Streaming PromQL engine: native histograms

pkg/streamingpromql/benchmarks/benchmarks.go

@@ -69,13 +69,18 @@ func (c BenchCase) Run(ctx context.Context, t testing.TB, start, end time.Time,
 	return res, qry.Close
 }
 
-// These test cases are taken from https://github.com/prometheus/prometheus/blob/main/promql/bench_test.go.
+// These test cases are taken from https://github.com/prometheus/prometheus/blob/main/promql/bench_test.go
+// and enhanced/added to.
 func TestCases(metricSizes []int) []BenchCase {
 	cases := []BenchCase{
 		// Plain retrieval.
 		{
 			Expr: "a_X",
 		},
+		// Histogram retrieval
+		{
+			Expr: "nh_X",
+		},
 		// Range vector selector.
 		{
 			Expr:             "a_X[1m]",

pkg/streamingpromql/benchmarks/comparison_test.go

@@ -163,6 +163,24 @@ func TestBenchmarkSetup(t *testing.T) {
 	}
 
 	require.Equal(t, expectedPoints, series.Floats)
+
+	// Check native histograms are set up correctly
+	query, err = streamingEngine.NewRangeQuery(ctx, q, nil, "nh_1", time.Unix(0, 0), time.Unix(int64(15*intervalSeconds), 0), interval)
+	require.NoError(t, err)
+
+	t.Cleanup(query.Close)
+	result = query.Exec(ctx)
+	require.NoError(t, result.Err)
+
+	matrix, err = result.Matrix()
+	require.NoError(t, err)
+
+	require.Len(t, matrix, 1)
+	series = matrix[0]
+	require.Equal(t, labels.FromStrings("__name__", "nh_1"), series.Metric)
+	require.Len(t, series.Floats, 0)
+	require.Len(t, series.Histograms, 16)
+	// TODO(jhesketh): Maybe check a histogram is set up as expected
 }
 
 // Why do we do this rather than require.Equal(t, expected, actual)?
@@ -194,7 +212,11 @@ func requireEqualResults(t testing.TB, expected, actual *promql.Result) {
 			require.Equal(t, expectedSample.Metric, actualSample.Metric)
 			require.Equal(t, expectedSample.T, actualSample.T)
 			require.Equal(t, expectedSample.H, actualSample.H)
-			require.InEpsilon(t, expectedSample.F, actualSample.F, 1e-10)
+			if expectedSample.F == 0 {
+				require.Equal(t, expectedSample.F, actualSample.F)
+			} else {
+				require.InEpsilon(t, expectedSample.F, actualSample.F, 1e-10)
+			}
 		}
 	case parser.ValueTypeMatrix:
 		expectedMatrix, err := expected.Matrix()
@@ -214,7 +236,11 @@ func requireEqualResults(t testing.TB, expected, actual *promql.Result) {
 				actualPoint := actualSeries.Floats[j]
 
 				require.Equal(t, expectedPoint.T, actualPoint.T)
-				require.InEpsilonf(t, expectedPoint.F, actualPoint.F, 1e-10, "expected series %v to have points %v, but result is %v", expectedSeries.Metric.String(), expectedSeries.Floats, actualSeries.Floats)
+				if expectedPoint.F == 0 {
+					require.Equal(t, expectedPoint.F, actualPoint.F)
+				} else {
+					require.InEpsilonf(t, expectedPoint.F, actualPoint.F, 1e-10, "expected series %v to have points %v, but result is %v", expectedSeries.Metric.String(), expectedSeries.Floats, actualSeries.Floats)
+				}
 			}
 		}
 	default:

pkg/streamingpromql/benchmarks/ingester.go

@@ -14,6 +14,7 @@ import (
 	"net"
 	"path/filepath"
 	"strconv"
+	"strings"
 	"time"
 
