// Timed Get
  // There are a few design constraints to consider
  // * We want to be responsive to small timeouts, unpark() has non trivial latency overheads (I
  //   have observed 12 micros on 64-bit linux systems to wake up a parked thread). So if the
  //   timeout is small we shouldn't park(). This needs to be traded off with the cpu overhead of
  //   spinning, so we use SPIN_THRESHOLD_NANOS which is what AbstractQueuedSynchronizer uses for

      </fields>
    </class>
    <class java.clone="deep">
      <name>ActivationOS</name>
      <version>1.0.0+</version>
      <description>
        This is an activator which will detect an operating system's attributes in order to activate
        its profile.
      </description>
      <fields>
        <field>
          <name>name</name>
          <version>1.0.0+</version>
          <type>String</type>

func detectUTF8(s string) (valid, require bool) {
	for i := 0; i < len(s); {
		r, size := utf8.DecodeRuneInString(s[i:])
		i += size
		// Officially, ZIP uses CP-437, but many readers use the system's
		// local character encoding. Most encoding are compatible with a large
		// subset of CP-437, which itself is ASCII-like.
		//
		// Forbid 0x7e and 0x5c since EUC-KR and Shift-JIS replace those

import jakarta.servlet.ServletContext;

/**
 * CrawlJob is responsible for executing the crawling process in Fess.
 * This job launches a separate crawler process that can crawl web sites, file systems,
 * and data sources based on the configured crawling settings.
 *
 * <p>The job supports selective crawling by specifying configuration IDs for different

  // Timed Get
  // There are a few design constraints to consider
  // * We want to be responsive to small timeouts, unpark() has non trivial latency overheads (I
  //   have observed 12 micros on 64-bit linux systems to wake up a parked thread). So if the
  //   timeout is small we shouldn't park(). This needs to be traded off with the cpu overhead of
  //   spinning, so we use SPIN_THRESHOLD_NANOS which is what AbstractQueuedSynchronizer uses for

            assertEquals(21474836480L, fileFsSizeInfo.getFree()); // 20GB
        }

        @Test
        @DisplayName("Should handle large file systems")
        void shouldHandleLargeFileSystems() throws SMBProtocolDecodingException {
            // Given - 10TB file system with 4KB clusters
            long totalClusters = 2684354560L; // 10TB / 4KB

* **Uvicorn** with `--workers`
    * One Uvicorn **process manager** would listen on the **IP** and **port**, and it would start **multiple Uvicorn worker processes**.
* **Kubernetes** and other distributed **container systems**
    * Something in the **Kubernetes** layer would listen on the **IP** and **port**. The replication would be by having **multiple containers**, each with **one Uvicorn process** running.
* **Cloud services** that handle this for you

 * - Scroll search for large result sets
 * - Document retrieval by ID
 * - Bulk document updates
 * - Search parameter serialization/deserialization for cookies
 * - Integration with search engines and logging systems
 */
public class SearchHelper {

    // ===================================================================================
    //                                                                            Constant

            assertEquals(21474836480L, fileFsFullSizeInfo.getFree()); // 20GB
        }

        @Test
        @DisplayName("Should handle large file systems")
        void shouldHandleLargeFileSystems() throws SMBProtocolDecodingException {
            // Given - 10TB file system with 4KB clusters
            long totalClusters = 2684354560L; // 10TB / 4KB

But you can also exploit the benefits of parallelism and multiprocessing (having multiple processes running in parallel) for **CPU bound** workloads like those in Machine Learning systems.