assertTrue(file.canCacheWrite());
    }
}
```

## 9. Performance Considerations

### 9.1 Memory Management
- Lease entries should be evicted based on LRU when max leases reached
- Implement periodic cleanup of expired leases

### 9.2 Thread Safety
- Use concurrent data structures for lease storage
- Minimize lock contention in hot paths
- Async handling of lease breaks

        }
    }

    /**
     * Evict oldest leases based on LRU
     *
     * @param count number of leases to evict
     */
    private void evictOldestLeases(int count) {
        if (count <= 0 || leases.isEmpty()) {
            return;
        }

        // Find oldest leases by last access time
        leases.entrySet()
                .stream()

    - 🧰 [SQLModel](https://sqlmodel.tiangolo.com) para las interacciones con bases de datos SQL en Python (ORM).
    - 🔍 [Pydantic](https://docs.pydantic.dev), utilizado por FastAPI, para la validación de datos y gestión de configuraciones.
    - 💾 [PostgreSQL](https://www.postgresql.org) como base de datos SQL.
- 🚀 [React](https://react.dev) para el frontend.

Voici un petit bout de cette histoire.

## Alternatives

Je crée des API avec des exigences complexes depuis plusieurs années (Machine Learning, systèmes distribués, jobs asynchrones, bases de données NoSQL, etc), en dirigeant plusieurs équipes de développeurs.

Dans ce cadre, j'ai dû étudier, tester et utiliser de nombreuses alternatives.

   *
   * <p><b>Note:</b> consider representing the array as a {@link java.util.BitSet} instead,
   * replacing {@code Booleans.contains(array, true)} with {@code !bitSet.isEmpty()} and {@code
   * Booleans.contains(array, false)} with {@code bitSet.nextClearBit(0) == sizeOfBitSet}.
   *
   * @param array an array of {@code boolean} values, possibly empty

   *
   * <p><b>Note:</b> consider representing the array as a {@link java.util.BitSet} instead,
   * replacing {@code Booleans.contains(array, true)} with {@code !bitSet.isEmpty()} and {@code
   * Booleans.contains(array, false)} with {@code bitSet.nextClearBit(0) == sizeOfBitSet}.
   *
   * @param array an array of {@code boolean} values, possibly empty

  @Param("2.5")
  double concentration;

  Random random = new Random();

  LoadingCache<Integer, Integer> cache;

  int max;

  static AtomicLong requests = new AtomicLong(0);
  static AtomicLong misses = new AtomicLong(0);

  @BeforeExperiment
  void setUp() {
    // random integers will be generated in this range, then raised to the
    // power of (1/concentration) and floor()ed

## Otros tipos de datos

Aquí hay algunos de los tipos de datos adicionales que puedes usar:

* `UUID`:
    * Un "Identificador Universalmente Único" estándar, común como un ID en muchas bases de datos y sistemas.
    * En requests y responses se representará como un `str`.
* `datetime.datetime`:
    * Un `datetime.datetime` de Python.

## Pydantic-Modelle für Eingabe und Ausgabe

Nehmen wir an, Sie haben ein Pydantic-Modell mit Defaultwerten wie dieses:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:7] hl[7] *}

### Modell für Eingabe

Wenn Sie dieses Modell wie hier als Eingabe verwenden:

{* ../../docs_src/separate_openapi_schemas/tutorial001_py310.py ln[1:15] hl[14] *}

        /**
         * Session ID for filtering thumbnail generation.
         */
        @Option(name = "-s", aliases = "--sessionId", metaVar = "sessionId", usage = "Session ID")
        protected String sessionId;

        /**
         * Name identifier for the thumbnail generation task.
         */
        @Option(name = "-n", aliases = "--name", metaVar = "name", usage = "Name")
        protected String name;

        /**