app/vmselect/promql: add outliersk(N, m) aggregate function for anomaly detection across groups of similar time series

app/vmselect/promql/aggr.go

@@ -44,6 +44,7 @@ var aggrFuncs = map[string]aggrFunc{
 	"bottomk_avg":    newAggrFuncRangeTopK(avgValue, true),
 	"bottomk_median": newAggrFuncRangeTopK(medianValue, true),
 	"any":            newAggrFunc(aggrFuncAny),
+	"outliersk":      aggrFuncOutliersK,
 }
 
 type aggrFunc func(afa *aggrFuncArg) ([]*timeseries, error)
@@ -588,16 +589,73 @@ func avgValue(values []float64) float64 {
 func medianValue(values []float64) float64 {
 	h := histogram.GetFast()
 	for _, v := range values {
-		if math.IsNaN(v) {
+		if !math.IsNaN(v) {
-			continue
+			h.Update(v)
 		}
-		h.Update(v)
 	}
 	value := h.Quantile(0.5)
 	histogram.PutFast(h)
 	return value
 }
 
+func aggrFuncOutliersK(afa *aggrFuncArg) ([]*timeseries, error) {
+	args := afa.args
+	if err := expectTransformArgsNum(args, 2); err != nil {
+		return nil, err
+	}
+	ks, err := getScalar(args[0], 0)
+	if err != nil {
+		return nil, err
+	}
+	afe := func(tss []*timeseries) []*timeseries {
+		// Calculate medians for each point across tss.
+		medians := make([]float64, len(ks))
+		h := histogram.GetFast()
+		for n := range ks {
+			h.Reset()
+			for j := range tss {
+				v := tss[j].Values[n]
+				if !math.IsNaN(v) {
+					h.Update(v)
+				}
+			}
+			medians[n] = h.Quantile(0.5)
+		}
+		histogram.PutFast(h)
+
+		// Calculate variation-like value for each tss.
+		type variation struct {
+			sum2 float64
+			ts   *timeseries
+		}
+		variations := make([]variation, len(tss))
+		for i, ts := range tss {
+			sum2 := float64(0)
+			for n, v := range ts.Values {
+				d := v - medians[n]
+				sum2 += d * d
+			}
+			variations[i] = variation{
+				sum2: sum2,
+				ts:   ts,
+			}
+		}
+
+		// Sort variations by sum2.
+		sort.Slice(variations, func(i, j int) bool {
+			a, b := variations[i], variations[j]
+			return lessWithNaNs(a.sum2, b.sum2)
+		})
+
+		// Return only up to k time series with the highest variation.
+		for i, k := range ks {
+			fillNaNsAtIdx(i, k, tss)
+		}
+		return removeNaNs(tss)
+	}
+	return aggrFuncExt(afe, args[1], &afa.ae.Modifier, afa.ae.Limit, true)
+}
+
 func aggrFuncLimitK(afa *aggrFuncArg) ([]*timeseries, error) {
 	args := afa.args
 	if err := expectTransformArgsNum(args, 2); err != nil {
@@ -658,24 +716,18 @@ func aggrFuncMedian(afa *aggrFuncArg) ([]*timeseries, error) {
 func newAggrQuantileFunc(phis []float64) func(tss []*timeseries) []*timeseries {
 	return func(tss []*timeseries) []*timeseries {
 		dst := tss[0]
+		h := histogram.GetFast()
+		defer histogram.PutFast(h)
 		for n := range dst.Values {
-			sort.Slice(tss, func(i, j int) bool {
+			h.Reset()
-				a := tss[i].Values[n]
+			for j := range tss {
-				b := tss[j].Values[n]
+				v := tss[j].Values[n]
-				return lessWithNaNs(a, b)
+				if !math.IsNaN(v) {
-			})
+					h.Update(v)
+				}
+			}
 			phi := phis[n]
-			if math.IsNaN(phi) {
+			dst.Values[n] = h.Quantile(phi)
-				phi = 1
-			}
-			if phi < 0 {
-				phi = 0
-			}
-			if phi > 1 {
-				phi = 1
-			}
-			idx := int(math.Round(float64(len(tss)-1) * phi))
-			dst.Values[n] = tss[idx].Values[n]
 		}
 		tss[0] = dst
 		return tss[:1]

