fix: check for unknown temporality

pkg/querier/querier.go

@@ -269,7 +269,7 @@ func (q *querier) QueryRange(ctx context.Context, orgID valuer.UUID, req *qbtype
 	var metricTemporality map[string]metrictypes.Temporality
 	if len(metricNames) > 0 {
 		var err error
-		metricTemporality, err = q.metadataStore.FetchTemporalityMulti(ctx, metricNames...)
+		metricTemporality, err = q.metadataStore.FetchTemporalityMulti(ctx, req.Start, req.End, metricNames...)
 		if err != nil {
 			q.logger.WarnContext(ctx, "failed to fetch metric temporality", "error", err, "metrics", metricNames)
 			// Continue without temporality - statement builder will handle unspecified

pkg/query-service/app/clickhouseReader/reader.go

@@ -830,7 +830,7 @@ func (r *ClickHouseReader) GetUsage(ctx context.Context, queryParams *model.GetU
 
 func (r *ClickHouseReader) GetSpansForTrace(ctx context.Context, traceID string, traceDetailsQuery string) ([]model.SpanItemV2, *model.ApiError) {
 	var traceSummary model.TraceSummary
-	summaryQuery := fmt.Sprintf("SELECT trace_id, min(start) AS start, max(end) AS end, sum(num_spans) AS num_spans FROM %s.%s WHERE trace_id=$1 GROUP BY trace_id", r.TraceDB, r.traceSummaryTable)
+	summaryQuery := fmt.Sprintf("SELECT * from %s.%s FINAL WHERE trace_id=$1", r.TraceDB, r.traceSummaryTable)
 	err := r.db.QueryRow(ctx, summaryQuery, traceID).Scan(&traceSummary.TraceID, &traceSummary.Start, &traceSummary.End, &traceSummary.NumSpans)
 	if err != nil {
 		if err == sql.ErrNoRows {
@@ -6458,7 +6458,7 @@ func (r *ClickHouseReader) SearchTraces(ctx context.Context, params *model.Searc
 	}
 
 	var traceSummary model.TraceSummary
-	summaryQuery := fmt.Sprintf("SELECT trace_id, min(start) AS start, max(end) AS end, sum(num_spans) AS num_spans FROM %s.%s WHERE trace_id=$1 GROUP BY trace_id", r.TraceDB, r.traceSummaryTable)
+	summaryQuery := fmt.Sprintf("SELECT * from %s.%s FINAL WHERE trace_id=$1", r.TraceDB, r.traceSummaryTable)
 	err := r.db.QueryRow(ctx, summaryQuery, params.TraceID).Scan(&traceSummary.TraceID, &traceSummary.Start, &traceSummary.End, &traceSummary.NumSpans)
 	if err != nil {
 		if err == sql.ErrNoRows {

pkg/telemetrymetadata/metadata.go

@@ -10,6 +10,7 @@ import (
 	"github.com/SigNoz/signoz/pkg/factory"
 	"github.com/SigNoz/signoz/pkg/querybuilder"
 	"github.com/SigNoz/signoz/pkg/telemetrylogs"
+	"github.com/SigNoz/signoz/pkg/telemetrymeter"
 	"github.com/SigNoz/signoz/pkg/telemetrymetrics"
 	"github.com/SigNoz/signoz/pkg/telemetrystore"
 	"github.com/SigNoz/signoz/pkg/telemetrytraces"
@@ -1597,12 +1598,12 @@ func (t *telemetryMetaStore) GetAllValues(ctx context.Context, fieldValueSelecto
 	return values, complete, nil
 }
 
-func (t *telemetryMetaStore) FetchTemporality(ctx context.Context, metricName string) (metrictypes.Temporality, error) {
+func (t *telemetryMetaStore) FetchTemporality(ctx context.Context, queryTimeRangeStartTs, queryTimeRangeEndTs uint64, metricName string) (metrictypes.Temporality, error) {
 	if metricName == "" {
 		return metrictypes.Unknown, errors.Newf(errors.TypeInternal, errors.CodeInternal, "metric name cannot be empty")
 	}
 
-	temporalityMap, err := t.FetchTemporalityMulti(ctx, metricName)
+	temporalityMap, err := t.FetchTemporalityMulti(ctx, queryTimeRangeStartTs, queryTimeRangeEndTs, metricName)
 	if err != nil {
 		return metrictypes.Unknown, err
 	}
@@ -1615,27 +1616,35 @@ func (t *telemetryMetaStore) FetchTemporality(ctx context.Context, metricName st
 	return temporality, nil
 }
 
