* iterators that don't support {@code remove()}. Otherwise, all optional operations are supported.
 * Null row keys, columns keys, and values are not supported.
 *
 * <p>Lookups by row key are often faster than lookups by column key, because the data is stored in
 * a {@code Map<R, Map<C, V>>}. A method call like {@code column(columnKey).get(rowKey)} still runs

#### Endpoints

Endpoints have an optimized code path, as they are by far the most frequently updated resource - in a steady cluster, this will often be the *only* change, caused by scale up/down.

      #
      # If the probe is disabled, it is recommended to have delay=1s,period=2s,failureThreshold=30.
      # This ensures the startup is reasonable fast (polling every 2s). 1s delay is used since the startup is not often ready instantly.
      startupProbe:
        enabled: true
        failureThreshold: 600 # 10 minutes
      # Resources for the sidecar.
      resources:
        requests:
          cpu: 100m

    // > - the opening parenthesis (,
    // > - the closing parenthesis ),
    // > - and the opening square bracket [,
    // > - the opening curly brace {,
    // > These special characters are often called "metacharacters".
    [
        /[\\$.|*+(){^]/g,
        match => `\\${match}`
    ],

    [
        // > a question mark (?) matches a single character
        /(?!\\)\?/g,

      #
      # If the probe is disabled, it is recommended to have delay=1s,period=2s,failureThreshold=30.
      # This ensures the startup is reasonable fast (polling every 2s). 1s delay is used since the startup is not often ready instantly.
      startupProbe:
        enabled: true
        failureThreshold: 600 # 10 minutes

      # Resources for the sidecar.
      resources:
        requests:
          cpu: 100m

   * remove()}. Otherwise, all optional operations are supported. Null row keys, columns keys, and
   * values are not supported.
   *
   * <p>Lookups by row key are often faster than lookups by column key, because the data is stored
   * in a {@code Map<R, Map<C, V>>}. A method call like {@code column(columnKey).get(rowKey)} still

  // +optional
  optional int64 observedGeneration = 1;

  // lastScaleTime is the last time the HorizontalPodAutoscaler scaled the number of pods;
  // used by the autoscaler to control how often the number of pods is changed.
  // +optional
  optional k8s.io.apimachinery.pkg.apis.meta.v1.Time lastScaleTime = 2;

  // currentReplicas is the current number of replicas of pods managed by this autoscaler.

 * asynchronous operations. You can chain them together manually with calls to methods like {@link
 * Futures#transform(ListenableFuture, Function, Executor) Futures.transform}, but you will often
 * find it easier to use a framework. Frameworks automate the process, often adding features like
 * monitoring, debugging, and cancellation. Examples of frameworks include:
 *
 * <ul>
 *   <li><a href="https://dagger.dev/producers.html">Dagger Producers</a>

	for i := 0; i < b.N; i++ {
		var b Buffer
		b.Write(buf[0:1])
		for i := 0; i < 5<<10; i++ {
			b.Write(buf)
			b.Read(buf)
		}
	}
}

// Check that we don't compact too often. From Issue 5154.
func BenchmarkBufferFullSmallReads(b *testing.B) {
	buf := make([]byte, 1024)
	for i := 0; i < b.N; i++ {
		var b Buffer
		b.Write(buf)
		for b.Len()+20 < b.Cap() {
			b.Write(buf[:10])

    alternating: List<Header>,
  ) {
    writer.headers(outFinished, streamId, alternating)
  }

  /**
   * Callers of this method are not thread safe, and sometimes on application threads. Most often,
   * this method will be called to send a buffer worth of data to the peer.
   *
   * Writes are subject to the write window of the stream and the connection. Until there is a