app/vmselect/promql/exec_test.go

@@ -4206,14 +4206,63 @@ func TestExecSuccess(t *testing.T) {
 	t.Run(`quantile(NaN)`, func(t *testing.T) {
 		t.Parallel()
 		q := `quantile(NaN, label_set(10, "foo", "bar") or label_set(time()/150, "baz", "sss"))`
+		resultExpected := []netstorage.Result{}
+		f(q, resultExpected)
+	})
+	t.Run(`outliersk(0)`, func(t *testing.T) {
+		t.Parallel()
+		q := `outliersk(0, (
+			label_set(1300, "foo", "bar"),
+			label_set(time(), "baz", "sss"),
+		))`
+		resultExpected := []netstorage.Result{}
+		f(q, resultExpected)
+	})
+	t.Run(`outliersk(1)`, func(t *testing.T) {
+		t.Parallel()
+		q := `outliersk(1, (
+			label_set(1300, "foo", "bar"),
+			label_set(time(), "baz", "sss"),
+		))`
 		r := netstorage.Result{
 			MetricName: metricNameExpected,
-			Values:     []float64{10, 10, 10, 10.666666666666666, 12, 13.333333333333334},
+			Values:     []float64{1000, 1200, 1400, 1600, 1800, 2000},
 			Timestamps: timestampsExpected,
 		}
+		r.MetricName.Tags = []storage.Tag{{
+			Key:   []byte("baz"),
+			Value: []byte("sss"),
+		}}
 		resultExpected := []netstorage.Result{r}
 		f(q, resultExpected)
 	})
+	t.Run(`outliersk(3)`, func(t *testing.T) {
+		t.Parallel()
+		q := `sort_desc(outliersk(3, (
+			label_set(1300, "foo", "bar"),
+			label_set(time(), "baz", "sss"),
+		)))`
+		r1 := netstorage.Result{
+			MetricName: metricNameExpected,
+			Values:     []float64{1000, 1200, 1400, 1600, 1800, 2000},
+			Timestamps: timestampsExpected,
+		}
+		r1.MetricName.Tags = []storage.Tag{{
+			Key:   []byte("baz"),
+			Value: []byte("sss"),
+		}}
+		r2 := netstorage.Result{
+			MetricName: metricNameExpected,
+			Values:     []float64{1300, 1300, 1300, 1300, 1300, 1300},
+			Timestamps: timestampsExpected,
+		}
+		r2.MetricName.Tags = []storage.Tag{{
+			Key:   []byte("foo"),
+			Value: []byte("bar"),
+		}}
+		resultExpected := []netstorage.Result{r1, r2}
+		f(q, resultExpected)
+	})
 	t.Run(`range_quantile(0.5)`, func(t *testing.T) {
 		t.Parallel()
 		q := `range_quantile(0.5, time())`
@@ -5545,6 +5594,8 @@ func TestExecError(t *testing.T) {
 	f(`hoeffding_bound_upper()`)
 	f(`hoeffding_bound_upper(1)`)
 	f(`hoeffding_bound_upper(0.99, foo, 1)`)
+	f(`outliersk()`)
+	f(`outliersk(1)`)
 
 	// Invalid argument type
 	f(`median_over_time({}, 2)`)
@@ -5584,6 +5635,7 @@ func TestExecError(t *testing.T) {
 	f(`alias(1, 2)`)
 	f(`aggr_over_time(1, 2)`)
 	f(`aggr_over_time(("foo", "bar"), 3)`)
+	f(`outliersk((label_set(1, "foo", "bar"), label_set(2, "x", "y")), 123)`)
 
