- 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:

import com.google.common.annotations.GwtIncompatible;
import com.google.common.annotations.J2ktIncompatible;
import java.io.DataOutput;
import java.io.IOException;

/**
 * An extension of {@code DataOutput} for writing to in-memory byte arrays; its methods offer
 * identical functionality but do not throw {@link IOException}.
 *
 * @author Jayaprabhakar Kadarkarai
 * @since 1.0
 */
@J2ktIncompatible
@GwtIncompatible

limitations under the License.
==============================================================================*/
#include "tensorflow/c/eager/gradient_checker.h"

#include <memory>

#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/experimental/ops/math_ops.h"
#include "tensorflow/c/tf_tensor.h"

namespace tensorflow {

 * <p>This class should not be assumed to be universally superior to {@code java.util.HashSet}.
 * 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 Dimitris Andreou
 * @author Jon Noack
 */
@GwtIncompatible // not worth using in GWT for now

 * 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

//
// Returns a TF_Buffer. The memory pointed to by the result is owned by
// lib_handle. The data in the buffer will be the serialized OpList proto for
// ops defined in the library.
TF_CAPI_EXPORT extern TF_Buffer TF_GetOpList(TF_Library* lib_handle);

// Frees the memory associated with the library handle.
// Does NOT unload the library.

 * 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

                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);

# ADR-0002 - Avoid using Java serialization

## Date

2012-12-01

## 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.

  public abstract HashCode hashBytes(byte[] input, int off, int len);

  @Override
  public HashCode hashBytes(ByteBuffer input) {
    return newHasher(input.remaining()).putBytes(input).hash();
  }

  /** In-memory stream-based implementation of Hasher. */
  private final class BufferingHasher extends AbstractHasher {
    final ExposedByteArrayOutputStream stream;

    BufferingHasher(int expectedInputSize) {