214 lines
11 KiB
Go
214 lines
11 KiB
Go
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package cumulative
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import (
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"fmt"
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"go.signoz.io/signoz/pkg/query-service/app/metrics/v4/helpers"
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"go.signoz.io/signoz/pkg/query-service/constants"
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v3 "go.signoz.io/signoz/pkg/query-service/model/v3"
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"go.signoz.io/signoz/pkg/query-service/utils"
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)
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// See https://clickhouse.com/docs/en/sql-reference/window-functions for more details on `lagInFrame` function
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//
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// Calculating the rate of change of a metric is a common use case.
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// Requests and errors are two examples of metrics that are often expressed as a rate of change.
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// The rate of change is the difference between the current value and the previous value divided by
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// the time difference between the current and previous values (i.e. the time interval).
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//
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// The value of a cumulative counter always increases. However, the rate of change can be negative
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// if the value decreases between two samples. This can happen if the counter is reset when the
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// application restarts or if the counter is reset manually. In this case, the rate of change is
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// not meaningful and should be ignored.
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//
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// The condition `(per_series_value - lagInFrame(per_series_value, 1, 0) OVER rate_window) < 0`
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// checks if the rate of change is negative. If it is negative, the value is replaced with `nan`.
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//
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// The condition `ts - lagInFrame(ts, 1, toDate('1970-01-01')) OVER rate_window) >= 86400` checks
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// if the time difference between the current and previous values is greater than or equal to 1 day.
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// The first sample of a metric is always `nan` because there is no previous value to compare it to.
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// When the first sample is encountered, the previous value for the time is set to default i.e `1970-01-01`.
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// Since any difference between the first sample timestamp and the previous value timestamp will be
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// greater than or equal to 1 day, the rate of change for the first sample will be `nan`.
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//
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// If neither of the above conditions are true, the rate of change is calculated as
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// `(per_series_value - lagInFrame(per_series_value, 1, 0) OVER rate_window) / (ts - lagInFrame(ts, 1, toDate('1970-01-01')) OVER rate_window)`
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// where `rate_window` is a window function that partitions the data by fingerprint and orders it by timestamp.
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// We want to calculate the rate of change for each time series, so we partition the data by fingerprint.
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//
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// The `increase` function is similar to the `rate` function, except that it does not divide by the time interval.
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const (
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rateWithoutNegative = `If((per_series_value - lagInFrame(per_series_value, 1, 0) OVER rate_window) < 0, nan, If((ts - lagInFrame(ts, 1, toDate('1970-01-01')) OVER rate_window) >= 86400, nan, (per_series_value - lagInFrame(per_series_value, 1, 0) OVER rate_window) / (ts - lagInFrame(ts, 1, toDate('1970-01-01')) OVER rate_window)))`
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increaseWithoutNegative = `If((per_series_value - lagInFrame(per_series_value, 1, 0) OVER rate_window) < 0, nan, If((ts - lagInFrame(ts, 1, toDate('1970-01-01')) OVER rate_window) >= 86400, nan, (per_series_value - lagInFrame(per_series_value, 1, 0) OVER rate_window)))`
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)
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// prepareTimeAggregationSubQueryTimeSeries prepares the sub-query to be used for temporal aggregation
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// of time series data
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// The following example illustrates how the sub-query is used to calculate the sume of values for each
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// time series in a 15 seconds interval:
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// ```
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// timestamp 01.00 01.05 01.10 01.15 01.20 01.25 01.30 01.35 01.40
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// +------+------+------+------+------+------+------+------+------+
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// | | | | | | | | | |
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// | v1 | v2 | v3 | v4 | v5 | v6 | v7 | v8 | v9 |
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// | | | | | | | | | |
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// +------+------+------+------+------+------+------+------+------+
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// | | | | | | | | |
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// | | | | | | | | |
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// | | |
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// +------+ +------+ +------+
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// | v1+ | | v4+ | | v7+ |
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// | v2+ | | v5+ | | v8+ |
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// | v3 | | v6 | | v9 |
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// +------+ +------+ +------+
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// 01.00 01.15 01.30
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// ```
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// Calculating the rate/increase involves an additional step. We first calculate the maximum value for each time series
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// in a 15 seconds interval. Then, we calculate the difference between the current maximum value and the previous
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// maximum value
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// The following example illustrates how the sub-query is used to calculate the rate of change for each time series
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// in a 15 seconds interval:
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// ```
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// timestamp 01.00 01.05 01.10 01.15 01.20 01.25 01.30 01.35 01.40
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// +------+------+------+------+------+------+------+------+------+
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// | | | | | | | | | |
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// | v1 | v2 | v3 | v4 | v5 | v6 | v7 | v8 | v9 |
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// | | | | | | | | | |
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// +------+------+------+------+------+------+------+------+------+
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// | | | | | | | | |
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// | | | | | | | | |
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// | | |
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// +------+ +------+ +------+
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// max(| v1, | max(| v4, | max(| v7, |
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// | v2, | | v5, | | v8, |
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// | v3 |) | v6 |) | v9 |)
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// +------+ +------+ +------+
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// 01.00 01.15 01.30
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// +-------+ +--------+
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// | V6-V2 | | V9-V6 |
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// | | | |
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// | | | |
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// +------+ +--------+
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// 01.00 01.15
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// ```
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// The rate of change is calculated as (Vy - Vx) / (Ty - Tx) where Vx and Vy are the values at time Tx and Ty respectively.
