* traditional {@code LinkedHashMap} implementation which stores keys and count values as map
 * entries, {@code ObjectCountLinkedHashMap} minimizes object allocation and reduces memory
 * footprint.
 */
@GwtCompatible
@NullMarked
final class ObjectCountLinkedHashMap<K extends @Nullable Object> extends ObjectCountHashMap<K> {
  /** Creates an empty {@code ObjectCountLinkedHashMap} instance. */

   *
   * {@snippet :
   * Stream.of("foo", "quux", "banana", "elephant").collect(least(2, comparingInt(String::length)))
   * // returns {"foo", "quux"}
   * }
   *
   * <p>This {@code Collector} uses O(k) memory and takes expected time O(n) (worst-case O(n log
   * k)), as opposed to e.g. {@code Stream.sorted(comparator).limit(k)}, which currently takes O(n
   * log n) time and O(n) space.
   *

   * having a hash table of size eight. Providing a large enough estimate at construction time
   * avoids the need for expensive resizing operations later, but setting this value unnecessarily
   * high wastes memory.
   *
   * @throws IllegalArgumentException if {@code initialCapacity} is negative
   * @throws IllegalStateException if an initial capacity was already set
   */
  @CanIgnoreReturnValue

   * interned, and so does not prevent these instances from being garbage-collected. This most
   * likely does not perform as well as {@link #newStrongInterner}, but is the best alternative when
   * the memory usage of that implementation is unacceptable.
   */
  @GwtIncompatible // java.lang.ref.WeakReference
  public static <E> Interner<E> newWeakInterner() {
    return newBuilder().weak().build();
  }

 * <p>
 * Unlike a cache, this session data is not subject to purging. For this same reason, session data should also not be
 * abused as a cache (i.e. for storing values that can be re-calculated) to avoid memory exhaustion.
 * <p>
 * <strong>Note:</strong> Actual implementations must be thread-safe.
 *
 * @see Session#getData()
 * @since 4.0.0
 */
@Experimental
@ThreadSafe
@Provider
public interface SessionData {

# Side Public License, v 1.

define_opts = {
  autostart: false
}.freeze

Vagrant.configure(2) do |config|

  config.vm.provider 'virtualbox' do |vbox|
    # Give the box more memory and cpu because our tests are beasts!
    vbox.memory = Integer(ENV['VAGRANT_MEMORY'] || 8192)
    vbox.cpus = Integer(ENV['VAGRANT_CPUS'] || 4)

    # see https://github.com/hashicorp/vagrant/issues/9524

  public
  static HashCode hash(File file, HashFunction hashFunction) throws IOException {
    return asByteSource(file).hash(hashFunction);
  }

  /**
   * Fully maps a file read-only in to memory as per {@link
   * FileChannel#map(java.nio.channels.FileChannel.MapMode, long, long)}.
   *
   * <p>Files are mapped from offset 0 to its length.
   *
   * <p>This only works for files ≤ {@link Integer#MAX_VALUE} bytes.

   *       and virtual memory, this is not a problem - because it is mapped read-only, the kernel
   *       can always page it to disk "for free". However, on systems where killing processes
   *       happens all the time in normal conditions (i.e., android) the OS must make a tradeoff

* Docker en Modo Swarm
* Systemd
* Supervisor
* Manejado internamente por un proveedor de nube como parte de sus servicios
* Otros...

## Replicación - Procesos y Memoria { #replication-processes-and-memory }

Con una aplicación FastAPI, usando un programa servidor como el comando `fastapi` que ejecuta Uvicorn, ejecutarlo una vez en **un proceso** puede servir a múltiples clientes concurrentemente.

现在仍然是对的。

因此，为了能够同时拥有**多个进程**，必须有一个**单个进程侦听端口**，然后以某种方式将通信传输到每个工作进程。

### 每个进程的内存 { #memory-per-process }

现在，当程序将内容加载到内存中时，例如，将机器学习模型加载到变量中，或者将大文件的内容加载到变量中，所有这些都会消耗服务器的一点内存 (RAM) 。

多个进程通常**不共享任何内存**。 这意味着每个正在运行的进程都有自己的东西、变量和内存。 如果您的代码消耗了大量内存，**每个进程**将消耗等量的内存。

### 服务器内存 { #server-memory }