fix: cleanup

deploy/docker-swarm/docker-compose.ha.yaml

@@ -190,7 +190,7 @@ services:
       # - ../common/clickhouse/storage.xml:/etc/clickhouse-server/config.d/storage.xml
   signoz:
     !!merge <<: *db-depend
-    image: signoz/signoz:v0.116.0
+    image: signoz/signoz:v0.115.0
     ports:
       - "8080:8080" # signoz port
     #   - "6060:6060"     # pprof port

deploy/docker-swarm/docker-compose.yaml

@@ -117,7 +117,7 @@ services:
       # - ../common/clickhouse/storage.xml:/etc/clickhouse-server/config.d/storage.xml
   signoz:
     !!merge <<: *db-depend
-    image: signoz/signoz:v0.116.0
+    image: signoz/signoz:v0.115.0
     ports:
       - "8080:8080" # signoz port
     volumes:

deploy/docker/docker-compose.ha.yaml

@@ -181,7 +181,7 @@ services:
       # - ../common/clickhouse/storage.xml:/etc/clickhouse-server/config.d/storage.xml
   signoz:
     !!merge <<: *db-depend
-    image: signoz/signoz:${VERSION:-v0.116.0}
+    image: signoz/signoz:${VERSION:-v0.115.0}
     container_name: signoz
     ports:
       - "8080:8080" # signoz port

deploy/docker/docker-compose.yaml

@@ -109,7 +109,7 @@ services:
       # - ../common/clickhouse/storage.xml:/etc/clickhouse-server/config.d/storage.xml
   signoz:
     !!merge <<: *db-depend
-    image: signoz/signoz:${VERSION:-v0.116.0}
+    image: signoz/signoz:${VERSION:-v0.115.0}
     container_name: signoz
     ports:
       - "8080:8080" # signoz port

pkg/querier/builder_query.go

@@ -361,6 +361,10 @@ func (q *builderQuery[T]) executeWindowList(ctx context.Context) (*qbtypes.Resul
 		if err != nil {
 			return nil, err
 		}
+		// skip = true means we have indentified from the query itself that we don't need to execute this bucket
+		if stmt.Skip {
+			continue
+		}
 		warnings = stmt.Warnings
 		warningsDocURL = stmt.WarningsDocURL
 		// Execute with proper context for partial value detection

pkg/querier/consume.go

@@ -69,7 +69,6 @@ func readAsTimeSeries(rows driver.Rows, queryWindow *qbtypes.TimeRange, step qbt
 		key string // deterministic join of label values
 	}
 	seriesMap := map[sKey]*qbtypes.TimeSeries{}
-	var keyOrder []sKey // preserves ClickHouse row-arrival order
 
 	stepMs := uint64(step.Duration.Milliseconds())
 
@@ -220,7 +219,6 @@ func readAsTimeSeries(rows driver.Rows, queryWindow *qbtypes.TimeRange, step qbt
 			if !ok {
 				series = &qbtypes.TimeSeries{Labels: lblObjs}
 				seriesMap[key] = series
-				keyOrder = append(keyOrder, key)
 			}
 			series.Values = append(series.Values, &qbtypes.TimeSeriesValue{
 				Timestamp: ts,
@@ -252,8 +250,8 @@ func readAsTimeSeries(rows driver.Rows, queryWindow *qbtypes.TimeRange, step qbt
 			Alias: "__result_" + strconv.Itoa(i),
 		}
 	}
-	for _, k := range keyOrder {
-		buckets[k.agg].Series = append(buckets[k.agg].Series, seriesMap[k])
+	for k, s := range seriesMap {
+		buckets[k.agg].Series = append(buckets[k.agg].Series, s)
 	}
 
 	var nonEmpty []*qbtypes.AggregationBucket

pkg/querier/postprocess.go

@@ -185,6 +185,22 @@ func postProcessMetricQuery(
 	query qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation],
 	req *qbtypes.QueryRangeRequest,
 ) *qbtypes.Result {
+
+	config := query.Aggregations[0]
+	spaceAggOrderBy := fmt.Sprintf("%s(%s)", config.SpaceAggregation.StringValue(), config.MetricName)
+	timeAggOrderBy := fmt.Sprintf("%s(%s)", config.TimeAggregation.StringValue(), config.MetricName)
+	timeSpaceAggOrderBy := fmt.Sprintf("%s(%s(%s))", config.SpaceAggregation.StringValue(), config.TimeAggregation.StringValue(), config.MetricName)
+
+	for idx := range query.Order {
+		if query.Order[idx].Key.Name == spaceAggOrderBy ||
+			query.Order[idx].Key.Name == timeAggOrderBy ||
+			query.Order[idx].Key.Name == timeSpaceAggOrderBy {
+			query.Order[idx].Key.Name = qbtypes.DefaultOrderByKey
+		}
+	}
+
+	result = q.applySeriesLimit(result, query.Limit, query.Order)
+
 	if len(query.Functions) > 0 {
 		step := query.StepInterval.Duration.Milliseconds()
 		functions := q.prepareFillZeroArgsWithStep(query.Functions, req, step)

pkg/telemetrymeter/stmt_builder_test.go

@@ -51,7 +51,7 @@ func TestStatementBuilder(t *testing.T) {
 				},
 			},
 			expected: qbtypes.Statement{
-				Query: "WITH __temporal_aggregation_cte AS (SELECT ts, `service.name`, multiIf(row_number() OVER rate_window = 1, nan, (per_series_value - lagInFrame(per_series_value, 1) OVER rate_window) < 0, per_series_value / (ts - lagInFrame(ts, 1) OVER rate_window), (per_series_value - lagInFrame(per_series_value, 1) OVER rate_window) / (ts - lagInFrame(ts, 1) OVER rate_window)) AS per_series_value FROM (SELECT fingerprint, toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(86400)) AS ts, JSONExtractString(labels, 'service.name') AS `service.name`, max(value) AS per_series_value FROM signoz_meter.distributed_samples AS points WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? AND JSONExtractString(labels, 'service.name') = ? AND LOWER(temporality) LIKE LOWER(?) GROUP BY fingerprint, ts, `service.name` ORDER BY fingerprint, ts) WINDOW rate_window AS (PARTITION BY fingerprint ORDER BY fingerprint, ts)), __spatial_aggregation_cte AS (SELECT ts, `service.name`, sum(per_series_value) AS value FROM __temporal_aggregation_cte WHERE isNaN(per_series_value) = ? GROUP BY ts, `service.name`) SELECT * FROM __spatial_aggregation_cte WHERE (`service.name`) IN (SELECT `service.name` FROM __spatial_aggregation_cte GROUP BY `service.name` ORDER BY avg(value) DESC LIMIT 10) ORDER BY avg(value) OVER (PARTITION BY `service.name`) DESC, `service.name`, ts ASC",
+				Query: "WITH __temporal_aggregation_cte AS (SELECT ts, `service.name`, multiIf(row_number() OVER rate_window = 1, nan, (per_series_value - lagInFrame(per_series_value, 1) OVER rate_window) < 0, per_series_value / (ts - lagInFrame(ts, 1) OVER rate_window), (per_series_value - lagInFrame(per_series_value, 1) OVER rate_window) / (ts - lagInFrame(ts, 1) OVER rate_window)) AS per_series_value FROM (SELECT fingerprint, toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(86400)) AS ts, JSONExtractString(labels, 'service.name') AS `service.name`, max(value) AS per_series_value FROM signoz_meter.distributed_samples AS points WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? AND JSONExtractString(labels, 'service.name') = ? AND LOWER(temporality) LIKE LOWER(?) GROUP BY fingerprint, ts, `service.name` ORDER BY fingerprint, ts) WINDOW rate_window AS (PARTITION BY fingerprint ORDER BY fingerprint, ts)), __spatial_aggregation_cte AS (SELECT ts, `service.name`, sum(per_series_value) AS value FROM __temporal_aggregation_cte WHERE isNaN(per_series_value) = ? GROUP BY ts, `service.name`) SELECT * FROM __spatial_aggregation_cte ORDER BY `service.name`, ts",
 				Args:  []any{"signoz_calls_total", uint64(1747785600000), uint64(1747983420000), "cartservice", "cumulative", 0},
 			},
 			expectedErr: nil,
@@ -84,7 +84,7 @@ func TestStatementBuilder(t *testing.T) {
 				},
 			},
 			expected: qbtypes.Statement{
-				Query: "WITH __spatial_aggregation_cte AS (SELECT toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(86400)) AS ts, JSONExtractString(labels, 'service.name') AS `service.name`, sum(value)/86400 AS value FROM signoz_meter.distributed_samples AS points WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? AND JSONExtractString(labels, 'service.name') = ? AND LOWER(temporality) LIKE LOWER(?) GROUP BY ts, `service.name`) SELECT * FROM __spatial_aggregation_cte WHERE (`service.name`) IN (SELECT `service.name` FROM __spatial_aggregation_cte GROUP BY `service.name` ORDER BY avg(value) DESC LIMIT 10) ORDER BY avg(value) OVER (PARTITION BY `service.name`) DESC, `service.name`, ts ASC",
+				Query: "WITH __spatial_aggregation_cte AS (SELECT toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(86400)) AS ts, JSONExtractString(labels, 'service.name') AS `service.name`, sum(value)/86400 AS value FROM signoz_meter.distributed_samples AS points WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? AND JSONExtractString(labels, 'service.name') = ? AND LOWER(temporality) LIKE LOWER(?) GROUP BY ts, `service.name`) SELECT * FROM __spatial_aggregation_cte ORDER BY `service.name`, ts",
 				Args:  []any{"signoz_calls_total", uint64(1747872000000), uint64(1747983420000), "cartservice", "delta"},
 			},
 			expectedErr: nil,
@@ -117,7 +117,7 @@ func TestStatementBuilder(t *testing.T) {
 				},
 			},
 			expected: qbtypes.Statement{
-				Query: "WITH __temporal_aggregation_cte AS (SELECT fingerprint, toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(86400)) AS ts, JSONExtractString(labels, 'service.name') AS `service.name`, sum(value)/86400 AS per_series_value FROM signoz_meter.distributed_samples AS points WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? AND JSONExtractString(labels, 'service.name') = ? AND LOWER(temporality) LIKE LOWER(?) GROUP BY fingerprint, ts, `service.name` ORDER BY fingerprint, ts), __spatial_aggregation_cte AS (SELECT ts, `service.name`, avg(per_series_value) AS value FROM __temporal_aggregation_cte WHERE isNaN(per_series_value) = ? GROUP BY ts, `service.name`) SELECT * FROM __spatial_aggregation_cte WHERE (`service.name`) IN (SELECT `service.name` FROM __spatial_aggregation_cte GROUP BY `service.name` ORDER BY avg(value) DESC LIMIT 10) ORDER BY avg(value) OVER (PARTITION BY `service.name`) DESC, `service.name`, ts ASC",
+				Query: "WITH __temporal_aggregation_cte AS (SELECT fingerprint, toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(86400)) AS ts, JSONExtractString(labels, 'service.name') AS `service.name`, sum(value)/86400 AS per_series_value FROM signoz_meter.distributed_samples AS points WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? AND JSONExtractString(labels, 'service.name') = ? AND LOWER(temporality) LIKE LOWER(?) GROUP BY fingerprint, ts, `service.name` ORDER BY fingerprint, ts), __spatial_aggregation_cte AS (SELECT ts, `service.name`, avg(per_series_value) AS value FROM __temporal_aggregation_cte WHERE isNaN(per_series_value) = ? GROUP BY ts, `service.name`) SELECT * FROM __spatial_aggregation_cte ORDER BY `service.name`, ts",
 				Args:  []any{"signoz_calls_total", uint64(1747872000000), uint64(1747983420000), "cartservice", "delta", 0},
 			},
 			expectedErr: nil,
@@ -150,7 +150,7 @@ func TestStatementBuilder(t *testing.T) {
 				},
 			},
 			expected: qbtypes.Statement{
-				Query: "WITH __temporal_aggregation_cte AS (SELECT fingerprint, toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(86400)) AS ts, JSONExtractString(labels, 'host.name') AS `host.name`, avg(value) AS per_series_value FROM signoz_meter.distributed_samples AS points WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? AND JSONExtractString(labels, 'host.name') = ? AND LOWER(temporality) LIKE LOWER(?) GROUP BY fingerprint, ts, `host.name` ORDER BY fingerprint, ts), __spatial_aggregation_cte AS (SELECT ts, `host.name`, sum(per_series_value) AS value FROM __temporal_aggregation_cte WHERE isNaN(per_series_value) = ? GROUP BY ts, `host.name`) SELECT * FROM __spatial_aggregation_cte WHERE (`host.name`) IN (SELECT `host.name` FROM __spatial_aggregation_cte GROUP BY `host.name` ORDER BY avg(value) DESC LIMIT 10) ORDER BY avg(value) OVER (PARTITION BY `host.name`) DESC, `host.name`, ts ASC",
+				Query: "WITH __temporal_aggregation_cte AS (SELECT fingerprint, toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(86400)) AS ts, JSONExtractString(labels, 'host.name') AS `host.name`, avg(value) AS per_series_value FROM signoz_meter.distributed_samples AS points WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? AND JSONExtractString(labels, 'host.name') = ? AND LOWER(temporality) LIKE LOWER(?) GROUP BY fingerprint, ts, `host.name` ORDER BY fingerprint, ts), __spatial_aggregation_cte AS (SELECT ts, `host.name`, sum(per_series_value) AS value FROM __temporal_aggregation_cte WHERE isNaN(per_series_value) = ? GROUP BY ts, `host.name`) SELECT * FROM __spatial_aggregation_cte ORDER BY `host.name`, ts",
 				Args:  []any{"system.memory.usage", uint64(1747872000000), uint64(1747983420000), "big-data-node-1", "unspecified", 0},
 			},
 			expectedErr: nil,

pkg/telemetrymetrics/statement_builder.go

@@ -4,7 +4,6 @@ import (
 	"context"
 	"fmt"
 	"log/slog"
-	"strings"
 
 	"github.com/SigNoz/signoz/pkg/factory"
 	"github.com/SigNoz/signoz/pkg/flagger"
@@ -540,13 +539,6 @@ func (b *MetricQueryStatementBuilder) buildSpatialAggregationCTE(
 	return fmt.Sprintf("__spatial_aggregation_cte AS (%s)", q), args
 }
 
-func isMetricAggOrderByKey(key string, config qbtypes.MetricAggregation) bool {
-	spaceAggOrderBy := fmt.Sprintf("%s(%s)", config.SpaceAggregation.StringValue(), config.MetricName)
-	timeAggOrderBy := fmt.Sprintf("%s(%s)", config.TimeAggregation.StringValue(), config.MetricName)
-	timeSpaceAggOrderBy := fmt.Sprintf("%s(%s(%s))", config.SpaceAggregation.StringValue(), config.TimeAggregation.StringValue(), config.MetricName)
-	return key == spaceAggOrderBy || key == timeAggOrderBy || key == timeSpaceAggOrderBy
-}
-
 func (b *MetricQueryStatementBuilder) BuildFinalSelect(
 	cteFragments []string,
 	cteArgs [][]any,
@@ -554,159 +546,73 @@ func (b *MetricQueryStatementBuilder) BuildFinalSelect(
 ) (*qbtypes.Statement, error) {
 	metricType := query.Aggregations[0].Type
 	spaceAgg := query.Aggregations[0].SpaceAggregation
-	finalCTE := "__spatial_aggregation_cte"
-	if metricType == metrictypes.HistogramType {
-		histogramCTE, histogramCTEArgs, err := b.buildHistogramCTE(query)
-		if err != nil {
-			return nil, err
-		}
-		cteFragments = append(cteFragments, histogramCTE)
-		cteArgs = append(cteArgs, histogramCTEArgs)
-		finalCTE = "__histogram_cte"
-	}
-
-	groupByKeys := querybuilder.GroupByKeys(query.GroupBy)
-	hasGroupBy := len(groupByKeys) > 0
-
-	if hasGroupBy {
-		cteWithAvgColumn := b.buildCTEWithAvgColumn(query, finalCTE)
-		cteFragments = append(cteFragments, cteWithAvgColumn)
-
-		cteWithGroupRankColumn := b.buildCTEWithGroupRank(query)
-		cteFragments = append(cteFragments, cteWithGroupRankColumn)
-		finalCTE = "__with_group_rank_cte"
-	}
 
 	combined := querybuilder.CombineCTEs(cteFragments)
+
 	var args []any
 	for _, a := range cteArgs {
 		args = append(args, a...)
 	}
 
 	sb := sqlbuilder.NewSelectBuilder()
-	sb.Select("ts")
-	if metricType == metrictypes.HistogramType && spaceAgg == metrictypes.SpaceAggregationCount && query.Aggregations[0].ComparisonSpaceAggregationParam == nil {
-		sb.SelectMore("le")
-	}
-	sb.SelectMore(groupByKeys...)
-	sb.SelectMore("value")
-	sb.From(finalCTE)
-	if query.Having != nil && query.Having.Expression != "" {
-		rewriter := querybuilder.NewHavingExpressionRewriter()
-		rewrittenExpr := rewriter.RewriteForMetrics(query.Having.Expression, query.Aggregations)
-		sb.Where(rewrittenExpr)
-	}
 
