//
    // TODO(user): Consider using
    // java/lang/management/OperatingSystemMXBean.html#getSystemLoadAverage()
    //
    // TODO(user): Consider scaling by number of mutator threads,
    // e.g. using Thread#activeCount()
    return max(10L, Runtime.getRuntime().totalMemory() / (32L * 1024L * 1024L));
  }

  /**

      return true;
    }

    boolean get(long bitIndex) {
      return (data.get((int) (bitIndex >>> LONG_ADDRESSABLE_BITS)) & (1L << bitIndex)) != 0;
    }

    /**
     * Careful here: if threads are mutating the atomicLongArray while this method is executing, the
     * final long[] will be a "rolling snapshot" of the state of the bit array. This is usually good
     * enough, but should be kept in mind.
     */

    //
    // TODO(user): Consider using
    // java/lang/management/OperatingSystemMXBean.html#getSystemLoadAverage()
    //
    // TODO(user): Consider scaling by number of mutator threads,
    // e.g. using Thread#activeCount()
    return max(10L, Runtime.getRuntime().totalMemory() / (32L * 1024L * 1024L));
  }

  /**

Mas antes disso, controlar código assíncrono era bem mais complexo e difícil.

Nas versões anteriores do Python, você poderia utilizar threads ou <a href="https://www.gevent.org/" class="external-link" target="_blank">Gevent</a>. Mas o código é bem mais complexo de entender, debugar, e pensar sobre.

labels.includedUrls=Included URLs for Crawling
labels.includedDocPaths=Included Paths for Searching
labels.includedDocUrls=Included URLs for Searching
labels.maxAccessCount=Max Access Count
labels.name=Name
labels.numOfThread=Number of Threads
labels.duplicateHostName=Duplicate Host Name
labels.pageNumber=Page Number
labels.password=Password
labels.paths=Paths
labels.port=Port
labels.regex=Regex
labels.regularName=Regular Name

            SECONDS,
            new ArrayBlockingQueue<Runnable>(1000));
    executorService.prestartAllCoreThreads();
    AtomicInteger integer = new AtomicInteger();
    // Execute a bunch of tasks to ensure that our threads are allocated and hot
    for (int i = 0; i < NUM_THREADS * 10; i++) {
      @SuppressWarnings("unused") // https://errorprone.info/bugpattern/FutureReturnValueIgnored
      Future<?> possiblyIgnoredError =

labels.includedDocPaths=Caminhos a serem incluídos na pesquisa
labels.includedDocUrls=URLs a serem incluídas na pesquisa
labels.maxAccessCount=Número máximo de acessos
labels.name=Nome
labels.numOfThread=Número de threads
labels.duplicateHostName=Nome duplicado
labels.pageNumber=Número da página
labels.password=Senha
labels.paths=Caminhos
labels.port=Porta
labels.regex=Expressão regular
labels.regularName=Nome regular

   * accommodate as many threads as will ever concurrently modify the table. Using a significantly
   * higher value than you need can waste space and time, and a significantly lower value can lead
   * to thread contention. But overestimates and underestimates within an order of magnitude do not
   * usually have much noticeable impact. A value of one permits only one thread to modify the cache

 * example involving two locks and two threads, deadlock occurs when one thread acquires Lock A, and
 * then Lock B, while another thread acquires Lock B, and then Lock A:
 *
 * <pre>
 * Thread1: acquire(LockA) --X acquire(LockB)
 * Thread2: acquire(LockB) --X acquire(LockA)
 * </pre>
 *
 * <p>Neither thread will progress because each is waiting for the other. In more complex

  }

  public void testNegativeTimeout() throws Exception {
    future.set(1);
    assertEquals(1, future.get(-1, SECONDS).intValue());
  }

  @J2ktIncompatible
  @GwtIncompatible // threads
  public void testOverflowTimeout() throws Exception {
    // First, sanity check that naive multiplication would really overflow to a negative number:
    long nanosPerSecond = NANOSECONDS.convert(1, SECONDS);