-func (t *telemetryMetaStore) FetchTemporalityMulti(ctx context.Context, metricNames ...string) (map[string]metrictypes.Temporality, error) {
+func (t *telemetryMetaStore) FetchTemporalityMulti(ctx context.Context, queryTimeRangeStartTs, queryTimeRangeEndTs uint64, metricNames ...string) (map[string]metrictypes.Temporality, error) {
 	if len(metricNames) == 0 {
 		return make(map[string]metrictypes.Temporality), nil
 	}
 
 	result := make(map[string]metrictypes.Temporality)
-	metricsTemporality, err := t.fetchMetricsTemporality(ctx, metricNames...)
+	metricsTemporality, err := t.fetchMetricsTemporality(ctx, queryTimeRangeStartTs, queryTimeRangeEndTs, metricNames...)
 	if err != nil {
 		return nil, err
 	}
 	// TODO: return error after table migration are run
-	meterMetricsTemporality, _ := t.fetchMeterSourceMetricsTemporality(ctx, metricNames...)
+	meterMetricsTemporality, _ := t.fetchMeterSourceMetricsTemporality(ctx, queryTimeRangeStartTs, queryTimeRangeEndTs, metricNames...)
 
 	// For metrics not found in the database, set to Unknown
 	for _, metricName := range metricNames {
-		if temporality, exists := metricsTemporality[metricName]; exists {
-			result[metricName] = temporality
+		if temporality, exists := metricsTemporality[metricName]; exists && len(temporality) > 0 {
+			if len(temporality) > 1 {
+				result[metricName] = metrictypes.Multiple
+			} else {
+				result[metricName] = temporality[0]
+			}
 			continue
 		}
-		if temporality, exists := meterMetricsTemporality[metricName]; exists {
-			result[metricName] = temporality
+		if temporality, exists := meterMetricsTemporality[metricName]; exists && len(temporality) > 0 {
+			if len(temporality) > 1 {
+				result[metricName] = metrictypes.Multiple
+			} else {
+				result[metricName] = temporality[0]
+			}
 			continue
 		}
 		result[metricName] = metrictypes.Unknown
@@ -1644,8 +1653,10 @@ func (t *telemetryMetaStore) FetchTemporalityMulti(ctx context.Context, metricNa
 	return result, nil
 }
 
-func (t *telemetryMetaStore) fetchMetricsTemporality(ctx context.Context, metricNames ...string) (map[string]metrictypes.Temporality, error) {
-	result := make(map[string]metrictypes.Temporality)
+func (t *telemetryMetaStore) fetchMetricsTemporality(ctx context.Context, queryTimeRangeStartTs, queryTimeRangeEndTs uint64, metricNames ...string) (map[string][]metrictypes.Temporality, error) {
+	result := make(map[string][]metrictypes.Temporality)
+
+	adjustedStartTs, adjustedEndTs, tsTableName, _ := telemetrymetrics.WhichTSTableToUse(queryTimeRangeStartTs, queryTimeRangeEndTs, nil)
 
 	// Build query to fetch temporality for all metrics
 	// We use attr_string_value where attr_name = '__temporality__'
@@ -1653,14 +1664,18 @@ func (t *telemetryMetaStore) fetchMetricsTemporality(ctx context.Context, metric
 	// and metric_name column contains temporality value, so we use the correct mapping
 	sb := sqlbuilder.Select(
 		"metric_name",
-		"argMax(temporality, last_reported_unix_milli) as temporality",
-	).From(t.metricsDBName + "." + t.metricsFieldsTblName)
+		"temporality",
+	).
+		From(t.metricsDBName + "." + tsTableName)
 
 	// Filter by metric names (in the temporality column due to data mix-up)
-	sb.Where(sb.In("metric_name", metricNames))
+	sb.Where(
+		sb.In("metric_name", metricNames),
+		sb.GTE("unix_milli", adjustedStartTs),
+		sb.LT("unix_milli", adjustedEndTs),
+	)
 
-	// Group by metric name to get one temporality per metric
-	sb.GroupBy("metric_name")
+	sb.GroupBy("metric_name", "temporality")
 
 	query, args := sb.BuildWithFlavor(sqlbuilder.ClickHouse)
 