-	if hasGroupBy {
-		sb.OrderBy("group_rank")
-		if query.Limit > 0 {
-			sb.Where(fmt.Sprintf("group_rank <= %d", query.Limit))
+	if metricType == metrictypes.HistogramType && spaceAgg.IsPercentile() {
+		quantile := query.Aggregations[0].SpaceAggregation.Percentile()
+		sb.Select("ts")
+		for _, g := range query.GroupBy {
+			sb.SelectMore(fmt.Sprintf("`%s`", g.TelemetryFieldKey.Name))
+		}
+		sb.SelectMore(fmt.Sprintf(
+			"histogramQuantile(arrayMap(x -> toFloat64(x), groupArray(le)), groupArray(value), %.3f) AS value",
+			quantile,
+		))
+		sb.From("__spatial_aggregation_cte")
+		sb.GroupBy(querybuilder.GroupByKeys(query.GroupBy)...)
+		sb.GroupBy("ts")
+		if query.Having != nil && query.Having.Expression != "" {
+			rewriter := querybuilder.NewHavingExpressionRewriter()
+			rewrittenExpr := rewriter.RewriteForMetrics(query.Having.Expression, query.Aggregations)
+			sb.Having(rewrittenExpr)
+		}
+	} else if metricType == metrictypes.HistogramType && spaceAgg == metrictypes.SpaceAggregationCount && query.Aggregations[0].ComparisonSpaceAggregationParam != nil {
+		sb.Select("ts")
+
+		for _, g := range query.GroupBy {
+			sb.SelectMore(fmt.Sprintf("`%s`", g.TelemetryFieldKey.Name))
+		}
+
+		aggQuery, err := AggregationQueryForHistogramCountWithParams(query.Aggregations[0].ComparisonSpaceAggregationParam)
+		if err != nil {
+			return nil, err
+		}
+		sb.SelectMore(aggQuery)
+
+		sb.From("__spatial_aggregation_cte")
+
+		sb.GroupBy(querybuilder.GroupByKeys(query.GroupBy)...)
+		sb.GroupBy("ts")
+
+		if query.Having != nil && query.Having.Expression != "" {
+			rewriter := querybuilder.NewHavingExpressionRewriter()
+			rewrittenExpr := rewriter.RewriteForMetrics(query.Having.Expression, query.Aggregations)
+			sb.Having(rewrittenExpr)
+		}
+	} else {
+		// for count aggregation on histograms with no params, the exact result of spatial aggregation can be sent forward
+		sb.Select("*")
+		sb.From("__spatial_aggregation_cte")
+		if query.Having != nil && query.Having.Expression != "" {
+			rewriter := querybuilder.NewHavingExpressionRewriter()
+			rewrittenExpr := rewriter.RewriteForMetrics(query.Having.Expression, query.Aggregations)
+			sb.Where(rewrittenExpr)
 		}
 	}
+	sb.OrderBy(querybuilder.GroupByKeys(query.GroupBy)...)
+	sb.OrderBy("ts")
 	if metricType == metrictypes.HistogramType && spaceAgg == metrictypes.SpaceAggregationCount && query.Aggregations[0].ComparisonSpaceAggregationParam == nil {
 		sb.OrderBy("toFloat64(le)")
 	}
-	sb.OrderBy("ts ASC")
 
 	q, a := sb.BuildWithFlavor(sqlbuilder.ClickHouse)
 	return &qbtypes.Statement{Query: combined + q, Args: append(args, a...)}, nil
 }
-
-func (b *MetricQueryStatementBuilder) buildHistogramCTE(
-	query qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation],
-) (string, []any, error) {
-	spaceAgg := query.Aggregations[0].SpaceAggregation
-	histogramCTEQueryBuilder := sqlbuilder.NewSelectBuilder()
-	if spaceAgg.IsPercentile() {
-		histogramCTEQueryBuilder.Select("ts")
-		for _, g := range query.GroupBy {
-			histogramCTEQueryBuilder.SelectMore(fmt.Sprintf("`%s`", g.TelemetryFieldKey.Name))
-		}
-		quantile := spaceAgg.Percentile()
-		histogramCTEQueryBuilder.SelectMore(fmt.Sprintf(
-			"histogramQuantile(arrayMap(x -> toFloat64(x), groupArray(le)), groupArray(value), %.3f) AS value",
-			quantile,
-		))
-		histogramCTEQueryBuilder.From("__spatial_aggregation_cte")
-		histogramCTEQueryBuilder.GroupBy(querybuilder.GroupByKeys(query.GroupBy)...)
-		histogramCTEQueryBuilder.GroupBy("ts")
-	} else if spaceAgg == metrictypes.SpaceAggregationCount && query.Aggregations[0].ComparisonSpaceAggregationParam != nil {
-		histogramCTEQueryBuilder.Select("ts")
-		for _, g := range query.GroupBy {
-			histogramCTEQueryBuilder.SelectMore(fmt.Sprintf("`%s`", g.TelemetryFieldKey.Name))
-		}
-		aggQuery, err := AggregationQueryForHistogramCountWithParams(query.Aggregations[0].ComparisonSpaceAggregationParam)
-		if err != nil {
-			return "", nil, err
-		}
-		histogramCTEQueryBuilder.SelectMore(aggQuery)
-		histogramCTEQueryBuilder.From("__spatial_aggregation_cte")
-		histogramCTEQueryBuilder.GroupBy(querybuilder.GroupByKeys(query.GroupBy)...)
-		histogramCTEQueryBuilder.GroupBy("ts")
-	} else {
-		// for count aggregation on histograms with no params, the exact result of spatial aggregation can be sent forward
-		histogramCTEQueryBuilder.Select("*")
-		histogramCTEQueryBuilder.From("__spatial_aggregation_cte")
-	}
-	histogramQueryCTE, histogramQueryCTEArgs := histogramCTEQueryBuilder.BuildWithFlavor(sqlbuilder.ClickHouse)
-	histogramCTE := fmt.Sprintf("__histogram_cte AS (%s)", histogramQueryCTE)
-
-	return histogramCTE, histogramQueryCTEArgs, nil
-}
-
-/*
-this receives a CTE (__spatial_aggregation_cte or __histogram_cte) that has columns ts, value, and a column each for all the group by keys
-it creates a CTE (__with_avg_cte) that adds a column avg_val which has the avg value for the group the row belongs in
-*/
-func (b *MetricQueryStatementBuilder) buildCTEWithAvgColumn(
-	query qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation],
-	latestCTE string,
-) string {
-	withAvgCTEBuilder := sqlbuilder.NewSelectBuilder()
-	withAvgCTEBuilder.Select("*")
-	groupByKeys := querybuilder.GroupByKeys(query.GroupBy)
-	withAvgCTEBuilder.SelectMore(fmt.Sprintf("avgIf(value, isNaN(value) = 0) OVER (PARTITION BY %s) AS avg_val", strings.Join(groupByKeys, ",")))
-	withAvgCTEBuilder.From(latestCTE)
-	withAvgCTEQuery, _ := withAvgCTEBuilder.BuildWithFlavor(sqlbuilder.ClickHouse) // no args so second return param is ignored
-	withAvgCTE := fmt.Sprintf("__with_avg_cte AS (%s)", withAvgCTEQuery)
-	return withAvgCTE
-}
-
-/*
-this receives the __with_avg_cte that has columns ts, value, a column each for all the group by keys, and avg_val which has the avg value for the group the row belongs in
-it creates a CTE (__with_group_rank_cte) that adds a column group_rank that ranks each group based on the order by keys (or by avg val if there are none)
-*/
-func (b *MetricQueryStatementBuilder) buildCTEWithGroupRank(
-	query qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation],
-) string {
-	withGroupByCTEBuilder := sqlbuilder.NewSelectBuilder()
-	withGroupByCTEBuilder.Select("*")
-
-	windowOrder := []string{}
-	orderedKeys := map[string]struct{}{} // this will be used to add the remaining keys as tie breakers in the end
-	if len(query.Order) > 0 {
-		for _, o := range query.Order {
-			key := o.Key.Name
-			if isMetricAggOrderByKey(key, query.Aggregations[0]) {
-				windowOrder = append(windowOrder, fmt.Sprintf("avg_val %s", o.Direction.StringValue()))
-			} else {
-				windowOrder = append(windowOrder, fmt.Sprintf("`%s` %s", key, o.Direction.StringValue()))
-				orderedKeys[fmt.Sprintf("`%s`", key)] = struct{}{}
-			}
-		}
-	} else {
-		windowOrder = append(windowOrder, "avg_val DESC")
-	}
-	groupByKeys := querybuilder.GroupByKeys(query.GroupBy)
-	for _, gk := range groupByKeys { // keys that haven't been added via order by keys will be added at the end as tie breakers
-		if _, ok := orderedKeys[gk]; !ok {
-			windowOrder = append(windowOrder, fmt.Sprintf("%s ASC", gk))
-		}
-	}
-	withGroupByCTEBuilder.SelectMore(fmt.Sprintf("dense_rank() OVER (ORDER BY %s) AS group_rank", strings.Join(windowOrder, ",")))
-
-	withGroupByCTEBuilder.From("__with_avg_cte")
-	withGroupRankCTEQuery, _ := withGroupByCTEBuilder.BuildWithFlavor(sqlbuilder.ClickHouse) // no args so second return param is ignored
-	withGroupRankCTE := fmt.Sprintf("__with_group_rank_cte AS (%s)", withGroupRankCTEQuery)
-	return withGroupRankCTE
-}

pkg/telemetrymetrics/stmt_builder_test.go

@@ -15,17 +15,16 @@ import (
 )
 
