app/vmselect/promql: add histogram aggregate function, which is useful for building heatmaps from multiple time series

app/vmselect/promql/aggr.go

@@ -9,6 +9,7 @@ import (
 
 	"github.com/VictoriaMetrics/VictoriaMetrics/lib/logger"
 	"github.com/VictoriaMetrics/VictoriaMetrics/lib/storage"
+	"github.com/VictoriaMetrics/metrics"
 )
 
 var aggrFuncs = map[string]aggrFunc{
@@ -31,6 +32,7 @@ var aggrFuncs = map[string]aggrFunc{
 	"distinct":  newAggrFunc(aggrFuncDistinct),
 	"sum2":      newAggrFunc(aggrFuncSum2),
 	"geomean":   newAggrFunc(aggrFuncGeomean),
+	"histogram": newAggrFunc(aggrFuncHistogram),
 }
 
 type aggrFunc func(afa *aggrFuncArg) ([]*timeseries, error)
@@ -184,6 +186,37 @@ func aggrFuncGeomean(tss []*timeseries) []*timeseries {
 	return tss[:1]
 }
 
+func aggrFuncHistogram(tss []*timeseries) []*timeseries {
+	m := make(map[string]*timeseries)
+	for i := range tss[0].Values {
+		var h metrics.Histogram
+		for _, ts := range tss {
+			v := ts.Values[i]
+			h.Update(v)
+		}
+		h.VisitNonZeroBuckets(func(vmrange string, count uint64) {
+			ts := m[vmrange]
+			if ts == nil {
+				ts = &timeseries{}
+				ts.CopyFromShallowTimestamps(tss[0])
+				ts.MetricName.RemoveTag("vmrange")
+				ts.MetricName.AddTag("vmrange", vmrange)
+				values := ts.Values
+				for k := range values {
+					values[k] = 0
+				}
+				m[vmrange] = ts
+			}
+			ts.Values[i] = float64(count)
+		})
+	}
+	rvs := make([]*timeseries, 0, len(m))
+	for _, ts := range m {
+		rvs = append(rvs, ts)
+	}
+	return vmrangeBucketsToLE(rvs)
+}
+
 func aggrFuncMin(tss []*timeseries) []*timeseries {
 	if len(tss) == 1 {
 		// Fast path - nothing to min.

app/vmselect/promql/exec.go

@@ -110,7 +110,7 @@ func timeseriesToResult(tss []*timeseries, maySort bool) ([]netstorage.Result, e
 	for i, ts := range tss {
 		bb.B = marshalMetricNameSorted(bb.B[:0], &ts.MetricName)
 		if _, ok := m[string(bb.B)]; ok {
-			return nil, fmt.Errorf(`duplicate output timeseries: %s%s`, ts.MetricName.MetricGroup, stringMetricName(&ts.MetricName))
+			return nil, fmt.Errorf(`duplicate output timeseries: %s`, stringMetricName(&ts.MetricName))
 		}
 		m[string(bb.B)] = struct{}{}
 

