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18 Commits
test/uplot
...
nv/6175
| Author | SHA1 | Date | |
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660e454dbf | ||
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b820cf9b81 |
@@ -269,7 +269,7 @@ func (q *querier) QueryRange(ctx context.Context, orgID valuer.UUID, req *qbtype
|
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var metricTemporality map[string]metrictypes.Temporality
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if len(metricNames) > 0 {
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var err error
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metricTemporality, err = q.metadataStore.FetchTemporalityMulti(ctx, metricNames...)
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metricTemporality, err = q.metadataStore.FetchTemporalityMulti(ctx, req.Start, req.End, metricNames...)
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if err != nil {
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q.logger.WarnContext(ctx, "failed to fetch metric temporality", "error", err, "metrics", metricNames)
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// Continue without temporality - statement builder will handle unspecified
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@@ -1597,12 +1597,12 @@ func (t *telemetryMetaStore) GetAllValues(ctx context.Context, fieldValueSelecto
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return values, complete, nil
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}
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func (t *telemetryMetaStore) FetchTemporality(ctx context.Context, metricName string) (metrictypes.Temporality, error) {
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func (t *telemetryMetaStore) FetchTemporality(ctx context.Context, queryTimeRangeStartTs, queryTimeRangeEndTs uint64, metricName string) (metrictypes.Temporality, error) {
|
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if metricName == "" {
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return metrictypes.Unknown, errors.Newf(errors.TypeInternal, errors.CodeInternal, "metric name cannot be empty")
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}
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|
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temporalityMap, err := t.FetchTemporalityMulti(ctx, metricName)
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temporalityMap, err := t.FetchTemporalityMulti(ctx, queryTimeRangeStartTs, queryTimeRangeEndTs, metricName)
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if err != nil {
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return metrictypes.Unknown, err
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}
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@@ -1615,27 +1615,35 @@ func (t *telemetryMetaStore) FetchTemporality(ctx context.Context, metricName st
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return temporality, nil
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}
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func (t *telemetryMetaStore) FetchTemporalityMulti(ctx context.Context, metricNames ...string) (map[string]metrictypes.Temporality, error) {
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func (t *telemetryMetaStore) FetchTemporalityMulti(ctx context.Context, queryTimeRangeStartTs, queryTimeRangeEndTs uint64, metricNames ...string) (map[string]metrictypes.Temporality, error) {
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if len(metricNames) == 0 {
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return make(map[string]metrictypes.Temporality), nil
|
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}
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|
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result := make(map[string]metrictypes.Temporality)
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metricsTemporality, err := t.fetchMetricsTemporality(ctx, metricNames...)
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metricsTemporality, err := t.fetchMetricsTemporality(ctx, queryTimeRangeStartTs, queryTimeRangeEndTs, metricNames...)
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if err != nil {
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return nil, err
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}
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// TODO: return error after table migration are run
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meterMetricsTemporality, _ := t.fetchMeterSourceMetricsTemporality(ctx, metricNames...)
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meterMetricsTemporality, _ := t.fetchMeterSourceMetricsTemporality(ctx, queryTimeRangeStartTs, queryTimeRangeEndTs, metricNames...)
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// For metrics not found in the database, set to Unknown
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for _, metricName := range metricNames {
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if temporality, exists := metricsTemporality[metricName]; exists {
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result[metricName] = temporality
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if temporality, exists := metricsTemporality[metricName]; exists && len(temporality) > 0 {
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if len(temporality) > 1 {
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result[metricName] = metrictypes.Multiple
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} else {
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result[metricName] = temporality[0]
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}
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continue
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}
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if temporality, exists := meterMetricsTemporality[metricName]; exists {
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result[metricName] = temporality
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if temporality, exists := meterMetricsTemporality[metricName]; exists && len(temporality) > 0 {
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if len(temporality) > 1 {
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result[metricName] = metrictypes.Multiple
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} else {
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result[metricName] = temporality[0]
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}
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continue
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}
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result[metricName] = metrictypes.Unknown
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@@ -1644,8 +1652,10 @@ func (t *telemetryMetaStore) FetchTemporalityMulti(ctx context.Context, metricNa
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return result, nil
|
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}
|
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|
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func (t *telemetryMetaStore) fetchMetricsTemporality(ctx context.Context, metricNames ...string) (map[string]metrictypes.Temporality, error) {
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result := make(map[string]metrictypes.Temporality)
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func (t *telemetryMetaStore) fetchMetricsTemporality(ctx context.Context, queryTimeRangeStartTs, queryTimeRangeEndTs uint64, metricNames ...string) (map[string][]metrictypes.Temporality, error) {
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result := make(map[string][]metrictypes.Temporality)
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_, _, _, tsTableName := telemetrymetrics.WhichTSTableToUse(queryTimeRangeStartTs, queryTimeRangeEndTs, nil)
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// Build query to fetch temporality for all metrics
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// We use attr_string_value where attr_name = '__temporality__'
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@@ -1653,14 +1663,18 @@ func (t *telemetryMetaStore) fetchMetricsTemporality(ctx context.Context, metric
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// and metric_name column contains temporality value, so we use the correct mapping
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sb := sqlbuilder.Select(
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"metric_name",
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"argMax(temporality, last_reported_unix_milli) as temporality",
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).From(t.metricsDBName + "." + t.metricsFieldsTblName)
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"temporality",
|
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).