 func TestStatementBuilder(t *testing.T) {
-	type baseQuery struct {
-		name     string
-		query    qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]
-		orderKey string
-		args     []any
-		cte      string
-	}
-
-	bases := []baseQuery{
+	cases := []struct {
+		name        string
+		requestType qbtypes.RequestType
+		query       qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]
+		expected    qbtypes.Statement
+		expectedErr error
+	}{
 		{
-			name: "cumulative_rate_sum",
+			name:        "test_cumulative_rate_sum",
+			requestType: qbtypes.RequestTypeTimeSeries,
 			query: qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]{
 				Signal:       telemetrytypes.SignalMetrics,
 				StepInterval: qbtypes.Step{Duration: 30 * time.Second},
@@ -41,16 +40,24 @@ func TestStatementBuilder(t *testing.T) {
 				Filter: &qbtypes.Filter{
 					Expression: "service.name = 'cartservice'",
 				},
+				Limit: 10,
 				GroupBy: []qbtypes.GroupByKey{
-					{TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{Name: "service.name"}},
+					{
+						TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{
+							Name: "service.name",
+						},
+					},
 				},
 			},
-			orderKey: "service.name",
-			args:     []any{"signoz_calls_total", uint64(1747936800000), uint64(1747983420000), "cumulative", false, "cartservice", "signoz_calls_total", uint64(1747947360000), uint64(1747983420000), 0},
-			cte:      "WITH __temporal_aggregation_cte AS (SELECT ts, `service.name`, multiIf(row_number() OVER rate_window = 1, nan, (per_series_value - lagInFrame(per_series_value, 1) OVER rate_window) < 0, per_series_value / (ts - lagInFrame(ts, 1) OVER rate_window), (per_series_value - lagInFrame(per_series_value, 1) OVER rate_window) / (ts - lagInFrame(ts, 1) OVER rate_window)) AS per_series_value FROM (SELECT fingerprint, toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(30)) AS ts, `service.name`, max(value) AS per_series_value FROM signoz_metrics.distributed_samples_v4 AS points INNER JOIN (SELECT fingerprint, JSONExtractString(labels, 'service.name') AS `service.name` FROM signoz_metrics.time_series_v4_6hrs WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli <= ? AND LOWER(temporality) LIKE LOWER(?) AND __normalized = ? AND JSONExtractString(labels, 'service.name') = ? GROUP BY fingerprint, `service.name`) AS filtered_time_series ON points.fingerprint = filtered_time_series.fingerprint WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? GROUP BY fingerprint, ts, `service.name` ORDER BY fingerprint, ts) WINDOW rate_window AS (PARTITION BY fingerprint ORDER BY fingerprint, ts)), __spatial_aggregation_cte AS (SELECT ts, `service.name`, sum(per_series_value) AS value FROM __temporal_aggregation_cte WHERE isNaN(per_series_value) = ? GROUP BY ts, `service.name`)",
+			expected: qbtypes.Statement{
+				Query: "WITH __temporal_aggregation_cte AS (SELECT ts, `service.name`, multiIf(row_number() OVER rate_window = 1, nan, (per_series_value - lagInFrame(per_series_value, 1) OVER rate_window) < 0, per_series_value / (ts - lagInFrame(ts, 1) OVER rate_window), (per_series_value - lagInFrame(per_series_value, 1) OVER rate_window) / (ts - lagInFrame(ts, 1) OVER rate_window)) AS per_series_value FROM (SELECT fingerprint, toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(30)) AS ts, `service.name`, max(value) AS per_series_value FROM signoz_metrics.distributed_samples_v4 AS points INNER JOIN (SELECT fingerprint, JSONExtractString(labels, 'service.name') AS `service.name` FROM signoz_metrics.time_series_v4_6hrs WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli <= ? AND LOWER(temporality) LIKE LOWER(?) AND __normalized = ? AND JSONExtractString(labels, 'service.name') = ? GROUP BY fingerprint, `service.name`) AS filtered_time_series ON points.fingerprint = filtered_time_series.fingerprint WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? GROUP BY fingerprint, ts, `service.name` ORDER BY fingerprint, ts) WINDOW rate_window AS (PARTITION BY fingerprint ORDER BY fingerprint, ts)), __spatial_aggregation_cte AS (SELECT ts, `service.name`, sum(per_series_value) AS value FROM __temporal_aggregation_cte WHERE isNaN(per_series_value) = ? GROUP BY ts, `service.name`) SELECT * FROM __spatial_aggregation_cte ORDER BY `service.name`, ts",
+				Args:  []any{"signoz_calls_total", uint64(1747936800000), uint64(1747983420000), "cumulative", false, "cartservice", "signoz_calls_total", uint64(1747947360000), uint64(1747983420000), 0},
+			},
+			expectedErr: nil,
 		},
 		{
-			name: "cumulative_rate_sum_with_mat_column",
+			name:        "test_cumulative_rate_sum_with_mat_column",
+			requestType: qbtypes.RequestTypeTimeSeries,
 			query: qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]{
 				Signal:       telemetrytypes.SignalMetrics,
 				StepInterval: qbtypes.Step{Duration: 30 * time.Second},
@@ -66,16 +73,24 @@ func TestStatementBuilder(t *testing.T) {
 				Filter: &qbtypes.Filter{
 					Expression: "materialized.key.name REGEXP 'cartservice' OR service.name = 'cartservice'",
 				},
+				Limit: 10,
 				GroupBy: []qbtypes.GroupByKey{
-					{TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{Name: "service.name"}},
+					{
+						TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{
+							Name: "service.name",
+						},
+					},
 				},
 			},
-			orderKey: "service.name",
-			args:     []any{"signoz_calls_total", uint64(1747936800000), uint64(1747983420000), "cumulative", false, "cartservice", "cartservice", "signoz_calls_total", uint64(1747947360000), uint64(1747983420000), 0},
-			cte:      "WITH __temporal_aggregation_cte AS (SELECT ts, `service.name`, multiIf(row_number() OVER rate_window = 1, nan, (per_series_value - lagInFrame(per_series_value, 1) OVER rate_window) < 0, per_series_value / (ts - lagInFrame(ts, 1) OVER rate_window), (per_series_value - lagInFrame(per_series_value, 1) OVER rate_window) / (ts - lagInFrame(ts, 1) OVER rate_window)) AS per_series_value FROM (SELECT fingerprint, toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(30)) AS ts, `service.name`, max(value) AS per_series_value FROM signoz_metrics.distributed_samples_v4 AS points INNER JOIN (SELECT fingerprint, JSONExtractString(labels, 'service.name') AS `service.name` FROM signoz_metrics.time_series_v4_6hrs WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli <= ? AND LOWER(temporality) LIKE LOWER(?) AND __normalized = ? AND (match(JSONExtractString(labels, 'materialized.key.name'), ?) OR JSONExtractString(labels, 'service.name') = ?) GROUP BY fingerprint, `service.name`) AS filtered_time_series ON points.fingerprint = filtered_time_series.fingerprint WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? GROUP BY fingerprint, ts, `service.name` ORDER BY fingerprint, ts) WINDOW rate_window AS (PARTITION BY fingerprint ORDER BY fingerprint, ts)), __spatial_aggregation_cte AS (SELECT ts, `service.name`, sum(per_series_value) AS value FROM __temporal_aggregation_cte WHERE isNaN(per_series_value) = ? GROUP BY ts, `service.name`)",
+			expected: qbtypes.Statement{
+				Query: "WITH __temporal_aggregation_cte AS (SELECT ts, `service.name`, multiIf(row_number() OVER rate_window = 1, nan, (per_series_value - lagInFrame(per_series_value, 1) OVER rate_window) < 0, per_series_value / (ts - lagInFrame(ts, 1) OVER rate_window), (per_series_value - lagInFrame(per_series_value, 1) OVER rate_window) / (ts - lagInFrame(ts, 1) OVER rate_window)) AS per_series_value FROM (SELECT fingerprint, toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(30)) AS ts, `service.name`, max(value) AS per_series_value FROM signoz_metrics.distributed_samples_v4 AS points INNER JOIN (SELECT fingerprint, JSONExtractString(labels, 'service.name') AS `service.name` FROM signoz_metrics.time_series_v4_6hrs WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli <= ? AND LOWER(temporality) LIKE LOWER(?) AND __normalized = ? AND (match(JSONExtractString(labels, 'materialized.key.name'), ?) OR JSONExtractString(labels, 'service.name') = ?) GROUP BY fingerprint, `service.name`) AS filtered_time_series ON points.fingerprint = filtered_time_series.fingerprint WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? GROUP BY fingerprint, ts, `service.name` ORDER BY fingerprint, ts) WINDOW rate_window AS (PARTITION BY fingerprint ORDER BY fingerprint, ts)), __spatial_aggregation_cte AS (SELECT ts, `service.name`, sum(per_series_value) AS value FROM __temporal_aggregation_cte WHERE isNaN(per_series_value) = ? GROUP BY ts, `service.name`) SELECT * FROM __spatial_aggregation_cte ORDER BY `service.name`, ts",
+				Args:  []any{"signoz_calls_total", uint64(1747936800000), uint64(1747983420000), "cumulative", false, "cartservice", "cartservice", "signoz_calls_total", uint64(1747947360000), uint64(1747983420000), 0},
+			},
+			expectedErr: nil,
 		},
 		{
-			name: "delta_rate_sum",
+			name:        "test_delta_rate_sum",
+			requestType: qbtypes.RequestTypeTimeSeries,
 			query: qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]{
 				Signal:       telemetrytypes.SignalMetrics,
 				StepInterval: qbtypes.Step{Duration: 30 * time.Second},
@@ -91,16 +106,24 @@ func TestStatementBuilder(t *testing.T) {
 				Filter: &qbtypes.Filter{
 					Expression: "service.name = 'cartservice'",
 				},
+				Limit: 10,
 				GroupBy: []qbtypes.GroupByKey{
-					{TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{Name: "service.name"}},
+					{
+						TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{
+							Name: "service.name",
+						},
+					},
 				},
 			},
-			orderKey: "service.name",
-			args:     []any{"signoz_calls_total", uint64(1747936800000), uint64(1747983420000), "delta", false, "cartservice", "signoz_calls_total", uint64(1747947390000), uint64(1747983420000)},
-			cte:      "WITH __spatial_aggregation_cte AS (SELECT toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(30)) AS ts, `service.name`, sum(value)/30 AS value FROM signoz_metrics.distributed_samples_v4 AS points INNER JOIN (SELECT fingerprint, JSONExtractString(labels, 'service.name') AS `service.name` FROM signoz_metrics.time_series_v4_6hrs WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli <= ? AND LOWER(temporality) LIKE LOWER(?) AND __normalized = ? AND JSONExtractString(labels, 'service.name') = ? GROUP BY fingerprint, `service.name`) AS filtered_time_series ON points.fingerprint = filtered_time_series.fingerprint WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? GROUP BY ts, `service.name`)",
+			expected: qbtypes.Statement{
+				Query: "WITH __spatial_aggregation_cte AS (SELECT toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(30)) AS ts, `service.name`, sum(value)/30 AS value FROM signoz_metrics.distributed_samples_v4 AS points INNER JOIN (SELECT fingerprint, JSONExtractString(labels, 'service.name') AS `service.name` FROM signoz_metrics.time_series_v4_6hrs WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli <= ? AND LOWER(temporality) LIKE LOWER(?) AND __normalized = ? AND JSONExtractString(labels, 'service.name') = ? GROUP BY fingerprint, `service.name`) AS filtered_time_series ON points.fingerprint = filtered_time_series.fingerprint WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? GROUP BY ts, `service.name`) SELECT * FROM __spatial_aggregation_cte ORDER BY `service.name`, ts",
+				Args:  []any{"signoz_calls_total", uint64(1747936800000), uint64(1747983420000), "delta", false, "cartservice", "signoz_calls_total", uint64(1747947390000), uint64(1747983420000)},
+			},
+			expectedErr: nil,
 		},
 		{
-			name: "histogram_percentile1",
+			name:        "test_histogram_percentile1",
+			requestType: qbtypes.RequestTypeTimeSeries,
 			query: qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]{
 				Signal:       telemetrytypes.SignalMetrics,
 				StepInterval: qbtypes.Step{Duration: 30 * time.Second},
@@ -116,38 +139,24 @@ func TestStatementBuilder(t *testing.T) {
 				Filter: &qbtypes.Filter{
 					Expression: "service.name = 'cartservice'",
 				},
+				Limit: 10,
 				GroupBy: []qbtypes.GroupByKey{
-					{TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{Name: "service.name"}},
-				},
-			},
-			orderKey: "service.name",
-			args:     []any{"signoz_latency", uint64(1747936800000), uint64(1747983420000), "delta", false, "cartservice", "signoz_latency", uint64(1747947390000), uint64(1747983420000)},
-			cte:      "WITH __spatial_aggregation_cte AS (SELECT toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(30)) AS ts, `service.name`, `le`, sum(value)/30 AS value FROM signoz_metrics.distributed_samples_v4 AS points INNER JOIN (SELECT fingerprint, JSONExtractString(labels, 'service.name') AS `service.name`, JSONExtractString(labels, 'le') AS `le` FROM signoz_metrics.time_series_v4_6hrs WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli <= ? AND LOWER(temporality) LIKE LOWER(?) AND __normalized = ? AND JSONExtractString(labels, 'service.name') = ? GROUP BY fingerprint, `service.name`, `le`) AS filtered_time_series ON points.fingerprint = filtered_time_series.fingerprint WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? GROUP BY ts, `service.name`, `le`), __histogram_cte AS (SELECT ts, `service.name`, histogramQuantile(arrayMap(x -> toFloat64(x), groupArray(le)), groupArray(value), 0.950) AS value FROM __spatial_aggregation_cte GROUP BY `service.name`, ts)",
-		},
-		{
-			name: "histogram_percentile2",
-			query: qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]{
-				Signal:       telemetrytypes.SignalMetrics,
-				StepInterval: qbtypes.Step{Duration: 30 * time.Second},
-				Aggregations: []qbtypes.MetricAggregation{
 					{
-						MetricName:       "http_server_duration_bucket",
-						Type:             metrictypes.HistogramType,
-						Temporality:      metrictypes.Cumulative,
-						TimeAggregation:  metrictypes.TimeAggregationRate,
-						SpaceAggregation: metrictypes.SpaceAggregationPercentile95,
+						TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{
+							Name: "service.name",
+						},
 					},
 				},
-				GroupBy: []qbtypes.GroupByKey{
-					{TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{Name: "service.name"}},
-				},
 			},
-			orderKey: "service.name",
-			args:     []any{"http_server_duration_bucket", uint64(1747936800000), uint64(1747983420000), "cumulative", false, "http_server_duration_bucket", uint64(1747947360000), uint64(1747983420000), 0},
-			cte:      "WITH __temporal_aggregation_cte AS (SELECT ts, `service.name`, `le`, multiIf(row_number() OVER rate_window = 1, nan, (per_series_value - lagInFrame(per_series_value, 1) OVER rate_window) < 0, per_series_value / (ts - lagInFrame(ts, 1) OVER rate_window), (per_series_value - lagInFrame(per_series_value, 1) OVER rate_window) / (ts - lagInFrame(ts, 1) OVER rate_window)) AS per_series_value FROM (SELECT fingerprint, toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(30)) AS ts, `service.name`, `le`, max(value) AS per_series_value FROM signoz_metrics.distributed_samples_v4 AS points INNER JOIN (SELECT fingerprint, JSONExtractString(labels, 'service.name') AS `service.name`, JSONExtractString(labels, 'le') AS `le` FROM signoz_metrics.time_series_v4_6hrs WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli <= ? AND LOWER(temporality) LIKE LOWER(?) AND __normalized = ? GROUP BY fingerprint, `service.name`, `le`) AS filtered_time_series ON points.fingerprint = filtered_time_series.fingerprint WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? GROUP BY fingerprint, ts, `service.name`, `le` ORDER BY fingerprint, ts) WINDOW rate_window AS (PARTITION BY fingerprint ORDER BY fingerprint, ts)), __spatial_aggregation_cte AS (SELECT ts, `service.name`, `le`, sum(per_series_value) AS value FROM __temporal_aggregation_cte WHERE isNaN(per_series_value) = ? GROUP BY ts, `service.name`, `le`), __histogram_cte AS (SELECT ts, `service.name`, histogramQuantile(arrayMap(x -> toFloat64(x), groupArray(le)), groupArray(value), 0.950) AS value FROM __spatial_aggregation_cte GROUP BY `service.name`, ts)",
+			expected: qbtypes.Statement{
+				Query: "WITH __spatial_aggregation_cte AS (SELECT toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(30)) AS ts, `service.name`, `le`, sum(value)/30 AS value FROM signoz_metrics.distributed_samples_v4 AS points INNER JOIN (SELECT fingerprint, JSONExtractString(labels, 'service.name') AS `service.name`, JSONExtractString(labels, 'le') AS `le` FROM signoz_metrics.time_series_v4_6hrs WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli <= ? AND LOWER(temporality) LIKE LOWER(?) AND __normalized = ? AND JSONExtractString(labels, 'service.name') = ? GROUP BY fingerprint, `service.name`, `le`) AS filtered_time_series ON points.fingerprint = filtered_time_series.fingerprint WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? GROUP BY ts, `service.name`, `le`) SELECT ts, `service.name`, histogramQuantile(arrayMap(x -> toFloat64(x), groupArray(le)), groupArray(value), 0.950) AS value FROM __spatial_aggregation_cte GROUP BY `service.name`, ts ORDER BY `service.name`, ts",
+				Args:  []any{"signoz_latency", uint64(1747936800000), uint64(1747983420000), "delta", false, "cartservice", "signoz_latency", uint64(1747947390000), uint64(1747983420000)},
+			},
+			expectedErr: nil,
 		},
 		{
-			name: "gauge_avg_sum",
+			name:        "test_gauge_avg_sum",
+			requestType: qbtypes.RequestTypeTimeSeries,
 			query: qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]{
 				Signal:       telemetrytypes.SignalMetrics,
 				StepInterval: qbtypes.Step{Duration: 30 * time.Second},
@@ -163,81 +172,51 @@ func TestStatementBuilder(t *testing.T) {
 				Filter: &qbtypes.Filter{
 					Expression: "host.name = 'big-data-node-1'",
 				},
+				Limit: 10,
 				GroupBy: []qbtypes.GroupByKey{
-					{TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{Name: "host.name"}},
+					{
+						TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{
+							Name: "host.name",
+						},
+					},
 				},
 			},
-			orderKey: "host.name",
-			args:     []any{"system.memory.usage", uint64(1747936800000), uint64(1747983420000), "unspecified", false, "big-data-node-1", "system.memory.usage", uint64(1747947390000), uint64(1747983420000), 0},
-			cte:      "WITH __temporal_aggregation_cte AS (SELECT fingerprint, toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(30)) AS ts, `host.name`, avg(value) AS per_series_value FROM signoz_metrics.distributed_samples_v4 AS points INNER JOIN (SELECT fingerprint, JSONExtractString(labels, 'host.name') AS `host.name` FROM signoz_metrics.time_series_v4_6hrs WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli <= ? AND LOWER(temporality) LIKE LOWER(?) AND __normalized = ? AND JSONExtractString(labels, 'host.name') = ? GROUP BY fingerprint, `host.name`) AS filtered_time_series ON points.fingerprint = filtered_time_series.fingerprint WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? GROUP BY fingerprint, ts, `host.name` ORDER BY fingerprint, ts), __spatial_aggregation_cte AS (SELECT ts, `host.name`, sum(per_series_value) AS value FROM __temporal_aggregation_cte WHERE isNaN(per_series_value) = ? GROUP BY ts, `host.name`)",
+			expected: qbtypes.Statement{
+				Query: "WITH __temporal_aggregation_cte AS (SELECT fingerprint, toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(30)) AS ts, `host.name`, avg(value) AS per_series_value FROM signoz_metrics.distributed_samples_v4 AS points INNER JOIN (SELECT fingerprint, JSONExtractString(labels, 'host.name') AS `host.name` FROM signoz_metrics.time_series_v4_6hrs WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli <= ? AND LOWER(temporality) LIKE LOWER(?) AND __normalized = ? AND JSONExtractString(labels, 'host.name') = ? GROUP BY fingerprint, `host.name`) AS filtered_time_series ON points.fingerprint = filtered_time_series.fingerprint WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? GROUP BY fingerprint, ts, `host.name` ORDER BY fingerprint, ts), __spatial_aggregation_cte AS (SELECT ts, `host.name`, sum(per_series_value) AS value FROM __temporal_aggregation_cte WHERE isNaN(per_series_value) = ? GROUP BY ts, `host.name`) SELECT * FROM __spatial_aggregation_cte ORDER BY `host.name`, ts",
+				Args:  []any{"system.memory.usage", uint64(1747936800000), uint64(1747983420000), "unspecified", false, "big-data-node-1", "system.memory.usage", uint64(1747947390000), uint64(1747983420000), 0},
+			},
+			expectedErr: nil,
+		},
+		{
+			name:        "test_histogram_percentile2",
+			requestType: qbtypes.RequestTypeTimeSeries,
+			query: qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]{
+				Signal:       telemetrytypes.SignalMetrics,
+				StepInterval: qbtypes.Step{Duration: 30 * time.Second},
+				Aggregations: []qbtypes.MetricAggregation{
+					{
+						MetricName:       "http_server_duration_bucket",
+						Type:             metrictypes.HistogramType,
+						Temporality:      metrictypes.Cumulative,
+						TimeAggregation:  metrictypes.TimeAggregationRate,
+						SpaceAggregation: metrictypes.SpaceAggregationPercentile95,
+					},
+				},
+				Limit: 10,
+				GroupBy: []qbtypes.GroupByKey{
+					{
+						TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{
+							Name: "service.name",
+						},
+					},
+				},
+			},
+			expected: qbtypes.Statement{
+				Query: "WITH __temporal_aggregation_cte AS (SELECT ts, `service.name`, `le`, multiIf(row_number() OVER rate_window = 1, nan, (per_series_value - lagInFrame(per_series_value, 1) OVER rate_window) < 0, per_series_value / (ts - lagInFrame(ts, 1) OVER rate_window), (per_series_value - lagInFrame(per_series_value, 1) OVER rate_window) / (ts - lagInFrame(ts, 1) OVER rate_window)) AS per_series_value FROM (SELECT fingerprint, toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(30)) AS ts, `service.name`, `le`, max(value) AS per_series_value FROM signoz_metrics.distributed_samples_v4 AS points INNER JOIN (SELECT fingerprint, JSONExtractString(labels, 'service.name') AS `service.name`, JSONExtractString(labels, 'le') AS `le` FROM signoz_metrics.time_series_v4_6hrs WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli <= ? AND LOWER(temporality) LIKE LOWER(?) AND __normalized = ? GROUP BY fingerprint, `service.name`, `le`) AS filtered_time_series ON points.fingerprint = filtered_time_series.fingerprint WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? GROUP BY fingerprint, ts, `service.name`, `le` ORDER BY fingerprint, ts) WINDOW rate_window AS (PARTITION BY fingerprint ORDER BY fingerprint, ts)), __spatial_aggregation_cte AS (SELECT ts, `service.name`, `le`, sum(per_series_value) AS value FROM __temporal_aggregation_cte WHERE isNaN(per_series_value) = ? GROUP BY ts, `service.name`, `le`) SELECT ts, `service.name`, histogramQuantile(arrayMap(x -> toFloat64(x), groupArray(le)), groupArray(value), 0.950) AS value FROM __spatial_aggregation_cte GROUP BY `service.name`, ts ORDER BY `service.name`, ts",
+				Args:  []any{"http_server_duration_bucket", uint64(1747936800000), uint64(1747983420000), "cumulative", false, "http_server_duration_bucket", uint64(1747947360000), uint64(1747983420000), 0},
+			},
+			expectedErr: nil,
 		},
-	}
-
-	type variant struct {
-		name     string
-		limit    int
-		hasOrder bool
-	}
-
-	variants := []variant{
-		{"with_limits", 10, false},
-		{"without_limits", 0, false},
-		{"with_order_by", 0, true},
-		{"with_order_by_and_limits", 10, true},
-	}
-
-	sumMetricsFinalSelects := map[string]string{
-		"with_limits":              " SELECT * FROM __spatial_aggregation_cte WHERE (`service.name`) IN (SELECT `service.name` FROM __spatial_aggregation_cte GROUP BY `service.name` ORDER BY avg(value) DESC LIMIT 10) ORDER BY avg(value) OVER (PARTITION BY `service.name`) DESC, `service.name`, ts ASC",
-		"without_limits":           " SELECT * FROM __spatial_aggregation_cte ORDER BY avg(value) OVER (PARTITION BY `service.name`) DESC, `service.name`, ts ASC",
-		"with_order_by":            " SELECT * FROM __spatial_aggregation_cte ORDER BY `service.name` asc, ts ASC",
-		"with_order_by_and_limits": " SELECT * FROM __spatial_aggregation_cte WHERE (`service.name`) IN (SELECT `service.name` FROM __spatial_aggregation_cte GROUP BY `service.name` ORDER BY `service.name` asc LIMIT 10) ORDER BY `service.name` asc, ts ASC",
-	}
-
-	histogramMetricsFinalSelects := map[string]string{
-		"with_limits":              " SELECT * FROM __histogram_cte WHERE (`service.name`) IN (SELECT `service.name` FROM __histogram_cte GROUP BY `service.name` ORDER BY avg(value) DESC LIMIT 10) ORDER BY avg(value) OVER (PARTITION BY `service.name`) DESC, `service.name`, ts ASC",
-		"without_limits":           " SELECT * FROM __histogram_cte ORDER BY avg(value) OVER (PARTITION BY `service.name`) DESC, `service.name`, ts ASC",
-		"with_order_by":            " SELECT * FROM __histogram_cte ORDER BY `service.name` asc, ts ASC",
-		"with_order_by_and_limits": " SELECT * FROM __histogram_cte WHERE (`service.name`) IN (SELECT `service.name` FROM __histogram_cte GROUP BY `service.name` ORDER BY `service.name` asc LIMIT 10) ORDER BY `service.name` asc, ts ASC",
-	}
-
-	// expectedFinalSelects maps "base/variant" to the final SELECT portion after the CTE.
-	// The full expected query is: base.cte + expectedFinalSelects[name]
-	expectedFinalSelects := map[string]string{
-		// cumulative_rate_sum
-		"cumulative_rate_sum/with_limits":              sumMetricsFinalSelects["with_limits"],
-		"cumulative_rate_sum/without_limits":           sumMetricsFinalSelects["without_limits"],
-		"cumulative_rate_sum/with_order_by":            sumMetricsFinalSelects["with_order_by"],
-		"cumulative_rate_sum/with_order_by_and_limits": sumMetricsFinalSelects["with_order_by_and_limits"],
-
-		// cumulative_rate_sum_with_mat_column
-		"cumulative_rate_sum_with_mat_column/with_limits":              sumMetricsFinalSelects["with_limits"],
-		"cumulative_rate_sum_with_mat_column/without_limits":           sumMetricsFinalSelects["without_limits"],
-		"cumulative_rate_sum_with_mat_column/with_order_by":            sumMetricsFinalSelects["with_order_by"],
-		"cumulative_rate_sum_with_mat_column/with_order_by_and_limits": sumMetricsFinalSelects["with_order_by_and_limits"],
-
-		// delta_rate_sum
-		"delta_rate_sum/with_limits":              sumMetricsFinalSelects["with_limits"],
-		"delta_rate_sum/without_limits":           sumMetricsFinalSelects["without_limits"],
-		"delta_rate_sum/with_order_by":            sumMetricsFinalSelects["with_order_by"],
-		"delta_rate_sum/with_order_by_and_limits": sumMetricsFinalSelects["with_order_by_and_limits"],
-
-		// histogram_percentile1
-		"histogram_percentile1/with_limits":              histogramMetricsFinalSelects["with_limits"],
-		"histogram_percentile1/without_limits":           histogramMetricsFinalSelects["without_limits"],
-		"histogram_percentile1/with_order_by":            histogramMetricsFinalSelects["with_order_by"],
-		"histogram_percentile1/with_order_by_and_limits": histogramMetricsFinalSelects["with_order_by_and_limits"],
-
-		// histogram_percentile2
-		"histogram_percentile2/with_limits":              histogramMetricsFinalSelects["with_limits"],
-		"histogram_percentile2/without_limits":           histogramMetricsFinalSelects["without_limits"],
-		"histogram_percentile2/with_order_by":            histogramMetricsFinalSelects["with_order_by"],
-		"histogram_percentile2/with_order_by_and_limits": histogramMetricsFinalSelects["with_order_by_and_limits"],
-
-		// gauge_avg_sum
-		"gauge_avg_sum/with_limits":              " SELECT * FROM __spatial_aggregation_cte WHERE (`host.name`) IN (SELECT `host.name` FROM __spatial_aggregation_cte GROUP BY `host.name` ORDER BY avg(value) DESC LIMIT 10) ORDER BY avg(value) OVER (PARTITION BY `host.name`) DESC, `host.name`, ts ASC",
-		"gauge_avg_sum/without_limits":           " SELECT * FROM __spatial_aggregation_cte ORDER BY avg(value) OVER (PARTITION BY `host.name`) DESC, `host.name`, ts ASC",
-		"gauge_avg_sum/with_order_by":            " SELECT * FROM __spatial_aggregation_cte ORDER BY `host.name` asc, ts ASC",
-		"gauge_avg_sum/with_order_by_and_limits": " SELECT * FROM __spatial_aggregation_cte WHERE (`host.name`) IN (SELECT `host.name` FROM __spatial_aggregation_cte GROUP BY `host.name` ORDER BY `host.name` asc LIMIT 10) ORDER BY `host.name` asc, ts ASC",
 	}
 
