<p>
Go approaches its memory model in much the same way as the rest of the language,
aiming to keep the semantics simple, understandable, and useful.
This section gives a general overview of the approach and should suffice for most programmers.
The memory model is specified more formally in the next section.
</p>

<p>
A data race is defined as

        // requireNonNull is safe because we always have either `file` or `memory`.
        requireNonNull(memory);
        return new ByteArrayInputStream(memory.getBuffer(), 0, memory.getCount());
      }
    }

    synchronized void reset() throws IOException {
      try {
        close();
      } finally {
        if (memory == null) {
          memory = new MemoryOutput();
        } else {
          memory.reset();

    public abstract ByteBuffer receive(int timeout) throws IOException;

    /**
     * Perform RDMA read operation
     *
     * @param localRegion local memory region to read into
     * @param remoteAddress remote memory address
     * @param remoteKey remote memory key
     * @param length number of bytes to read
     * @throws IOException if read fails
     */

    }

    /**
     * Returns whether the content is cached in memory.
     *
     * @return true if the content is in memory, false if it is in a file
     */
    public boolean isInMemory() {
        return data != null;
    }

    /**
     * Returns the content as a byte array.
     * If the content is cached in memory, a clone of the data is returned.

# Como Fazer - Receitas { #how-to-recipes }

Aqui você encontrará diferentes exemplos práticos ou tutoriais de "como fazer" para **vários tópicos**.

A maioria dessas ideias será mais ou menos **independente**, e na maioria dos casos você deveria estudá-las apenas se elas se aplicarem diretamente ao **seu projeto**.

Se algo parecer interessante e útil para o seu projeto, vá em frente e dê uma olhada. Caso contrário, você pode simplesmente ignorá-las.

/// tip | Dica

| `minio_node_mem_buffers_avg`   | Buffers memory on the node (avg).         |
| `minio_node_mem_buffers_max`   | Buffers memory on the node (max).         |
| `minio_node_mem_cache`         | Cache memory on the node.                 |
| `minio_node_mem_cache_avg`     | Cache memory on the node (avg).           |
| `minio_node_mem_cache_max`     | Cache memory on the node (max).           |

### Memory per Process { #memory-per-process }

Now, when the program loads things in memory, for example, a machine learning model in a variable, or the contents of a large file in a variable, all that **consumes a bit of the memory (RAM)** of the server.

import java.util.concurrent.atomic.AtomicLong;

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/**
 * RDMA buffer manager for efficient memory region pooling.
 *
 * Manages pools of pre-registered memory regions to avoid the overhead
 * of frequent registration/deregistration during RDMA operations.
 */
public class RdmaBufferManager implements AutoCloseable {

   * value slightly too high than slightly too low. If the value is not exact, the {@link
   * ImmutableLongArray} that is built will very likely occupy more memory than strictly necessary;
   * to trim memory usage, build using {@code builder.build().trimmed()}.
   */
  public static Builder builder(int initialCapacity) {
    checkArgument(initialCapacity >= 0, "Invalid initialCapacity: %s", initialCapacity);

    TFE_TensorHandle* h, TF_Status* status);

// This function will block till the operation that produces `h` has
// completed. The memory returned might alias the internal memory used by
// TensorFlow. Hence, callers should not mutate this memory (for example by
// modifying the memory region pointed to by TF_TensorData() on the returned
// TF_Tensor).
TF_CAPI_EXPORT extern TF_Tensor* TFE_TensorHandleResolve(TFE_TensorHandle* h,