stream(tags).of(stream -> stream.sorted().reduce((l, r) -> l + r).ifPresent(v -> buf.append(v)));
        buf.append(CACHE_KEY_SPLITTER);
        stream(roles).of(stream -> stream.sorted().reduce((l, r) -> l + r).ifPresent(v -> buf.append(v)));
        buf.append(CACHE_KEY_SPLITTER);
        stream(fields).of(stream -> stream.sorted().reduce((l, r) -> l + r).ifPresent(v -> buf.append(v)));

      return DummyProxy.this;
    }

    @Override
    public String toString() {
      return "Dummy proxy for " + interfaceType;
    }

    // Since type variables aren't serializable, reduce the type down to raw type before
    // serialization.
        private Object writeReplace() {
      return new DummyHandler(TypeToken.of(interfaceType.getRawType()));
    }
  }

    R apply(double from, long index);
  }

  /**
   * Returns the last element of the specified stream, or {@link java.util.Optional#empty} if the
   * stream is empty.
   *
   * <p>Equivalent to {@code stream.reduce((a, b) -> b)}, but may perform significantly better. This
   * method's runtime will be between O(log n) and O(n), performing better on <a

      return DummyProxy.this;
    }

    @Override
    public String toString() {
      return "Dummy proxy for " + interfaceType;
    }

    // Since type variables aren't serializable, reduce the type down to raw type before
    // serialization.
        private Object writeReplace() {
      return new DummyHandler(TypeToken.of(interfaceType.getRawType()));
    }
  }

/**
 * One or more variables that together maintain an initially zero {@code long} sum. When updates
 * (method {@link #add}) are contended across threads, the set of variables may grow dynamically to
 * reduce contention. Method {@link #sum} (or, equivalently, {@link #longValue}) returns the current
 * total combined across the variables maintaining the sum.
 *

 *       hand, is multiple-use, and does implement {@link Iterable}.
 *   <li>Streams offer many features not found here, including {@code min/max}, {@code distinct},
 *       {@code reduce}, {@code sorted}, the very powerful {@code collect}, and built-in support for
 *       parallelizing stream operations.
 *   <li>{@code FluentIterable} contains several features not available on {@code Stream}, which are

 *       hand, is multiple-use, and does implement {@link Iterable}.
 *   <li>Streams offer many features not found here, including {@code min/max}, {@code distinct},
 *       {@code reduce}, {@code sorted}, the very powerful {@code collect}, and built-in support for
 *       parallelizing stream operations.
 *   <li>{@code FluentIterable} contains several features not available on {@code Stream}, which are

                .filter(q -> StringUtil.isNotBlank(q) && q.length() <= maxQueryLength)
                .forEach(oq -> split(oq, " ")
                        .get(s -> s.filter(StringUtil::isNotBlank).reduce((q1, q2) -> escape(q1, "(", ")") + OR + escape(q2, "(", ")")))
                        .ifPresent(q -> {
                            appendQuery(queryBuf, q);
                        })));

   * following conditions:
   *
   * <ul>
   *   <li>You want to assign the same fraction of inputs to each bucket.
   *   <li>When you reduce the number of buckets, you can accept that the most recently added
   *       buckets will be removed first. More concretely, if you are dividing traffic among tasks,

    @SuppressWarnings("SynchronizeOnNonFinalField")
    public T get() {
      // Another variant of Double Checked Locking.
      //
      // We use two volatile reads. We could reduce this to one by
      // putting our fields into a holder class, but (at least on x86)
      // the extra memory consumption and indirection are more
      // expensive than the extra volatile reads.