 	"github.com/go-kit/log"
@@ -174,7 +175,7 @@ func pushTestData(ing *ingester.Ingester, metricSizes []int) error {
 	totalMetrics := 0
 
 	for _, size := range metricSizes {
-		totalMetrics += (2 + histogramBuckets + 1) * size // 2 non-histogram metrics + 5 metrics for histogram buckets + 1 metric for +Inf histogram bucket
+		totalMetrics += (2 + histogramBuckets + 1 + 1) * size // 2 non-histogram metrics + 5 metrics for histogram buckets + 1 metric for +Inf histogram bucket + 1 metric for native-histograms
 	}
 
 	metrics := make([]labels.Labels, 0, totalMetrics)
@@ -183,6 +184,7 @@ func pushTestData(ing *ingester.Ingester, metricSizes []int) error {
 		aName := "a_" + strconv.Itoa(size)
 		bName := "b_" + strconv.Itoa(size)
 		histogramName := "h_" + strconv.Itoa(size)
+		nativeHistogramName := "nh_" + strconv.Itoa(size)
 
 		if size == 1 {
 			// We don't want a "l" label on metrics with one series (some test cases rely on this label not being present).
@@ -192,6 +194,7 @@ func pushTestData(ing *ingester.Ingester, metricSizes []int) error {
 				metrics = append(metrics, labels.FromStrings("__name__", histogramName, "le", strconv.Itoa(le)))
 			}
 			metrics = append(metrics, labels.FromStrings("__name__", histogramName, "le", "+Inf"))
+			metrics = append(metrics, labels.FromStrings("__name__", nativeHistogramName))
 		} else {
 			for i := 0; i < size; i++ {
 				metrics = append(metrics, labels.FromStrings("__name__", aName, "l", strconv.Itoa(i)))
@@ -200,34 +203,66 @@ func pushTestData(ing *ingester.Ingester, metricSizes []int) error {
 					metrics = append(metrics, labels.FromStrings("__name__", histogramName, "l", strconv.Itoa(i), "le", strconv.Itoa(le)))
 				}
 				metrics = append(metrics, labels.FromStrings("__name__", histogramName, "l", strconv.Itoa(i), "le", "+Inf"))
+				metrics = append(metrics, labels.FromStrings("__name__", nativeHistogramName, "l", strconv.Itoa(i)))
 			}
 		}
 	}
 
 	ctx := user.InjectOrgID(context.Background(), UserID)
-	req := &mimirpb.WriteRequest{
-		Timeseries: make([]mimirpb.PreallocTimeseries, len(metrics)),
-	}
 
-	for i, m := range metrics {
-		series := mimirpb.PreallocTimeseries{TimeSeries: &mimirpb.TimeSeries{
-			Labels:  mimirpb.FromLabelsToLabelAdapters(m),
-			Samples: make([]mimirpb.Sample, NumIntervals),
-		}}
+	// Batch samples into separate requests
+	batchSize := 100
+	for i := 0; i < NumIntervals; i += batchSize {
+		end := i + batchSize
+		if end > NumIntervals {
+			end = NumIntervals
+		}
 
-		for s := 0; s < NumIntervals; s++ {
-			series.Samples[s].TimestampMs = int64(s) * interval.Milliseconds()
-			series.Samples[s].Value = float64(s) + float64(i)/float64(len(metrics))
+		req := &mimirpb.WriteRequest{
+			Timeseries: make([]mimirpb.PreallocTimeseries, len(metrics)),
 		}
 