@@ -1692,26 +1707,35 @@ func (t *telemetryMetaStore) fetchMetricsTemporality(ctx context.Context, metric
 			// Unknown or empty temporality
 			temporality = metrictypes.Unknown
 		}
-
-		result[metricName] = temporality
+		if temporality != metrictypes.Unknown {
+			result[metricName] = append(result[metricName], temporality)
+		}
+	}
+	if err := rows.Err(); err != nil {
+		return nil, errors.Wrapf(err, errors.TypeInternal, errors.CodeInternal, "error iterating over metrics temporality rows")
 	}
 
 	return result, nil
 }
 
-func (t *telemetryMetaStore) fetchMeterSourceMetricsTemporality(ctx context.Context, metricNames ...string) (map[string]metrictypes.Temporality, error) {
-	result := make(map[string]metrictypes.Temporality)
+func (t *telemetryMetaStore) fetchMeterSourceMetricsTemporality(ctx context.Context, queryTimeRangeStartTs, queryTimeRangeEndTs uint64, metricNames ...string) (map[string][]metrictypes.Temporality, error) {
+	result := make(map[string][]metrictypes.Temporality)
 
 	sb := sqlbuilder.Select(
 		"metric_name",
-		"argMax(temporality, unix_milli) as temporality",
-	).From(t.meterDBName + "." + t.meterFieldsTblName)
+		"temporality",
+	).
+		From(t.meterDBName + "." + t.meterFieldsTblName)
 
+	adjustedStartTs := telemetrymeter.AdjustStartTsForTSTable(queryTimeRangeStartTs, t.meterFieldsTblName)
 	// Filter by metric names (in the temporality column due to data mix-up)
-	sb.Where(sb.In("metric_name", metricNames))
+	sb.Where(
+		sb.In("metric_name", metricNames),
+		sb.GTE("unix_milli", adjustedStartTs),
+		sb.LT("unix_milli", queryTimeRangeEndTs),
+	)
 
-	// Group by metric name to get one temporality per metric
-	sb.GroupBy("metric_name")
+	sb.GroupBy("metric_name", "temporality")
 
 	query, args := sb.BuildWithFlavor(sqlbuilder.ClickHouse)
 
@@ -1743,8 +1767,12 @@ func (t *telemetryMetaStore) fetchMeterSourceMetricsTemporality(ctx context.Cont
 			// Unknown or empty temporality
 			temporality = metrictypes.Unknown
 		}
-
-		result[metricName] = temporality
+		if temporality != metrictypes.Unknown {
+			result[metricName] = append(result[metricName], temporality)
+		}
+	}
+	if err := rows.Err(); err != nil {
+		return nil, errors.Wrapf(err, errors.TypeInternal, errors.CodeInternal, "error iterating over meter temporality rows")
 	}
 
 	return result, nil

pkg/telemetrymeter/tables.go

@@ -15,6 +15,8 @@ const (
 )
 
 var (
+	oneHourInMilliseconds  = uint64(time.Hour.Milliseconds() * 1)
+	oneDayInMilliseconds   = uint64(time.Hour.Milliseconds() * 24)
 	oneMonthInMilliseconds = uint64(time.Hour.Milliseconds() * 24 * 30)
 
 	// when the query requests for almost 1 day, but not exactly 1 day, we need to add an offset to the end time
@@ -57,6 +59,13 @@ func WhichSamplesTableToUse(
 
 }
 
+func AdjustStartTsForTSTable(start uint64, table string) uint64 {
+	if table == SamplesTableName || table == SamplesLocalTableName {
+		return start - (start % oneHourInMilliseconds)
+	}
+	return start - (start % oneDayInMilliseconds)
+}
+
 func AggregationColumnForSamplesTable(
 	start, end uint64,
 	metricType metrictypes.Type,

