"@platforms//cpu:x86_64",
    ],
    visibility = ["//visibility:public"],
)

# Features that are default ON are handled differently below.
#
config_setting(
    name = "no_gcp_support",
    define_values = {"no_gcp_support": "true"},
    visibility = ["//visibility:public"],
)

# Experimental features
config_setting(
    name = "stackdriver_support",
    define_values = {"stackdriver_support": "true"},

 * <Long>}:
 *
 * <ul>
 *   <li>Improved memory compactness and locality.
 *   <li>Can be queried without allocating garbage.
 *   <li>Access to {@code LongStream} features (like {@link LongStream#sum}) using {@code stream()}
 *       instead of the awkward {@code stream().mapToLong(v -> v)}.
 * </ul>
 *
 * <p>Disadvantages compared to {@code ImmutableList<Long>}:
 *
 * <ul>

 * <Integer>}:
 *
 * <ul>
 *   <li>Improved memory compactness and locality.
 *   <li>Can be queried without allocating garbage.
 *   <li>Access to {@code IntStream} features (like {@link IntStream#sum}) using {@code stream()}
 *       instead of the awkward {@code stream().mapToInt(v -> v)}.
 * </ul>
 *
 * <p>Disadvantages compared to {@code ImmutableList<Integer>}:
 *
 * <ul>

 * types of crawlers (web, file, data). It manages the crawler process lifecycle,
 * handles timeout scenarios, and ensures proper cleanup of resources.</p>
 *
 * <p>Key features:</p>
 * <ul>
 *   <li>Concurrent crawler process management with configurable limits</li>
 *   <li>Selective crawling based on configuration IDs</li>
 *   <li>Document expiration handling</li>

 * crawling and indexing documents, including incremental crawling capabilities, content
 * modification checking, and integration with the Fess search engine backend.
 *
 * <p>Key features include:</p>
 * <ul>
 * <li>Incremental crawling support with last-modified timestamp checking</li>
 * <li>Document expiration handling</li>
 * <li>Child URL extraction and queueing</li>

The same way as with Pydantic models, you declare class attributes with type annotations, and possibly default values.

You can use all the same validation features and tools you use for Pydantic models, like different data types and additional validations with `Field()`.

//// tab | Pydantic v2

{* ../../docs_src/settings/tutorial001.py hl[2,5:8,11] *}

////

import com.google.common.collect.testing.ListTestSuiteBuilder;
import com.google.common.collect.testing.NavigableSetTestSuiteBuilder;
import com.google.common.collect.testing.features.CollectionFeature;
import com.google.common.collect.testing.features.CollectionSize;
import com.google.common.collect.testing.google.SetGenerators.ImmutableSortedSetAsListGenerator;

/**
 * Admin action for Synonym management.
 *
 */
public class AdminDictSynonymAction extends FessAdminAction {

    /**
     * Role name required for accessing synonym dictionary administration features.
     */
    public static final String ROLE = "admin-dict";

    private static final Logger logger = LogManager.getLogger(AdminDictSynonymAction.class);

#### Continuous Improvement
- Project sustainability is not just about features. Many SIGs have been working on  improving test coverage, ensuring the basics stay reliable, and stability of the core feature set and working on maturing existing features and cleaning up the backlog.

#### Extensibility

      - [Removed Deprecations](#removed-deprecations)
      - [Graduated to Stable/GA](#graduated-to-stablega)
      - [Graduated to Beta](#graduated-to-beta)
    - [New alpha features](#new-alpha-features)
  - [Other Notable Changes](#other-notable-changes-10)
    - [SIG API Machinery](#sig-api-machinery-1)
    - [SIG Apps](#sig-apps)
    - [SIG Auth](#sig-auth-1)
    - [SIG Autoscaling](#sig-autoscaling)