// requirements, and is immutable.
  optional string runtimeHandler = 1;

  // overhead represents the resource overhead associated with running a pod for a
  // given RuntimeClass. For more details, see
  // https://git.k8s.io/enhancements/keps/sig-node/688-pod-overhead/README.md
  // +optional
  optional Overhead overhead = 2;

  // scheduling holds the scheduling constraints to ensure that pods running

  // and is immutable.
  optional string handler = 2;

  // overhead represents the resource overhead associated with running a pod for a
  // given RuntimeClass. For more details, see
  //  https://kubernetes.io/docs/concepts/scheduling-eviction/pod-overhead/
  // +optional
  optional Overhead overhead = 3;

  // scheduling holds the scheduling constraints to ensure that pods running

  // and is immutable.
  optional string handler = 2;

  // overhead represents the resource overhead associated with running a pod for a
  // given RuntimeClass. For more details, see
  // https://git.k8s.io/enhancements/keps/sig-node/688-pod-overhead/README.md
  // +optional
  optional Overhead overhead = 3;

  // scheduling holds the scheduling constraints to ensure that pods running

    * If you didn't use FastAPI and used Starlette directly (or another tool, like Sanic, Flask, Responder, etc) you would have to implement all the data validation and serialization yourself. So, your final application would still have the same overhead as if it was built using FastAPI. And in many cases, this data validation and serialization...

    * Wenn Sie Uvicorn vergleichen, vergleichen Sie es mit Anwendungsservern wie Daphne, Hypercorn, uWSGI, usw.
* **Starlette**:

`jstream` is a streaming JSON parser and value extraction library for Go.

Unlike most JSON parsers, `jstream` is document position- and depth-aware -- this enables the extraction of values at a specified depth, eliminating the overhead of allocating encompassing arrays or objects; e.g:

Using the below example document:
<img width="85%" src="https://bradley.codes/static/img/jstream-levels.gif" alt="jstream"/>

  @Override
  public int hashCode() {
    return delegate.hashCode();
  }

  private void validate() {
    // Don't use checkState(), because we don't want the overhead of generating the error message
    // unless it's actually going to be used; validate() is called for all set method calls, so it
    // needs to be fast.

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.

import com.google.caliper.Benchmark;
import com.google.caliper.Param;
import java.util.Arrays;
import java.util.Comparator;
import java.util.Random;

/**
 * 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.
 *

    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;