}
    return dummy;
  }

  @Benchmark
  double percentile90(int reps) {
    double dummy = 0.0;
    for (int i = 0; i < reps; i++) {
      dummy += algorithm.singleQuantile(90, 100, dataset(i));
    }
    return dummy;
  }

  @Benchmark
  double percentile99(int reps) {
    double dummy = 0.0;
    for (int i = 0; i < reps; i++) {

 *
 * <p>To compute the 99th percentile:
 *
 * {@snippet :
 * double myPercentile99 = percentiles().index(99).compute(myDataset);
 * }
 *
 * where {@link #percentiles()} has been statically imported.
 *
 * <p>To compute median and the 90th and 99th percentiles:
 *
 * {@snippet :
 * Map<Integer, Double> myPercentiles =
 *     percentiles().indexes(50, 90, 99).compute(myDataset);
 * }
 *

          .that(percentiles().index(index).compute(PSEUDORANDOM_DATASET))
          .isWithin(ALLOWED_ERROR)
          .of(expectedLargeDatasetPercentile(index));
    }
  }

  @AndroidIncompatible // slow
  public void testPercentiles_index_computeInPlace() {
    // Assert that the computation gives the correct result for all possible percentiles.
    for (int index = 0; index <= 100; index++) {

          .that(percentiles().index(index).compute(PSEUDORANDOM_DATASET))
          .isWithin(ALLOWED_ERROR)
          .of(expectedLargeDatasetPercentile(index));
    }
  }

  @AndroidIncompatible // slow
  public void testPercentiles_index_computeInPlace() {
    // Assert that the computation gives the correct result for all possible percentiles.
    for (int index = 0; index <= 100; index++) {

        regA(builder);
        return builder;
    }

    protected PercentilesAggregationBuilder regPercentilesA(String name, String field) {
        PercentilesAggregationBuilder builder = AggregationBuilders.percentiles(name).field(field);
        regA(builder);
        return builder;
    }

    protected PercentileRanksAggregationBuilder regPercentileRanksA(String name, String field, double[] values) {

        regA(builder);
        return builder;
    }

    protected PercentilesAggregationBuilder regPercentilesA(String name, String field) {
        PercentilesAggregationBuilder builder = AggregationBuilders.percentiles(name).field(field);
        regA(builder);
        return builder;
    }

    protected PercentileRanksAggregationBuilder regPercentileRanksA(String name, String field, double[] values) {

        regA(builder);
        return builder;
    }

    protected PercentilesAggregationBuilder regPercentilesA(String name, String field) {
        PercentilesAggregationBuilder builder = AggregationBuilders.percentiles(name).field(field);
        regA(builder);
        return builder;
    }

    protected PercentileRanksAggregationBuilder regPercentileRanksA(String name, String field, double[] values) {

  - `http_404_request_total` (the number of 404 requests handled)
  - `http_404_request_duration_ms` (the amount of time the server took to respond in ms)

  Also includes percentile groupings. The directory for the default 404 handler includes instructions on how to enable prometheus for monitoring and setting alerts.

introduced significant changes to its garbage collection algorithm. As a result, we observed an increase in memory usage for kube-apiserver in larger an heavily loaded clusters up to ~25% (with the benefit of API call latencies drop by up to 10x on 99th percentiles). If the memory increase is not acceptable for you you can mitigate by setting GOGC env variable (for our tests using GOGC=63 brings memory usage back to original value, although the exact value may depend on usage patterns on your cluster). ([...