crawler.document.file.default.exclude.index.patterns=
crawler.document.file.default.include.search.patterns=
crawler.document.file.default.exclude.search.patterns=

# cache
crawler.document.cache.enabled=true
crawler.document.cache.max.size=2621440
crawler.document.cache.supported.mimetypes=text/html

        PluginVersionResult result = resolveFromProject(request);

        if (result == null) {
            ConcurrentMap<Key, PluginVersionResult> cache = getCache(request);
            Key key = getKey(request);
            result = cache.get(key);

            if (result == null) {
                result = resolveFromRepository(request);

                logger.debug(

`@lru_cache` is part of `functools` which is part of Python's standard library, you can read more about it in the <a href="https://docs.python.org/3/library/functools.html#functools.lru_cache" class="external-link" target="_blank">Python docs for `@lru_cache`</a>.

## Recap

                }
            }
        }
    }


    /**
     * Resolve an array of SIDs using a cache and at most one MSRPC request.
     * <p>
     * This method will attempt
     * to resolve SIDs using a cache and cache the results of any SIDs that
     * required resolving with the authority. SID cache entries are currently not
     * expired because under normal circumstances SID information never changes.
     *

	}

	// Cache the Schema for performance,
	// Use the modelType or modelType + schemaTable (if it present) as cache key.
	var schemaCacheKey interface{}
	if specialTableName != "" {
		schemaCacheKey = fmt.Sprintf("%p-%s", modelType, specialTableName)
	} else {
		schemaCacheKey = modelType
	}

	// Load exist schema cache, return if exists

        .url("https://square.com")
        .build()
    assertThat(request.headers("Cache-Control")).containsExactly("no-cache")
    assertThat(request.cacheControl.noCache).isTrue()
  }

  @Test
  fun emptyCacheControlClearsAllCacheControlHeaders() {
    val request =
      Request.Builder()
        .header("Cache-Control", "foo")
        .cacheControl(CacheControl.Builder().build())

	defer xioutil.SafeClose(updates)

	st, err := storageNSScannerRPC.Call(ctx, client.gridConn, &nsScannerOptions{
		DiskID:   *client.diskID.Load(),
		ScanMode: int(scanMode),
		Cache:    &cache,
	})
	if err != nil {
		return cache, toStorageErr(err)
	}

这样，它的行为几乎就像是一个全局变量。但是由于它使用了依赖项函数，因此我们可以轻松地进行测试时的覆盖。

`@lru_cache` 是 `functools` 的一部分，它是 Python 标准库的一部分，您可以在<a href="https://docs.python.org/3/library/functools.html#functools.lru_cache" class="external-link" target="_blank">Python 文档中了解有关 `@lru_cache` 的更多信息</a>。

## 小结

您可以使用 Pydantic 设置处理应用程序的设置或配置，利用 Pydantic 模型的所有功能。

* 通过使用依赖项，您可以简化测试。
* 您可以使用 `.env` 文件。

import org.codelibs.fess.taglib.FessFunctions;
import org.codelibs.fess.util.ComponentUtil;
import org.dbflute.optional.OptionalThing;

import com.google.common.cache.CacheBuilder;
import com.google.common.cache.CacheLoader;
import com.google.common.cache.LoadingCache;

/**
 * @author shinsuke
 *
 */
public class CrawlerStatsHelper {
    private static final Logger logger = LogManager.getLogger(CrawlerStatsHelper.class);

    open fun headers(headers: Headers) = commonHeaders(headers)

    /**
     * Sets this request's `Cache-Control` header, replacing any cache control headers already
     * present. If [cacheControl] doesn't define any directives, this clears this request's
     * cache-control headers.
     */
    open fun cacheControl(cacheControl: CacheControl): Builder = commonCacheControl(cacheControl)