*       be "done" as soon as <i>that</i> {@code Future} completes in any way. Notably, a {@code
 *       Future} is "completed" even if it is cancelled while its underlying work continues on a
 *       thread, an RPC, etc. The {@code Future} is also "completed" if it fails "early" -- for
 *       example, if the deadline expires on a {@code Future} returned from {@link

   * allows the user to close all the closeable objects that were captured during it for later
   * closing.
   *
   * <p>The asynchronous operation will have completed before this object is created.
   *
   * @param <V> the type of the value of a successful operation
   * @see ClosingFuture#finishToValueAndCloser(ValueAndCloserConsumer, Executor)
   */

            generateProject(projectDir, version)

            logger.lifecycle("Building project: $projectName")
            buildProject(projectDir)
        }

        logger.lifecycle("Android SDK warmup completed successfully!")
    }

    private fun generateProject(projectDir: File, version: SdkVersion) {
        projectDir.mkdirs()

        File(projectDir, "build.gradle").writeText(generateBuildGradle(version))

            urlQueueService.delete(sessionId);
            dataService.delete(sessionId);
            urlFilter.clear();
            if (logger.isInfoEnabled()) {
                logger.info("Completed crawler cleanup: sessionId={}", sessionId);
            }
        } catch (final Exception e) {
            logger.warn("Failed to cleanup crawler data: sessionId={}", sessionId, e);
        }
    }

    /**

    }, 1, TimeUnit.SECONDS);

    System.out.printf("%.2f Executing call.%n", (System.nanoTime() - startNanos) / 1e9f);
    try (Response response = call.execute()) {
      System.out.printf("%.2f Call was expected to fail, but completed: %s%n",
          (System.nanoTime() - startNanos) / 1e9f, response);
    } catch (IOException e) {
      System.out.printf("%.2f Call failed as expected: %s%n",
          (System.nanoTime() - startNanos) / 1e9f, e);
    }

name: Deploy Docs
on:
  workflow_run:
    workflows:
      - Build Docs
    types:
      - completed

permissions:
  deployments: write
  issues: write
  pull-requests: write
  statuses: write

env:
  UV_SYSTEM_PYTHON: 1

jobs:
  deploy-docs:
    runs-on: ubuntu-latest
    steps:
      - name: Dump GitHub context
        env:
          GITHUB_CONTEXT: ${{ toJson(github) }}

   *
   * <p>Caches loaded by a {@link CacheLoader} will call {@link CacheLoader#load} to load new values
   * into the cache. Newly loaded values are added to the cache using {@code
   * Cache.asMap().putIfAbsent} after loading has completed; if another value was associated with
   * {@code key} while the new value was loading then a removal notification will be sent for the
   * new value.
   *

Java serialization is one way to implement this, however, despite its simplicity of implementation, it has several drawbacks:

- **Performance:**
Java's built-in serialization mechanism is often slower compared to other serialization solutions.
This is due to Java's use of reflection and the need to maintain a lot of metadata.

- **Size of Serialized Data:**

You can combine normal FastAPI *path operations* with GraphQL on the same application.

/// tip

**GraphQL** solves some very specific use cases.

It has **advantages** and **disadvantages** when compared to common **web APIs**.

Make sure you evaluate if the **benefits** for your use case compensate the **drawbacks**. 🤓

///

## GraphQL Libraries { #graphql-libraries }

   * <p>The {@link OutputStream} should not be written to before or after the hand-off.
   */
  // TODO(user): Evaluate whether it makes sense to always piggyback the computation of a
  // HashCode on an existing OutputStream, compared to creating a separate OutputStream that could
  // be (optionally) be combined with another if needed (with something like
  // MultiplexingOutputStream).
  public HashingOutputStream(HashFunction hashFunction, OutputStream out) {