 	fm := NewFieldMapper()
@@ -248,13 +227,15 @@ func TestStatementBuilder(t *testing.T) {
 		t.Fatalf("failed to load field keys: %v", err)
 	}
 	mockMetadataStore.KeysMap = keys
+	// NOTE: LoadFieldKeysFromJSON doesn't set Materialized field
+	// for keys, so we have to set it manually here for testing
 	if _, ok := mockMetadataStore.KeysMap["materialized.key.name"]; ok {
 		if len(mockMetadataStore.KeysMap["materialized.key.name"]) > 0 {
 			mockMetadataStore.KeysMap["materialized.key.name"][0].Materialized = true
 		}
 	}
 
-	fl, err := flagger.New(context.Background(), instrumentationtest.New().ToProviderSettings(), flagger.Config{}, flagger.MustNewRegistry())
+	flagger, err := flagger.New(context.Background(), instrumentationtest.New().ToProviderSettings(), flagger.Config{}, flagger.MustNewRegistry())
 	if err != nil {
 		t.Fatalf("failed to create flagger: %v", err)
 	}
@@ -264,30 +245,23 @@ func TestStatementBuilder(t *testing.T) {
 		mockMetadataStore,
 		fm,
 		cb,
-		fl,
+		flagger,
 	)
 
-	for _, b := range bases {
-		for _, v := range variants {
-			name := b.name + "/" + v.name
-			t.Run(name, func(t *testing.T) {
-				q := b.query
-				q.Limit = v.limit
-				if v.hasOrder {
-					q.Order = []qbtypes.OrderBy{
-						{
-							Key:       qbtypes.OrderByKey{TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{Name: b.orderKey}},
-							Direction: qbtypes.OrderDirectionAsc,
-						},
-					}
-				}
+	for _, c := range cases {
+		t.Run(c.name, func(t *testing.T) {
 
-				result, err := statementBuilder.Build(context.Background(), 1747947419000, 1747983448000, qbtypes.RequestTypeTimeSeries, q, nil)
+			q, err := statementBuilder.Build(context.Background(), 1747947419000, 1747983448000, c.requestType, c.query, nil)
 
+			if c.expectedErr != nil {
+				require.Error(t, err)
+				require.Contains(t, err.Error(), c.expectedErr.Error())
+			} else {
 				require.NoError(t, err)
-				require.Equal(t, b.cte+expectedFinalSelects[name], result.Query)
-				require.Equal(t, b.args, result.Args)
-			})
-		}
+				require.Equal(t, c.expected.Query, q.Query)
+				require.Equal(t, c.expected.Args, q.Args)
+				require.Equal(t, c.expected.Warnings, q.Warnings)
+			}
+		})
 	}
 }

pkg/telemetrytraces/statement_builder.go

@@ -121,8 +121,18 @@ func (b *traceQueryStatementBuilder) Build(
 			if !ok {
 				b.logger.DebugContext(ctx, "failed to get trace time range", "trace_ids", traceIDs)
 			} else if traceStart > 0 && traceEnd > 0 {
-				start = uint64(traceStart)
-				end = uint64(traceEnd)
+				// we don't  need to query if the start and end are non overlapping
+				if uint64(traceStart) > end || uint64(traceEnd) < start {
+					return &qbtypes.Statement{Skip: true}, nil
+				}
+
+				// clamp start/end to trace time range to avoid overlap between buckets
+				if uint64(traceStart) > start {
+					start = uint64(traceStart)
+				}
+				if uint64(traceEnd) < end {
+					end = uint64(traceEnd)
+				}
 				b.logger.DebugContext(ctx, "optimized time range for traces", "trace_ids", traceIDs, "start", start, "end", end)
 			}
 		}

pkg/types/querybuildertypes/querybuildertypesv5/qb.go

@@ -47,6 +47,7 @@ type Statement struct {
 	Args           []any
 	Warnings       []string
 	WarningsDocURL string
+	Skip           bool // don't run this query
 }
 
 // StatementBuilder builds the query.

tests/integration/fixtures/querier.py

@@ -58,8 +58,6 @@ def build_builder_query(
     step_interval: int = DEFAULT_STEP_INTERVAL,
     group_by: Optional[List[str]] = None,
     filter_expression: Optional[str] = None,
-    order_by: Optional[List[Dict]] = None,
-    limit: Optional[int] = None,
     functions: Optional[List[Dict]] = None,
     disabled: bool = False,
 ) -> Dict:
@@ -95,12 +93,6 @@ def build_builder_query(
     if filter_expression:
         spec["filter"] = {"expression": filter_expression}
 
-    if order_by:
-        spec["order"] = order_by
-
-    if limit is not None:
-        spec["limit"] = limit
-
     if functions:
         spec["functions"] = functions
 

tests/integration/src/querier/04_traces.py

@@ -696,7 +696,6 @@ def test_traces_list_with_corrupt_data(
     assert response.status_code == status_code
 
     if response.status_code == HTTPStatus.OK:
-
         if not results(traces):
             # No results expected
             assert response.json()["data"]["data"]["results"][0]["rows"] is None
@@ -2026,3 +2025,249 @@ def test_traces_fill_zero_formula_with_group_by(
             expected_by_ts=expectations[service_name],
             context=f"traces/fillZero/F1/{service_name}",
         )
+
+
+def test_traces_list_filter_by_trace_id(
+    signoz: types.SigNoz,
+    create_user_admin: None,  # pylint: disable=unused-argument
+    get_token: Callable[[str, str], str],
+    insert_traces: Callable[[List[Traces]], None],
+) -> None:
+    """
+    Tests that filtering by trace_id:
+    1. Returns the matching span (narrow window, single bucket).
+    2. Does not return duplicate spans when the query window spans multiple
+       exponential buckets (>1 h) — regression test for the expand-vs-clamp bug.
+    3. Returns no results when the query window does not contain the trace.
+    """
+    target_trace_id = TraceIdGenerator.trace_id()
+    other_trace_id = TraceIdGenerator.trace_id()
+    span_id_root = TraceIdGenerator.span_id()
+    other_span_id = TraceIdGenerator.span_id()
+
+    now = datetime.now(tz=timezone.utc).replace(second=0, microsecond=0)
+
+    common_resources = {
+        "deployment.environment": "production",
+        "service.name": "trace-filter-service",
+        "cloud.provider": "integration",
+    }
+
+    insert_traces(
+        [
+            Traces(
+                timestamp=now - timedelta(seconds=10),
+                duration=timedelta(seconds=5),
+                trace_id=target_trace_id,
+                span_id=span_id_root,
+                parent_span_id="",
+                name="root-span",
+                kind=TracesKind.SPAN_KIND_SERVER,
+                status_code=TracesStatusCode.STATUS_CODE_OK,
+                status_message="",
+                resources=common_resources,
+                attributes={"http.request.method": "GET"},
+            ),
+            # span from a different trace — must not appear in results
+            Traces(
+                timestamp=now - timedelta(seconds=5),
+                duration=timedelta(seconds=1),
+                trace_id=other_trace_id,
+                span_id=other_span_id,
+                parent_span_id="",
+                name="other-root-span",
+                kind=TracesKind.SPAN_KIND_SERVER,
+                status_code=TracesStatusCode.STATUS_CODE_OK,
+                status_message="",
+                resources=common_resources,
+                attributes={},
+            ),
+        ]
+    )
+
+    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
+
+    trace_filter = f"trace_id = '{target_trace_id}'"
+
+    def _query(start_ms: int, end_ms: int) -> List:
+        response = make_query_request(
+            signoz,
+            token,
+            start_ms=start_ms,
+            end_ms=end_ms,
+            request_type="raw",
+            queries=[
+                {
+                    "type": "builder_query",
+                    "spec": {
+                        "name": "A",
+                        "signal": "traces",
+                        "limit": 100,
+                        "offset": 0,
+                        "filter": {"expression": trace_filter},
+                        "order": [{"key": {"name": "timestamp"}, "direction": "desc"}],
+                        "selectFields": [
+                            {
+                                "name": "name",
+                                "fieldDataType": "string",
+                                "fieldContext": "span",
+                                "signal": "traces",
+                            }
+                        ],
+                    },
+                }
+            ],
+        )
+        assert response.status_code == HTTPStatus.OK
+        assert response.json()["status"] == "success"
+        return response.json()["data"]["data"]["results"][0]["rows"] or []
+
+    now_ms = int(now.timestamp() * 1000)
+
+    # --- Test 1: narrow window (single bucket, <1 h) ---
+    narrow_start_ms = int((now - timedelta(minutes=5)).timestamp() * 1000)
+    narrow_rows = _query(narrow_start_ms, now_ms)
+
+    assert len(narrow_rows) == 1, (
+        f"Expected 1 span for trace_id filter (narrow window), got {len(narrow_rows)}"
+    )
+    assert narrow_rows[0]["data"]["span_id"] == span_id_root
+    assert narrow_rows[0]["data"]["trace_id"] == target_trace_id
+
+    # --- Test 2: wide window (>1 h, triggers multiple exponential buckets) ---
+    # should just return 1 span, not duplicate
+    wide_start_ms = int((now - timedelta(hours=12)).timestamp() * 1000)
+    wide_rows = _query(wide_start_ms, now_ms)
+
+    assert len(wide_rows) == 1, (
+        f"Expected 1 span for trace_id filter (wide window, multi-bucket), "
+        f"got {len(wide_rows)} — possible duplicate-span regression"
+    )
+    assert wide_rows[0]["data"]["span_id"] == span_id_root
+    assert wide_rows[0]["data"]["trace_id"] == target_trace_id
+
+    # --- Test 3: window that does not contain the trace returns no results ---
+    past_start_ms = int((now - timedelta(hours=6)).timestamp() * 1000)
+    past_end_ms = int((now - timedelta(hours=2)).timestamp() * 1000)
+    past_rows = _query(past_start_ms, past_end_ms)
+
+    assert len(past_rows) == 0, (
+        f"Expected 0 spans for trace_id filter outside time window, "
+        f"got {len(past_rows)}"
+    )
+
+
+def test_traces_list_filter_by_trace_id_cross_bucket(
+    signoz: types.SigNoz,
+    create_user_admin: None,  # pylint: disable=unused-argument
+    get_token: Callable[[str, str], str],
+    insert_traces: Callable[[List[Traces]], None],
+) -> None:
+    """
+    Regression test for multi-bucket trace_id queries.
+
+    Setup: a single trace with two spans placed in different exponential buckets.
+      makeBuckets over a 3-hour window produces (newest-first):
+        bucket 1: [now-1h,  now]       ← span_a lives here (now-30min)
+        bucket 2: [now-3h,  now-1h]    ← span_b lives here (now-90min)
+
+    Without the fix: both buckets expanded their window to [traceStart, traceEnd]
+    and therefore each returned both spans → 4 rows total (duplicates).
+
+    With the fix: each bucket is tied to its intersection with the trace range,
+    so bucket 1 returns span_a and bucket 2 returns span_b → exactly 2 rows.
+    """
+    trace_id = TraceIdGenerator.trace_id()
+    span_id_a = TraceIdGenerator.span_id()
+    span_id_b = TraceIdGenerator.span_id()
+
+    now = datetime.now(tz=timezone.utc).replace(second=0, microsecond=0)
+
+    common_resources = {
+        "deployment.environment": "production",
+        "service.name": "cross-bucket-service",
+        "cloud.provider": "integration",
+    }
+
+    insert_traces(
+        [
+            # span_a: sits in bucket 1 [now-1h, now]
+            Traces(
+                timestamp=now - timedelta(minutes=30),
+                duration=timedelta(seconds=1),
+                trace_id=trace_id,
+                span_id=span_id_a,
+                parent_span_id="",
+                name="span-a",
+                kind=TracesKind.SPAN_KIND_SERVER,
+                status_code=TracesStatusCode.STATUS_CODE_OK,
+                status_message="",
+                resources=common_resources,
+                attributes={},
+            ),
+            # span_b: sits in bucket 2 [now-3h, now-1h]
+            Traces(
+                timestamp=now - timedelta(minutes=90),
+                duration=timedelta(seconds=1),
+                trace_id=trace_id,
+                span_id=span_id_b,
+                parent_span_id=span_id_a,
+                name="span-b",
+                kind=TracesKind.SPAN_KIND_CLIENT,
+                status_code=TracesStatusCode.STATUS_CODE_OK,
+                status_message="",
+                resources=common_resources,
+                attributes={},
+            ),
+        ]
+    )
+
+    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
+
+    # 3-hour window forces makeBuckets to create two buckets:
+    #   bucket 1: [now-1h, now]
+    #   bucket 2: [now-3h, now-1h]
+    start_ms = int((now - timedelta(hours=3)).timestamp() * 1000)
+    end_ms = int(now.timestamp() * 1000)
+
+    response = make_query_request(
+        signoz,
+        token,
+        start_ms=start_ms,
+        end_ms=end_ms,
+        request_type="raw",
+        queries=[
+            {
+                "type": "builder_query",
+                "spec": {
+                    "name": "A",
+                    "signal": "traces",
+                    "limit": 100,
+                    "offset": 0,
+                    "filter": {"expression": f"trace_id = '{trace_id}'"},
+                    "order": [{"key": {"name": "timestamp"}, "direction": "desc"}],
+                    "selectFields": [
+                        {
+                            "name": "name",
+                            "fieldDataType": "string",
+                            "fieldContext": "span",
+                            "signal": "traces",
+                        }
+                    ],
+                },
+            }
+        ],
+    )
+
+    assert response.status_code == HTTPStatus.OK
+    assert response.json()["status"] == "success"
+
+    rows = response.json()["data"]["data"]["results"][0]["rows"] or []
+
+    assert len(rows) == 2, (
+        f"Expected exactly 2 spans (one per bucket), got {len(rows)} — "
+        f"likely a duplicate-span regression from bucket window expansion"
+    )
+    returned_span_ids = {r["data"]["span_id"] for r in rows}
+    assert returned_span_ids == {span_id_a, span_id_b}
+    assert all(r["data"]["trace_id"] == trace_id for r in rows)

tests/integration/src/querier/05_metrics_rate_cumulative_counter.py

@@ -2,20 +2,16 @@
 Look at the cumulative_counters_1h.jsonl file for the relevant data
 """
 
-import logging
 import os
 from datetime import datetime, timedelta, timezone
 from http import HTTPStatus
-from typing import Any, Callable, List, Optional, Union
-
-import pytest
+from typing import Any, Callable, List
 
 from fixtures import types
 from fixtures.auth import USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD
 from fixtures.metrics import Metrics
 from fixtures.querier import (
     build_builder_query,
-    build_order_by,
     get_all_series,
     get_series_values,
     make_query_request,
@@ -75,200 +71,16 @@ def test_rate_with_steady_values_and_reset(
         assert v["value"] >= 0, f"Rate should not be negative: {v['value']}"
 
