feat: added min_over_time TraceQL metric

CHANGELOG.md

@@ -10,6 +10,7 @@
 * [ENHANCEMENT] Update metrics-generator config in Tempo distributed docker compose example to serve TraceQL metrics [#4003](https://github.com/grafana/tempo/pull/4003) (@javiermolinar)
 * [ENHANCEMENT] Reduce allocs related to marshalling dedicated columns repeatedly in the query frontend. [#4007](https://github.com/grafana/tempo/pull/4007) (@joe-elliott)
 * [ENHANCEMENT] Support exporting internal Tempo traces via OTLP exporter when `use_otel_tracer` is enabled. Use the OpenTelemetry SDK environment variables to configure the span exporter [#4028](https://github.com/grafana/tempo/pull/4028) (@andreasgerstmayr)
+* [ENHANCEMENT] TraceQL metrics queries: add min_over_time  [#3975](https://github.com/grafana/tempo/pull/3975) (@javiermolinar)
 
 # v2.6.0-rc.1
 

docs/sources/tempo/traceql/metrics-queries.md

@@ -56,6 +56,9 @@ These functions can be added as an operator at the end of any TraceQL query.
 `count_over_time`
 : Counts the number of matching spans per time interval (see the `step` API parameter)
 
+`min_over_time`
+: Returns the minimin value of matching spans values per time interval (see the `step` API parameter)
+
 `quantile_over_time`
 : The quantile of the values in the specified interval
 
@@ -91,7 +94,25 @@ down by HTTP route.
 This might let you determine that `/api/sad` had a higher rate of erroring
 spans than `/api/happy`, for example.
 
-### The `quantile_over_time` and `histogram_over_time` functions
+### The `<aggregation>_over_time()` functions
+
+The `counts_over_time()` let you counts the number of matching spans per time interval.
+
+```
+{ name = "GET /:endpoint" } | count_over_time() by (span.http.status_code)
+
+```
+The `min_over_time()` let you aggregate numerical values by computing the minimun value of them, such as the all important span duration.
+
+```
+{ name = "GET /:endpoint" } | min_over_time(duration) by (span.http.target)
+```
+
+Any numerical attribute on the span is fair game.
+
+```
+{ name = "GET /:endpoint" } | min_over_time(span.http.status_code)
+```
 
 The `quantile_over_time()` and `histogram_over_time()` functions let you aggregate numerical values, such as the all important span duration.
 You can specify multiple quantiles in the same query.

modules/querier/http.go

@@ -403,6 +403,10 @@ func (q *Querier) QueryRangeHandler(w http.ResponseWriter, r *http.Request) {
 		errHandler(ctx, span, err)
 		return
 	}
+	// This is to prevent a panic marshaling nil
+	if resp == nil {
+		resp = &tempopb.QueryRangeResponse{}
+	}
 
 	if resp != nil && resp.Metrics != nil {
 		span.SetTag("inspectedBytes", resp.Metrics.InspectedBytes)

pkg/traceql/ast.go

@@ -1048,6 +1048,8 @@ type MetricsAggregate struct {
 	agg        SpanAggregator
 	seriesAgg  SeriesAggregator
 	exemplarFn getExemplar
+	// Type of operation for simple aggregatation in layers 2 and 3
+	simpleAggregationOp SimpleAggregationOp
 }
 
 func newMetricsAggregate(agg MetricsAggregateOp, by []Attribute) *MetricsAggregate {
@@ -1057,6 +1059,14 @@ func newMetricsAggregate(agg MetricsAggregateOp, by []Attribute) *MetricsAggrega
 	}
 }
 
+func newMetricsAggregateWithAttr(agg MetricsAggregateOp, attr Attribute, by []Attribute) *MetricsAggregate {
+	return &MetricsAggregate{
+		op:   agg,
+		attr: attr,
+		by:   by,
+	}
+}
+
 func newMetricsAggregateQuantileOverTime(attr Attribute, qs []float64, by []Attribute) *MetricsAggregate {
 	return &MetricsAggregate{
 		op:     metricsAggregateQuantileOverTime,
@@ -1066,24 +1076,13 @@ func newMetricsAggregateQuantileOverTime(attr Attribute, qs []float64, by []Attr
 	}
 }
 
