return Spliterators.spliterator(array, SPLITERATOR_CHARACTERISTICS);
  }

  // TODO(lowasser): benchmark optimizations for equals() and see if they're worthwhile

  // redeclare to help optimizers with b/310253115
  @SuppressWarnings("RedundantOverride")
  @Override
  @J2ktIncompatible
  @GwtIncompatible
    Object writeReplace() {
    return super.writeReplace();
  }

import okio.ByteString
import okio.ByteString.Companion.encodeUtf8
import okio.Options

/**
 * A decoded [mapping table] that can perform the [mapping step] of IDNA processing.
 *
 * This implementation is optimized for readability over efficiency.
 *
 * This implements non-transitional processing by preserving deviation characters.
 *
 * This implementation's STD3 rules are configured to `UseSTD3ASCIIRules=false`. This is

      return entries.iterator();
    }

    @Override
    ImmutableList<Entry<K, V>> createAsList() {
      return entries;
    }

    // redeclare to help optimizers with b/310253115
    @SuppressWarnings("RedundantOverride")
    @Override
    @J2ktIncompatible
    @GwtIncompatible
        Object writeReplace() {
      return super.writeReplace();
    }
  }

      @Override
      boolean isPartialView() {
        return true;
      }

      @Override
      public int size() {
        return entryList.size();
      }

      // redeclare to help optimizers with b/310253115
      @SuppressWarnings("RedundantOverride")
      @Override
      @J2ktIncompatible
      @GwtIncompatible
            Object writeReplace() {
        return super.writeReplace();
      }

  }

  @Override
  boolean isPartialView() {
    return false;
  }

  @Override
  public int size() {
    return Ints.saturatedCast(size);
  }

  // redeclare to help optimizers with b/310253115
  @SuppressWarnings("RedundantOverride")
  @Override
  @J2ktIncompatible
  @GwtIncompatible
    Object writeReplace() {
    return super.writeReplace();
  }

   *
   * The alternative natural refactorings into void or Exception-returning methods are much slower.
   * This is a big deal - we're talking factors of 2-8 in microbenchmarks, not just 10-20%. (This is
   * a hotspot optimizer bug, which should be fixed, but that's a separate, big project).
   *
   * The coding pattern above is heavily used in java.util, e.g. in ArrayList. There is a

MinIO uses [`klauspost/compress/s2`](https://github.com/klauspost/compress/tree/master/s2)
streaming compression due to its stability and performance.

This algorithm is specifically optimized for machine generated content.
Write throughput is typically at least 500MB/s per CPU core,
and scales with the number of available CPU cores.
Decompression speed is typically at least 1GB/s.

import java.io.IOException;
import java.nio.file.Path;
import java.util.List;
import java.util.Map;
import java.util.concurrent.TimeUnit;

/**
 * A race between Lucene Expressions, Painless, and a hand optimized script
 * implementing a {@link ScriptScoreQuery}.
 */
@Fork(2)
@Warmup(iterations = 10)
@Measurement(iterations = 5)
@BenchmarkMode(Mode.AverageTime)
@OutputTimeUnit(TimeUnit.NANOSECONDS)

        size(),
        ImmutableList.SPLITERATOR_CHARACTERISTICS | Spliterator.SORTED | Spliterator.DISTINCT,
        delegateList()::get,
        comparator());
  }

  // redeclare to help optimizers with b/310253115
  @SuppressWarnings("RedundantOverride")
  @Override
  @J2ktIncompatible
  @GwtIncompatible
    Object writeReplace() {
    return super.writeReplace();
  }

    this.allowsSelfLoops = builder.allowsSelfLoops;
    this.nodeOrder = builder.nodeOrder.cast();
    this.edgeOrder = builder.edgeOrder.cast();
    // Prefer the heavier "MapRetrievalCache" for nodes if lookup is expensive. This optimizes
    // methods that access the same node(s) repeatedly, such as Graphs.removeEdgesConnecting().
    this.nodeConnections =
        (nodeConnections instanceof TreeMap)