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From(t.metricsDBName + "." + tsTableName)
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// Filter by metric names (in the temporality column due to data mix-up)
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sb.Where(sb.In("metric_name", metricNames))
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sb.Where(
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sb.In("metric_name", metricNames),
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sb.GTE("unix_milli", queryTimeRangeStartTs),
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sb.LT("unix_milli", queryTimeRangeEndTs),
|
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)
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// Group by metric name to get one temporality per metric
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sb.GroupBy("metric_name")
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sb.GroupBy("metric_name", "temporality")
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query, args := sb.BuildWithFlavor(sqlbuilder.ClickHouse)
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@@ -1693,25 +1707,30 @@ func (t *telemetryMetaStore) fetchMetricsTemporality(ctx context.Context, metric
|
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temporality = metrictypes.Unknown
|
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}
|
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|
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result[metricName] = temporality
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result[metricName] = append(result[metricName], temporality)
|
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}
|
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|
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return result, nil
|
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}
|
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|
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func (t *telemetryMetaStore) fetchMeterSourceMetricsTemporality(ctx context.Context, metricNames ...string) (map[string]metrictypes.Temporality, error) {
|
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result := make(map[string]metrictypes.Temporality)
|
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func (t *telemetryMetaStore) fetchMeterSourceMetricsTemporality(ctx context.Context, queryTimeRangeStartTs, queryTimeRangeEndTs uint64, metricNames ...string) (map[string][]metrictypes.Temporality, error) {
|
||||
result := make(map[string][]metrictypes.Temporality)
|
||||
|
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sb := sqlbuilder.Select(
|
||||
"metric_name",
|
||||
"argMax(temporality, unix_milli) as temporality",
|
||||
).From(t.meterDBName + "." + t.meterFieldsTblName)
|
||||
"temporality",
|
||||
).
|
||||
Distinct().
|
||||
From(t.meterDBName + "." + 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", queryTimeRangeStartTs),
|
||||
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)
|
||||
|
||||
@@ -1744,7 +1763,7 @@ func (t *telemetryMetaStore) fetchMeterSourceMetricsTemporality(ctx context.Cont
|
||||
temporality = metrictypes.Unknown
|
||||
}
|
||||
|
||||
result[metricName] = temporality
|
||||
result[metricName] = append(result[metricName], temporality)
|
||||
}
|
||||
|
||||
return result, nil
|
||||
|
||||
@@ -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,
|
||||
|
||||
@@ -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) {
|
||||
|
||||
@@ -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)
|
||||
|
||||
@@ -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 {
|
||||
|
||||
@@ -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"] = [
|
||||
|
||||
396
tests/integration/src/querier/07_metrics_multi_temporality.py
Normal file
396
tests/integration/src/querier/07_metrics_multi_temporality.py
Normal file
@@ -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[Any](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
|
||||
84
tests/integration/testdata/multi_temporality_counters_10h.jsonl
vendored
Normal file
84
tests/integration/testdata/multi_temporality_counters_10h.jsonl
vendored
Normal file
@@ -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":{}}
|
||||
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|
||||
288
tests/integration/testdata/multi_temporality_counters_1h.jsonl
vendored
Normal file
288
tests/integration/testdata/multi_temporality_counters_1h.jsonl
vendored
Normal file
@@ -0,0 +1,288 @@
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|
||||
196
tests/integration/testdata/multi_temporality_counters_24h.jsonl
vendored
Normal file
196
tests/integration/testdata/multi_temporality_counters_24h.jsonl
vendored
Normal file
@@ -0,0 +1,196 @@
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{"metric_name":"http.request.count","labels":{"__temporality__":"Cumulative","service":"web","endpoint":"/checkout","status_code":"200"},"timestamp":"2025-01-10T21:30:00+00:00","value":44,"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":"web","endpoint":"/checkout","status_code":"200"},"timestamp":"2025-01-10T22:00:00+00:00","value":45,"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":"web","endpoint":"/checkout","status_code":"200"},"timestamp":"2025-01-10T22:30:00+00:00","value":46,"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":"web","endpoint":"/checkout","status_code":"200"},"timestamp":"2025-01-10T23:00:00+00:00","value":47,"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":"web","endpoint":"/checkout","status_code":"200"},"timestamp":"2025-01-10T23:30:00+00:00","value":48,"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":"web","endpoint":"/checkout","status_code":"200"},"timestamp":"2025-01-11T24:00:00+00:00","value":49,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
|
||||
Reference in New Issue
Block a user