-func newMetricsAggregateHistogramOverTime(attr Attribute, by []Attribute) *MetricsAggregate {
-	return &MetricsAggregate{
-		op:   metricsAggregateHistogramOverTime,
-		by:   by,
-		attr: attr,
-	}
-}
-
 func (a *MetricsAggregate) extractConditions(request *FetchSpansRequest) {
-	switch a.op {
-	case metricsAggregateRate, metricsAggregateCountOverTime:
-		// No extra conditions, start time is already enough
-	case metricsAggregateQuantileOverTime, metricsAggregateHistogramOverTime:
-		if !request.HasAttribute(a.attr) {
-			request.SecondPassConditions = append(request.SecondPassConditions, Condition{
-				Attribute: a.attr,
-			})
-		}
+	// For metrics aggregators based on a span attribute we have to include it
+	includeAttribute := a.attr != (Attribute{}) && !request.HasAttribute(a.attr)
+	if includeAttribute {
+		request.SecondPassConditions = append(request.SecondPassConditions, Condition{
+			Attribute: a.attr,
+		})
 	}
 
 	for _, b := range a.by {
@@ -1096,16 +1095,6 @@ func (a *MetricsAggregate) extractConditions(request *FetchSpansRequest) {
 }
 
 func (a *MetricsAggregate) init(q *tempopb.QueryRangeRequest, mode AggregateMode) {
-	switch mode {
-	case AggregateModeSum:
-		a.initSum(q)
-		return
-
-	case AggregateModeFinal:
-		a.initFinal(q)
-		return
-	}
-
 	// Raw mode:
 
 	var innerAgg func() VectorAggregator
@@ -1116,12 +1105,20 @@ func (a *MetricsAggregate) init(q *tempopb.QueryRangeRequest, mode AggregateMode
 	switch a.op {
 	case metricsAggregateCountOverTime:
 		innerAgg = func() VectorAggregator { return NewCountOverTimeAggregator() }
+		a.simpleAggregationOp = sumAggregation
+		exemplarFn = func(s Span) (float64, uint64) {
+			return math.NaN(), a.spanStartTimeMs(s)
+		}
+	case metricsAggregateMinOverTime:
+		innerAgg = func() VectorAggregator { return NewMinOverTimeAggregator(a.attr) }
+		a.simpleAggregationOp = minAggregation
 		exemplarFn = func(s Span) (float64, uint64) {
 			return math.NaN(), a.spanStartTimeMs(s)
 		}
 
 	case metricsAggregateRate:
 		innerAgg = func() VectorAggregator { return NewRateAggregator(1.0 / time.Duration(q.Step).Seconds()) }
+		a.simpleAggregationOp = sumAggregation
 		exemplarFn = func(s Span) (float64, uint64) {
 			return math.NaN(), a.spanStartTimeMs(s)
 		}
@@ -1131,6 +1128,7 @@ func (a *MetricsAggregate) init(q *tempopb.QueryRangeRequest, mode AggregateMode
 		// I.e. a duration of 500ms will be in __bucket==0.512s
 		innerAgg = func() VectorAggregator { return NewCountOverTimeAggregator() }
 		byFuncLabel = internalLabelBucket
+		a.simpleAggregationOp = sumAggregation
 		switch a.attr {
 		case IntrinsicDurationAttribute:
 			// Optimal implementation for duration attribute
@@ -1142,12 +1140,22 @@ func (a *MetricsAggregate) init(q *tempopb.QueryRangeRequest, mode AggregateMode
 			// Basic implementation for all other attributes
 			byFunc = a.bucketizeAttribute
 			exemplarFn = func(s Span) (float64, uint64) {
-				v, _ := a.floatizeAttribute(s)
+				v, _ := FloatizeAttribute(s, a.attr)
 				return v, a.spanStartTimeMs(s)
 			}
 		}
 	}
 
