For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.

And if your remote server or virtual machine only has 3 GB of RAM, trying to load more than 4 GB of RAM will cause problems. 🚨

		return false
	}

	size := b.uint64()
	if size != directory64EndLen-12 {
		t.Errorf("expected length of %d, got %d", directory64EndLen-12, size)
	}
	return true
}

// Zip64 is required if the total size of the records is uint32max.
func TestZip64LargeDirectory(t *testing.T) {
	if runtime.GOARCH == "wasm" {
		t.Skip("too slow on wasm")
	}
	if testing.Short() {
		t.Skip("skipping in short mode")
	}

	ID              string            `msg:"id"`     // ID of the ongoing rebalance operation
	PercentFreeGoal float64           `msg:"pf"`     // Computed from total free space and capacity at the start of rebalance
	PoolStats       []*rebalanceStats `msg:"rss"`    // Per-pool rebalance stats keyed by pool index
}

var errRebalanceNotStarted = errors.New("rebalance not started")

- **Custom Systems** - REST API for bulk document indexing

---

## Pre-Migration Planning

### 1. Assessment Phase

Before starting migration, assess your current system:

- **Document Count**: Total number of indexed documents
- **Index Size**: Storage requirements for search indices
- **Crawl Sources**: URLs, file shares, databases being indexed
- **Custom Configurations**: URL patterns, security rules, metadata fields

     *
     * @param params The search request parameters
     * @param cursor Function to process each document in the result set
     * @param userBean Optional user information for permission checking
     * @return Total number of documents processed
     */
    public long scrollSearch(final SearchRequestParams params, final BooleanFunction<Map<String, Object>> cursor,
            final OptionalThing<FessUserBean> userBean) {

Por ejemplo, si tu código carga un modelo de Machine Learning con **1 GB de tamaño**, cuando ejecutas un proceso con tu API, consumirá al menos 1 GB de RAM. Y si inicias **4 procesos** (4 workers), cada uno consumirá 1 GB de RAM. Así que, en total, tu API consumirá **4 GB de RAM**.

Y si tu servidor remoto o máquina virtual solo tiene 3 GB de RAM, intentar cargar más de 4 GB de RAM causará problemas. 🚨

E se o seu servidor remoto ou máquina virtual tiver apenas 3 GB de RAM, tentar carregar mais de 4 GB de RAM causará problemas. 🚨

  }

  @GwtIncompatible // Shorts.fromByteArray, Shorts.toByteArray
  public void testByteArrayRoundTrips() {
    Random r = new Random(5);
    byte[] b = new byte[Shorts.BYTES];

    // total overkill, but, it takes 0.1 sec so why not...
    for (int i = 0; i < 10000; i++) {
      short num = (short) r.nextInt();
      assertThat(Shorts.fromByteArray(Shorts.toByteArray(num))).isEqualTo(num);

    return this;
  }

  Equivalence<Object> getValueEquivalence() {
    return MoreObjects.firstNonNull(valueEquivalence, getValueStrength().defaultEquivalence());
  }

  /**
   * Sets the minimum total size for the internal hash tables. For example, if the initial capacity
   * is {@code 60}, and the concurrency level is {@code 8}, then eight segments are created, each

   * absent, it will fall back to a heavyweight operation that will open a stream, read (or {@link
   * InputStream#skip(long) skip}, if possible) to the end of the stream and return the total number
   * of bytes that were read.
   *
   * <p>Note that for some sources that implement {@link #sizeIfKnown} to provide a more efficient