pkg/telemetrymetrics/statement_builder.go

@@ -21,6 +21,10 @@ const (
 	RateWithoutNegative     = `If((per_series_value - lagInFrame(per_series_value, 1, 0) OVER rate_window) < 0, per_series_value / (ts - lagInFrame(ts, 1, toDateTime(fromUnixTimestamp64Milli(%d))) OVER rate_window), (per_series_value - lagInFrame(per_series_value, 1, 0) OVER rate_window) / (ts - lagInFrame(ts, 1, toDateTime(fromUnixTimestamp64Milli(%d))) OVER rate_window))`
 	IncreaseWithoutNegative = `If((per_series_value - lagInFrame(per_series_value, 1, 0) OVER rate_window) < 0, per_series_value, ((per_series_value - lagInFrame(per_series_value, 1, 0) OVER rate_window) / (ts - lagInFrame(ts, 1, toDateTime(fromUnixTimestamp64Milli(%d))) OVER rate_window)) * (ts - lagInFrame(ts, 1, toDateTime(fromUnixTimestamp64Milli(%d))) OVER rate_window))`
 
+	RateWithoutNegativeMultiTemporality     = `IF(LOWER(temporality) LIKE LOWER('delta'), %s, IF((%s - lagInFrame(%s, 1, 0) OVER rate_window) < 0, %s / (ts - lagInFrame(ts, 1, toDateTime(fromUnixTimestamp64Milli(%d))) OVER rate_window), (%s - lagInFrame(%s, 1, 0) OVER rate_window) / (ts - lagInFrame(ts, 1, toDateTime(fromUnixTimestamp64Milli(%d))) OVER rate_window))) AS per_series_value`
+	IncreaseWithoutNegativeMultiTemporality = `IF(LOWER(temporality) LIKE LOWER('delta'), %s, IF((%s - lagInFrame(%s, 1, 0) OVER rate_window) < 0, %s, ((%s - lagInFrame(%s, 1, 0) OVER rate_window) / (ts - lagInFrame(ts, 1, toDateTime(fromUnixTimestamp64Milli(%d))) OVER rate_window)) * (ts - lagInFrame(ts, 1, toDateTime(fromUnixTimestamp64Milli(%d))) OVER rate_window))) AS per_series_value`
+	OthersMultiTemporality                  = `IF(LOWER(temporality) LIKE LOWER('delta'), %s, %s) AS per_series_value`
+
 	RateWithInterpolation = `
 		CASE 
 			WHEN row_number() OVER rate_window = 1 THEN 
@@ -377,7 +381,7 @@ func (b *MetricQueryStatementBuilder) buildTimeSeriesCTE(
 		sb.LTE("unix_milli", end),
 	)
 
-	if query.Aggregations[0].Temporality != metrictypes.Unknown {
+	if query.Aggregations[0].Temporality != metrictypes.Multiple && query.Aggregations[0].Temporality != metrictypes.Unknown {
 		sb.Where(sb.ILike("temporality", query.Aggregations[0].Temporality.StringValue()))
 	}
 
@@ -407,8 +411,10 @@ func (b *MetricQueryStatementBuilder) buildTemporalAggregationCTE(
 ) (string, []any, error) {
 	if query.Aggregations[0].Temporality == metrictypes.Delta {
 		return b.buildTemporalAggDelta(ctx, start, end, query, timeSeriesCTE, timeSeriesCTEArgs)
+	} else if query.Aggregations[0].Temporality != metrictypes.Multiple {
+		return b.buildTemporalAggCumulativeOrUnspecified(ctx, start, end, query, timeSeriesCTE, timeSeriesCTEArgs)
 	}
-	return b.buildTemporalAggCumulativeOrUnspecified(ctx, start, end, query, timeSeriesCTE, timeSeriesCTEArgs)
+	return b.buildTemporalAggForMultipleTemporalities(ctx, start, end, query, timeSeriesCTE, timeSeriesCTEArgs)
 }
 
 func (b *MetricQueryStatementBuilder) buildTemporalAggDelta(
@@ -528,6 +534,57 @@ func (b *MetricQueryStatementBuilder) buildTemporalAggCumulativeOrUnspecified(
 	}
 }
 
