*
 * <h3>Alternatives</h3>
 *
 * <h4>Frameworks</h4>
 *
 * <p>When chaining together a graph of asynchronous operations, you will often find it easier to
 * use a framework. Frameworks automate the process, often adding features like monitoring,
 * debugging, and cancellation. Examples of frameworks include:
 *
 * <ul>
 *   <li><a href="https://dagger.dev/producers.html">Dagger Producers</a>
 * </ul>
 *

* `Field(index=True)` indique à SQLModel qu'il doit créer un **index SQL** pour cette colonne, ce qui permettra des recherches plus rapides dans la base lors de la lecture de données filtrées par cette colonne.

    SQLModel saura que quelque chose déclaré comme `str` sera une colonne SQL de type `TEXT` (ou `VARCHAR`, selon la base).

### Créer un engine { #create-an-engine }

* <dfn title="également connu sous le nom de : sérialisation, parsing, marshalling">Conversion</dfn> des données d'entrée : venant du réseau vers les données et types Python. Lecture depuis :
    * JSON.
    * Paramètres de chemin.
    * Paramètres de requête.
    * Cookies.
    * En-têtes.
    * Formulaires.
    * Fichiers.

 * <Double>}:
 *
 * <ul>
 *   <li>Improved memory compactness and locality.
 *   <li>Can be queried without allocating garbage.
 *   <li>Access to {@code DoubleStream} features (like {@link DoubleStream#sum}) using {@code
 *       stream()} instead of the awkward {@code stream().mapToDouble(v -> v)}.
 * </ul>
 *
 * <p>Disadvantages compared to {@code ImmutableList<Double>}:
 *
 * <ul>

 * <Integer>}:
 *
 * <ul>
 *   <li>Improved memory compactness and locality.
 *   <li>Can be queried without allocating garbage.
 *   <li>Access to {@code IntStream} features (like {@link IntStream#sum}) using {@code stream()}
 *       instead of the awkward {@code stream().mapToInt(v -> v)}.
 * </ul>
 *
 * <p>Disadvantages compared to {@code ImmutableList<Integer>}:
 *
 * <ul>

 *       However, utilities that <i>use</i> that executor have the ability to interrupt tasks
 *       running on it. This class, by contrast, does not expose an {@code Executor} API.)
 * </ul>
 *
 * <p>If you don't need the features of this class, you may prefer {@code newSequentialExecutor} for
 * its simplicity and ability to accommodate interruption.
 *
 * @since 26.0
 */
@J2ktIncompatible
@GwtIncompatible

 * scroll search, and bulk operations. It handles search request parameter processing,
 * query building, response formatting, and search log management.
 *
 * Key features:
 * - Document search with pagination and faceting
 * - Scroll search for large result sets
 * - Document retrieval by ID
 * - Bulk document updates
 * - Search parameter serialization/deserialization for cookies

 * This executor implements a weave-mode build strategy, where builds are executed phase-by-phase rather than
 * project-by-project.
 *
 * <h2>Key Features:</h2>
 * <ul>
 *   <li>Concurrent execution of compatible build steps across projects</li>
 *   <li>Thread-safety validation for plugins</li>
 *   <li>Support for forked executions and lifecycle phases</li>

        return Strings.fromOEMBytes(src, srcIndex, Strings.findTermination(src, srcIndex, maxLen), getConfig());
    }

    /**
     * Calculates the wire format length of a string
     * @param str string to measure
     * @param offset current buffer offset for alignment calculation
     * @return string length
     */
    public int stringWireLength(final String str, final int offset) {
        int len = str.length() + 1;

///

### [Webargs](https://webargs.readthedocs.io/en/latest/) { #webargs }

Une autre grande fonctionnalité requise par les API est l’<dfn title="lecture et conversion en données Python">analyse</dfn> des données provenant des requêtes entrantes.

Webargs est un outil qui a été créé pour fournir cela par-dessus plusieurs frameworks, dont Flask.