if (resetArtifact.getVersion() == null && resetArtifact.getVersionRange() != null) {

                                    // go find the version. This is a total hack. See previous comment.
                                    List<ArtifactVersion> versions = resetArtifact.getAvailableVersions();
                                    if (versions == null) {

        if (logger.isDebugEnabled()) {
            logger.debug("[updateMemberOf] Direct groups retrieved. Total groups: {}, Total roles: {}, Group IDs for parent lookup: {}",
                    groupList.size(), roleList.size(), groupIdsForParentLookup.size());
        }

        // Set initial groups

        suggester.refresh();

        long oldWordsCount = suggester.getAllWordsNum();
        assertTrue(oldWordsCount > 0);

        // Minimal sleep to ensure timestamp separation (reduced from 2000ms to 100ms total)
        Thread.sleep(50);
        ZonedDateTime threshold = ZonedDateTime.now();
        Thread.sleep(50);

        // Index new data after threshold
        document = new HashMap<>();

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")
	}

- **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);