private static final long serialVersionUID = 1L;

            @Override
            public int getCrawlingThreadCount() {
                return 1; // Reduced from 2 to minimize thread overhead
            }

            @Override
            public String getIndexFieldConfigId() {
                return "config_id";
            }

            @Override

This is due to Java's use of reflection and the need to maintain a lot of metadata.

- **Size of Serialized Data:**
Java serialization tends to produce larger serialized objects because it includes class metadata and other overhead.

- **Flexibility and Control:**
Java serialization offers limited control over the serialization process, such as excluding certain fields, customizing naming conventions, and handling complex data structures more gracefully.

    private final List<EventSpy> eventSpies;

    @Inject
    public EventSpyDispatcher(List<EventSpy> eventSpies) {
        // make copy to get rid of needless overhead for dynamic lookups
        this.eventSpies = new ArrayList<>(eventSpies);
    }

    public ExecutionListener chainListener(ExecutionListener listener) {
        if (eventSpies.isEmpty()) {
            return listener;

 *
 * @author David Richter
 */
@NullUnmarked
public class IteratorBenchmark {
  @Param({"0", "1", "16", "256", "4096", "65536"})
  int size;

  // use concrete classes to remove any possible polymorphic overhead?
  Object[] array;
  ArrayList<Object> arrayList;
  LinkedList<Object> linkedList;

  @BeforeExperiment
  void setUp() {
    array = new Object[size];
    arrayList = Lists.newArrayListWithCapacity(size);

   * intended to be used as a metric: smaller headers are more efficient to encode and transmit.
   */
  fun byteCount(): Long {
    // Each header name has 2 bytes of overhead for ': ' and every header value has 2 bytes of
    // overhead for '\r\n'.
    var result = (namesAndValues.size * 2).toLong()

    for (i in 0 until namesAndValues.size) {
      result += namesAndValues[i].length.toLong()
    }

import com.google.common.base.Function;
import java.util.ListIterator;
import org.jspecify.annotations.Nullable;

/**
 * An iterator that transforms a backing list iterator; for internal use. This avoids the object
 * overhead of constructing a {@link Function} for internal methods.
 *
 * @author Louis Wasserman
 */
@GwtCompatible
abstract class TransformedListIterator<F extends @Nullable Object, T extends @Nullable Object>

import com.google.common.base.Function;
import java.util.ListIterator;
import org.jspecify.annotations.Nullable;

/**
 * An iterator that transforms a backing list iterator; for internal use. This avoids the object
 * overhead of constructing a {@link Function} for internal methods.
 *
 * @author Louis Wasserman
 */
@GwtCompatible
abstract class TransformedListIterator<F extends @Nullable Object, T extends @Nullable Object>

The main limitation is that to update or delete content of a file inside a ZIP file the entire ZIP file must be replaced.

## How to enable S3 ZIP behavior ?

import com.google.caliper.Param;
import java.util.Arrays;
import java.util.Comparator;
import java.util.Random;
import org.jspecify.annotations.NullUnmarked;

/**
 * A benchmark to determine the overhead of sorting with {@link Ordering#from(Comparator)}, or with
 * {@link Ordering#natural()}, as opposed to using the inlined {@link Arrays#sort(Object[])}
 * implementation, which uses {@link Comparable#compareTo} directly.
 *

   *
   * <p>Although all tasks are immediately executed in the thread that submitted the task, this
   * {@code ExecutorService} imposes a small locking overhead on each task submission in order to
   * implement shutdown and termination behavior.
   *
   * <p>Because of the nature of single-thread execution, the methods {@code scheduleAtFixedRate}