-		req.Timeseries[i] = series
-	}
+		for j, m := range metrics {
+			if strings.HasPrefix(m.Get("__name__"), "nh_") {
+				series := mimirpb.PreallocTimeseries{TimeSeries: &mimirpb.TimeSeries{
+					Labels:     mimirpb.FromLabelsToLabelAdapters(m.Copy()),
+					Histograms: make([]mimirpb.Histogram, end-i),
+				}}
+
+				for s := i; s < end; s++ {
+					// TODO(jhesketh): Fix this with some better data
+					series.Histograms[s-i].Timestamp = int64(s) * interval.Milliseconds()
+					series.Histograms[s-i].Count = &mimirpb.Histogram_CountInt{CountInt: 12}
+					series.Histograms[s-i].ZeroCount = &mimirpb.Histogram_ZeroCountInt{ZeroCountInt: 2}
+					series.Histograms[s-i].ZeroThreshold = 0.001
+					series.Histograms[s-i].Sum = 18.4
+					series.Histograms[s-i].Schema = 0
+					series.Histograms[s-i].NegativeSpans = []mimirpb.BucketSpan{{Offset: 0, Length: 2}, {Offset: 1, Length: 2}}
+					series.Histograms[s-i].NegativeDeltas = []int64{1, 1, -1, 0}
+					series.Histograms[s-i].PositiveSpans = []mimirpb.BucketSpan{{Offset: 0, Length: 2}, {Offset: 1, Length: 2}}
+					series.Histograms[s-i].PositiveDeltas = []int64{1, 1, -1, 0}
+				}
 
-	if _, err := ing.Push(ctx, req); err != nil {
-		return fmt.Errorf("failed to push samples to ingester: %w", err)
-	}
+				req.Timeseries[j] = series
+			} else {
+				series := mimirpb.PreallocTimeseries{TimeSeries: &mimirpb.TimeSeries{
+					Labels:  mimirpb.FromLabelsToLabelAdapters(m.Copy()),
+					Samples: make([]mimirpb.Sample, end-i),
+				}}
 
-	ing.Flush()
+				for s := i; s < end; s++ {
+					series.Samples[s-i].TimestampMs = int64(s) * interval.Milliseconds()
+					series.Samples[s-i].Value = float64(s) + float64(j)/float64(len(metrics))
+				}
+
+				req.Timeseries[j] = series
+			}
+		}
+		if _, err := ing.Push(ctx, req); err != nil {
+			return fmt.Errorf("failed to push samples to ingester: %w", err)
+		}
+		ing.Flush()
+	}
 
 	return nil
 }

pkg/streamingpromql/operator/instant_vector_selector.go

@@ -7,7 +7,6 @@ package operator
 
 import (
 	"context"
-	"errors"
 	"fmt"
 
 	"github.com/prometheus/prometheus/model/histogram"
@@ -48,13 +47,11 @@ func (v *InstantVectorSelector) NextSeries(_ context.Context) (InstantVectorSeri
 
 	v.memoizedIterator.Reset(v.chunkIterator)
 
-	data := InstantVectorSeriesData{
-		Floats: GetFPointSlice(v.numSteps), // TODO: only allocate this if we have any floats (once we support native histograms)
-	}
+	data := InstantVectorSeriesData{}
 
 	for stepT := v.Selector.Start; stepT <= v.Selector.End; stepT += v.Selector.Interval {
 		var t int64
-		var val float64
+		var f float64
 		var h *histogram.FloatHistogram
 
 		ts := stepT
@@ -70,26 +67,42 @@ func (v *InstantVectorSelector) NextSeries(_ context.Context) (InstantVectorSeri
 				return InstantVectorSeriesData{}, v.memoizedIterator.Err()
 			}
 		case chunkenc.ValFloat:
-			t, val = v.memoizedIterator.At()
+			t, f = v.memoizedIterator.At()
+		case chunkenc.ValHistogram, chunkenc.ValFloatHistogram:
+			t, h = v.memoizedIterator.AtFloatHistogram()
 		default:
 			return InstantVectorSeriesData{}, fmt.Errorf("streaming PromQL engine: unknown value type %s", valueType.String())
 		}
 
 		if valueType == chunkenc.ValNone || t > ts {
 			var ok bool
-			t, val, h, ok = v.memoizedIterator.PeekPrev()
-			if h != nil {
-				return InstantVectorSeriesData{}, errors.New("streaming PromQL engine doesn't support histograms yet")
-			}
+			t, f, h, ok = v.memoizedIterator.PeekPrev()
 			if !ok || t < ts-v.Selector.LookbackDelta.Milliseconds() {
 				continue
 			}
 		}
-		if value.IsStaleNaN(val) || (h != nil && value.IsStaleNaN(h.Sum)) {
+		if value.IsStaleNaN(f) || (h != nil && value.IsStaleNaN(h.Sum)) {
 			continue
 		}
 