 
-def _assert_endpoint_rate_values(endpoint_values: dict) -> None:
-    # /health: 60 data points (t01-t60), steady +10/min
-    # rate = 10/60 = 0.167
-    if "/health" in endpoint_values:
-        health_values = endpoint_values["/health"]
-        assert (
-            len(health_values) >= 58
-        ), f"Expected >= 58 values for /health, got {len(health_values)}"
-        count_steady_health = sum(1 for v in health_values if v["value"] == 0.167)
-        assert (
-            count_steady_health >= 57
-        ), f"Expected >= 57 steady rate values (0.167) for /health, got {count_steady_health}"
-        # all /health rates should be 0.167 except possibly first/last due to boundaries
-        for v in health_values[1:-1]:
-            assert v["value"] == 0.167, f"Expected /health rate 0.167, got {v['value']}"
-
-    # /products: 51 data points with 10-minute gap (t20-t29 missing), steady +20/min
-    # rate = 20/60 = 0.333, gap causes lower averaged rate at boundary
-    if "/products" in endpoint_values:
-        products_values = endpoint_values["/products"]
-        assert (
-            len(products_values) >= 49
-        ), f"Expected >= 49 values for /products, got {len(products_values)}"
-        count_steady_products = sum(1 for v in products_values if v["value"] == 0.333)
-        # most values should be 0.333, some boundary values differ due to 10-min gap
-        assert (
-            count_steady_products >= 46
-        ), f"Expected >= 46 steady rate values (0.333) for /products, got {count_steady_products}"
-        # check that non-0.333 values are due to gap averaging (should be lower)
-        gap_boundary_values = [v["value"] for v in products_values if v["value"] != 0.333]
-        for val in gap_boundary_values:
-            assert (
-                0 < val < 0.333
-            ), f"Gap boundary values should be between 0 and 0.333, got {val}"
-
-    # /checkout: 61 data points (t00-t60), +1/min normal, +50/min spike at t40-t44
-    # normal rate = 1/60 = 0.0167, spike rate = 50/60 = 0.833
-    if "/checkout" in endpoint_values:
-        checkout_values = endpoint_values["/checkout"]
-        assert (
-            len(checkout_values) >= 59
-        ), f"Expected >= 59 values for /checkout, got {len(checkout_values)}"
-        count_steady_checkout = sum(1 for v in checkout_values if v["value"] == 0.0167)
-        assert (
-            count_steady_checkout >= 53
-        ), f"Expected >= 53 steady rate values (0.0167) for /checkout, got {count_steady_checkout}"
-        # check that spike values exist (traffic spike +50/min at t40-t44)
-        count_spike_checkout = sum(1 for v in checkout_values if v["value"] == 0.833)
-        assert (
-            count_spike_checkout >= 4
-        ), f"Expected >= 4 spike rate values (0.833) for /checkout, got {count_spike_checkout}"
-        # spike values should be consecutive
-        spike_indices = [
-            i for i, v in enumerate[Any](checkout_values) if v["value"] == 0.833
-        ]
-        assert len(spike_indices) >= 4, f"Expected >= 4 spike indices, got {spike_indices}"
-        for i in range(1, len(spike_indices)):
-            assert (
-                spike_indices[i] == spike_indices[i - 1] + 1
-            ), f"Spike indices should be consecutive, got {spike_indices}"
-
-    # /orders: 60 data points (t00-t60) with gap at t30, counter reset at t31 (150->2)
-    # rate = 5/60 = 0.0833
-    # reset at t31 causes: rate at t30 includes gap (lower), t31 has high rate after reset
-    if "/orders" in endpoint_values:
-        orders_values = endpoint_values["/orders"]
-        assert (
-            len(orders_values) >= 58
-        ), f"Expected >= 58 values for /orders, got {len(orders_values)}"
-        count_steady_orders = sum(1 for v in orders_values if v["value"] == 0.0833)
-        assert (
-            count_steady_orders >= 55
-        ), f"Expected >= 55 steady rate values (0.0833) for /orders, got {count_steady_orders}"
-        # check for counter reset effects - there should be some non-standard values
-        non_standard_orders = [v["value"] for v in orders_values if v["value"] != 0.0833]
-        assert (
-            len(non_standard_orders) >= 2
-        ), f"Expected >= 2 non-standard values due to counter reset, got {non_standard_orders}"
-        # post-reset value should be higher (new counter value / interval)
-        high_rate_orders = [v for v in non_standard_orders if v > 0.0833]
-        assert (
-            len(high_rate_orders) >= 1
-        ), f"Expected at least one high rate value after counter reset, got {non_standard_orders}"
-
-    # /users: 56 data points (t05-t60), sparse +1 every 5 minutes
-    # Rate = 1/60 = 0.0167 during increment, 0 during flat periods
-    if "/users" in endpoint_values:
-        users_values = endpoint_values["/users"]
-        assert (
-            len(users_values) >= 54
-        ), f"Expected >= 54 values for /users, got {len(users_values)}"
-        count_zero_users = sum(1 for v in users_values if v["value"] == 0)
-        # most values should be 0 (flat periods between increments)
-        assert (
-            count_zero_users >= 40
-        ), f"Expected >= 40 zero rate values for /users (sparse data), got {count_zero_users}"
-        # non-zero values should be 0.0167 (1/60 increment rate)
-        non_zero_users = [v["value"] for v in users_values if v["value"] != 0]
-        count_increment_rate = sum(1 for v in non_zero_users if v == 0.0167)
-        assert (
-            count_increment_rate >= 8
-        ), f"Expected >= 8 increment rate values (0.0167) for /users, got {count_increment_rate}"
-
-
-@pytest.mark.parametrize(
-    "metric_suffix,order_by,limit,expected_count,expected_endpoints",
-    [
-        (
-            "no_order",
-            None, # this is equivalent to sum(metric_name)
-            None,
-            5,
-            ["/products", "/health", "/checkout", "/orders", "/users"],
-        ),
-        (
-            "only_limit",
-            None, # this is equivalent to sum(metric_name)
-            3,
-            3,
-            ["/products", "/health", "/checkout"],
-        ),
-        (
-            "asc",
-            [build_order_by("endpoint", "asc")],
-            None,
-            5,
-            ["/checkout", "/health", "/orders", "/products", "/users"],
-        ),
-        (
-            "asc_lim3",
-            [build_order_by("endpoint", "asc")],
-            3,
-            3,
-            ["/checkout", "/health", "/orders"],
-        ),
-        (
-            "desc",
-            [build_order_by("endpoint", "desc")],
-            None,
-            5,
-            ["/users", "/products", "/orders", "/health", "/checkout"],
-        ),
-        (
-            "desc_lim3",
-            [build_order_by("endpoint", "desc")],
-            3,
-            3,
-            ["/users", "/products", "/orders"],
-        ),
-        (
-            "asc_metric_name",
-            [build_order_by("sum(test_rate_groupby_asc_metric_name)", "asc")],
-            None,
-            5,
-            ["/users", "/orders", "/checkout", "/health", "/products"],
-        ),
-        (
-            "asc_metric_name_lim3",
-            [build_order_by("sum(test_rate_groupby_asc_metric_name_lim3)", "asc")],
-            3,
-            3,
-            ["/users", "/orders", "/checkout"],
-        ),
-        (
-            "desc_metric_name",
-            [build_order_by("sum(test_rate_groupby_desc_metric_name)", "desc")],
-            None,
-            5,
-            ["/products", "/health", "/checkout", "/orders", "/users"],
-        ),
-        (
-            "desc_metric_name_lim3",
-            [build_order_by("sum(test_rate_groupby_desc_metric_name_lim3)", "desc")],
-            3,
-            3,
-            ["/products", "/health", "/checkout"],
-        ),
-    ],
-)
 def test_rate_group_by_endpoint(
     signoz: types.SigNoz,
     create_user_admin: None,  # pylint: disable=unused-argument
     get_token: Callable[[str, str], str],
     insert_metrics: Callable[[List[Metrics]], None],
-    metric_suffix: str,
-    order_by: Optional[List],
-    limit: Optional[int],
-    expected_count: int,
-    expected_endpoints: Union[set, List[str]],
 ) -> None:
     now = datetime.now(tz=timezone.utc).replace(second=0, microsecond=0)
     start_ms = int((now - timedelta(minutes=65)).timestamp() * 1000)
     end_ms = int(now.timestamp() * 1000)
-    metric_name = f"test_rate_groupby_{metric_suffix}"
+    metric_name = "test_rate_groupby"
 
     metrics = Metrics.load_from_file(
         CUMULATIVE_COUNTERS_FILE,
@@ -285,8 +97,6 @@ def test_rate_group_by_endpoint(
         "sum",
         temporality="cumulative",
         group_by=["endpoint"],
-        order_by=order_by,
-        limit=limit,
     )
 
     response = make_query_request(signoz, token, start_ms, end_ms, [query])
@@ -295,23 +105,10 @@ def test_rate_group_by_endpoint(
     data = response.json()
     all_series = get_all_series(data, "A")
 
+    # Should have 5 different endpoints
     assert (
-        len(all_series) == expected_count
-    ), f"Expected {expected_count} series, got {len(all_series)}"
-
-    endpoint_labels = [
-        series.get("labels", [{}])[0].get("value", "unknown")
-        for series in all_series
-    ]
-
-    if isinstance(expected_endpoints, set):
-        assert (
-            set(endpoint_labels) == expected_endpoints
-        ), f"Expected endpoints {expected_endpoints}, got {set(endpoint_labels)}"
-    else:
-        assert endpoint_labels == expected_endpoints, (
-            f"Expected endpoints {expected_endpoints}, got {endpoint_labels}"
-        )
+        len(all_series) == 5
+    ), f"Expected 5 series for 5 endpoints, got {len(all_series)}"
 
     # endpoint -> values
     endpoint_values = {}
@@ -320,6 +117,11 @@ def test_rate_group_by_endpoint(
         values = sorted(series.get("values", []), key=lambda x: x["timestamp"])
         endpoint_values[endpoint] = values
 
+    expected_endpoints = {"/products", "/health", "/checkout", "/orders", "/users"}
+    assert (
+        set(endpoint_values.keys()) == expected_endpoints
+    ), f"Expected endpoints {expected_endpoints}, got {set(endpoint_values.keys())}"
+
     # at no point rate should be negative
     for endpoint, values in endpoint_values.items():
         for v in values:
@@ -327,4 +129,103 @@ def test_rate_group_by_endpoint(
                 v["value"] >= 0
             ), f"Rate for {endpoint} should not be negative: {v['value']}"
 
-    _assert_endpoint_rate_values(endpoint_values)
+    # /health: 60 data points (t01-t60), steady +10/min
+    # rate = 10/60 = 0.167
+    health_values = endpoint_values["/health"]
+    assert (
+        len(health_values) >= 58
+    ), f"Expected >= 58 values for /health, got {len(health_values)}"
+    count_steady_health = sum(1 for v in health_values if v["value"] == 0.167)
+    assert (
+        count_steady_health >= 57
+    ), f"Expected >= 57 steady rate values (0.167) for /health, got {count_steady_health}"
+    # all /health rates should be 0.167 except possibly first/last due to boundaries
+    for v in health_values[1:-1]:
+        assert v["value"] == 0.167, f"Expected /health rate 0.167, got {v['value']}"
+
+    # /products: 51 data points with 10-minute gap (t20-t29 missing), steady +20/min
+    # rate = 20/60 = 0.333, gap causes lower averaged rate at boundary
+    products_values = endpoint_values["/products"]
+    assert (
+        len(products_values) >= 49
+    ), f"Expected >= 49 values for /products, got {len(products_values)}"
+    count_steady_products = sum(1 for v in products_values if v["value"] == 0.333)
+
+    # most values should be 0.333, some boundary values differ due to 10-min gap
+    assert (
+        count_steady_products >= 46
+    ), f"Expected >= 46 steady rate values (0.333) for /products, got {count_steady_products}"
+
+    # check that non-0.333 values are due to gap averaging (should be lower)
+    gap_boundary_values = [v["value"] for v in products_values if v["value"] != 0.333]
+    for val in gap_boundary_values:
+        assert (
+            0 < val < 0.333
+        ), f"Gap boundary values should be between 0 and 0.333, got {val}"
+
+    # /checkout: 61 data points (t00-t60), +1/min normal, +50/min spike at t40-t44
+    # normal rate = 1/60 = 0.0167, spike rate = 50/60 = 0.833
+    checkout_values = endpoint_values["/checkout"]
+    assert (
+        len(checkout_values) >= 59
+    ), f"Expected >= 59 values for /checkout, got {len(checkout_values)}"
+    count_steady_checkout = sum(1 for v in checkout_values if v["value"] == 0.0167)
+    assert (
+        count_steady_checkout >= 53
+    ), f"Expected >= 53 steady rate values (0.0167) for /checkout, got {count_steady_checkout}"
+    # check that spike values exist (traffic spike +50/min at t40-t44)
+    count_spike_checkout = sum(1 for v in checkout_values if v["value"] == 0.833)
+    assert (
+        count_spike_checkout >= 4
+    ), f"Expected >= 4 spike rate values (0.833) for /checkout, got {count_spike_checkout}"
+
+    # spike values should be consecutive
+    spike_indices = [
+        i for i, v in enumerate[Any](checkout_values) if v["value"] == 0.833
+    ]
+    assert len(spike_indices) >= 4, f"Expected >= 4 spike indices, got {spike_indices}"
+    # consecutiveness
+    for i in range(1, len(spike_indices)):
+        assert (
+            spike_indices[i] == spike_indices[i - 1] + 1
+        ), f"Spike indices should be consecutive, got {spike_indices}"
+
+    # /orders: 60 data points (t00-t60) with gap at t30, counter reset at t31 (150->2)
+    # rate = 5/60 = 0.0833
+    # reset at t31 causes: rate at t30 includes gap (lower), t31 has high rate after reset
+    orders_values = endpoint_values["/orders"]
+    assert (
+        len(orders_values) >= 58
+    ), f"Expected >= 58 values for /orders, got {len(orders_values)}"
+    count_steady_orders = sum(1 for v in orders_values if v["value"] == 0.0833)
+    assert (
+        count_steady_orders >= 55
+    ), f"Expected >= 55 steady rate values (0.0833) for /orders, got {count_steady_orders}"
+    # check for counter reset effects - there should be some non-standard values
+    non_standard_orders = [v["value"] for v in orders_values if v["value"] != 0.0833]
+    assert (
+        len(non_standard_orders) >= 2
+    ), f"Expected >= 2 non-standard values due to counter reset, got {non_standard_orders}"
+    # post-reset value should be higher (new counter value / interval)
+    high_rate_orders = [v for v in non_standard_orders if v > 0.0833]
+    assert (
+        len(high_rate_orders) >= 1
+    ), f"Expected at least one high rate value after counter reset, got {non_standard_orders}"
+
+    # /users: 56 data points (t05-t60), sparse +1 every 5 minutes
+    # Rate = 1/60 = 0.0167 during increment, 0 during flat periods
+    users_values = endpoint_values["/users"]
+    assert (
+        len(users_values) >= 54
+    ), f"Expected >= 54 values for /users, got {len(users_values)}"
+    count_zero_users = sum(1 for v in users_values if v["value"] == 0)
+    # most values should be 0 (flat periods between increments)
+    assert (
+        count_zero_users >= 40
+    ), f"Expected >= 40 zero rate values for /users (sparse data), got {count_zero_users}"
+    # non-zero values should be 0.0167 (1/60 increment rate)
+    non_zero_users = [v["value"] for v in users_values if v["value"] != 0]
+    count_increment_rate = sum(1 for v in non_zero_users if v == 0.0167)
+    assert (
+        count_increment_rate >= 8
+    ), f"Expected >= 8 increment rate values (0.0167) for /users, got {count_increment_rate}"

tests/integration/src/querier/07_metrics_multi_temporality.py

@@ -5,7 +5,7 @@ Look at the multi_temporality_counters_1h.jsonl file for the relevant data
 import random
 from datetime import datetime, timedelta, timezone
 from http import HTTPStatus
-from typing import Callable, List, Optional, Union
+from typing import Callable, List
 
 import pytest
 
@@ -14,7 +14,6 @@ from fixtures.auth import USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD
 from fixtures.metrics import Metrics
 from fixtures.querier import (
     build_builder_query,
-    build_order_by,
     get_all_series,
     get_series_values,
     make_query_request,
@@ -92,198 +91,6 @@ def test_with_steady_values_and_reset(
         ), f"{time_aggregation} should not be negative: {v['value']}"
 
