return spliteratorWithCharacteristics(SPLITERATOR_CHARACTERISTICS);
  }

  @GwtIncompatible // Spliterator
  Spliterator<E> spliteratorWithCharacteristics(int characteristics) {
    return CollectSpliterators.indexed(size(), characteristics, this::get);
  }

  @Override
  int copyIntoArray(@Nullable Object[] dst, int offset) {
    // this loop is faster for RandomAccess instances, which ImmutableLists are

reverseBits(i int) int { i = ((i << 2) & 0xc) | ((i >> 2) & 0x3) i = ((i << 1) & 0xa) | ((i >> 1) & 0x5) return i } // ghashAdd adds two elements of GF(2¹²⁸) and returns the sum. func ghashAdd(x, y *gcmFieldElement) gcmFieldElement { // Addition in a characteristic 2 field is just XOR. return gcmFieldElement{x.low ^ y.low, x.high ^ y.high} } // ghashDouble returns the result of doubling an element of GF(2¹²⁸). func ghashDouble(x *gcmFieldElement) (double gcmFieldElement) { msbSet := x.high&1 == 1 // Because...

  public void testNotDistinctEntrySpliterator() {
    ImmutableListMultimap<String, String> multimap =
        ImmutableListMultimap.of("foo", "bar", "foo", "bar");
    assertThat(multimap.entries().spliterator().characteristics() & Spliterator.DISTINCT)
        .isEqualTo(0);
  }

  // Yes, we want to test that inverse() returns the same instance each time that it's called.
  @SuppressWarnings("SelfAssertion")

        StatsAccumulator::new,
        (a, x) -> a.add(x.doubleValue()),
        (l, r) -> {
          l.addAll(r);
          return l;
        },
        StatsAccumulator::snapshot,
        Collector.Characteristics.UNORDERED);
  }

  /** Returns the number of values. */
  public long count() {
    return count;
  }

  /**

        entrySpliterator,
        (Entry<E> entry) -> Collections.nCopies(entry.getCount(), entry.getElement()).spliterator(),
        Spliterator.SIZED
            | (entrySpliterator.characteristics()
                & (Spliterator.ORDERED | Spliterator.NONNULL | Spliterator.IMMUTABLE)),
        multiset.size());
  }

  /** An implementation of {@link Multiset#size}. */

  }

  /**
   * Implementation of a transforming random access list. We try to make as many of these methods
   * pass-through to the source list as possible so that the performance characteristics of the
   * source list and transformed list are similar.
   *
   * @see Lists#transform
   */
  private static final class TransformingRandomAccessList<

  }

  /**
   * Implementation of a transforming random access list. We try to make as many of these methods
   * pass-through to the source list as possible so that the performance characteristics of the
   * source list and transformed list are similar.
   *
   * @see Lists#transform
   */
  private static final class TransformingRandomAccessList<

....
127.0.0.1       localhost ElasticMBP.local
255.255.255.255 broadcasthost
::1             localhost ElasticMBP.local`
....

== Benchmarking

For changes that might affect the performance characteristics of Elasticsearch
you should also run macrobenchmarks. We maintain a macrobenchmarking tool
called https://github.com/elastic/rally[Rally]
which you can use to measure the performance impact. It comes with a set of

    result.removeAll(collection);
    return result;
  }

  /**
   * Returns a set backed by the specified map. The resulting set displays the same ordering,
   * concurrency, and performance characteristics as the backing map. In essence, this factory
   * method provides a {@link Set} implementation corresponding to any {@link Map} implementation.

    result.removeAll(collection);
    return result;
  }

  /**
   * Returns a set backed by the specified map. The resulting set displays the same ordering,
   * concurrency, and performance characteristics as the backing map. In essence, this factory
   * method provides a {@link Set} implementation corresponding to any {@link Map} implementation.