+func (b *MetricQueryStatementBuilder) buildTemporalAggForMultipleTemporalities(
+	_ context.Context,
+	start, end uint64,
+	query qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation],
+	timeSeriesCTE string,
+	timeSeriesCTEArgs []any,
+) (string, []any, error) {
+	stepSec := int64(query.StepInterval.Seconds())
+	sb := sqlbuilder.NewSelectBuilder()
+
+	sb.SelectMore(fmt.Sprintf(
+		"toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(%d)) AS ts",
+		stepSec,
+	))
+	for _, g := range query.GroupBy {
+		sb.SelectMore(fmt.Sprintf("`%s`", g.TelemetryFieldKey.Name))
+	}
+
+	aggForDeltaTemporality := AggregationColumnForSamplesTable(start, end, query.Aggregations[0].Type, metrictypes.Delta, query.Aggregations[0].TimeAggregation, query.Aggregations[0].TableHints)
+	aggForCumulativeTemporality := AggregationColumnForSamplesTable(start, end, query.Aggregations[0].Type, metrictypes.Cumulative, query.Aggregations[0].TimeAggregation, query.Aggregations[0].TableHints)
+	if query.Aggregations[0].TimeAggregation == metrictypes.TimeAggregationRate {
+		aggForDeltaTemporality = fmt.Sprintf("%s/%d", aggForDeltaTemporality, stepSec)
+	}
+
+	switch query.Aggregations[0].TimeAggregation {
+	case metrictypes.TimeAggregationRate:
+		rateExpr := fmt.Sprintf(RateWithoutNegativeMultiTemporality, aggForDeltaTemporality, aggForCumulativeTemporality, aggForCumulativeTemporality, aggForCumulativeTemporality, start, aggForCumulativeTemporality, aggForCumulativeTemporality, start)
+		sb.SelectMore(rateExpr)
+	case metrictypes.TimeAggregationIncrease:
+		increaseExpr := fmt.Sprintf(IncreaseWithoutNegativeMultiTemporality, aggForDeltaTemporality, aggForCumulativeTemporality, aggForCumulativeTemporality, aggForCumulativeTemporality, aggForCumulativeTemporality, aggForCumulativeTemporality, start, start)
+		sb.SelectMore(increaseExpr)
+	default:
+		expr := fmt.Sprintf(OthersMultiTemporality, aggForDeltaTemporality, aggForCumulativeTemporality)
+		sb.SelectMore(expr)
+	}
+
+	tbl := WhichSamplesTableToUse(start, end, query.Aggregations[0].Type, query.Aggregations[0].TimeAggregation, query.Aggregations[0].TableHints)
+	sb.From(fmt.Sprintf("%s.%s AS points", DBName, tbl))
+	sb.JoinWithOption(sqlbuilder.InnerJoin, timeSeriesCTE, "points.fingerprint = filtered_time_series.fingerprint")
+	sb.Where(
+		sb.In("metric_name", query.Aggregations[0].MetricName),
+		sb.GTE("unix_milli", start),
+		sb.LT("unix_milli", end),
+	)
+	sb.GroupBy("fingerprint", "ts", "temporality")
+	sb.GroupBy(querybuilder.GroupByKeys(query.GroupBy)...)
+	queryWithoutWindow, args := sb.BuildWithFlavor(sqlbuilder.ClickHouse, timeSeriesCTEArgs...)
+	queryWithWindowAndOrder := queryWithoutWindow + " WINDOW rate_window AS (PARTITION BY fingerprint ORDER BY fingerprint ASC, ts ASC) ORDER BY ts"
+	return fmt.Sprintf("__temporal_aggregation_cte AS (%s)", queryWithWindowAndOrder), args, nil
+}
+
 func (b *MetricQueryStatementBuilder) buildSpatialAggregationCTE(
 	_ context.Context,
 	_ uint64,

pkg/types/metrictypes/metrictypes.go

@@ -19,6 +19,7 @@ var (
 	Cumulative  = Temporality{valuer.NewString("cumulative")}
 	Unspecified = Temporality{valuer.NewString("unspecified")}
 	Unknown     = Temporality{valuer.NewString("")}
+	Multiple    = Temporality{valuer.NewString("__multiple__")}
 )
 
 func (t Temporality) Value() (driver.Value, error) {

pkg/types/telemetrytypes/store.go

@@ -27,10 +27,10 @@ type MetadataStore interface {
 	GetAllValues(ctx context.Context, fieldValueSelector *FieldValueSelector) (*TelemetryFieldValues, bool, error)
 
 	// FetchTemporality fetches the temporality for metric
-	FetchTemporality(ctx context.Context, metricName string) (metrictypes.Temporality, error)
+	FetchTemporality(ctx context.Context, queryTimeRangeStartTs, queryTimeRangeEndTs uint64, metricName string) (metrictypes.Temporality, error)
 