+	switch mode {
+	case AggregateModeSum:
+		a.initSum(q)
+		return
+
+	case AggregateModeFinal:
+		a.initFinal(q)
+		return
+	}
+
 	a.agg = NewGroupingAggregator(a.op.String(), func() RangeAggregator {
 		return NewStepAggregator(q.Start, q.End, q.Step, innerAgg)
 	}, a.by, byFunc, byFuncLabel)
@@ -1167,28 +1175,8 @@ func (a *MetricsAggregate) bucketizeSpanDuration(s Span) (Static, bool) {
 	return NewStaticFloat(Log2Bucketize(d) / float64(time.Second)), true
 }
 
-func (a *MetricsAggregate) floatizeAttribute(s Span) (float64, StaticType) {
-	v, ok := s.AttributeFor(a.attr)
-	if !ok {
-		return 0, TypeNil
-	}
-
-	switch v.Type {
-	case TypeInt:
-		n, _ := v.Int()
-		return float64(n), v.Type
-	case TypeDuration:
-		d, _ := v.Duration()
-		return float64(d.Nanoseconds()), v.Type
-	case TypeFloat:
-		return v.Float(), v.Type
-	default:
-		return 0, TypeNil
-	}
-}
-
 func (a *MetricsAggregate) bucketizeAttribute(s Span) (Static, bool) {
-	f, t := a.floatizeAttribute(s)
+	f, t := FloatizeAttribute(s, a.attr)
 
 	switch t {
 	case TypeInt:
@@ -1213,7 +1201,7 @@ func (a *MetricsAggregate) bucketizeAttribute(s Span) (Static, bool) {
 func (a *MetricsAggregate) initSum(q *tempopb.QueryRangeRequest) {
 	// Currently all metrics are summed by job to produce
 	// intermediate results. This will change when adding min/max/topk/etc
-	a.seriesAgg = NewSimpleAdditionCombiner(q)
+	a.seriesAgg = NewSimpleCombiner(q, a.simpleAggregationOp)
 }
 
 func (a *MetricsAggregate) initFinal(q *tempopb.QueryRangeRequest) {
@@ -1222,7 +1210,7 @@ func (a *MetricsAggregate) initFinal(q *tempopb.QueryRangeRequest) {
 		a.seriesAgg = NewHistogramAggregator(q, a.floats)
 	default:
 		// These are simple additions by series
-		a.seriesAgg = NewSimpleAdditionCombiner(q)
+		a.seriesAgg = NewSimpleCombiner(q, a.simpleAggregationOp)
 	}
 }
 
@@ -1252,6 +1240,7 @@ func (a *MetricsAggregate) result() SeriesSet {
 func (a *MetricsAggregate) validate() error {
 	switch a.op {
 	case metricsAggregateCountOverTime:
+	case metricsAggregateMinOverTime:
 	case metricsAggregateRate:
 	case metricsAggregateHistogramOverTime:
 		if len(a.by) >= maxGroupBys {

pkg/traceql/ast_stringer.go

@@ -179,9 +179,11 @@ func (a MetricsAggregate) String() string {
 
 	s.WriteString(a.op.String())
 	s.WriteString("(")
+	if a.attr != (Attribute{}) {
+		s.WriteString(a.attr.String())
+	}
 	switch a.op {
 	case metricsAggregateQuantileOverTime:
-		s.WriteString(a.attr.String())
 		s.WriteString(",")
 		for i, f := range a.floats {
 			s.WriteString(strconv.FormatFloat(f, 'f', 5, 64))

pkg/traceql/ast_test.go

@@ -854,6 +854,11 @@ func (m *mockSpan) WithSpanString(key string, value string) *mockSpan {
 	return m
 }
 
+func (m *mockSpan) WithSpanInt(key string, value int) *mockSpan {
+	m.attributes[NewScopedAttribute(AttributeScopeSpan, false, key)] = NewStaticInt(value)
+	return m
+}
+
 func (m *mockSpan) WithAttrBool(key string, value bool) *mockSpan {
 	m.attributes[NewAttribute(key)] = NewStaticBool(value)
 	return m