// Restore normal operation parameters
        // Verify all registrations are still valid
    }
}
```

## 9. Performance and Optimization

### 9.1 Witness Event Batching
```java
public class WitnessEventBatcher {
    private final Queue<WitnessNotification> pendingNotifications;
    private final ScheduledExecutorService scheduler;
    private final int batchSize = 10;

 * {@link Iterables} class.
 *
 * <p><i>Performance notes:</i> Unless otherwise noted, all of the iterators produced in this class
 * are <i>lazy</i>, which means that they only advance the backing iteration when absolutely
 * necessary.
 *
 * <p>See the Guava User Guide section on <a href=
 * "https://github.com/google/guava/wiki/CollectionUtilitiesExplained#iterables">{@code
 * Iterators}</a>.
 *

  class RowMap extends ViewCachingAbstractMap<R, Map<C, V>> {
    @Override
    public boolean containsKey(@Nullable Object key) {
      return containsRow(key);
    }

    // performing cast only when key is in backing map and has the correct type
    @SuppressWarnings("unchecked")
    @Override
    public @Nullable Map<C, V> get(@Nullable Object key) {
      // requireNonNull is safe because of the containsRow check.

- Hashing of KeyID in Logs
  

     *
     * @param hook The shutdown hook to add.
     */
    public void addShutdownHook(final Runnable hook) {
        shutdownHookList.add(hook);
    }

    /**
     * Gets the hostname of the local machine.
     *
     * @return The hostname.
     */
    public String getHostname() {
        final Map<String, String> env = getEnvMap();
        if (env.containsKey("COMPUTERNAME")) {

    }
  }


  @J2ktIncompatible // slow enough to cause flakiness
  @GwtIncompatible // far too slow
  public void testSqrtOfPerfectSquareAsDoubleIsPerfect() {
    // This takes just over a minute on my machine.
    for (long n = 0; n <= LongMath.FLOOR_SQRT_MAX_LONG; n++) {
      long actual = (long) Math.sqrt((double) (n * n));
      assertTrue(actual == n);
    }
  }

### Memória do servidor

Si tu aplicación es **simple**, probablemente esto **no será un problema**, y puede que no necesites especificar límites de memoria estrictos. Pero si estás **usando mucha memoria** (por ejemplo, con modelos de **Machine Learning**), deberías verificar cuánta memoria estás consumiendo y ajustar el **número de contenedores** que se ejecutan en **cada máquina** (y tal vez agregar más máquinas a tu cluster).

She is such a dear quiet thing,' Alice went on, half to herself,
as she swam lazily about in the pool, `and she sits purring so
nicely by the fire, licking her paws and washing her face--and
she is such a nice soft thing to nurse--and she's such a capital
one for catching mice--oh, I beg your pardon!' cried Alice again,
for this time the Mouse was bristling all over, and she felt
certain it must be really offended.  `We won't talk about her any

- Supporting Cross RG resources (disks, Azure File and node [Experimental]

Besides the above new features, support for Azure Virtual Machine Scale Sets (VMSS) and Cluster-Autoscaler is now stable and considered GA:

- Azure virtual machine scale sets (VMSS) allow you to create and manage identical load balanced VMs that automatically increase or decrease based on demand or a set schedule.