any given second, with sustained requests being smoothly
   * spread over each second. When the incoming request rate exceeds {@code permitsPerSecond} the

labels.searchlog_log_type_search_user_hour=User Count/Hour
labels.searchlog_log_type_search_user_day=User Count/Day
labels.searchlog_log_type_search_reqtimeavg_hour=Request Average Time/Hour
labels.searchlog_log_type_search_reqtimeavg_day=Request Average Time/Day
labels.searchlog_log_type_click_count=Click Count
labels.searchlog_log_type_favorite_count=Favorite Count
labels.searchlog_log_message=Message

    // twice as many reads and writes. But benchmarking shows that they usually perform better than
    // Dolphin. Reversal is about as good as Successive on average, and it is much simpler,
    // especially since we already have a `reverse` method.
    checkNotNull(array);
    checkPositionIndexes(fromIndex, toIndex, array.length);
    if (array.length <= 1) {
      return;

    static final int MAX_RUN_MULTIPLIER = 13;

    /**
     * Checks the whole hash table for poor hash distribution. Takes O(n) in the worst case, O(n /
     * log n) on average.
     *
     * <p>The online hash flooding detecting in RegularSetBuilderImpl.add can detect e.g. many
     * exactly matching hash codes, which would cause construction to take O(n^2), but can't detect

}

// ServerHTTPAPIStats holds total number of HTTP operations from/to the server,
// including the average duration the call was spent.
type ServerHTTPAPIStats struct {
	APIStats map[string]int `json:"apiStats"`
}

// ServerHTTPStats holds all type of http operations performed to/from the server
// including their average execution time.
type ServerHTTPStats struct {

			// Experiments (using IndexPeriodic) suggest
			// the cutover is about 16 byte skips.
			// TODO: if large prefixes of sep are matching
			// we should cutover at even larger average skips,
			// because Equal becomes that much more expensive.
			// This code does not take that effect into account.
			j := bytealg.IndexRabinKarp(s[i:], sep)
			if j < 0 {
				return -1
			}

     * traverse. When nodes would otherwise be changed, new nodes are created to replace them. This
     * works well for hash tables since the bin lists tend to be short. (The average length is less
     * than two.)
     *
     * Read operations can thus proceed without locking, but rely on selected uses of volatiles to