}

  /**
   * Similar to load64, but allows offset + 8 > input.length, padding the result with zeroes. This
   * has to explicitly reverse the order of the bytes as it packs them into the result which makes
   * it slower than the native version.
   *
   * @param input the input bytes
   * @param offset the offset into the array at which to start reading
   * @param length the number of bytes from the input to read

Java serialization is one way to implement this, however, despite its simplicity of implementation, it has several drawbacks:

- **Performance:**
Java's built-in serialization mechanism is often slower compared to other serialization solutions.
This is due to Java's use of reflection and the need to maintain a lot of metadata.

- **Size of Serialized Data:**

* `compresslevel` - Used during GZip compression. It is an integer ranging from 1 to 9. Defaults to `9`. Lower value results in faster compression but larger file sizes, while higher value results in slower compression but smaller file sizes.

## Other middlewares { #other-middlewares }

There are many other ASGI middlewares.

For example:

* **Starlette**:
    * Will have the next best performance, after Uvicorn. In fact, Starlette uses Uvicorn to run. So, it probably can only get "slower" than Uvicorn by having to execute more code.
    * But it provides you the tools to build simple web applications, with routing based on paths, etc.

When using a `response_model` or return type, FastAPI won't use the `jsonable_encoder` to convert the data (which would be slower) nor the `JSONResponse` class.

Instead it takes the JSON bytes generated with Pydantic using the response model (or return type) and returns a `Response` with the right media type for JSON directly (`application/json`).

        int strideLimit = minLength & ~(stride - 1);
        int i;

        /*
         * Compare 8 bytes at a time. Benchmarking on x86 shows a stride of 8 bytes is no slower
         * than 4 bytes even on 32-bit. On the other hand, it is substantially faster on 64-bit.
         */
        for (i = 0; i < strideLimit; i += stride) {

On the other hand, if you have 2 servers and you are using **100% of their CPU and RAM**, at some point one process will ask for more memory, and the server will have to use the disk as "memory" (which can be thousands of times slower), or even **crash**. Or one process might need to do some computation and would have to wait until the CPU is free again.

// additional operations in the lookup path. Alternative approaches were tested:
//   - [128]bool with explicit bounds check (if c >= 128): introduces branches
//     that cause pipeline stalls, resulting in ~70% slower performance
//   - [128]bool with masking (c&0x7f): eliminates bounds checks but the AND
//     operation still costs ~10% performance compared to direct indexing
//

# Environnements virtuels { #virtual-environments }

Lorsque vous travaillez sur des projets Python, vous devriez probablement utiliser un environnement virtuel (ou un mécanisme similaire) pour isoler les packages que vous installez pour chaque projet.

/// info

Si vous connaissez déjà les environnements virtuels, comment les créer et les utiliser, vous pouvez passer cette section. 🤓

///

/// tip | Astuce

C'est parce que nous voulons inclure leurs *chemins d'accès* dans le schéma OpenAPI et les interfaces utilisateur.

Comme nous ne pouvons pas simplement les isoler et les « monter » indépendamment du reste, les *chemins d'accès* sont « clonés » (recréés), pas inclus directement.

///

## Configurer l'`entrypoint` dans `pyproject.toml` { #configure-the-entrypoint-in-pyproject-toml }