 
-def _assert_endpoint_group_values(  # pylint: disable=too-many-arguments
-    endpoint_values: dict,
-    stable_health_value: float,
-    stable_products_value: float,
-    stable_checkout_value: float,
-    spike_checkout_value: float,
-    stable_orders_value: float,
-    spike_users_value: float,
-    time_aggregation: str,
-) -> None:
-    # /health: 60 data points (t01-t60), steady +10/min
-    if "/health" in endpoint_values:
-        health_values = endpoint_values["/health"]
-        assert (
-            len(health_values) >= 58
-        ), f"Expected >= 58 values for /health, got {len(health_values)}"
-        count_steady_health = sum(
-            1 for v in health_values if v["value"] == stable_health_value
-        )
-        assert (
-            count_steady_health >= 57
-        ), f"Expected >= 57 steady rate values ({stable_health_value}) for /health, got {count_steady_health}"
-        # all /health rates should be stable except possibly first/last due to boundaries
-        for v in health_values[1:-1]:
-            assert (
-                v["value"] == stable_health_value
-            ), f"Expected /health rate {stable_health_value}, got {v['value']}"
-
-    # /products: 51 data points with 10-minute gap (t20-t29 missing), steady +20/min
-    if "/products" in endpoint_values:
-        products_values = endpoint_values["/products"]
-        assert (
-            len(products_values) >= 49
-        ), f"Expected >= 49 values for /products, got {len(products_values)}"
-        count_steady_products = sum(
-            1 for v in products_values if v["value"] == stable_products_value
-        )
-        # most values should be stable, some boundary values differ due to 10-min gap
-        assert (
-            count_steady_products >= 46
-        ), f"Expected >= 46 steady rate values ({stable_products_value}) for /products, got {count_steady_products}"
-        # check that non-stable values are due to gap averaging (should be lower)
-        gap_boundary_values = [
-            v["value"] for v in products_values if v["value"] != stable_products_value
-        ]
-        for val in gap_boundary_values:
-            assert (
-                0 < val < stable_products_value
-            ), f"Gap boundary values should be between 0 and {stable_products_value}, got {val}"
-
-    # /checkout: 61 data points (t00-t60), +1/min normal, +50/min spike at t40-t44
-    if "/checkout" in endpoint_values:
-        checkout_values = endpoint_values["/checkout"]
-        assert (
-            len(checkout_values) >= 59
-        ), f"Expected >= 59 values for /checkout, got {len(checkout_values)}"
-        count_steady_checkout = sum(
-            1 for v in checkout_values if v["value"] == stable_checkout_value
-        )
-        assert (
-            count_steady_checkout >= 53
-        ), f"Expected >= 53 steady {time_aggregation} values ({stable_checkout_value}) for /checkout, got {count_steady_checkout}"
-        # check that spike values exist (traffic spike +50/min at t40-t44)
-        count_spike_checkout = sum(
-            1 for v in checkout_values if v["value"] == spike_checkout_value
-        )
-        assert (
-            count_spike_checkout >= 4
-        ), f"Expected >= 4 spike {time_aggregation} values ({spike_checkout_value}) for /checkout, got {count_spike_checkout}"
-        # spike values should be consecutive
-        spike_indices = [
-            i for i, v in enumerate(checkout_values) if v["value"] == spike_checkout_value
-        ]
-        assert len(spike_indices) >= 4, f"Expected >= 4 spike indices, got {spike_indices}"
-        for i in range(1, len(spike_indices)):
-            assert (
-                spike_indices[i] == spike_indices[i - 1] + 1
-            ), f"Spike indices should be consecutive, got {spike_indices}"
-
-    # /orders: 60 data points (t00-t60) with gap at t30, counter reset at t31 (150->2)
-    # reset at t31 causes: rate/increase at t30 includes gap (lower), t31 has high rate after reset
-    if "/orders" in endpoint_values:
-        orders_values = endpoint_values["/orders"]
-        assert (
-            len(orders_values) >= 58
-        ), f"Expected >= 58 values for /orders, got {len(orders_values)}"
-        count_steady_orders = sum(
-            1 for v in orders_values if v["value"] == stable_orders_value
-        )
-        assert (
-            count_steady_orders >= 55
-        ), f"Expected >= 55 steady {time_aggregation} values ({stable_orders_value}) for /orders, got {count_steady_orders}"
-        # check for counter reset effects - there should be some non-standard values
-        non_standard_orders = [
-            v["value"] for v in orders_values if v["value"] != stable_orders_value
-        ]
-        assert (
-            len(non_standard_orders) >= 2
-        ), f"Expected >= 2 non-standard values due to counter reset, got {non_standard_orders}"
-        # post-reset value should be higher (new counter value / interval)
-        high_rate_orders = [v for v in non_standard_orders if v > stable_orders_value]
-        assert (
-            len(high_rate_orders) >= 1
-        ), f"Expected at least one high {time_aggregation} value after counter reset, got {non_standard_orders}"
-
-    # /users: 56 data points (t05-t60), sparse +1 every 5 minutes
-    if "/users" in endpoint_values:
-        users_values = endpoint_values["/users"]
-        assert (
-            len(users_values) >= 54
-        ), f"Expected >= 54 values for /users, got {len(users_values)}"
-        count_zero_users = sum(1 for v in users_values if v["value"] == 0)
-        # most values should be 0 (flat periods between increments)
-        assert (
-            count_zero_users >= 40
-        ), f"Expected >= 40 zero {time_aggregation} values for /users (sparse data), got {count_zero_users}"
-        # non-zero values should be stable increment rate
-        non_zero_users = [v["value"] for v in users_values if v["value"] != 0]
-        count_increment_rate = sum(1 for v in non_zero_users if v == spike_users_value)
-        assert (
-            count_increment_rate >= 8
-        ), f"Expected >= 8 increment {time_aggregation} values ({spike_users_value}) for /users, got {count_increment_rate}"
-
-
-@pytest.mark.parametrize(
-    "order_suffix,order_by_spec,limit,expected_count,expected_endpoints",
-    [
-        (
-            "no_order",
-            None,
-            None,
-            5,
-            ["/products", "/health", "/checkout", "/orders", "/users"],
-        ),
-        (
-            "asc",
-            ("endpoint", "asc"),
-            None,
-            5,
-            ["/checkout", "/health", "/orders", "/products", "/users"],
-        ),
-        (
-            "asc_lim3",
-            ("endpoint", "asc"),
-            3,
-            3,
-            ["/checkout", "/health", "/orders"],
-        ),
-        (
-            "desc",
-            ("endpoint", "desc"),
-            None,
-            5,
-            ["/users", "/products", "/orders", "/health", "/checkout"],
-        ),
-        (
-            "desc_lim3",
-            ("endpoint", "desc"),
-            3,
-            3,
-            ["/users", "/products", "/orders"],
-        ),
-        (
-            "asc_metric_name",
-            ("sum_metric", "asc"),
-            None,
-            5,
-            ["/users", "/orders", "/checkout", "/health", "/products"],
-        ),
-        (
-            "asc_metric_name_lim3",
-            ("sum_metric", "asc"),
-            3,
-            3,
-            ["/users", "/orders", "/checkout"],
-        ),
-        (
-            "desc_metric_name",
-            ("sum_metric", "desc"),
-            None,
-            5,
-            ["/products", "/health", "/checkout", "/orders", "/users"],
-        ),
-        (
-            "desc_metric_name_lim3",
-            ("sum_metric", "desc"),
-            3,
-            3,
-            ["/products", "/health", "/checkout"],
-        ),
-    ],
-)
 @pytest.mark.parametrize(
     "time_aggregation, stable_health_value, stable_products_value, stable_checkout_value, spike_checkout_value, stable_orders_value, spike_users_value",
     [
@@ -303,24 +110,11 @@ def test_group_by_endpoint(
     spike_checkout_value: float,
     stable_orders_value: float,
     spike_users_value: float,
-    order_suffix: str,
-    order_by_spec: Optional[tuple],
-    limit: Optional[int],
-    expected_count: int,
-    expected_endpoints: Union[set, List[str]],
 ) -> None:
     now = datetime.now(tz=timezone.utc).replace(second=0, microsecond=0)
     start_ms = int((now - timedelta(minutes=65)).timestamp() * 1000)
     end_ms = int(now.timestamp() * 1000)
-    metric_name = f"test_{time_aggregation}_groupby_{order_suffix}"
-
-    # Build order_by at runtime so metric name reflects actual time_aggregation
-    order_by = None
-    if order_by_spec is not None:
-        key, direction = order_by_spec
-        if key == "sum_metric":
-            key = f"sum({metric_name})"
-        order_by = [build_order_by(key, direction)]
+    metric_name = f"test_{time_aggregation}_groupby"
 
     metrics = Metrics.load_from_file(
         MULTI_TEMPORALITY_FILE,
@@ -336,8 +130,6 @@ def test_group_by_endpoint(
         time_aggregation,
         "sum",
         group_by=["endpoint"],
-        order_by=order_by,
-        limit=limit,
     )
 
     response = make_query_request(signoz, token, start_ms, end_ms, [query])
@@ -345,23 +137,10 @@ def test_group_by_endpoint(
 
     data = response.json()
     all_series = get_all_series(data, "A")
+    # Should have 5 different endpoints
     assert (
-        len(all_series) == expected_count
-    ), f"Expected {expected_count} series, got {len(all_series)}"
-
-    endpoint_labels = [
-        series.get("labels", [{}])[0].get("value", "unknown")
-        for series in all_series
-    ]
-
-    if isinstance(expected_endpoints, set):
-        assert (
-            set(endpoint_labels) == expected_endpoints
-        ), f"Expected endpoints {expected_endpoints}, got {set(endpoint_labels)}"
-    else:
-        assert endpoint_labels == expected_endpoints, (
-            f"Expected endpoints {expected_endpoints}, got {endpoint_labels}"
-        )
+        len(all_series) == 5
+    ), f"Expected 5 series for 5 endpoints, got {len(all_series)}"
 
     # endpoint -> values
     endpoint_values = {}
@@ -370,6 +149,11 @@ def test_group_by_endpoint(
         values = sorted(series.get("values", []), key=lambda x: x["timestamp"])
         endpoint_values[endpoint] = values
 
+    expected_endpoints = {"/products", "/health", "/checkout", "/orders", "/users"}
+    assert (
+        set(endpoint_values.keys()) == expected_endpoints
+    ), f"Expected endpoints {expected_endpoints}, got {set(endpoint_values.keys())}"
+
     # at no point rate should be negative
     for endpoint, values in endpoint_values.items():
         for v in values:
@@ -377,16 +161,117 @@ def test_group_by_endpoint(
                 v["value"] >= 0
             ), f"Rate for {endpoint} should not be negative: {v['value']}"
 
-    _assert_endpoint_group_values(
-        endpoint_values,
-        stable_health_value,
-        stable_products_value,
-        stable_checkout_value,
-        spike_checkout_value,
-        stable_orders_value,
-        spike_users_value,
-        time_aggregation,
+    # /health: 60 data points (t01-t60), steady +10/min
+    health_values = endpoint_values["/health"]
+    assert (
+        len(health_values) >= 58
+    ), f"Expected >= 58 values for /health, got {len(health_values)}"
+    count_steady_health = sum(
+        1 for v in health_values if v["value"] == stable_health_value
     )
+    assert (
+        count_steady_health >= 57
+    ), f"Expected >= 57 steady rate values ({stable_health_value}) for /health, got {count_steady_health}"
+    # all /health rates should be state except possibly first/last due to boundaries
+    for v in health_values[1:-1]:
+        assert (
+            v["value"] == stable_health_value
+        ), f"Expected /health rate {stable_health_value}, got {v['value']}"
+
+    # /products: 51 data points with 10-minute gap (t20-t29 missing), steady +20/min
+    products_values = endpoint_values["/products"]
+    assert (
+        len(products_values) >= 49
+    ), f"Expected >= 49 values for /products, got {len(products_values)}"
+    count_steady_products = sum(
+        1 for v in products_values if v["value"] == stable_products_value
+    )
+
+    # most values should be stable, some boundary values differ due to 10-min gap
+    assert (
+        count_steady_products >= 46
+    ), f"Expected >= 46 steady rate values ({stable_products_value}) for /products, got {count_steady_products}"
+
+    # check that non-stable values are due to gap averaging (should be lower)
+    gap_boundary_values = [
+        v["value"] for v in products_values if v["value"] != stable_products_value
+    ]
+    for val in gap_boundary_values:
+        assert (
+            0 < val < stable_products_value
+        ), f"Gap boundary values should be between 0 and {stable_products_value}, got {val}"
+
+    # /checkout: 61 data points (t00-t60), +1/min normal, +50/min spike at t40-t44
+    checkout_values = endpoint_values["/checkout"]
+    assert (
+        len(checkout_values) >= 59
+    ), f"Expected >= 59 values for /checkout, got {len(checkout_values)}"
+    count_steady_checkout = sum(
+        1 for v in checkout_values if v["value"] == stable_checkout_value
+    )
+    assert (
+        count_steady_checkout >= 53
+    ), f"Expected >= 53 steady {time_aggregation} values ({stable_checkout_value}) for /checkout, got {count_steady_checkout}"
+    # check that spike values exist (traffic spike +50/min at t40-t44)
+    count_spike_checkout = sum(
+        1 for v in checkout_values if v["value"] == spike_checkout_value
+    )
+    assert (
+        count_spike_checkout >= 4
+    ), f"Expected >= 4 spike {time_aggregation} values ({spike_checkout_value}) for /checkout, got {count_spike_checkout}"
+
+    # spike values should be consecutive
+    spike_indices = [
+        i for i, v in enumerate(checkout_values) if v["value"] == spike_checkout_value
+    ]
+    assert len(spike_indices) >= 4, f"Expected >= 4 spike indices, got {spike_indices}"
+    # consecutiveness
+    for i in range(1, len(spike_indices)):
+        assert (
+            spike_indices[i] == spike_indices[i - 1] + 1
+        ), f"Spike indices should be consecutive, got {spike_indices}"
+
+    # /orders: 60 data points (t00-t60) with gap at t30, counter reset at t31 (150->2)
+    # reset at t31 causes: rate/increase at t30 includes gap (lower), t31 has high rate after reset
+    orders_values = endpoint_values["/orders"]
+    assert (
+        len(orders_values) >= 58
+    ), f"Expected >= 58 values for /orders, got {len(orders_values)}"
+    count_steady_orders = sum(
+        1 for v in orders_values if v["value"] == stable_orders_value
+    )
+    assert (
+        count_steady_orders >= 55
+    ), f"Expected >= 55 steady {time_aggregation} values ({stable_orders_value}) for /orders, got {count_steady_orders}"
+    # check for counter reset effects - there should be some non-standard values
+    non_standard_orders = [
+        v["value"] for v in orders_values if v["value"] != stable_orders_value
+    ]
+    assert (
+        len(non_standard_orders) >= 2
+    ), f"Expected >= 2 non-standard values due to counter reset, got {non_standard_orders}"
+    # post-reset value should be higher (new counter value / interval)
+    high_rate_orders = [v for v in non_standard_orders if v > stable_orders_value]
+    assert (
+        len(high_rate_orders) >= 1
+    ), f"Expected at least one high {time_aggregation} value after counter reset, got {non_standard_orders}"
+
+    # /users: 56 data points (t05-t60), sparse +1 every 5 minutes
+    users_values = endpoint_values["/users"]
+    assert (
+        len(users_values) >= 54
+    ), f"Expected >= 54 values for /users, got {len(users_values)}"
+    count_zero_users = sum(1 for v in users_values if v["value"] == 0)
+    # most values should be 0 (flat periods between increments)
+    assert (
+        count_zero_users >= 40
+    ), f"Expected >= 40 zero {time_aggregation} values for /users (sparse data), got {count_zero_users}"
+    # non-zero values should be 0.0167 (1/60 increment rate)
+    non_zero_users = [v["value"] for v in users_values if v["value"] != 0]
+    count_increment_rate = sum(1 for v in non_zero_users if v == spike_users_value)
+    assert (
+        count_increment_rate >= 8
+    ), f"Expected >= 8 increment {time_aggregation} values ({spike_users_value}) for /users, got {count_increment_rate}"
 
 
 @pytest.mark.parametrize(

tests/integration/src/querier/08_metrics_histogram.py

@@ -4,7 +4,7 @@ Look at the histogram_data_1h.jsonl file for the relevant data
 
 from datetime import datetime, timedelta, timezone
 from http import HTTPStatus
-from typing import Callable, List, Optional, Union
+from typing import Callable, List
 
 import pytest
 
@@ -13,16 +13,13 @@ from fixtures.auth import USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD
 from fixtures.metrics import Metrics
 from fixtures.querier import (
     build_builder_query,
-    build_order_by,
     get_all_series,
     get_series_values,
     make_query_request,
 )
 from fixtures.utils import get_testdata_file_path
 
-
 FILE = get_testdata_file_path("histogram_data_1h.jsonl")
-FILE_WITH_MANY_GROUPS = get_testdata_file_path("histogram_data_1h_many_groups.jsonl")
 
