fix: handle series with different types of labels

pkg/querier/postprocess.go

@@ -627,19 +627,29 @@ func convertTimeSeriesDataToScalar(tsData *qbtypes.TimeSeriesData, queryName str
 		return &qbtypes.ScalarData{QueryName: queryName}
 	}
 
-	columns := []*qbtypes.ColumnDescriptor{}
-
-	// Add group columns from first series
-	if len(tsData.Aggregations[0].Series) > 0 {
-		for _, label := range tsData.Aggregations[0].Series[0].Labels {
-			columns = append(columns, &qbtypes.ColumnDescriptor{
-				TelemetryFieldKey: label.Key,
-				QueryName:         queryName,
-				Type:              qbtypes.ColumnTypeGroup,
-			})
+	// Series can have ragged label sets; build the column schema from the
+	// union of all label keys (first-seen order) and fill rows by key lookup.
+	keyOrder := []telemetrytypes.TelemetryFieldKey{}
+	keyIndex := map[string]int{}
+	for _, series := range tsData.Aggregations[0].Series {
+		for _, label := range series.Labels {
+			if _, ok := keyIndex[label.Key.Name]; ok {
+				continue
+			}
+			keyIndex[label.Key.Name] = len(keyOrder)
+			keyOrder = append(keyOrder, label.Key)
 		}
 	}
 
+	columns := make([]*qbtypes.ColumnDescriptor, 0, len(keyOrder)+len(tsData.Aggregations))
+	for _, key := range keyOrder {
+		columns = append(columns, &qbtypes.ColumnDescriptor{
+			TelemetryFieldKey: key,
+			QueryName:         queryName,
+			Type:              qbtypes.ColumnTypeGroup,
+		})
+	}
+
 	// Add aggregation columns
 	for _, agg := range tsData.Aggregations {
 		name := agg.Alias
@@ -655,18 +665,18 @@ func convertTimeSeriesDataToScalar(tsData *qbtypes.TimeSeriesData, queryName str
 		})
 	}
 
-	// Build rows
+	// Build rows.
+	groupColCount := len(keyOrder)
 	data := [][]any{}
 	for seriesIdx, series := range tsData.Aggregations[0].Series {
 		row := make([]any, len(columns))
 
-		// Add group values
-		for i, label := range series.Labels {
-			row[i] = label.Value
+		// Place each label under its key's column (by lookup, not index).
+		for _, label := range series.Labels {
+			row[keyIndex[label.Key.Name]] = label.Value
 		}
 
 		// Add aggregation values (last value)
-		groupColCount := len(series.Labels)
 		for aggIdx, agg := range tsData.Aggregations {
 			if seriesIdx < len(agg.Series) && len(agg.Series[seriesIdx].Values) > 0 {
 				lastValue := agg.Series[seriesIdx].Values[len(agg.Series[seriesIdx].Values)-1].Value

pkg/querier/postprocess_test.go

@@ -0,0 +1,53 @@
+package querier
+
+import (
+	"testing"
+
+	"github.com/stretchr/testify/assert"
+	"github.com/stretchr/testify/require"
+
+	qbtypes "github.com/SigNoz/signoz/pkg/types/querybuildertypes/querybuildertypesv5"
+	"github.com/SigNoz/signoz/pkg/types/telemetrytypes"
+)
+
+// Multiple series with different number of labels, shouldn't panic and should align labels correctly.
+func TestConvertTimeSeriesDataToScalar_RaggedLabels(t *testing.T) {
+	label := func(name string, value any) *qbtypes.Label {
+		return &qbtypes.Label{
+			Key:   telemetrytypes.TelemetryFieldKey{Name: name},
+			Value: value,
+		}
+	}
+	series := func(labels []*qbtypes.Label, value float64) *qbtypes.TimeSeries {
+		return &qbtypes.TimeSeries{
+			Labels: labels,
+			Values: []*qbtypes.TimeSeriesValue{{Timestamp: 1, Value: value}},
+		}
+	}
+
+	tsData := &qbtypes.TimeSeriesData{
+		QueryName: "A",
+		Aggregations: []*qbtypes.AggregationBucket{{
+			Index: 0,
+			Series: []*qbtypes.TimeSeries{
+				series([]*qbtypes.Label{label("label_1", "orphan-0")}, 20),
+				series([]*qbtypes.Label{label("label_1", "box-0"), label("label_2", "rpc-0")}, 10),
+			},
+		}},
+	}
+
+	var sd *qbtypes.ScalarData
+	require.NotPanics(t, func() {
+		sd = convertTimeSeriesDataToScalar(tsData, "A")
+	})
+
+	require.NotNil(t, sd)
+	require.Len(t, sd.Columns, 3)
+	assert.Equal(t, "label_1", sd.Columns[0].Name)
+	assert.Equal(t, "label_2", sd.Columns[1].Name)
+	assert.Equal(t, "__result_0", sd.Columns[2].Name)
+
+	require.Len(t, sd.Data, 2)
+	assert.Equal(t, []any{"orphan-0", nil, 20.0}, sd.Data[0])
+	assert.Equal(t, []any{"box-0", "rpc-0", 10.0}, sd.Data[1])
+}