-		data.Floats = append(data.Floats, promql.FPoint{T: stepT, F: val})
+		// if (f, h) have been set by PeekPrev, we do not know if f is 0 because that's the actual value, or because
+		// the previous value had a histogram.
+		// PeekPrev will set the histogram to nil, or the value to 0 if the other type exists.
+		// So check if histograms is nil first. If we don't have a histogram, then we should have a value and vice-versa.
+		if h != nil {
+			if len(data.Histograms) == 0 {
+				// Only create the slice once we know the series is a histogram or not.
+				// (It is possible to over-allocate in the case where we have both floats and histograms, but that won't be common).
+				data.Histograms = GetHPointSlice(v.numSteps)
+			}
+			data.Histograms = append(data.Histograms, promql.HPoint{T: stepT, H: h})
+		} else {
+			if len(data.Floats) == 0 {
+				// Only create the slice once we know the series is a histogram or not
+				data.Floats = GetFPointSlice(v.numSteps)
+			}
+			data.Floats = append(data.Floats, promql.FPoint{T: stepT, F: f})
+		}
 	}
 
 	if v.memoizedIterator.Err() != nil {

pkg/streamingpromql/operator/pool.go

@@ -21,6 +21,10 @@ var (
 		return make([]promql.FPoint, 0, size)
 	})
 
+	hPointSlicePool = pool.NewBucketedPool(1, maxExpectedPointsPerSeries, seriesPerResultBucketFactor, func(size int) []promql.HPoint {
+		return make([]promql.HPoint, 0, size)
+	})
+
 	matrixPool = pool.NewBucketedPool(1, maxExpectedSeriesPerResult, seriesPerResultBucketFactor, func(size int) promql.Matrix {
 		return make(promql.Matrix, 0, size)
 	})
@@ -51,6 +55,14 @@ func PutFPointSlice(s []promql.FPoint) {
 	fPointSlicePool.Put(s)
 }
 
+func GetHPointSlice(size int) []promql.HPoint {
+	return hPointSlicePool.Get(size)
+}
+
+func PutHPointSlice(s []promql.HPoint) {
+	hPointSlicePool.Put(s)
+}
+
 func GetMatrix(size int) promql.Matrix {
 	return matrixPool.Get(size)
 }

pkg/streamingpromql/query.go

@@ -278,6 +278,11 @@ func (q *Query) Exec(ctx context.Context) *promql.Result {
 	return q.result
 }
 
+func returnSeriesDataSlices(d operator.InstantVectorSeriesData) {
+	operator.PutFPointSlice(d.Floats)
+	operator.PutHPointSlice(d.Histograms)
+}
+
 func (q *Query) populateVectorFromInstantVectorOperator(ctx context.Context, o operator.InstantVectorOperator, series []operator.SeriesMetadata) (promql.Vector, error) {
 	ts := timeMilliseconds(q.statement.Start)
 	v := operator.GetVector(len(series))
@@ -292,26 +297,29 @@ func (q *Query) populateVectorFromInstantVectorOperator(ctx context.Context, o o
 			return nil, err
 		}
 