 
 @pytest.mark.parametrize(
@@ -524,564 +521,4 @@ def test_histogram_percentile_for_delta_service(
     assert len(result_values) == 60
     assert result_values[0]["value"] == zeroth_value
     assert result_values[1]["value"] == first_value
-    assert result_values[-1]["value"] == last_value
-
-
-def _assert_series_endpoint_labels(
-    series: list,
-    expected_endpoints: Union[set, List[str]],
-    prefix: str,
-) -> None:
-    labels = [s.get("labels", [{}])[0].get("value", "unknown") for s in series]
-    if isinstance(expected_endpoints, set):
-        assert (
-            set(labels) == expected_endpoints
-        ), f"Expected {prefix} endpoints {expected_endpoints}, got {set(labels)}"
-    else:
-        assert labels == expected_endpoints, (
-            f"Expected {prefix} endpoints in order {expected_endpoints}, got {labels}"
-        )
-
-
-@pytest.mark.parametrize(
-    "order_suffix,order_by,limit,expected_count,expected_endpoints",
-    [
-        (
-            "no_order",
-            None,
-            None,
-            3,
-            ["/checkout", "/health", "/orders"],
-        ),
-        (
-            "asc",
-            [build_order_by("endpoint", "asc")],
-            None,
-            3,
-            ["/checkout", "/health", "/orders"],
-        ),
-        (
-            "asc_lim2",
-            [build_order_by("endpoint", "asc")],
-            2,
-            2,
-            ["/checkout", "/health"],
-        ),
-        (
-            "desc",
-            [build_order_by("endpoint", "desc")],
-            None,
-            3,
-            ["/orders", "/health", "/checkout"],
-        ),
-        (
-            "desc_lim2",
-            [build_order_by("endpoint", "desc")],
-            2,
-            2,
-            ["/orders", "/health"],
-        ),
-        (
-            "asc_metric_name",
-            [build_order_by("count(test_histogram_count_groupby_asc_metric_name)", "asc")],
-            None,
-            3,
-            ["/health", "/orders", "/checkout"], ## health and orders have the same size so they are then sorted endpoint as a tiebreaker
-        ),
-        (
-            "asc_metric_name_lim2",
-            [build_order_by("count(test_histogram_count_groupby_asc_metric_name_lim2)", "asc")],
-            2,
-            2,
-            ["/health", "/orders"],
-        ),
-        (
-            "desc_metric_name",
-            [build_order_by("count(test_histogram_count_groupby_desc_metric_name)", "desc")],
-            None,
-            3,
-            ["/checkout", "/health", "/orders"], ## health and orders have the same size so they are then sorted endpoint as a tiebreaker
-        ),
-        (
-            "desc_metric_name_lim2",
-            [build_order_by("count(test_histogram_count_groupby_desc_metric_name_lim2)", "desc")],
-            2,
-            2,
-            ["/checkout", "/health"],
-        ),
-    ],
-)
-def test_histogram_count_group_by_endpoint(
-    signoz: types.SigNoz,
-    create_user_admin: None,  # pylint: disable=unused-argument
-    get_token: Callable[[str, str], str],
-    insert_metrics: Callable[[List[Metrics]], None],
-    order_suffix: str,
-    order_by: Optional[List],
-    limit: Optional[int],
-    expected_count: int,
-    expected_endpoints: Union[set, List[str]],
-) -> None:
-    now = datetime.now(tz=timezone.utc).replace(second=0, microsecond=0)
-    start_ms = int((now - timedelta(minutes=65)).timestamp() * 1000)
-    end_ms = int(now.timestamp() * 1000)
-    metric_name = f"test_histogram_count_groupby_{order_suffix}"
-
-    metrics = Metrics.load_from_file(
-        FILE,
-        base_time=now - timedelta(minutes=60),
-        metric_name_override=metric_name,
-    )
-    insert_metrics(metrics)
-
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-    query_count = build_builder_query(
-        "A",
-        metric_name,
-        "increase",
-        "count",
-        comparisonSpaceAggregationParam={"threshold": 1000, "operator": "<="},
-        group_by=["endpoint"],
-        order_by=order_by,
-        limit=limit,
-    )
-
-    response = make_query_request(signoz, token, start_ms, end_ms, [query_count])
-    assert response.status_code == HTTPStatus.OK
-
-    data = response.json()
-    count_all_series = get_all_series(data, "A")
-
-    assert (
-        len(count_all_series) == expected_count
-    ), f"Expected {expected_count} series, got {len(count_all_series)}"
-
-    _assert_series_endpoint_labels(count_all_series, expected_endpoints, "count")
-
-    count_values = {}
-    for series in count_all_series:
-        endpoint = series.get("labels", [{}])[0].get("value", "unknown")
-        count_values[endpoint] = sorted(
-            series.get("values", []), key=lambda x: x["timestamp"]
-        )
-
-    for endpoint, values in count_values.items():
-        for v in values:
-            assert v["value"] >= 0, f"Count for {endpoint} should not be negative: {v['value']}"
-
-    # /health (cumulative, service=api): 59 points, increase starts at 11/min → 69/min
-    if "/health" in count_values:
-        vals = count_values["/health"]
-        assert vals[0]["value"] == 11, f"Expected /health count first=11, got {vals[0]['value']}"
-        assert vals[-1]["value"] == 69, f"Expected /health count last=69, got {vals[-1]['value']}"
-
-    # /orders (cumulative, service=api): same distribution as /health
-    if "/orders" in count_values:
-        vals = count_values["/orders"]
-        assert vals[0]["value"] == 11, f"Expected /orders count first=11, got {vals[0]['value']}"
-        assert vals[-1]["value"] == 69, f"Expected /orders count last=69, got {vals[-1]['value']}"
-
-    # /checkout (delta, service=web): 60 points, zeroth=12345 (raw delta), then 11/min → 69/min
-    if "/checkout" in count_values:
-        vals = count_values["/checkout"]
-        assert vals[0]["value"] == 12345, f"Expected /checkout count zeroth=12345, got {vals[0]['value']}"
-        assert vals[1]["value"] == 11, f"Expected /checkout count first=11, got {vals[1]['value']}"
-        assert vals[-1]["value"] == 69, f"Expected /checkout count last=69, got {vals[-1]['value']}"
-
-
-@pytest.mark.parametrize(
-    "order_suffix,order_by,limit,expected_count,expected_endpoints",
-    [
-        (
-            "no_order",
-            None,
-            None,
-            4,
-            [ "/checkout", "/health", "/orders", "/coupon"],
-        ),
-        (
-            "only_limit",
-            None,
-            1,
-            1,
-            [ "/checkout"], ##health and checkout have the same size so they are then sorted endpoint as a tiebreaker, and only checkout makes the limit
-        ),
-        (
-            "asc",
-            [build_order_by("endpoint", "asc")],
-            None,
-            4,
-            [ "/checkout", "/coupon", "/health", "/orders"],
-        ),
-        (
-            "asc_lim2",
-            [build_order_by("endpoint", "asc")],
-            2,
-            2,
-            ["/checkout", "/coupon"],
-        ),
-        (
-            "desc",
-            [build_order_by("endpoint", "desc")],
-            None,
-            4,
-            ["/orders", "/health", "/coupon", "/checkout"],
-        ),
-        (
-            "desc_lim2",
-            [build_order_by("endpoint", "desc")],
-            2,
-            2,
-            ["/orders", "/health"],
-        ),
-        (
-            "asc_metric_name",
-            [build_order_by("p75(test_histogram_p75_groupby_asc_metric_name)", "asc")],
-            None,
-            4,
-            ["/coupon", "/orders", "/checkout", "/health"], ## health and checkout have the same size so they are then sorted endpoint as a tiebreaker
-        ),
-        (
-            "asc_metric_name_lim2",
-            [build_order_by("p75(test_histogram_p75_groupby_asc_metric_name_lim2)", "asc")],
-            2,
-            2,
-            ["/coupon", "/orders"],
-        ),
-        (
-            "asc_metric_name_lim3",
-            [build_order_by("p75(test_histogram_p75_groupby_asc_metric_name_lim3)", "asc")],
-            3,
-            3,
-            ["/coupon", "/orders", "/checkout"], ##health and checkout have the same size so they are then sorted endpoint as a tiebreaker, and only checkout makes the limit
-        ),
-        (
-            "desc_metric_name",
-            [build_order_by("p75(test_histogram_p75_groupby_desc_metric_name)", "desc")],
-            None,
-            4,
-            [ "/checkout", "/health", "/orders", "/coupon"],
-        ),
-        (
-            "desc_metric_name_lim2",
-            [build_order_by("p75(test_histogram_p75_groupby_desc_metric_name_lim2)", "desc")],
-            2,
-            2,
-            [ "/checkout", "/health"],
-        ),
-        (
-            "desc_metric_name_lim2",
-            [build_order_by("p75(test_histogram_p75_groupby_desc_metric_name_lim2)", "desc")],
-            1,
-            1,
-            [ "/checkout"], ##health and checkout have the same size so they are then sorted endpoint as a tiebreaker, and only checkout makes the limit
-        ),
-    ],
-)
-def test_histogram_percentile_group_by_endpoint(
-    signoz: types.SigNoz,
-    create_user_admin: None,  # pylint: disable=unused-argument
-    get_token: Callable[[str, str], str],
-    insert_metrics: Callable[[List[Metrics]], None],
-    order_suffix: str,
-    order_by: Optional[List],
-    limit: Optional[int],
-    expected_count: int,
-    expected_endpoints: Union[set, List[str]],
-) -> None:
-    now = datetime.now(tz=timezone.utc).replace(second=0, microsecond=0)
-    start_ms = int((now - timedelta(minutes=65)).timestamp() * 1000)
-    end_ms = int(now.timestamp() * 1000)
-    metric_name = f"test_histogram_p75_groupby_{order_suffix}"
-
-    metrics = Metrics.load_from_file(
-        FILE_WITH_MANY_GROUPS,
-        base_time=now - timedelta(minutes=60),
-        metric_name_override=metric_name,
-    )
-    insert_metrics(metrics)
-
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-    query_p75 = build_builder_query(
-        "A",
-        metric_name,
-        "doesnotreallymatter",
-        "p75",
-        group_by=["endpoint"],
-        order_by=order_by,
-        limit=limit,
-    )
-
-    response = make_query_request(signoz, token, start_ms, end_ms, [query_p75])
-    assert response.status_code == HTTPStatus.OK
-
-    data = response.json()
-    p75_series = get_all_series(data, "A")
-
-    for series in p75_series:
-        endpoint = series.get("labels", [{}])[0].get("value", "unknown")
-
-    assert (
-        len(p75_series) == expected_count
-    ), f"Expected {expected_count} p75 series, got {len(p75_series)}"
-
-    _assert_series_endpoint_labels(p75_series, expected_endpoints, "p75")
-
-    p75_values = {}
-    for series in p75_series:
-        endpoint = series.get("labels", [{}])[0].get("value", "unknown")
-        p75_values[endpoint] = sorted(
-            series.get("values", []), key=lambda x: x["timestamp"]
-        )
-
-    for endpoint, values in p75_values.items():
-        for v in values:
-            assert v["value"] >= 0, f"p75 for {endpoint} should not be negative: {v['value']}"
-
-    # /health (cumulative, service=api)
-    if "/health" in p75_values:
-        vals = p75_values["/health"]
-        assert vals[0]["value"] == 6000, f"Expected /health p75 first=6000, got {vals[0]['value']}"
-        assert vals[-1]["value"] == 6000, f"Expected /health p75 last=6000, got {vals[-1]['value']}"
-
-    # /orders (cumulative, service=api): same distribution as /health
-    if "/orders" in p75_values:
-        vals = p75_values["/orders"]
-        assert vals[0]["value"] == 4500, f"Expected /orders p75 first=4500, got {vals[0]['value']}"
-        assert vals[-1]["value"] == 4500, f"Expected /orders p75 last=4500, got {vals[-1]['value']}"
-
-    # /checkout (delta, service=web): 60 points
-    if "/checkout" in p75_values:
-        vals = p75_values["/checkout"]
-        assert vals[0]["value"] == 6000, f"Expected /checkout p75 zeroth=6000, got {vals[0]['value']}"
-        assert vals[1]["value"] == 6000, f"Expected /checkout p75 first=6000, got {vals[1]['value']}"
-        assert vals[-1]["value"] == 6000, f"Expected /checkout p75 last=6000, got {vals[-1]['value']}"
-
-    # /coupon (delta, service=web): 60 points
-    if "/coupon" in p75_values:
-        vals = p75_values["/coupon"]
-        assert vals[0]["value"] == 1125, f"Expected /coupon p75 zeroth=1125, got {vals[0]['value']}"
-        assert vals[1]["value"] == 1125, f"Expected /coupon p75 first=1125, got {vals[1]['value']}"
-        assert vals[-1]["value"] == 1125, f"Expected /coupon p75 last=1125, got {vals[-1]['value']}"
-
-
-@pytest.mark.parametrize(
-    "order_suffix,order_by,limit,expected_count,expected_endpoints, expected_status_codes",
-    [
-        (
-            "no_order",
-            None,
-            None,
-            5,
-            [ "/checkout", "/health", "/orders", "/coupon", "/coupon"], ## coupon has 200 and 500 status codes so it will appear twice
-            [ "200", "200", "200", "200", "500"],
-        ),
-        (
-            "only_limit",
-            None,
-            1,
-            1,
-            [ "/checkout"], ##health and checkout have the same size so they are then sorted endpoint as a tiebreaker, and only checkout makes the limit
-            [ "200"]
-        ),
-        (
-            "asc_endpoint",
-            [build_order_by("endpoint", "asc")],
-            None,
-            5,
-            [ "/checkout", "/coupon", "/coupon", "/health", "/orders"],
-            [ "200", "200", "500", "200", "200"],
-        ),
-        (
-            "asc_endpoint_status_code",
-            [build_order_by("endpoint", "asc"), build_order_by("status_code", "asc")],
-            None,
-            5,
-            [ "/checkout", "/coupon", "/coupon", "/health", "/orders"],
-            [ "200", "200", "500", "200", "200"],
-        ),
-        (
-            "asc_status_code_endpoint",
-            [build_order_by("status_code", "asc"), build_order_by("endpoint", "asc")],
-            None,
-            5,
-            [ "/checkout", "/coupon", "/health", "/orders", "/coupon"],
-            [ "200", "200", "200", "200", "500"],
-        ),
-        (
-            "asc_endpoint_limit_2",
-            [build_order_by("endpoint", "asc")],
-            2,
-            2,
-            [ "/checkout", "/coupon"],
-            [ "200", "200"],
-        ),
-        (
-            "asc_endpoint_status_code_limit_2",
-            [build_order_by("endpoint", "asc"), build_order_by("status_code", "asc")],
-            2,
-            2,
-            [ "/checkout", "/coupon"],
-            [ "200", "200"],
-        ),
-        (
-            "asc_status_code_endpoint_limit_4",
-            [build_order_by("status_code", "asc"), build_order_by("endpoint", "asc")],
-            4,
-            4,
-            [ "/checkout", "/coupon", "/health", "/orders"],
-            [ "200", "200", "200", "200"],
-        ),
-        (
-            "desc_endpoint",
-            [build_order_by("endpoint", "desc")],
-            None,
-            5,
-            ["/orders", "/health", "/coupon", "/coupon", "/checkout"],
-            [ "200", "200", "200", "500", "200"],
-        ),
-        (
-            "desc_endpoint_status_code",
-            [build_order_by("endpoint", "desc"), build_order_by("status_code", "desc")],
-            None,
-            5,
-            ["/orders", "/health", "/coupon", "/coupon", "/checkout"],
-            [ "200", "200", "500", "200", "200"],
-        ),
-        (
-            "desc_status_code_endpoint",
-            [build_order_by("status_code", "desc"), build_order_by("endpoint", "desc")],
-            None,
-            5,
-            ["/coupon", "/orders", "/health", "/coupon", "/checkout"],
-            [ "500", "200", "200", "200", "200"],
-        ),
-        (
-            "desc_endpoint_limit2",
-            [build_order_by("endpoint", "desc")],
-            3,
-            3,
-            ["/orders", "/health", "/coupon"],
-            [ "200", "200", "200"],
-        ),
-        (
-            "desc_endpoint_status_code_limit3",
-            [build_order_by("endpoint", "desc"), build_order_by("status_code", "desc")],
-            3,
-            3,
-            ["/orders", "/health", "/coupon"],
-            [ "200", "200", "500"],
-        ),
-        (
-            "desc_status_code_endpoint_limit2",
-            [build_order_by("status_code", "desc"), build_order_by("endpoint", "desc")],
-            2,
-            2,
-            ["/coupon", "/orders"],
-            [ "500", "200"],
-        ),
-    ],
-)
-def test_histogram_percentile_group_by_endpoint_and_status_code(
-    signoz: types.SigNoz,
-    create_user_admin: None,  # pylint: disable=unused-argument
-    get_token: Callable[[str, str], str],
-    insert_metrics: Callable[[List[Metrics]], None],
-    order_suffix: str,
-    order_by: Optional[List],
-    limit: Optional[int],
-    expected_count: int,
-    expected_endpoints: Union[set, List[str]],
-    expected_status_codes: Union[set, List[str]],
-) -> None:
-    now = datetime.now(tz=timezone.utc).replace(second=0, microsecond=0)
-    start_ms = int((now - timedelta(minutes=65)).timestamp() * 1000)
-    end_ms = int(now.timestamp() * 1000)
-    metric_name = f"test_histogram_p75_groupby_{order_suffix}"
-
-    metrics = Metrics.load_from_file(
-        FILE_WITH_MANY_GROUPS,
-        base_time=now - timedelta(minutes=60),
-        metric_name_override=metric_name,
-    )
-    insert_metrics(metrics)
-
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-    query_p75 = build_builder_query(
-        "A",
-        metric_name,
-        "doesnotreallymatter",
-        "p75",
-        group_by=["endpoint", "status_code"],
-        order_by=order_by,
-        limit=limit,
-    )
-
-    response = make_query_request(signoz, token, start_ms, end_ms, [query_p75])
-    assert response.status_code == HTTPStatus.OK
-
-    data = response.json()
-    p75_series = get_all_series(data, "A")
-
-    for series in p75_series:
-        endpoint = series.get("labels", [{}])[0].get("value", "unknown")
-        status_code = series.get("labels", [{}])[1].get("value", "unknown")
-
-    assert (
-        len(p75_series) == expected_count
-    ), f"Expected {expected_count} p75 series, got {len(p75_series)}"
-
-    _assert_series_endpoint_labels(p75_series, expected_endpoints, "p75")
-    
-    endpoints = [s.get("labels", [{}])[0].get("value", "unknown") for s in p75_series]
-    assert endpoints == expected_endpoints, (
-        f"Expected p75 endpoints in order {expected_endpoints}, got {endpoints}"
-    )
-    status_codes = [s.get("labels", [{}])[1].get("value", "unknown") for s in p75_series]
-    assert status_codes == expected_status_codes, (
-        f"Expected p75 endpoints in order {expected_status_codes}, got {status_codes}"
-    )
-
-    p75_values = {}
-    for series in p75_series:
-        endpoint = series.get("labels", [{}])[0].get("value", "unknown")
-        status_code = series.get("labels", [{}])[1].get("value", "unknown")
-        p75_values[endpoint+status_code] = sorted(
-            series.get("values", []), key=lambda x: x["timestamp"]
-        )
-
-    for endpoint, values in p75_values.items():
-        for v in values:
-            assert v["value"] >= 0, f"p75 for {endpoint} should not be negative: {v['value']}"
-
-    # /health (cumulative, service=api)
-    if "/health200" in p75_values:
-        vals = p75_values["/health200"]
-        assert vals[0]["value"] == 6000, f"Expected /health p75 first=6000, got {vals[0]['value']}"
-        assert vals[-1]["value"] == 6000, f"Expected /health p75 last=6000, got {vals[-1]['value']}"
-
-    # /orders (cumulative, service=api): same distribution as /health
-    if "/orders200" in p75_values:
-        vals = p75_values["/orders200"]
-        assert vals[0]["value"] == 4500, f"Expected /orders p75 first=4500, got {vals[0]['value']}"
-        assert vals[-1]["value"] == 4500, f"Expected /orders p75 last=4500, got {vals[-1]['value']}"
-
-    # /checkout (delta, service=web): 60 points
-    if "/checkout200" in p75_values:
-        vals = p75_values["/checkout200"]
-        assert vals[0]["value"] == 6000, f"Expected /checkout p75 zeroth=6000, got {vals[0]['value']}"
-        assert vals[1]["value"] == 6000, f"Expected /checkout p75 first=6000, got {vals[1]['value']}"
-        assert vals[-1]["value"] == 6000, f"Expected /checkout p75 last=6000, got {vals[-1]['value']}"
-
-    # /coupon (delta, service=web): 60 points
-    if "/coupon200" in p75_values:
-        vals = p75_values["/coupon200"]
-        assert vals[0]["value"] == 1250, f"Expected /coupon200 p75 zeroth=1250, got {vals[0]['value']}"
-        assert vals[1]["value"] == 1250, f"Expected /coupon200 p75 first=1250, got {vals[1]['value']}"
-        assert vals[-1]["value"] == 1250, f"Expected /coupon200 p75 last=1250, got {vals[-1]['value']}"
-
-    if "/coupon500" in p75_values:
-        vals = p75_values["/coupon500"]
-        assert vals[0]["value"] == 750, f"Expected /coupon500 p75 zeroth=750, got {vals[0]['value']}"
-        assert vals[1]["value"] == 750, f"Expected /coupon500 p75 first=750, got {vals[1]['value']}"
-        assert vals[-1]["value"] == 750, f"Expected /coupon500 p75 last=750, got {vals[-1]['value']}"
+    assert result_values[-1]["value"] == last_value