 	// FetchTemporalityMulti fetches the temporality for multiple metrics
-	FetchTemporalityMulti(ctx context.Context, metricNames ...string) (map[string]metrictypes.Temporality, error)
+	FetchTemporalityMulti(ctx context.Context, queryTimeRangeStartTs, queryTimeRangeEndTs uint64, metricNames ...string) (map[string]metrictypes.Temporality, error)
 
 	// ListLogsJSONIndexes lists the JSON indexes for the logs table.
 	ListLogsJSONIndexes(ctx context.Context, filters ...string) (map[string][]schemamigrator.Index, error)

pkg/types/telemetrytypes/telemetrytypestest/metadata_store.go

@@ -265,7 +265,7 @@ func (m *MockMetadataStore) SetAllValues(lookupKey string, values *telemetrytype
 }
 
 // FetchTemporality fetches the temporality for a metric
-func (m *MockMetadataStore) FetchTemporality(ctx context.Context, metricName string) (metrictypes.Temporality, error) {
+func (m *MockMetadataStore) FetchTemporality(ctx context.Context, queryTimeRangeStartTs, queryTimeRangeEndTs uint64, metricName string) (metrictypes.Temporality, error) {
 	if temporality, exists := m.TemporalityMap[metricName]; exists {
 		return temporality, nil
 	}
@@ -273,7 +273,7 @@ func (m *MockMetadataStore) FetchTemporality(ctx context.Context, metricName str
 }
 
 // FetchTemporalityMulti fetches the temporality for multiple metrics
-func (m *MockMetadataStore) FetchTemporalityMulti(ctx context.Context, metricNames ...string) (map[string]metrictypes.Temporality, error) {
+func (m *MockMetadataStore) FetchTemporalityMulti(ctx context.Context, queryTimeRangeStartTs, queryTimeRangeEndTs uint64, metricNames ...string) (map[string]metrictypes.Temporality, error) {
 	result := make(map[string]metrictypes.Temporality)
 
 	for _, metricName := range metricNames {

tests/integration/fixtures/querier.py

@@ -52,7 +52,7 @@ def build_builder_query(
     time_aggregation: str,
     space_aggregation: str,
     *,
-    temporality: str = "cumulative",
+    temporality: Optional[str] = None,
     step_interval: int = DEFAULT_STEP_INTERVAL,
     group_by: Optional[List[str]] = None,
     filter_expression: Optional[str] = None,
@@ -65,7 +65,6 @@ def build_builder_query(
         "aggregations": [
             {
                 "metricName": metric_name,
-                "temporality": temporality,
                 "timeAggregation": time_aggregation,
                 "spaceAggregation": space_aggregation,
             }
@@ -73,6 +72,8 @@ def build_builder_query(
         "stepInterval": step_interval,
         "disabled": disabled,
     }
+    if temporality:
+        spec["aggregations"][0]["temporality"] = temporality
 
