<p>
The <code>sync</code> package provides additional synchronization abstractions,
including <a href="/pkg/sync/#Cond">condition variables</a>,
<a href="/pkg/sync/#Map">lock-free maps</a>,
<a href="/pkg/sync/#Pool">allocation pools</a>,
and
<a href="/pkg/sync/#WaitGroup">wait groups</a>.
The documentation for each of these specifies the guarantees it
makes concerning synchronization.
</p>

<p>

        new LinkedBlockingDeque<Object>(),
        new LinkedBlockingDeque<Object>(10),
        new PriorityBlockingQueue<Object>(10, Ordering.arbitrary()));
  }

  /*
   * We need to perform operations in a thread pool, even for simple cases, because the queue might
   * be a SynchronousQueue.
   */
  private ExecutorService threadPool;

  @Override
  public void setUp() {
    threadPool = newCachedThreadPool();
  }

**Suggestions Not Appearing**
- Confirm documents are indexed: `suggester.refresh()`
- Check query formatting and filters
- Verify analyzer configurations

**Performance Issues**
- Increase thread pool size in SuggesterBuilder
- Optimize batch sizes for indexing operations
- Review OpenSearch cluster performance

**Memory Usage**
- Configure appropriate JVM heap settings
- Monitor index sizes and optimize mappings

Dies kann sehr nützlich sein, um **Ressourcen** einzurichten, die Sie in der gesamten App verwenden wollen und die von Requests **gemeinsam genutzt** werden und/oder die Sie anschließend **aufräumen** müssen. Zum Beispiel ein Pool von Datenbankverbindungen oder das Laden eines gemeinsam genutzten Modells für maschinelles Lernen.

## Anwendungsfall { #use-case }

Esto puede ser muy útil para configurar **recursos** que necesitas usar para toda la app, y que son **compartidos** entre requests, y/o que necesitas **limpiar** después. Por ejemplo, un pool de conexiones a una base de datos, o cargando un modelo de machine learning compartido.

## Caso de Uso { #use-case }

Empecemos con un ejemplo de **caso de uso** y luego veamos cómo resolverlo con esto.

   * instance. This method is guaranteed to only be called once.
   *
   * <p>By default this returns a new {@link ScheduledExecutorService} with a single thread pool
   * that sets the name of the thread to the {@linkplain #serviceName() service name}. Also, the
   * pool will be {@linkplain ScheduledExecutorService#shutdown() shut down} when the service
   * {@linkplain Service.State#TERMINATED terminates} or {@linkplain Service.State#TERMINATED

 *  New: `ConnectionPool.setPolicy()` configures a minimum connection pool size for a target
    address. Use this to proactively open HTTP connections.

    Connections opened to fulfill this policy are subject to the connection pool's
    `keepAliveDuration` but do not count against the pool-wide `maxIdleConnections` limit.

    -   Uses `pycpp.sh`, `code_check_changed_files.sh`

These "env" files match up with an environment matrix that roughly covers:

-   Different Python versions
-   Linux, MacOS, and Windows machines (these pool definitions are internal)
-   x86 and arm64
-   CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs

## How to Test Your Changes to TensorFlow

You may check how your changes will affect TensorFlow by:

        ).build()
    assertThat(client.retryOnConnectionFailure).isTrue()
    executeSynchronously("/").assertBody("seed connection pool")
    executeSynchronously("/").assertBody("retry success")

    // The call that seeds the connection pool.
    eventRecorder.removeUpToEvent(CallEnd::class.java)

    // The ResponseFailed event is not necessarily fatal!

   * instance. This method is guaranteed to only be called once.
   *
   * <p>By default this returns a new {@link ScheduledExecutorService} with a single thread pool
   * that sets the name of the thread to the {@linkplain #serviceName() service name}. Also, the
   * pool will be {@linkplain ScheduledExecutorService#shutdown() shut down} when the service
   * {@linkplain Service.State#TERMINATED terminates} or {@linkplain Service.State#TERMINATED