tests/integration/src/querier/09_metrics_gauge.py

@@ -1,6 +1,6 @@
 from datetime import datetime, timedelta, timezone
 from http import HTTPStatus
-from typing import Callable, List, Optional, Union
+from typing import Callable, List
 
 import pytest
 
@@ -9,8 +9,6 @@ from fixtures.auth import USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD
 from fixtures.metrics import Metrics
 from fixtures.querier import (
     build_builder_query,
-    build_order_by,
-    get_all_series,
     get_series_values,
     make_query_request,
 )
@@ -141,137 +139,3 @@ def test_for_multiple_aggregations(
     assert result_values[19]["value"] == twentieth_min_val
     assert result_values[20]["value"] == twenty_first_min_val
     assert result_values[30]["value"] == thirty_first_min_val
-
-
-@pytest.mark.parametrize(
-    "metric_suffix,order_by,limit,expected_count,expected_services",
-    [
-        (
-            "no_order",
-            None,  # default ordering: desc by avg of all metric values for a group
-            None,
-            3,
-            ["lab", "web", "api"],  # sum of all values: lab=42000, api=36000, web=34000. avg of all sums: lab=700, api=600, web=680
-        ),
-        (
-            "only_limit",
-            None,
-            2,
-            2,
-            ["lab", "web"],  # top 2 by default desc: lab=42000, api=36000
-        ),
-        (
-            "asc",
-            [build_order_by("service", "asc")],
-            None,
-            3,
-            ["api", "lab", "web"],
-        ),
-        (
-            "asc_lim2",
-            [build_order_by("service", "asc")],
-            2,
-            2,
-            ["api", "lab"],
-        ),
-        (
-            "desc",
-            [build_order_by("service", "desc")],
-            None,
-            3,
-            ["web", "lab", "api"],
-        ),
-        (
-            "desc_lim2",
-            [build_order_by("service", "desc")],
-            2,
-            2,
-            ["web", "lab"],
-        ),
-        (
-            "asc_metric_name",
-            [build_order_by("sum(test_gauge_groupby_asc_metric_name)", "asc")],
-            None,
-            3,
-            ["api", "web", "lab"],
-        ),
-        (
-            "asc_metric_name_lim2",
-            [build_order_by("sum(test_gauge_groupby_asc_metric_name_lim2)", "asc")],
-            2,
-            2,
-            ["api", "web"],
-        ),
-        (
-            "desc_metric_name",
-            [build_order_by("sum(test_gauge_groupby_desc_metric_name)", "desc")],
-            None,
-            3,
-            ["lab", "web", "api"], 
-        ),
-        (
-            "desc_metric_name_lim2",
-            [build_order_by("sum(test_gauge_groupby_desc_metric_name_lim2)", "desc")],
-            2,
-            2,
-            ["lab", "web"],
-        ),
-    ],
-)
-def test_gauge_group_by_service(
-    signoz: types.SigNoz,
-    create_user_admin: None,  # pylint: disable=unused-argument
-    get_token: Callable[[str, str], str],
-    insert_metrics: Callable[[List[Metrics]], None],
-    metric_suffix: str,
-    order_by: Optional[List],
-    limit: Optional[int],
-    expected_count: int,
-    expected_services: Union[set, List[str]],
-) -> None:
-    now = datetime.now(tz=timezone.utc).replace(second=0, microsecond=0)
-    start_ms = int((now - timedelta(minutes=65)).timestamp() * 1000)
-    end_ms = int(now.timestamp() * 1000)
-    metric_name = f"test_gauge_groupby_{metric_suffix}"
-
-    metrics = Metrics.load_from_file(
-        FILE,
-        base_time=now - timedelta(minutes=60),
-        metric_name_override=metric_name,
-    )
-    insert_metrics(metrics)
-
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-    query = build_builder_query(
-        "A",
-        metric_name,
-        "max",
-        "sum",
-        group_by=["service"],
-        order_by=order_by,
-        limit=limit,
-    )
-
-    response = make_query_request(signoz, token, start_ms, end_ms, [query])
-    assert response.status_code == HTTPStatus.OK
-
-    data = response.json()
-    all_series = get_all_series(data, "A")
-
-    assert (
-        len(all_series) == expected_count
-    ), f"Expected {expected_count} series, got {len(all_series)}"
-
-    service_labels = [
-        series.get("labels", [{}])[0].get("value", "unknown")
-        for series in all_series
-    ]
-
-    if isinstance(expected_services, set):
-        assert (
-            set(service_labels) == expected_services
-        ), f"Expected services {expected_services}, got {set(service_labels)}"
-    else:
-        assert service_labels == expected_services, (
-            f"Expected services {expected_services}, got {service_labels}"
-        )

tests/integration/src/querier/10_metrics_rate_delta_counter.py

@@ -5,16 +5,13 @@ Look at the delta_counters_1h.jsonl file for the relevant data
 import os
 from datetime import datetime, timedelta, timezone
 from http import HTTPStatus
-from typing import Any, Callable, List, Optional, Union
-
-import pytest
+from typing import Any, Callable, List
 
 from fixtures import types
 from fixtures.auth import USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD
 from fixtures.metrics import Metrics
 from fixtures.querier import (
     build_builder_query,
-    build_order_by,
     get_all_series,
     get_series_values,
     make_query_request,
@@ -72,61 +69,16 @@ def test_rate_with_steady_values_and_reset(
         assert v["value"] >= 0, f"Rate should not be negative: {v['value']}"
 
 
-@pytest.mark.parametrize(
-    "order_suffix,order_by,limit,expected_count,expected_endpoints",
-    [
-        (
-            "no_order",
-            None,
-            None,
-            5,
-            {"/products", "/health", "/checkout", "/orders", "/users"},
-        ),
-        (
-            "asc",
-            [build_order_by("endpoint", "asc")],
-            None,
-            5,
-            ["/checkout", "/health", "/orders", "/products", "/users"],
-        ),
-        (
-            "asc_lim3",
-            [build_order_by("endpoint", "asc")],
-            3,
-            3,
-            ["/checkout", "/health", "/orders"],
-        ),
-        (
-            "desc",
-            [build_order_by("endpoint", "desc")],
-            None,
-            5,
-            ["/users", "/products", "/orders", "/health", "/checkout"],
-        ),
-        (
-            "desc_lim3",
-            [build_order_by("endpoint", "desc")],
-            3,
-            3,
-            ["/users", "/products", "/orders"],
-        ),
-    ],
-)
 def test_rate_group_by_endpoint(
     signoz: types.SigNoz,
     create_user_admin: None,  # pylint: disable=unused-argument
     get_token: Callable[[str, str], str],
     insert_metrics: Callable[[List[Metrics]], None],
-    order_suffix: str,
-    order_by: Optional[List],
-    limit: Optional[int],
-    expected_count: int,
-    expected_endpoints: Union[set, List[str]],
 ) -> None:
     now = datetime.now(tz=timezone.utc).replace(second=0, microsecond=0)
     start_ms = int((now - timedelta(minutes=65)).timestamp() * 1000)
     end_ms = int(now.timestamp() * 1000)
-    metric_name = f"test_rate_groupby_{order_suffix}"
+    metric_name = "test_rate_groupby"
 
     metrics = Metrics.load_from_file(
         DELTA_COUNTERS_FILE,
@@ -142,8 +94,6 @@ def test_rate_group_by_endpoint(
         "rate",
         "sum",
         group_by=["endpoint"],
-        order_by=order_by,
-        limit=limit,
     )
 
     response = make_query_request(signoz, token, start_ms, end_ms, [query])
@@ -152,23 +102,10 @@ def test_rate_group_by_endpoint(
     data = response.json()
     all_series = get_all_series(data, "A")
 
+    # Should have 5 different endpoints
     assert (
-        len(all_series) == expected_count
-    ), f"Expected {expected_count} series, got {len(all_series)}"
-
-    endpoint_labels = [
-        series.get("labels", [{}])[0].get("value", "unknown")
-        for series in all_series
-    ]
-
-    if isinstance(expected_endpoints, set):
-        assert (
-            set(endpoint_labels) == expected_endpoints
-        ), f"Expected endpoints {expected_endpoints}, got {set(endpoint_labels)}"
-    else:
-        assert endpoint_labels == expected_endpoints, (
-            f"Expected endpoints {expected_endpoints}, got {endpoint_labels}"
-        )
+        len(all_series) == 5
+    ), f"Expected 5 series for 5 endpoints, got {len(all_series)}"
 
     # endpoint -> values
     endpoint_values = {}
@@ -177,6 +114,11 @@ def test_rate_group_by_endpoint(
         values = sorted(series.get("values", []), key=lambda x: x["timestamp"])
         endpoint_values[endpoint] = values
 
+    expected_endpoints = {"/products", "/health", "/checkout", "/orders", "/users"}
+    assert (
+        set(endpoint_values.keys()) == expected_endpoints
+    ), f"Expected endpoints {expected_endpoints}, got {set(endpoint_values.keys())}"
+
     # at no point rate should be negative
     for endpoint, values in endpoint_values.items():
         for v in values:
@@ -186,95 +128,93 @@ def test_rate_group_by_endpoint(
 
     # /health: 60 data points (t01-t60), steady +10/min
     # rate = 10/60 = 0.167
-    if "/health" in endpoint_values:
-        health_values = endpoint_values["/health"]
-        assert (
-            len(health_values) == 60
-        ), f"Expected 60 values for /health, got {len(health_values)}"
-        count_steady_health = sum(1 for v in health_values if v["value"] == 0.167)
-        assert (
-            count_steady_health == 60
-        ), f"Expected == 60 steady rate values (0.167) for /health, got {count_steady_health}"
-        # all /health rates should be 0.167 except possibly first/last due to boundaries
-        for v in health_values[1:-1]:
-            assert v["value"] == 0.167, f"Expected /health rate 0.167, got {v['value']}"
+    health_values = endpoint_values["/health"]
+    assert (
+        len(health_values) == 60
+    ), f"Expected 60 values for /health, got {len(health_values)}"
+    count_steady_health = sum(1 for v in health_values if v["value"] == 0.167)
+    assert (
+        count_steady_health == 60
+    ), f"Expected == 60 steady rate values (0.167) for /health, got {count_steady_health}"
+    # all /health rates should be 0.167 except possibly first/last due to boundaries
+    for v in health_values[1:-1]:
+        assert v["value"] == 0.167, f"Expected /health rate 0.167, got {v['value']}"
 
     # /products: 51 data points with 10-minute gap (t20-t29 missing), steady +20/min
     # rate = 20/60 = 0.333, gap causes lower averaged rate at boundary
-    if "/products" in endpoint_values:
-        products_values = endpoint_values["/products"]
-        assert (
-            len(products_values) == 51
-        ), f"Expected 51 values for /products, got {len(products_values)}"
-        count_steady_products = sum(1 for v in products_values if v["value"] == 0.333)
-        assert (
-            count_steady_products == 51
-        ), f"Expected 51 steady rate values (0.333) for /products, got {count_steady_products}"
+    products_values = endpoint_values["/products"]
+    assert (
+        len(products_values) == 51
+    ), f"Expected 51 values for /products, got {len(products_values)}"
+    count_steady_products = sum(1 for v in products_values if v["value"] == 0.333)
+
+    assert (
+        count_steady_products == 51
+    ), f"Expected 51 steady rate values (0.333) for /products, got {count_steady_products}"
 
     # /checkout: 61 data points (t00-t60), +1/min normal, +50/min spike at t40-t44
     # normal rate = 1/60 = 0.0167, spike rate = 50/60 = 0.833
-    if "/checkout" in endpoint_values:
-        checkout_values = endpoint_values["/checkout"]
+    checkout_values = endpoint_values["/checkout"]
+    assert (
+        len(checkout_values) == 61
+    ), f"Expected 61 values for /checkout, got {len(checkout_values)}"
+    count_steady_checkout = sum(1 for v in checkout_values if v["value"] == 0.0167)
+    assert (
+        count_steady_checkout == 56
+    ), f"Expected 56 steady rate values (0.0167) for /checkout, got {count_steady_checkout}"
+    # check that spike values exist (traffic spike +50/min at t40-t44)
+    count_spike_checkout = sum(1 for v in checkout_values if v["value"] == 0.833)
+    assert (
+        count_spike_checkout == 5
+    ), f"Expected 5 spike rate values (0.833) for /checkout, got {count_spike_checkout}"
+
+    # spike values should be consecutive
+    spike_indices = [
+        i for i, v in enumerate[Any](checkout_values) if v["value"] == 0.833
+    ]
+    assert len(spike_indices) == 5, f"Expected 5 spike indices, got {spike_indices}"
+    # consecutiveness
+    for i in range(1, len(spike_indices)):
         assert (
-            len(checkout_values) == 61
-        ), f"Expected 61 values for /checkout, got {len(checkout_values)}"
-        count_steady_checkout = sum(1 for v in checkout_values if v["value"] == 0.0167)
-        assert (
-            count_steady_checkout == 56
-        ), f"Expected 56 steady rate values (0.0167) for /checkout, got {count_steady_checkout}"
-        # check that spike values exist (traffic spike +50/min at t40-t44)
-        count_spike_checkout = sum(1 for v in checkout_values if v["value"] == 0.833)
-        assert (
-            count_spike_checkout == 5
-        ), f"Expected 5 spike rate values (0.833) for /checkout, got {count_spike_checkout}"
-        # spike values should be consecutive
-        spike_indices = [
-            i for i, v in enumerate[Any](checkout_values) if v["value"] == 0.833
-        ]
-        assert len(spike_indices) == 5, f"Expected 5 spike indices, got {spike_indices}"
-        for i in range(1, len(spike_indices)):
-            assert (
-                spike_indices[i] == spike_indices[i - 1] + 1
-            ), f"Spike indices should be consecutive, got {spike_indices}"
+            spike_indices[i] == spike_indices[i - 1] + 1
+        ), f"Spike indices should be consecutive, got {spike_indices}"
 
     # /orders: 60 data points (t00-t60) with gap at t30, counter reset at t31 (150->2)
     # rate = 5/60 = 0.0833
     # reset at t31 causes: rate at t30 includes gap (lower), t31 has high rate after reset
-    if "/orders" in endpoint_values:
-        orders_values = endpoint_values["/orders"]
-        assert (
-            len(orders_values) == 60
-        ), f"Expected 60 values for /orders, got {len(orders_values)}"
-        count_steady_orders = sum(1 for v in orders_values if v["value"] == 0.0833)
-        assert (
-            count_steady_orders == 58
-        ), f"Expected 58 steady rate values (0.0833) for /orders, got {count_steady_orders}"
-        # check for counter reset effects - there should be some non-standard values
-        non_standard_orders = [v["value"] for v in orders_values if v["value"] != 0.0833]
-        assert (
-            len(non_standard_orders) == 2
-        ), f"Expected 2 non-standard values due to counter reset, got {non_standard_orders}"
-        # post-reset value should be higher (new counter value / interval)
-        high_rate_orders = [v for v in non_standard_orders if v > 0.0833]
-        assert (
-            len(high_rate_orders) == 1
-        ), f"Expected one high rate value after counter reset, got {non_standard_orders}"
+    orders_values = endpoint_values["/orders"]
+    assert (
+        len(orders_values) == 60
+    ), f"Expected 59 values for /orders, got {len(orders_values)}"
+    count_steady_orders = sum(1 for v in orders_values if v["value"] == 0.0833)
+    assert (
+        count_steady_orders == 58
+    ), f"Expected 58 steady rate values (0.0833) for /orders, got {count_steady_orders}"
+    # check for counter reset effects - there should be some non-standard values
+    non_standard_orders = [v["value"] for v in orders_values if v["value"] != 0.0833]
+    assert (
+        len(non_standard_orders) == 2
+    ), f"Expected 2 non-standard values due to counter reset, got {non_standard_orders}"
+    # post-reset value should be higher (new counter value / interval)
+    high_rate_orders = [v for v in non_standard_orders if v > 0.0833]
+    assert (
+        len(high_rate_orders) == 1
+    ), f"Expected one high rate value after counter reset, got {non_standard_orders}"
 
     # /users: 56 data points (t05-t60), sparse +1 every 5 minutes (12 of them)
     # Rate = 1/60 = 0.0167 during increment, 0 during flat periods
-    if "/users" in endpoint_values:
-        users_values = endpoint_values["/users"]
-        assert (
-            len(users_values) == 56
-        ), f"Expected 56 values for /users, got {len(users_values)}"
-        count_zero_users = sum(1 for v in users_values if v["value"] == 0)
-        # most values should be 0 (flat periods between increments)
-        assert (
-            count_zero_users == 44
-        ), f"Expected 44 zero rate values for /users (sparse data), got {count_zero_users}"
-        # non-zero values should be 0.0167 (1/60 increment rate)
-        non_zero_users = [v["value"] for v in users_values if v["value"] != 0]
-        count_increment_rate = sum(1 for v in non_zero_users if v == 0.0167)
-        assert (
-            count_increment_rate == 12
-        ), f"Expected 12 increment rate values (0.0167) for /users, got {count_increment_rate}"
+    users_values = endpoint_values["/users"]
+    assert (
+        len(users_values) == 56
+    ), f"Expected 56 values for /users, got {len(users_values)}"
+    count_zero_users = sum(1 for v in users_values if v["value"] == 0)
+    # most values should be 0 (flat periods between increments)
+    assert (
+        count_zero_users == 44
+    ), f"Expected 44 zero rate values for /users (sparse data), got {count_zero_users}"
+    # non-zero values should be 0.0167 (1/60 increment rate)
+    non_zero_users = [v["value"] for v in users_values if v["value"] != 0]
+    count_increment_rate = sum(1 for v in non_zero_users if v == 0.0167)
+    assert (
+        count_increment_rate == 12
+    ), f"Expected 12 increment rate values (0.0167) for /users, got {count_increment_rate}"