import java.util.Iterator;
import java.util.List;
import java.util.Map.Entry;
import java.util.Set;
import org.jspecify.annotations.NullMarked;
import org.jspecify.annotations.Nullable;

/**
 * A series of tests that support asserting that collections cannot be modified, either through
 * direct or indirect means.
 *
 * @author Robert Konigsberg
 */
@GwtCompatible
@NullMarked

Por ejemplo, podrías querer probar:

* [Speakeasy](https://speakeasy.com/editor?utm_source=fastapi+repo&utm_medium=github+sponsorship)
* [Stainless](https://www.stainless.com/?utm_source=fastapi&utm_medium=referral)

import java.util.Iterator;
import java.util.List;
import java.util.Map.Entry;
import java.util.Set;
import org.jspecify.annotations.NullMarked;
import org.jspecify.annotations.Nullable;

/**
 * A series of tests that support asserting that collections cannot be modified, either through
 * direct or indirect means.
 *
 * @author Robert Konigsberg
 */
@GwtCompatible
@NullMarked

     * the type of the concrete implementation.
     */
    abstract S self();

    /** Drains the reference queues used by this segment, if any. */
    @GuardedBy("this")
    void maybeDrainReferenceQueues() {}

    /** Clears the reference queues used by this segment, if any. */
    void maybeClearReferenceQueues() {}

    /** Sets the value of the given {@code entry}. */

   */
  static final int DRAIN_THRESHOLD = 0x3F;

  /**
   * Maximum number of entries to be drained in a single cleanup run. This applies independently to
   * the cleanup queue and both reference queues.
   */
  // TODO(fry): empirically optimize this
  static final int DRAIN_MAX = 16;

  // Fields

  static final Logger logger = Logger.getLogger(LocalCache.class.getName());

  /**

Neste exemplo, usaremos **SQLite**, porque ele usa um único arquivo e o Python tem suporte integrado. Assim, você pode copiar este exemplo e executá-lo como está.

Mais tarde, para sua aplicação em produção, você pode querer usar um servidor de banco de dados como o **PostgreSQL**.

/// tip | Dica

### Criando o `Settings` apenas uma vez com `lru_cache` { #creating-the-settings-only-once-with-lru-cache }

Ler um arquivo do disco normalmente é uma operação custosa (lenta), então você provavelmente vai querer fazer isso apenas uma vez e depois reutilizar o mesmo objeto de configurações, em vez de lê-lo a cada requisição.

Mas toda vez que fizermos:

```Python
Settings()
```

 * </ul>
 *
 * <p>Advantages compared to {@link com.google.common.collect.ImmutableList ImmutableList}{@code
 * <Double>}:
 *
 * <ul>
 *   <li>Improved memory compactness and locality.
 *   <li>Can be queried without allocating garbage.
 *   <li>Access to {@code DoubleStream} features (like {@link DoubleStream#sum}) using {@code
 *       stream()} instead of the awkward {@code stream().mapToDouble(v -> v)}.
 * </ul>
 *

 * </ul>
 *
 * <p>Advantages compared to {@link com.google.common.collect.ImmutableList ImmutableList}{@code
 * <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>
 *

public class CrawlerThread implements Runnable {
    /**
     * Constructs a new CrawlerThread.
     */
    public CrawlerThread() {
        // Default constructor
    }

    /**
     * Service for managing URL queues during crawling.
     */
    @Resource
    protected UrlQueueService<UrlQueue<?>> urlQueueService;

    /**
     * Service for managing access result data.
     */
    @Resource