Function BenchmarkHistogramAggregator_Results

pkg/traceql/engine_metrics_test.go:3142–3188

(b *testing.B)

}

func BenchmarkHistogramAggregator_Results(b *testing.B) {
	// nolint:gosec // G115
	req := &tempopb.QueryRangeRequest{
		Start:     uint64(time.Now().Add(-1 * time.Hour).UnixNano()),
		End:       uint64(time.Now().UnixNano()),
		Step:      uint64(15 * time.Second.Nanoseconds()),
		Exemplars: 100,
	}

	benchmarks := []struct {
		name          string
		seriesCount   int
		samplesCount  int
		exemplarCount int
		quantiles     []float64
	}{
		{"Small_3Quantiles", 6, 10, 5, []float64{0.5, 0.9, 0.99}},
		{"Medium_3Quantiles", 10, 100, 20, []float64{0.5, 0.9, 0.99}},
		{"Large_3Quantiles", 20, 1000, 100, []float64{0.5, 0.9, 0.99}},
		// These test the bucket rescanning optimization specifically
		{"Small_5Quantiles", 6, 10, 5, []float64{0.5, 0.75, 0.9, 0.95, 0.99}},
		{"Medium_5Quantiles", 10, 100, 20, []float64{0.5, 0.75, 0.9, 0.95, 0.99}},
		{"Large_5Quantiles", 20, 1000, 100, []float64{0.5, 0.75, 0.9, 0.95, 0.99}},
		// High exemplar density to test caching benefits
		{"High_Exemplars", 10, 100, 200, []float64{0.5, 0.9, 0.99}},
	}

	for _, bm := range benchmarks {
		b.Run(bm.name, func(b *testing.B) {
			// Generate test data
			series := generateTestTimeSeries(bm.seriesCount, bm.samplesCount, bm.exemplarCount, req.Start, req.End)

			// Create histogram aggregator
			h := NewHistogramAggregator(req, bm.quantiles, req.Exemplars)

			// Combine the series (this is setup, not benchmarked)
			h.Combine(series)

			// Benchmark the Results method
			b.ResetTimer()
			for i := 0; i < b.N; i++ {
				results := h.Results()
				_ = results // Prevent optimization
			}
		})
	}
}

// generateTestTimeSeries creates test time series data for benchmarking
// nolint:gosec // G115