-		if len(d.Floats)+len(d.Histograms) != 1 {
-			operator.PutFPointSlice(d.Floats)
-			// TODO: put histogram point slice back in pool
-
-			if len(d.Floats)+len(d.Histograms) == 0 {
+		if len(d.Floats) == 1 && len(d.Histograms) == 0 {
+			point := d.Floats[0]
+			v = append(v, promql.Sample{
+				Metric: s.Labels,
+				T:      ts,
+				F:      point.F,
+			})
+		} else if len(d.Floats) == 0 && len(d.Histograms) == 1 {
+			point := d.Histograms[0]
+			v = append(v, promql.Sample{
+				Metric: s.Labels,
+				T:      ts,
+				H:      point.H,
+			})
+		} else {
+			returnSeriesDataSlices(d)
+			// A series may have no data points.
+			if len(d.Floats) == 0 && len(d.Histograms) == 0 {
 				continue
 			}
-
-			return nil, fmt.Errorf("expected exactly one sample for series %s, but got %v", s.Labels.String(), len(d.Floats))
+			return nil, fmt.Errorf("expected exactly one sample for series %s, but got %v floats, %v histograms", s.Labels.String(), len(d.Floats), len(d.Histograms))
 		}
-
-		point := d.Floats[0]
-		v = append(v, promql.Sample{
-			Metric: s.Labels,
-			T:      ts,
-			F:      point.F,
-		})
-
-		operator.PutFPointSlice(d.Floats)
-		// TODO: put histogram point slice back in pool
+		returnSeriesDataSlices(d)
 	}
 
 	return v, nil
@@ -331,9 +339,7 @@ func (q *Query) populateMatrixFromInstantVectorOperator(ctx context.Context, o o
 		}
 
 		if len(d.Floats) == 0 && len(d.Histograms) == 0 {
-			operator.PutFPointSlice(d.Floats)
-			// TODO: put histogram point slice back in pool
-
+			returnSeriesDataSlices(d)
 			continue
 		}
 
@@ -394,7 +400,7 @@ func (q *Query) Close() {
 	case promql.Matrix:
 		for _, s := range v {
 			operator.PutFPointSlice(s.Floats)
-			// TODO: put histogram point slice back in pool
+			operator.PutHPointSlice(s.Histograms)
 		}
 
 		operator.PutMatrix(v)

pkg/streamingpromql/testdata/ours/native_histograms.test

@@ -0,0 +1,23 @@
+# SPDX-License-Identifier: AGPL-3.0-only
+# Provenance-includes-location: https://github.com/prometheus/prometheus/tree/main/promql/testdata/native_histograms.test
+# Provenance-includes-license: Apache-2.0
+# Provenance-includes-copyright: The Prometheus Authors
+
+# buckets:[1 2 1] means 1 observation in the 1st bucket, 2 observations in the 2nd and 1 observation in the 3rd (total 4).
+load 5m
+	single_histogram {{schema:0 sum:5 count:4 buckets:[1 2 1]}} {{schema:0 sum:20 count:7 buckets:[9 10 1]}}
+
+eval instant at 5m single_histogram
+	{__name__="single_histogram"} {{schema:0 sum:20 count:7 buckets:[9 10 1]}}
+
+eval range from 0 to 5m step 1m single_histogram
+	{__name__="single_histogram"} {{schema:0 sum:5 count:4 buckets:[1 2 1]}} {{schema:0 sum:5 count:4 buckets:[1 2 1]}} {{schema:0 sum:5 count:4 buckets:[1 2 1]}} {{schema:0 sum:5 count:4 buckets:[1 2 1]}} {{schema:0 sum:5 count:4 buckets:[1 2 1]}} {{schema:0 sum:20 count:7 buckets:[9 10 1]}}
+
+clear
+
+# Test metric with mixed floats and histograms
+load 1m
+	mixed_metric 1 2 3 {{schema:0 sum:5 count:4 buckets:[1 2 1]}} {{schema:0 sum:8 count:6 buckets:[1 4 1]}}
+
+eval range from 0 to 4m step 1m mixed_metric
+	{__name__="mixed_metric"} 1 2 3 {{count:4 sum:5 buckets:[1 2 1]}} {{count:6 sum:8 buckets:[1 4 1]}}