// specified in the Forwarded header. The first element will always be
		// the 'for=', which we ignore, subsequently we proceed to look for
		// 'proto=' which should precede right after `for=` if not
		// we simply ignore the values and return empty. This is in line
		// with the approach we took for returning first ip from multiple
		// params.
		if match := forRegex.FindStringSubmatch(proto); len(match) > 1 {

    private ReserializedListGenerator(OneSizeTestContainerGenerator<Collection<E>, E> gen) {
      this.gen = gen;
    }

    @Override
    public SampleElements<E> samples() {
      return gen.samples();
    }

    @Override
    public List<E> create(Object... elements) {
      return (List<E>) SerializableTester.reserialize(gen.create(elements));
    }

    @Override

    private final Pattern javadocLinkPattern = Pattern.compile("link:\\{javadocPath\\}/(.*?\\.html)");
    // link:../samples/sample_problems_api_usage.html[end-to-end sample]
    private final Pattern samplesLinkPattern = Pattern.compile("link:../samples/(.*?\\.html)");
    // link:https://kotlinlang.org/docs/reference/using-gradle.html#targeting-the-jvm[Kotlin]

    return {"fresh_value": fresh_value}
```

////

## Zusammenfassung { #recap }

Abgesehen von all den ausgefallenen Wörtern, die hier verwendet werden, ist das **Dependency Injection**-System recht simpel.

Einfach Funktionen, die genauso aussehen wie *Pfadoperation-Funktionen*.

Dennoch ist es sehr mächtig und ermöglicht Ihnen die Deklaration beliebig tief verschachtelter Abhängigkeits-„Graphen“ (Bäume).

/// tip | Tipp

        // The elements in the list are so small that they can't even be divided into {expectedBucketNumber}.
        // For example, how do you split [0,0,0,0,0] into 3 buckets?
        // In this case, we simply put the elements into these buckets evenly.
        return list.chunked(list.size / expectedBucketNumber, smallElementAggregateFunction)
    }

    val largestElement = list.removeFirst()!!

Vemos que precisaremos de algumas dependências usadas em vários lugares da aplicação.

Então, as colocamos em seu próprio módulo de `dependencies` (`app/dependencies.py`).

Agora usaremos uma dependência simples para ler um cabeçalho `X-Token` personalizado:

{* ../../docs_src/bigger_applications/app_an_py39/dependencies.py hl[3,6:8] title["app/dependencies.py"] *}

/// tip | Dica

  }

  public void testCopyOf_concurrentlyMutating() {
    List<String> sample = Lists.newArrayList("a", "b", "c");
    for (int delta : new int[] {-1, 0, 1}) {
      for (int i = 0; i < sample.size(); i++) {
        Collection<String> misleading = misleadingSizeCollection(delta);
        List<String> expected = sample.subList(0, i);
        misleading.addAll(expected);

import org.opensearch.search.aggregations.bucket.missing.MissingAggregationBuilder;
import org.opensearch.search.aggregations.bucket.range.DateRangeAggregationBuilder;
import org.opensearch.search.aggregations.bucket.sampler.SamplerAggregationBuilder;
import org.opensearch.search.aggregations.metrics.CardinalityAggregationBuilder;
import org.opensearch.search.aggregations.metrics.ScriptedMetricAggregationBuilder;

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;

two parity drives, 100 MiB file takes only approximately 114 MiB.

Below is a list of data/parity drives and corresponding _approximate_ storage space usage on a 16 drive MinIO deployment. The field _storage
usage ratio_ is simply the drive space used by the file after erasure-encoding, divided by actual file size.

| Total Drives (N) | Data Drives (D) | Parity Drives (P) | Storage Usage Ratio |