mirror of
https://git.mirrors.martin98.com/https://github.com/SigNoz/signoz
synced 2025-07-28 12:42:00 +08:00
220 lines
8.2 KiB
Go
220 lines
8.2 KiB
Go
package delta
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import (
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"fmt"
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"github.com/SigNoz/signoz/pkg/query-service/app/metrics/v4/helpers"
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"github.com/SigNoz/signoz/pkg/query-service/constants"
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v3 "github.com/SigNoz/signoz/pkg/query-service/model/v3"
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"github.com/SigNoz/signoz/pkg/query-service/utils"
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)
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// TODO(srikanthccv): support multiple quantiles; see https://github.com/SigNoz/signoz/issues/4016#issuecomment-1838583305
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var (
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sketchFmt = "quantilesDDMerge(0.01, %f)(sketch)[1]"
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)
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// prepareTimeAggregationSubQuery builds the sub-query to be used for temporal aggregation
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func prepareTimeAggregationSubQuery(start, end, step int64, mq *v3.BuilderQuery) (string, error) {
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var subQuery string
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timeSeriesSubQuery, err := helpers.PrepareTimeseriesFilterQuery(start, end, mq)
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if err != nil {
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return "", err
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}
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samplesTableFilter := fmt.Sprintf("metric_name IN %s AND unix_milli >= %d AND unix_milli < %d", utils.ClickHouseFormattedMetricNames(mq.AggregateAttribute.Key), start, end)
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tableName := helpers.WhichSamplesTableToUse(start, end, mq)
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samplesTableFilter = helpers.AddFlagsFilters(samplesTableFilter, tableName)
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// Select the aggregate value for interval
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queryTmpl :=
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"SELECT fingerprint, %s" +
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" toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), INTERVAL %d SECOND) as ts," +
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" %s as per_series_value" +
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" FROM " + constants.SIGNOZ_METRIC_DBNAME + "." + tableName +
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" INNER JOIN" +
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" (%s) as filtered_time_series" +
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" USING fingerprint" +
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" WHERE " + samplesTableFilter +
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" GROUP BY fingerprint, ts" +
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" ORDER BY fingerprint, ts"
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selectLabelsAny := helpers.SelectLabelsAny(mq.GroupBy)
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op := helpers.AggregationColumnForSamplesTable(start, end, mq)
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switch mq.TimeAggregation {
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case v3.TimeAggregationAvg:
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subQuery = fmt.Sprintf(queryTmpl, selectLabelsAny, step, op, timeSeriesSubQuery)
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case v3.TimeAggregationSum:
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subQuery = fmt.Sprintf(queryTmpl, selectLabelsAny, step, op, timeSeriesSubQuery)
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case v3.TimeAggregationMin:
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subQuery = fmt.Sprintf(queryTmpl, selectLabelsAny, step, op, timeSeriesSubQuery)
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case v3.TimeAggregationMax:
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subQuery = fmt.Sprintf(queryTmpl, selectLabelsAny, step, op, timeSeriesSubQuery)
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case v3.TimeAggregationCount:
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subQuery = fmt.Sprintf(queryTmpl, selectLabelsAny, step, op, timeSeriesSubQuery)
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case v3.TimeAggregationCountDistinct:
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subQuery = fmt.Sprintf(queryTmpl, selectLabelsAny, step, op, timeSeriesSubQuery)
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case v3.TimeAggregationAnyLast:
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subQuery = fmt.Sprintf(queryTmpl, selectLabelsAny, step, op, timeSeriesSubQuery)
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case v3.TimeAggregationRate:
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op := fmt.Sprintf("%s/%d", op, step)
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subQuery = fmt.Sprintf(queryTmpl, selectLabelsAny, step, op, timeSeriesSubQuery)
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case v3.TimeAggregationIncrease:
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subQuery = fmt.Sprintf(queryTmpl, selectLabelsAny, step, op, timeSeriesSubQuery)
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}
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return subQuery, nil
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}
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// See `canShortCircuit` below for details
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func prepareQueryOptimized(start, end, step int64, mq *v3.BuilderQuery) (string, error) {
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groupBy := helpers.GroupingSetsByAttributeKeyTags(mq.GroupBy...)