 	// Duplicate timeseries
 	f(`(label_set(1, "foo", "bar") or label_set(2, "foo", "baz"))

docs/MetricsQL.md

@@ -116,3 +116,5 @@ This functionality can be tried at [an editable Grafana dashboard](http://play-g
   for the given `phi` in the range `[0..1]`.
 - `last_over_time(m[d])` - returns the last value for `m` on the time range `d`.
 - `first_over_time(m[d])` - returns the first value for `m` on the time range `d`.
+- `outliersk(N, m)` - returns up to `N` outlier time series for `m`. Outlier time series have the highest deviation from the `median(m)`.
+  This aggregate function is useful to detect anomalies across groups of similar time series.

go.mod

@@ -8,7 +8,7 @@ require (
 	// like https://github.com/valyala/fasthttp/commit/996610f021ff45fdc98c2ce7884d5fa4e7f9199b
 	github.com/VictoriaMetrics/fasthttp v1.0.1
 	github.com/VictoriaMetrics/metrics v1.11.2
-	github.com/VictoriaMetrics/metricsql v0.2.1
+	github.com/VictoriaMetrics/metricsql v0.2.2
 	github.com/aws/aws-sdk-go v1.30.28
 	github.com/cespare/xxhash/v2 v2.1.1
 	github.com/golang/protobuf v1.4.2 // indirect

go.sum

@@ -45,8 +45,8 @@ github.com/VictoriaMetrics/fasthttp v1.0.1 h1:I7YdbswTIW63WxoFoUOSNxeOEGB46rdKUL
 github.com/VictoriaMetrics/fasthttp v1.0.1/go.mod h1:BqgsieH90PR7x97c89j+eqZDloKkDhAEQTwhLw6jw/4=
 github.com/VictoriaMetrics/metrics v1.11.2 h1:t/ceLP6SvagUqypCKU7cI7+tQn54+TIV/tGoxihHvx8=
 github.com/VictoriaMetrics/metrics v1.11.2/go.mod h1:LU2j9qq7xqZYXz8tF3/RQnB2z2MbZms5TDiIg9/NHiQ=
-github.com/VictoriaMetrics/metricsql v0.2.1 h1:OI/W2QCFiQiFULVN3ZiC/iCqZFt25rXp/O7P2NiAwYU=
+github.com/VictoriaMetrics/metricsql v0.2.2 h1:3PhBV4g2z7lm8adPShC4vr1PfSkRcLoSq5XOEpSgJPg=
-github.com/VictoriaMetrics/metricsql v0.2.1/go.mod h1:UIjd9S0W1UnTWlJdM0wLS+2pfuPqjwqKoK8yTos+WyE=
+github.com/VictoriaMetrics/metricsql v0.2.2/go.mod h1:UIjd9S0W1UnTWlJdM0wLS+2pfuPqjwqKoK8yTos+WyE=
 github.com/allegro/bigcache v1.2.1-0.20190218064605-e24eb225f156 h1:eMwmnE/GDgah4HI848JfFxHt+iPb26b4zyfspmqY0/8=
 github.com/allegro/bigcache v1.2.1-0.20190218064605-e24eb225f156/go.mod h1:Cb/ax3seSYIx7SuZdm2G2xzfwmv3TPSk2ucNfQESPXM=
 github.com/aws/aws-sdk-go v1.30.28 h1:SaPM7dlmp7h3Lj1nJ4jdzOkTdom08+g20k7AU5heZYg=

vendor/github.com/VictoriaMetrics/metricsql/aggr.go

@@ -34,6 +34,7 @@ var aggrFuncs = map[string]bool{
 	"bottomk_avg":    true,
 	"bottomk_median": true,
 	"any":            true,
+	"outliersk":      true,
 }
 
 func isAggrFunc(s string) bool {

vendor/modules.txt

@@ -18,7 +18,7 @@ github.com/VictoriaMetrics/fasthttp/fasthttputil
 github.com/VictoriaMetrics/fasthttp/stackless
 # github.com/VictoriaMetrics/metrics v1.11.2
 github.com/VictoriaMetrics/metrics
-# github.com/VictoriaMetrics/metricsql v0.2.1
+# github.com/VictoriaMetrics/metricsql v0.2.2
 github.com/VictoriaMetrics/metricsql
 github.com/VictoriaMetrics/metricsql/binaryop
 # github.com/aws/aws-sdk-go v1.30.28