// Test with Japanese characters
        String word = "日本語テスト";
        float boost = 2.0f;
        List<String> readings = Arrays.asList("ニホンゴテスト");
        List<String> fields = Arrays.asList("content_ja");
        List<String> tags = Arrays.asList("japanese");
        List<String> roles = Arrays.asList("admin");

        elevateWord = new ElevateWord(word, boost, readings, fields, tags, roles);

- Lease entries should be evicted based on LRU when max leases reached
- Implement periodic cleanup of expired leases

### 9.2 Thread Safety
- Use concurrent data structures for lease storage
- Minimize lock contention in hot paths
- Async handling of lease breaks

### 9.3 Network Efficiency
- Batch lease requests when possible
- Implement lease key reuse for related files
- Optimize lease break acknowledgment timing

 * <i>stripes</i>, increasing the granularity of a single lock and allowing independent operations
 * to lock different stripes and proceed concurrently, instead of creating contention for a single
 * lock.
 *
 * <p>The guarantee provided by this class is that equal keys lead to the same lock (or semaphore),
 * i.e. {@code if (key1.equals(key2))} then {@code striped.get(key1) == striped.get(key2)} (assuming

  /**
   * Guides the allowed concurrency among update operations. Used as a hint for internal sizing. The
   * table is internally partitioned to try to permit the indicated number of concurrent updates
   * without contention. Because assignment of entries to these partitions is not necessarily
   * uniform, the actual concurrency observed may vary. Ideally, you should choose a value to

    Castle is great! But it's a large dependency (6.5 MiB) and its security provider feature
    impacts VM-wide behavior.

 *  New: Reduce contention for applications that make a very high number of concurrent requests.
    Previously OkHttp used its connection pool as a lock when making changes to connections and

   * of reads may not be immediately reflected on the algorithm's data structures. These structures
   * are guarded by a lock and operations are applied in batches to avoid lock contention. The
   * penalty of applying the batches is spread across threads so that the amortized cost is slightly
   * higher than performing just the operation without enforcing the capacity constraint.
   *

   * of reads may not be immediately reflected on the algorithm's data structures. These structures
   * are guarded by a lock and operations are applied in batches to avoid lock contention. The
   * penalty of applying the batches is spread across threads so that the amortized cost is slightly
   * higher than performing just the operation without enforcing the capacity constraint.
   *

* Add a flag allowing contention profiling of the API server ([#36756](https://github.com/kubernetes/kubernetes/pull/36756), [@gmarek](https://github.com/gmarek))

* Fix comment and optimize code ([#38084](https://github.com/kubernetes/kubernetes/pull/38084), [@tanshanshan](https://github.com/tanshanshan))
* Add flag to enable contention profiling in scheduler. ([#37357](https://github.com/kubernetes/kubernetes/pull/37357), [@gmarek](https://github.com/gmarek))

    * cAdvisor: Fix brtfs filesystem discovery
    * cAdvisor: Fix race condition with AllDockerContainers
    * cAdvisor: Don't watch .mount cgroups
    * cAdvisor: Reduce lock contention during list containers
* Promote ScheduleDaemonSetPods by default scheduler to beta ([#67899](https://github.com/kubernetes/kubernetes/pull/67899), [@ravisantoshgudimetla](https://github.com/ravisantoshgudimetla))