}
        }
        return documentItems;
    }

    /**
     * Performs a scroll search for processing large result sets efficiently.
     *
     * This method uses OpenSearch scroll API to iterate through large numbers of
     * documents without loading them all into memory at once.
     *
     * @param params The search request parameters

        testCoverage: TestCoverage,
        parallelization: (Int) -> ParallelizationMethod,
    ): List<SmallSubprojectBucket> {
        // splitIntoBuckets() method expects us to split large element into N elements,
        // but we want to have a single bucket with N batches.
        // As a workaround, we repeat the bucket N times, and deduplicate the result at the end
        val resultIncludingDuplicates =

        // Should return uncompressed data for small inputs
        assertArrayEquals(smallData, compressed);
    }

    @Test
    @DisplayName("Test large data compression")
    public void testLargeDataCompression() throws CIFSException {
        byte[] compressed = compressionService.compress(largeTestData, CompressionService.COMPRESSION_LZ77);

			// in obtaining service accounts by this cred.
			cred.ParentUser = lookupResult.NormDN

			// Set this value to LDAP groups, LDAP user can be part
			// of large number of groups
			cred.Groups = targetGroups

			// Set the newly generated credentials, policyName is empty on purpose
			// LDAP policies are applied automatically using their ldapUser, ldapGroups
			// mapping.

before a garbage collection delays that cycle. Less frequent cycles means
the CPU cost of the garbage collector is incurred less frequently.

This functionality in this package is mostly captured in the Arena type.
Arenas allocate large chunks of memory for Go values, so they're likely to
be inefficient for allocating only small amounts of small Go values. They're
best used in bulk, on the order of MiB of memory allocated on each use.

    private ChannelInfo selectAdaptive(SMBMessage message, Collection<ChannelInfo> channels) {
        // Adaptive strategy based on message type and size
        
        if (isLargeTransfer(message)) {
            // For large transfers, prefer high-bandwidth channels
            return channels.stream()
                .max(Comparator.comparingInt(c -> c.getRemoteInterface().getLinkSpeed()))
                .orElseThrow();
        }
        

        assertNotNull(suggestItem.getRoles());
    }

    @Test
    public void testToSuggestItemWithLargeBoost() {
        // Test toSuggestItem with large boost value
        String word = "large boost";
        float boost = Float.MAX_VALUE;
        List<String> readings = Arrays.asList("reading1");
        List<String> fields = Arrays.asList("field1");

                if (params != null && params.size() > 100) {
                    return createMockRescorerBuilder();
                }
                return null;
            }
        };

        // Test with large number of parameters
        Map<String, Object> largeParams = new HashMap<>();
        for (int i = 0; i < 150; i++) {
            largeParams.put("key" + i, "value" + i);
        }

        thumbnailManager.setExceptionMessage(null);

        String result = purgeThumbnailJob.execute();

        assertTrue(result.contains("Test exception"));
    }

    // Test execute with large number of deleted files
    public void test_execute_largeNumberOfFiles() {
        thumbnailManager.setPurgeCallCount(Long.MAX_VALUE);

        String result = purgeThumbnailJob.execute();

        .addEqualityGroup(isNull)
        .addEqualityGroup(Equivalence.identity().equivalentTo("1"))
        .testEquals();
  }

  /*
   * We use large numbers to avoid the integer cache. Normally, we'd accomplish that merely by using
   * `new Integer` (as we do) instead of `Integer.valueOf`. However, under J2KT, `new Integer`