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orderBy := helpers.OrderByAttributeKeyTags(mq.OrderBy, mq.GroupBy)
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selectLabels := helpers.SelectLabels(mq.GroupBy)
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var query string
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timeSeriesSubQuery, err := helpers.PrepareTimeseriesFilterQuery(start, end, mq)
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if err != nil {
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return "", err
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}
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samplesTableFilter := fmt.Sprintf("metric_name IN %s AND unix_milli >= %d AND unix_milli < %d", utils.ClickHouseFormattedMetricNames(mq.AggregateAttribute.Key), start, end)
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tableName := helpers.WhichSamplesTableToUse(start, end, mq)
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samplesTableFilter = helpers.AddFlagsFilters(samplesTableFilter, tableName)
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// Select the aggregate value for interval
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queryTmpl :=
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"SELECT %s" +
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" toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), INTERVAL %d SECOND) as ts," +
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" %s as value" +
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" FROM " + constants.SIGNOZ_METRIC_DBNAME + "." + tableName +
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" INNER JOIN" +
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" (%s) as filtered_time_series" +
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" USING fingerprint" +
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" WHERE " + samplesTableFilter +
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" GROUP BY %s" +
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" ORDER BY %s"
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switch mq.SpaceAggregation {
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case v3.SpaceAggregationSum:
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op := helpers.AggregationColumnForSamplesTable(start, end, mq)
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if mq.TimeAggregation == v3.TimeAggregationRate {
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op = fmt.Sprintf("%s/%d", op, step)
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}
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query = fmt.Sprintf(queryTmpl, selectLabels, step, op, timeSeriesSubQuery, groupBy, orderBy)
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case v3.SpaceAggregationMin:
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op := helpers.AggregationColumnForSamplesTable(start, end, mq)
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query = fmt.Sprintf(queryTmpl, selectLabels, step, op, timeSeriesSubQuery, groupBy, orderBy)
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case v3.SpaceAggregationMax:
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op := helpers.AggregationColumnForSamplesTable(start, end, mq)
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query = fmt.Sprintf(queryTmpl, selectLabels, step, op, timeSeriesSubQuery, groupBy, orderBy)
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case v3.SpaceAggregationPercentile50,
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v3.SpaceAggregationPercentile75,
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v3.SpaceAggregationPercentile90,
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v3.SpaceAggregationPercentile95,
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v3.SpaceAggregationPercentile99:
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op := fmt.Sprintf(sketchFmt, v3.GetPercentileFromOperator(mq.SpaceAggregation))
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query = fmt.Sprintf(queryTmpl, selectLabels, step, op, timeSeriesSubQuery, groupBy, orderBy)
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}
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return query, nil
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}
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// PrepareMetricQueryDeltaTimeSeries builds the query to be used for fetching metrics
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func PrepareMetricQueryDeltaTimeSeries(start, end, step int64, mq *v3.BuilderQuery) (string, error) {
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if canShortCircuit(mq) {
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return prepareQueryOptimized(start, end, step, mq)
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}
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var query string
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temporalAggSubQuery, err := prepareTimeAggregationSubQuery(start, end, step, mq)
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if err != nil {
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return "", err
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}
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groupBy := helpers.GroupingSetsByAttributeKeyTags(mq.GroupBy...)
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orderBy := helpers.OrderByAttributeKeyTags(mq.OrderBy, mq.GroupBy)
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selectLabels := helpers.GroupByAttributeKeyTags(mq.GroupBy...)
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valueFilter := " WHERE isNaN(per_series_value) = 0"
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if mq.MetricValueFilter != nil {
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valueFilter += fmt.Sprintf(" AND per_series_value = %f", mq.MetricValueFilter.Value)
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}
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queryTmpl :=
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"SELECT %s," +
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" %s as value" +
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" FROM (%s)" +
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valueFilter +
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" GROUP BY %s" +
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" ORDER BY %s"
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switch mq.SpaceAggregation {
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case v3.SpaceAggregationAvg:
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op := "avg(per_series_value)"
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query = fmt.Sprintf(queryTmpl, selectLabels, op, temporalAggSubQuery, groupBy, orderBy)
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case v3.SpaceAggregationSum:
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op := "sum(per_series_value)"
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query = fmt.Sprintf(queryTmpl, selectLabels, op, temporalAggSubQuery, groupBy, orderBy)
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case v3.SpaceAggregationMin:
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op := "min(per_series_value)"
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query = fmt.Sprintf(queryTmpl, selectLabels, op, temporalAggSubQuery, groupBy, orderBy)
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case v3.SpaceAggregationMax:
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op := "max(per_series_value)"
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query = fmt.Sprintf(queryTmpl, selectLabels, op, temporalAggSubQuery, groupBy, orderBy)
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case v3.SpaceAggregationCount:
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op := "count(per_series_value)"
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query = fmt.Sprintf(queryTmpl, selectLabels, op, temporalAggSubQuery, groupBy, orderBy)
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}
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return query, nil
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}
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// canShortCircuit returns true if we can use the optimized query
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// for the given query
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// This is used to avoid the group by fingerprint thus improving the performance
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// for certain queries
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// cases where we can short circuit:
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// 1. time aggregation = (rate|increase) and space aggregation = sum
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// - rate = sum(value)/step, increase = sum(value) - sum of sums is same as sum of all values
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//
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// 2. time aggregation = sum and space aggregation = sum
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// - sum of sums is same as sum of all values
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//
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// 3. time aggregation = min and space aggregation = min
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// - min of mins is same as min of all values
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//
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// 4. time aggregation = max and space aggregation = max
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// - max of maxs is same as max of all values
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//
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// 5. special case exphist, there is no need for per series/fingerprint aggregation
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// we can directly use the quantilesDDMerge function
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//
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// all of this is true only for delta metrics
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func canShortCircuit(mq *v3.BuilderQuery) bool {
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if (mq.TimeAggregation == v3.TimeAggregationRate || mq.TimeAggregation == v3.TimeAggregationIncrease) && mq.SpaceAggregation == v3.SpaceAggregationSum {
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return true
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}
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if mq.TimeAggregation == v3.TimeAggregationSum && mq.SpaceAggregation == v3.SpaceAggregationSum {
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return true
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}
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if mq.TimeAggregation == v3.TimeAggregationMin && mq.SpaceAggregation == v3.SpaceAggregationMin {
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return true
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}
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if mq.TimeAggregation == v3.TimeAggregationMax && mq.SpaceAggregation == v3.SpaceAggregationMax {
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return true
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}
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if mq.AggregateAttribute.Type == v3.AttributeKeyType(v3.MetricTypeExponentialHistogram) && v3.IsPercentileOperator(mq.SpaceAggregation) {
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return true
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}
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return false
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}
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