# ADR-0002 - Avoid using Java serialization

## Status

- ACCEPTED on 2023-12-01

## Context

In Gradle we often need to serialize in-memory objects for caching, or to transmit them across process barriers, etc.
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.

                String.format(Locale.ROOT, "Caught an exception in %s.  Shutting down.", t),
                e);
      } catch (Throwable errorInLogging) { // sneaky checked exception
        // If logging fails, e.g. due to missing memory, at least try to log the
        // message and the cause for the failed logging.
        System.err.println(e.getMessage());
        System.err.println(errorInLogging.getMessage());
      } finally {
        runtime.exit(1);

Específicamente, ver Uvicorn, Starlette y FastAPI comparados juntos (entre muchas otras herramientas).

Cuanto más simple sea el problema resuelto por la herramienta, mejor rendimiento tendrá. Y la mayoría de los benchmarks no prueban las funcionalidades adicionales proporcionadas por la herramienta.

La jerarquía es como:

* **Uvicorn**: un servidor ASGI
    * **Starlette**: (usa Uvicorn) un microframework web

   - sanjoy
# filesystem path
filesystem_path:
   - tensorflow/c/experimental/filesystem
# security path
security_path:
   - tensorflow/security
# words checklist
segfault_memory:
   - segfault
   - memory leaks
# assignees
filesystem_security_assignee:
   - mihaimaruseac
   
# Cuda Comment
cuda_comment: >
   From the template it looks like you are installing **TensorFlow** (TF) prebuilt binaries:

Each <code>DATA</code> directive initializes a section of the
corresponding memory.
The memory not explicitly initialized is zeroed.
The general form of the <code>DATA</code> directive is

<pre>
DATA	symbol+offset(SB)/width, value
</pre>

<p>
which initializes the symbol memory at the given offset and width with the given value.

limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_C_EAGER_ABSTRACT_CONTEXT_H_
#define TENSORFLOW_C_EAGER_ABSTRACT_CONTEXT_H_

#include <memory>

#include "tensorflow/c/eager/abstract_function.h"
#include "tensorflow/c/eager/abstract_operation.h"

namespace tensorflow {

// Abstract interface to a context.
//

///

## Conversión automática { #automatic-conversion }

`Header` tiene un poquito de funcionalidad extra además de lo que proporcionan `Path`, `Query` y `Cookie`.

La mayoría de los headers estándar están separados por un carácter "guion", también conocido como el "símbolo menos" (`-`).

Pero una variable como `user-agent` es inválida en Python.

    int NT_STATUS_NOT_IMPLEMENTED = 0xC0000002;
    /** The specified information class is invalid */
    int NT_STATUS_INVALID_INFO_CLASS = 0xC0000003;
    /** Invalid access to memory location */
    int NT_STATUS_ACCESS_VIOLATION = 0xC0000005;
    /** The handle is invalid */
    int NT_STATUS_INVALID_HANDLE = 0xC0000008;
    /** The parameter is incorrect */

### Arbeitsspeicher pro Prozess { #memory-per-process }

Wenn das Programm nun Dinge in den Arbeitsspeicher lädt, zum Beispiel ein Modell für maschinelles Lernen in einer Variablen oder den Inhalt einer großen Datei in einer Variablen, verbraucht das alles **einen Teil des Arbeitsspeichers (RAM – Random Access Memory)** des Servers.

 * java.util.LinkedHashMap}. Generally speaking, this class reduces object allocation and memory
 * consumption at the price of moderately increased constant factors of CPU. Only use this class
 * when there is a specific reason to prioritize memory over CPU.
 *
 * @author Louis Wasserman
 */
@J2ktIncompatible // no support for access-order mode in LinkedHashMap delegate