import org.opensearch.search.aggregations.bucket.range.IpRangeAggregationBuilder;
import org.opensearch.search.aggregations.bucket.range.RangeAggregationBuilder;
import org.opensearch.search.aggregations.bucket.sampler.SamplerAggregationBuilder;
import org.opensearch.search.aggregations.bucket.terms.SignificantTermsAggregationBuilder;
import org.opensearch.search.aggregations.bucket.terms.TermsAggregationBuilder;

 *         .requireBracketsForIPv6();
 * hp.getHost();  // returns "2001:db8::1"
 * hp.getPort();  // returns 80
 * hp.toString(); // returns "[2001:db8::1]:80"
 * }
 *
 * <p>Here are some examples of recognized formats:
 *
 * <ul>
 *   <li>example.com
 *   <li>example.com:80
 *   <li>192.0.2.1
 *   <li>192.0.2.1:80
 *   <li>[2001:db8::1] - {@link #getHost()} omits brackets

{* ../../docs_src/dependencies/tutorial011_an_py310.py hl[22] *}

/// tip

All this might seem contrived. And it might not be very clear how is it useful yet.

These examples are intentionally simple, but show how it all works.

In the chapters about security, there are utility functions that are implemented in this same way.

import org.opensearch.search.aggregations.bucket.range.IpRangeAggregationBuilder;
import org.opensearch.search.aggregations.bucket.range.RangeAggregationBuilder;
import org.opensearch.search.aggregations.bucket.sampler.SamplerAggregationBuilder;
import org.opensearch.search.aggregations.bucket.terms.SignificantTermsAggregationBuilder;
import org.opensearch.search.aggregations.bucket.terms.TermsAggregationBuilder;

 *       at this goal. Important note: it may be easy to achieve the theoretical minimum collision
 *       rate when using completely <i>random</i> sample input. The true test of a hash function is
 *       how it performs on representative real-world data, which tends to contain many hidden
 *       patterns and clumps. The goal of a good hash function is to stamp these patterns out as

   * @throws IllegalStateException if the dataset is empty
   */
  public double populationCovariance() {
    checkState(count() != 0);
    return sumOfProductsOfDeltas / count();
  }

  /**
   * Returns the sample covariance of the values. The count must be greater than one.
   *
   * <p>This is not guaranteed to return zero when the dataset consists of the same pair of values
   * multiple times, due to numerical errors.
   *

흥미로운 점은 각 프로세스의 **CPU 사용률**은 시간에 따라 크게 **변동**할 수 있지만, **메모리(RAM)**는 보통 대체로 **안정적**으로 유지된다는 것입니다.

매번 비슷한 양의 연산을 수행하는 API이고 클라이언트가 많다면, **CPU 사용률**도 (급격히 오르내리기보다는) *안정적일* 가능성이 큽니다.

### 복제 도구와 전략 예시 { #examples-of-replication-tools-and-strategies }

이를 달성하는 접근 방식은 여러 가지가 있을 수 있으며, 다음 장들에서 Docker와 컨테이너를 설명할 때 구체적인 전략을 더 알려드리겠습니다.

    for (method in EventListener::class.java.declaredMethods) {
      if (method.name == "plus") continue

      val args =
        method.parameters
          .map { sampleValues[it.type] ?: error("no sample value for ${it.type}") }
          .toTypedArray()

      method.invoke(solo.eventListener, *args)
      method.invoke(composite, *args)

      val expectedEvent = solo.takeEvent()

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## Assine a newsletter { #subscribe-to-the-newsletter }

    <artifactId>corelib</artifactId>
    <version>0.7.0</version>
</dependency>
```

### Gradle Dependency
```gradle
implementation 'org.codelibs:corelib:0.7.0'
```

## 📖 Usage Examples

### Bean Utilities
```java
import org.codelibs.core.beans.*;
import org.codelibs.core.beans.factory.BeanDescFactory;
import org.codelibs.core.beans.util.BeanUtil;
import org.codelibs.core.beans.util.CopyOptions;