     if group_by:
         spec["groupBy"] = [

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

@@ -0,0 +1,396 @@
+"""
+Look at the multi_temporality_counters_1h.jsonl file for the relevant data
+"""
+
+import os
+from datetime import datetime, timedelta, timezone
+from http import HTTPStatus
+import random
+from typing import Any, Callable, List
+import pytest
+
+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,
+    get_all_series,
+    get_series_values,
+    make_query_request,
+)
+from fixtures.utils import get_testdata_file_path
+
+MULTI_TEMPORALITY_FILE = get_testdata_file_path("multi_temporality_counters_1h.jsonl")
+MULTI_TEMPORALITY_FILE_10h = get_testdata_file_path("multi_temporality_counters_10h.jsonl")
+MULTI_TEMPORALITY_FILE_24h = get_testdata_file_path("multi_temporality_counters_24h.jsonl")
+
+@pytest.mark.parametrize(
+    "time_aggregation, expected_value_at_31st_minute, expected_value_at_32nd_minute, steady_value",
+    [
+        ("rate", 0.0167, 0.133, 0.0833),
+        ("increase", 2, 8, 5),
+    ],
+)
+def test_with_steady_values_and_reset(
+    signoz: types.SigNoz,
+    create_user_admin: None,  # pylint: disable=unused-argument
+    get_token: Callable[[str, str], str],
+    insert_metrics: Callable[[List[Metrics]], None],
+    time_aggregation: str,
+    expected_value_at_31st_minute: float,
+    expected_value_at_32nd_minute: float,
+    steady_value: float
+) -> 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}_stale"
+
+    metrics = Metrics.load_from_file(
+        MULTI_TEMPORALITY_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,
+        time_aggregation,
+        "sum",
+        filter_expression='endpoint = "/orders"',
+    )
+
+    response = make_query_request(signoz, token, start_ms, end_ms, [query])
+    assert response.status_code == HTTPStatus.OK
+
+    data = response.json()
+    result_values = sorted(get_series_values(data, "A"), key=lambda x: x["timestamp"])
+    assert len(result_values) >= 59
+    # the counter reset happened at 31st minute
+    assert (
+        result_values[30]["value"] == expected_value_at_31st_minute
+    )
+    assert (
+        result_values[31]["value"] == expected_value_at_32nd_minute
+    )
+    assert (
+        result_values[39]["value"] == steady_value
+    ) # 39th minute is when cumulative shifts to delta
+    count_of_steady_rate = sum(1 for v in result_values if v["value"] == steady_value)
+    assert (
+        count_of_steady_rate >= 56
+    )  # 59 - (1 reset + 1 high rate + 1 at the beginning)
+    # All rates should be non-negative (stale periods = 0 rate)
+    for v in result_values:
+        assert v["value"] >= 0, f"{time_aggregation} should not be negative: {v['value']}"
+
+@pytest.mark.parametrize(
+    "time_aggregation, stable_health_value, stable_products_value, stable_checkout_value, spike_checkout_value, stable_orders_value, spike_users_value",
+    [
+        ("rate", 0.167, 0.333, 0.0167, 0.833, 0.0833, 0.0167),
+        ("increase", 10, 20, 1, 50, 5, 1),
+    ],
+)
+def test_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],
+    time_aggregation: str,
+    stable_health_value: float,
+    stable_products_value: float,
+    stable_checkout_value: float,
+    spike_checkout_value: float,
+    stable_orders_value: float,
+    spike_users_value: float,
+) -> 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"
+
+    metrics = Metrics.load_from_file(
+        MULTI_TEMPORALITY_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,
+        time_aggregation,
+        "sum",
+        group_by=["endpoint"],
+    )
+
+    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")
+    # Should have 5 different endpoints
+    assert (
+        len(all_series) == 5
+    ), f"Expected 5 series for 5 endpoints, got {len(all_series)}"
+
+    # endpoint -> values
+    endpoint_values = {}
+    for series in all_series:
+        endpoint = series.get("labels", [{}])[0].get("value", "unknown")
+        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:
+            assert (
+                v["value"] >= 0
+            ), f"Rate for {endpoint} should not be negative: {v['value']}"
+
+    # /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(
+    "time_aggregation, expected_value_at_30th_minute, expected_value_at_31st_minute, steady_value",
+    [
+        ("rate", 0.183, 0.183, 0.25),
+        ("increase", 11, 12, 15),
+    ],
+)
+def test_for_service_with_switch(
+    signoz: types.SigNoz,
+    create_user_admin: None,  # pylint: disable=unused-argument
+    get_token: Callable[[str, str], str],
+    insert_metrics: Callable[[List[Metrics]], None],
+    time_aggregation: str,
+    expected_value_at_30th_minute: float,
+    expected_value_at_31st_minute: float,
+    steady_value: float
+) -> 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}_count"
+
+    metrics = Metrics.load_from_file(
+        MULTI_TEMPORALITY_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,
+        time_aggregation,
+        "sum",
+        filter_expression='service = "api"',
+    )
+
+    response = make_query_request(signoz, token, start_ms, end_ms, [query])
+    assert response.status_code == HTTPStatus.OK
+
+    data = response.json()
+    result_values = sorted(get_series_values(data, "A"), key=lambda x: x["timestamp"])