app/vmselect/promql/exec_test.go

@@ -2459,12 +2459,12 @@ func TestExecSuccess(t *testing.T) {
 	t.Run(`prometheus_buckets(missing-vmrange)`, func(t *testing.T) {
 		t.Parallel()
 		q := `sort(prometheus_buckets((
-			alias(label_set(time()/20, "foo", "bar", "le", "0.2"), "xxx"),
+			alias(label_set(time()/20, "foo", "bar", "le", "0.2"), "xyz"),
 			alias(label_set(time()/100, "foo", "bar", "vmrange", "foobar"), "xxx"),
 			alias(label_set(time()/100, "foo", "bar", "vmrange", "30...foobar"), "xxx"),
 			alias(label_set(time()/100, "foo", "bar", "vmrange", "30...40"), "xxx"),
 			alias(label_set(time()/80, "foo", "bar", "vmrange", "0...900", "le", "54"), "yyy"),
-			alias(label_set(time()/40, "foo", "bar", "vmrange", "900...1000", "le", "2343"), "yyy"),
+			alias(label_set(time()/40, "foo", "bar", "vmrange", "900...+Inf", "le", "2343"), "yyy"),
 		)))`
 		r1 := netstorage.Result{
 			MetricName: metricNameExpected,
@@ -2500,10 +2500,10 @@ func TestExecSuccess(t *testing.T) {
 		}
 		r3 := netstorage.Result{
 			MetricName: metricNameExpected,
-			Values:     []float64{12.5, 15, 17.5, 20, 22.5, 25},
+			Values:     []float64{10, 12, 14, 16, 18, 20},
 			Timestamps: timestampsExpected,
 		}
-		r3.MetricName.MetricGroup = []byte("yyy")
+		r3.MetricName.MetricGroup = []byte("xxx")
 		r3.MetricName.Tags = []storage.Tag{
 			{
 				Key:   []byte("foo"),
@@ -2511,12 +2511,12 @@ func TestExecSuccess(t *testing.T) {
 			},
 			{
 				Key:   []byte("le"),
-				Value: []byte("900"),
+				Value: []byte("+Inf"),
 			},
 		}
 		r4 := netstorage.Result{
 			MetricName: metricNameExpected,
-			Values:     []float64{37.5, 45, 52.5, 60, 67.5, 75},
+			Values:     []float64{12.5, 15, 17.5, 20, 22.5, 25},
 			Timestamps: timestampsExpected,
 		}
 		r4.MetricName.MetricGroup = []byte("yyy")
@@ -2527,16 +2527,32 @@ func TestExecSuccess(t *testing.T) {
 			},
 			{
 				Key:   []byte("le"),
-				Value: []byte("1000"),
+				Value: []byte("900"),
 			},
 		}
 		r5 := netstorage.Result{
+			MetricName: metricNameExpected,
+			Values:     []float64{37.5, 45, 52.5, 60, 67.5, 75},
+			Timestamps: timestampsExpected,
+		}
+		r5.MetricName.MetricGroup = []byte("yyy")
+		r5.MetricName.Tags = []storage.Tag{
+			{
+				Key:   []byte("foo"),
+				Value: []byte("bar"),
+			},
+			{
+				Key:   []byte("le"),
+				Value: []byte("+Inf"),
+			},
+		}
+		r6 := netstorage.Result{
 			MetricName: metricNameExpected,
 			Values:     []float64{50, 60, 70, 80, 90, 100},
 			Timestamps: timestampsExpected,
 		}
-		r5.MetricName.MetricGroup = []byte("xxx")
-		r5.MetricName.Tags = []storage.Tag{
+		r6.MetricName.MetricGroup = []byte("xyz")
+		r6.MetricName.Tags = []storage.Tag{
 			{
 				Key:   []byte("foo"),
 				Value: []byte("bar"),
@@ -2546,7 +2562,7 @@ func TestExecSuccess(t *testing.T) {
 				Value: []byte("0.2"),
 			},
 		}
-		resultExpected := []netstorage.Result{r1, r2, r3, r4, r5}
+		resultExpected := []netstorage.Result{r1, r2, r3, r4, r5, r6}
 		f(q, resultExpected)
 	})
 	t.Run(`prometheus_buckets(valid)`, func(t *testing.T) {
@@ -2674,6 +2690,99 @@ func TestExecSuccess(t *testing.T) {
 		resultExpected := []netstorage.Result{r}
 		f(q, resultExpected)
 	})
+	t.Run(`histogram(scalar)`, func(t *testing.T) {
+		t.Parallel()
+		q := `histogram(123)`
+		r1 := netstorage.Result{
+			MetricName: metricNameExpected,
+			Values:     []float64{0, 0, 0, 0, 0, 0},
+			Timestamps: timestampsExpected,
+		}
+		r1.MetricName.Tags = []storage.Tag{
+			{
+				Key:   []byte("le"),
+				Value: []byte("1e2"),
+			},
+		}
+		r2 := netstorage.Result{
+			MetricName: metricNameExpected,
+			Values:     []float64{1, 1, 1, 1, 1, 1},
+			Timestamps: timestampsExpected,
+		}
+		r2.MetricName.Tags = []storage.Tag{
+			{
+				Key:   []byte("le"),
+				Value: []byte("2e2"),
+			},
+		}
+		r3 := netstorage.Result{
+			MetricName: metricNameExpected,
+			Values:     []float64{1, 1, 1, 1, 1, 1},
+			Timestamps: timestampsExpected,
+		}
+		r3.MetricName.Tags = []storage.Tag{
+			{
+				Key:   []byte("le"),
+				Value: []byte("+Inf"),
+			},
+		}
+		resultExpected := []netstorage.Result{r1, r2, r3}
+		f(q, resultExpected)
+	})
+	t.Run(`histogram(vector)`, func(t *testing.T) {
+		t.Parallel()
+		q := `sort(histogram((
+			label_set(1, "foo", "bar"),
+			label_set(1.5, "xx", "yy"),
+			alias(1.9, "foobar"),
+		)))`
+		r1 := netstorage.Result{
+			MetricName: metricNameExpected,
+			Values:     []float64{0, 0, 0, 0, 0, 0},
+			Timestamps: timestampsExpected,
+		}
+		r1.MetricName.Tags = []storage.Tag{
+			{
+				Key:   []byte("le"),
+				Value: []byte("9e-1"),
+			},
+		}
+		r2 := netstorage.Result{
+			MetricName: metricNameExpected,
+			Values:     []float64{1, 1, 1, 1, 1, 1},
+			Timestamps: timestampsExpected,
+		}
+		r2.MetricName.Tags = []storage.Tag{
+			{
+				Key:   []byte("le"),
+				Value: []byte("1"),
+			},
+		}
+		r3 := netstorage.Result{
+			MetricName: metricNameExpected,
+			Values:     []float64{3, 3, 3, 3, 3, 3},
+			Timestamps: timestampsExpected,
+		}
+		r3.MetricName.Tags = []storage.Tag{
+			{
+				Key:   []byte("le"),
+				Value: []byte("2"),
+			},
+		}
+		r4 := netstorage.Result{
+			MetricName: metricNameExpected,
+			Values:     []float64{3, 3, 3, 3, 3, 3},
+			Timestamps: timestampsExpected,
+		}
+		r4.MetricName.Tags = []storage.Tag{
+			{
+				Key:   []byte("le"),
+				Value: []byte("+Inf"),
+			},
+		}
+		resultExpected := []netstorage.Result{r1, r2, r3, r4}
+		f(q, resultExpected)
+	})
 	t.Run(`avg(scalar) wiTHout (xx, yy)`, func(t *testing.T) {
 		t.Parallel()
 		q := `avg wiTHout (xx, yy) (123)`
@@ -4520,7 +4629,7 @@ func testMetricNamesEqual(t *testing.T, mn, mnExpected *storage.MetricName, pos
 			t.Fatalf(`unexpected tag key at #%d,%d; got %q; want %q`, pos, i, tag.Key, tagExpected.Key)
 		}
 		if string(tag.Value) != string(tagExpected.Value) {
-			t.Fatalf(`unexpected tag value at #%d,%d; got %q; want %q`, pos, i, tag.Value, tagExpected.Value)
+			t.Fatalf(`unexpected tag value for key %q at #%d,%d; got %q; want %q`, tag.Key, pos, i, tag.Value, tagExpected.Value)
 		}
 	}
 }