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// In an ideal scenario, the last value of each interval could be used to calculate the rate of change. Instead, we use
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// the maximum value of each interval to calculate the rate of change. This is because any process restart can cause the
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// value to be reset to 0. This will produce an inaccurate result. The max is the best approximation we can get.
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// We don't expect the process to restart very often, so this should be a good approximation.
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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 = %s AND unix_milli >= %d AND unix_milli < %d", utils.ClickHouseFormattedValue(mq.AggregateAttribute.Key), start, end)
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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 + "." + constants.SIGNOZ_SAMPLES_V4_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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selectLabels := helpers.SelectLabels(mq.GroupBy)
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switch mq.TimeAggregation {
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case v3.TimeAggregationAvg:
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op := "avg(value)"
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subQuery = fmt.Sprintf(queryTmpl, selectLabelsAny, step, op, timeSeriesSubQuery)
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case v3.TimeAggregationSum:
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op := "sum(value)"
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subQuery = fmt.Sprintf(queryTmpl, selectLabelsAny, step, op, timeSeriesSubQuery)
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case v3.TimeAggregationMin:
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op := "min(value)"
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subQuery = fmt.Sprintf(queryTmpl, selectLabelsAny, step, op, timeSeriesSubQuery)
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case v3.TimeAggregationMax:
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op := "max(value)"
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subQuery = fmt.Sprintf(queryTmpl, selectLabelsAny, step, op, timeSeriesSubQuery)
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case v3.TimeAggregationCount:
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op := "count(value)"
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subQuery = fmt.Sprintf(queryTmpl, selectLabelsAny, step, op, timeSeriesSubQuery)
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case v3.TimeAggregationCountDistinct:
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op := "count(distinct(value))"
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subQuery = fmt.Sprintf(queryTmpl, selectLabelsAny, step, op, timeSeriesSubQuery)
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case v3.TimeAggregationAnyLast:
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op := "anyLast(value)"
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subQuery = fmt.Sprintf(queryTmpl, selectLabelsAny, step, op, timeSeriesSubQuery)
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case v3.TimeAggregationRate:
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op := "max(value)"
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innerSubQuery := fmt.Sprintf(queryTmpl, selectLabelsAny, step, op, timeSeriesSubQuery)
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rateQueryTmpl :=
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"SELECT %s ts, " + rateWithoutNegative +
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" as per_series_value FROM (%s) WINDOW rate_window as (PARTITION BY fingerprint ORDER BY fingerprint, ts)"
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subQuery = fmt.Sprintf(rateQueryTmpl, selectLabels, innerSubQuery)
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case v3.TimeAggregationIncrease:
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op := "max(value)"
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innerSubQuery := fmt.Sprintf(queryTmpl, selectLabelsAny, step, op, timeSeriesSubQuery)
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rateQueryTmpl :=
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"SELECT %s ts, " + increaseWithoutNegative +
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" as per_series_value FROM (%s) WINDOW rate_window as (PARTITION BY fingerprint ORDER BY fingerprint, ts)"
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subQuery = fmt.Sprintf(rateQueryTmpl, selectLabels, innerSubQuery)
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}
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return subQuery, nil
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}
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// PrepareMetricQueryCumulativeTimeSeries prepares the query to be used for fetching metrics
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func PrepareMetricQueryCumulativeTimeSeries(start, end, step int64, mq *v3.BuilderQuery) (string, error) {
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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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queryTmpl :=
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"SELECT %s," +
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" %s as value" +
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" FROM (%s)" +
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" WHERE isNaN(per_series_value) = 0" +
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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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