.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++) {

 *
 * <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);
 * }
 *

        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) {

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). ([...

    }
    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++) {

    }
    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++) {

     * @return average response time in milliseconds
     */
    public long getAverageResponseTime() {
        return avgResponseTime.get();
    }

    /**
     * Get 95th percentile response time
     *
     * @return 95th percentile response time in milliseconds
     */
    public long getP95ResponseTime() {
        return p95ResponseTime.get();
    }

    /**
     * Get minimum response time
     *