+    assert len(result_values) >= 60
+    assert (
+        result_values[30]["value"] == expected_value_at_30th_minute #0.183
+    ) 
+    assert (
+        result_values[31]["value"] == expected_value_at_31st_minute # 0.183
+    )
+    assert (
+        result_values[39]["value"] == steady_value # 0.25
+    ) # 39th minute is when cumulative shifts to delta
+    # All rates should be non-negative (stale periods = 0 rate)
+    for v in result_values:
+        assert v["value"] >= 0, f"{time_aggregation} should not be negative: {v['value']}"
+
+@pytest.mark.parametrize(
+    "time_aggregation, expected_value",
+    [
+        ("rate", 0.0122),
+        ("increase", 22),
+    ],
+)
+def test_for_week_long_time_range(
+    signoz: types.SigNoz,
+    create_user_admin: None,  # pylint: disable=unused-argument
+    get_token: Callable[[str, str], str],
+    insert_metrics: Callable[[List[Metrics]], None],
+    time_aggregation: str,
+    expected_value: float,
+) -> None:
+    now = datetime.now(tz=timezone.utc).replace(second=0, microsecond=0)
+    start_ms = int((now - timedelta(days=7)).timestamp() * 1000)
+    end_ms = int(now.timestamp() * 1000)
+    metric_name = f"test_{time_aggregation}_" + hex(random.getrandbits(32))[2:]
+
+    metrics = Metrics.load_from_file(
+        MULTI_TEMPORALITY_FILE_10h,
+        base_time=now - timedelta(minutes=600),
+        metric_name_override=metric_name,
+    )
+    insert_metrics(metrics)
+
+    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
+    query = build_builder_query(
+        "A",
+        metric_name,
+        time_aggregation,
+        "sum",
+    )
+
+    response = make_query_request(signoz, token, start_ms, end_ms, [query])
+    assert response.status_code == HTTPStatus.OK
+    data = response.json()
+    result_values = sorted(get_series_values(data, "A"), key=lambda x: x["timestamp"])
+    # the zeroth value may not cover the whole time range
+    for value in result_values[1:]:
+        assert value["value"] == expected_value
+
+@pytest.mark.parametrize(
+    "time_aggregation, expected_value",
+    [
+        ("rate", 0.0122),
+        ("increase", 110),
+    ],
+)
+def test_for_month_long_time_range(
+    signoz: types.SigNoz,
+    create_user_admin: None,  # pylint: disable=unused-argument
+    get_token: Callable[[str, str], str],
+    insert_metrics: Callable[[List[Metrics]], None],
+    time_aggregation: str,
+    expected_value: float,
+) -> None:
+    now = datetime.now(tz=timezone.utc).replace(second=0, microsecond=0)
+    start_ms = int((now - timedelta(days=31)).timestamp() * 1000)
+    end_ms = int(now.timestamp() * 1000)
+    metric_name = f"test_{time_aggregation}_" + hex(random.getrandbits(32))[2:]
+
+    metrics = Metrics.load_from_file(
+        MULTI_TEMPORALITY_FILE_24h,
+        base_time=now - timedelta(minutes=600),
+        metric_name_override=metric_name,
+    )
+    insert_metrics(metrics)
+
+    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
+    query = build_builder_query(
+        "A",
+        metric_name,
+        time_aggregation,
+        "sum",
+    )
+
+    response = make_query_request(signoz, token, start_ms, end_ms, [query])
+    assert response.status_code == HTTPStatus.OK
+    data = response.json()
+    result_values = sorted(get_series_values(data, "A"), key=lambda x: x["timestamp"])
+    ## the zeroth and last values may not cover the whole 9000 seconds
+    for value in result_values[1:-1]:
+        assert value["value"] == expected_value

tests/integration/testdata/multi_temporality_counters_10h.jsonl

@@ -0,0 +1,84 @@
+{"metric_name":"http.request.count","labels":{"__temporality__":"Cumulative","service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:00:00+00:00","value":10,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
+{"metric_name":"http.request.count","labels":{"__temporality__":"Cumulative","service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:30:00+00:00","value":20,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
+{"metric_name":"http.request.count","labels":{"__temporality__":"Cumulative","service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T11:00:00+00:00","value":30,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
+{"metric_name":"http.request.count","labels":{"__temporality__":"Cumulative","service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T11:30:00+00:00","value":40,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
+{"metric_name":"http.request.count","labels":{"__temporality__":"Delta","service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T12:00:00+00:00","value":10,"temporality":"Delta","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
+{"metric_name":"http.request.count","labels":{"__temporality__":"Delta","service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T12:30:00+00:00","value":10,"temporality":"Delta","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
+{"metric_name":"http.request.count","labels":{"__temporality__":"Delta","service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T13:00:00+00:00","value":10,"temporality":"Delta","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
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