pkg/querier/querier.go

@@ -426,17 +426,9 @@ func (q *querier) QueryRange(ctx context.Context, orgID valuer.UUID, req *qbtype
 	}
 	nonExistentMetrics := []string{}
 	var dormantMetricsWarningMsg string
-	// internal metrics aren't user-controlled — skip errors/warnings for them since users can't act on them
-	isInternalMetric := func(n string) bool { return strings.HasPrefix(n, "signoz.") || strings.HasPrefix(n, "signoz_") }
-	externalMissingMetrics := make([]string, 0, len(missingMetrics))
-	for _, m := range missingMetrics {
-		if !isInternalMetric(m) {
-			externalMissingMetrics = append(externalMissingMetrics, m)
-		}
-	}
-	if len(externalMissingMetrics) > 0 {
-		lastSeenInfo, _ := q.metadataStore.FetchLastSeenInfoMulti(ctx, externalMissingMetrics...)
-		for _, missingMetricName := range externalMissingMetrics {
+	if len(missingMetrics) > 0 {
+		lastSeenInfo, _ := q.metadataStore.FetchLastSeenInfoMulti(ctx, missingMetrics...)
+		for _, missingMetricName := range missingMetrics {
 			if ts, ok := lastSeenInfo[missingMetricName]; ok && ts > 0 {
 				continue
 			}
@@ -448,22 +440,24 @@ func (q *querier) QueryRange(ctx context.Context, orgID valuer.UUID, req *qbtype
 			return nil, errors.NewNotFoundf(errors.CodeNotFound, "the following metrics were not found: %s", strings.Join(nonExistentMetrics, ", "))
 		}
 		lastSeenStr := func(name string) string {
-			ts := lastSeenInfo[name]
-			ago := humanize.RelTime(time.UnixMilli(ts), time.Now(), "ago", "from now")
-			return fmt.Sprintf("%s (last seen %s)", name, ago)
+			if ts, ok := lastSeenInfo[name]; ok && ts > 0 {
+				ago := humanize.RelTime(time.UnixMilli(ts), time.Now(), "ago", "from now")
+				return fmt.Sprintf("%s (last seen %s)", name, ago)
+			}
+			return name // this case won't come cuz lastSeenStr is never called for metrics in nonExistentMetrics
 		}
-		if len(externalMissingMetrics) == 1 {
-			dormantMetricsWarningMsg = fmt.Sprintf("no data found for the metric %s in the query time range", lastSeenStr(externalMissingMetrics[0]))
+		if len(missingMetrics) == 1 {
+			dormantMetricsWarningMsg = fmt.Sprintf("no data found for the metric %s in the query time range", lastSeenStr(missingMetrics[0]))
 		} else {
-			parts := make([]string, len(externalMissingMetrics))
-			for i, m := range externalMissingMetrics {
+			parts := make([]string, len(missingMetrics))
+			for i, m := range missingMetrics {
 				parts[i] = lastSeenStr(m)
 			}
 			dormantMetricsWarningMsg = fmt.Sprintf("no data found for the following metrics in the query time range: %s", strings.Join(parts, ", "))
 		}
 	}
 	preseededResults := make(map[string]any)
-	for _, name := range missingMetricQueries {
+	for _, name := range missingMetricQueries { // at this point missing metrics will not have any non existent metrics, only normal ones
 		switch req.RequestType {
 		case qbtypes.RequestTypeTimeSeries:
 			preseededResults[name] = &qbtypes.TimeSeriesData{QueryName: name}

tests/integration/tests/querier/03_metrics.py

@@ -640,32 +640,6 @@ def test_non_existent_metrics_returns_404(
     assert get_error_message(response.json()) == "could not find the metric whatevergoennnsgoeshere"
 
 
-def test_non_existent_internal_metrics_returns_no_warning(
-    signoz: types.SigNoz,
-    create_user_admin: None,  # pylint: disable=unused-argument
-    get_token: Callable[[str, str], str],
-) -> None:
-
-    now = datetime.now(tz=UTC).replace(second=0, microsecond=0)
-    metric_name = "signoz_calls_total"
-
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-    query = build_builder_query(
-        "A",
-        metric_name,
-        "doesnotreallymatter",
-        "sum",
-    )
-
-    end_ms = int(now.timestamp() * 1000)
-
-    start_2h = int((now - timedelta(hours=2)).timestamp() * 1000)
-    response = make_query_request(signoz, token, start_2h, end_ms, [query])
-    assert response.status_code == HTTPStatus.OK
-    data = response.json()
-    assert get_all_warnings(data) == []
-
-
 # Verify /api/v1/fields/values filters label values by metricNamespace prefix.
 # Inserts metrics under ns.a and ns.b, then asserts a specific prefix returns
 # only matching values while a common prefix returns both.