app/vmselect/promql/transform.go

@@ -332,29 +332,40 @@ func vmrangeBucketsToLE(tss []*timeseries) []*timeseries {
 	}
 
 	// Convert `vmrange` label in each group of time series to `le` label.
-	for _, xss := range m {
-		sort.Slice(xss, func(i, j int) bool { return xss[i].end < xss[j].end })
-		xssNew := make([]x, 0, len(xss))
-		endStrPrev := "0"
-		for _, xs := range xss {
-			ts := xs.ts
-			if xs.startStr != endStrPrev {
-				var tsDummy timeseries
-				tsDummy.CopyFromShallowTimestamps(ts)
-				values := tsDummy.Values
+	copyTS := func(src *timeseries, leStr string) *timeseries {
+		var ts timeseries
+		ts.CopyFromShallowTimestamps(src)
+		values := ts.Values
 		for i := range values {
 			values[i] = 0
 		}
-				tsDummy.MetricName.AddTag("le", xs.startStr)
+		ts.MetricName.RemoveTag("le")
+		ts.MetricName.AddTag("le", leStr)
+		return &ts
+	}
+	for _, xss := range m {
+		sort.Slice(xss, func(i, j int) bool { return xss[i].end < xss[j].end })
+		xssNew := make([]x, 0, len(xss)+2)
+		var xsPrev x
+		for _, xs := range xss {
+			ts := xs.ts
+			if xs.start != xsPrev.end {
 				xssNew = append(xssNew, x{
 					endStr: xs.startStr,
 					end:    xs.start,
-					ts:     &tsDummy,
+					ts:     copyTS(ts, xs.startStr),
 				})
 			}
 			ts.MetricName.AddTag("le", xs.endStr)
 			xssNew = append(xssNew, xs)
-			endStrPrev = xs.endStr
+			xsPrev = xs
+		}
+		if !math.IsInf(xsPrev.end, 1) {
+			xssNew = append(xssNew, x{
+				endStr: "+Inf",
+				end:    math.Inf(1),
+				ts:     copyTS(xsPrev.ts, "+Inf"),
+			})
 		}
 		xss = xssNew
 		for i := range xss[0].ts.Values {