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chore: add prepare query for cumulative/unspecified timeseries (#4166)
This commit is contained in:
Srikanth Chekuri
GitHub
parent
55664872bd
commit
29b1344557
57
pkg/query-service/app/metrics/v4/cumulative/helper.go
Normal file
57
pkg/query-service/app/metrics/v4/cumulative/helper.go
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@ -0,0 +1,57 @@
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package cumulative
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import (
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"fmt"
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"strings"
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v3 "go.signoz.io/signoz/pkg/query-service/model/v3"
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)
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// groupingSets returns a string of comma separated tags for group by clause
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// `ts` is always added to the group by clause
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func groupingSets(tags ...string) string {
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withTs := append(tags, "ts")
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return fmt.Sprintf(`GROUPING SETS ( (%s), (%s) )`, strings.Join(withTs, ", "), strings.Join(tags, ", "))
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}
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// groupingSetsByAttributeKeyTags returns a string of comma separated tags for group by clause
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func groupingSetsByAttributeKeyTags(tags ...v3.AttributeKey) string {
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groupTags := []string{}
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for _, tag := range tags {
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groupTags = append(groupTags, tag.Key)
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}
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return groupingSets(groupTags...)
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}
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// groupBy returns a string of comma separated tags for group by clause
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func groupByAttributeKeyTags(tags ...v3.AttributeKey) string {
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groupTags := []string{}
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for _, tag := range tags {
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groupTags = append(groupTags, tag.Key)
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}
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groupTags = append(groupTags, "ts")
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return strings.Join(groupTags, ", ")
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}
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// orderBy returns a string of comma separated tags for order by clause
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// if the order is not specified, it defaults to ASC
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func orderByAttributeKeyTags(items []v3.OrderBy, tags []v3.AttributeKey) string {
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var orderBy []string
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for _, tag := range tags {
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found := false
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for _, item := range items {
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if item.ColumnName == tag.Key {
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found = true
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orderBy = append(orderBy, fmt.Sprintf("%s %s", item.ColumnName, item.Order))
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break
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}
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}
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if !found {
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orderBy = append(orderBy, fmt.Sprintf("%s ASC", tag.Key))
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}
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}
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orderBy = append(orderBy, "ts ASC")
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return strings.Join(orderBy, ", ")
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}
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220
pkg/query-service/app/metrics/v4/cumulative/timeseries.go
Normal file
220
pkg/query-service/app/metrics/v4/cumulative/timeseries.go
Normal file
@ -0,0 +1,220 @@
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package cumulative
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import (
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"fmt"
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v4 "go.signoz.io/signoz/pkg/query-service/app/metrics/v4"
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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 prepareTimeAggregationSubQueryTimeSeries(start, end, step int64, mq *v3.BuilderQuery) (string, error) {
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var subQuery string
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timeSeriesSubQuery, err := v4.PrepareTimeseriesFilterQuery(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 timestamp_ms >= %d AND timestamp_ms <= %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(timestamp_ms, 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_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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var selectLabelsAny string
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for _, tag := range mq.GroupBy {
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selectLabelsAny += fmt.Sprintf("any(%s) as %s,", tag.Key, tag.Key)
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}
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var selectLabels string
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for _, tag := range mq.GroupBy {
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selectLabels += tag.Key + ","
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}
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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 := prepareTimeAggregationSubQueryTimeSeries(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 := groupingSetsByAttributeKeyTags(mq.GroupBy...)
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orderBy := orderByAttributeKeyTags(mq.OrderBy, mq.GroupBy)
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selectLabels := 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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229
pkg/query-service/app/metrics/v4/cumulative/timeseries_test.go
Normal file
229
pkg/query-service/app/metrics/v4/cumulative/timeseries_test.go
Normal file
@ -0,0 +1,229 @@
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package cumulative
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import (
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"testing"
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"github.com/stretchr/testify/assert"
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v3 "go.signoz.io/signoz/pkg/query-service/model/v3"
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)
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func TestPrepareTimeAggregationSubQuery(t *testing.T) {
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// The time aggregation is performed for each unique series - since the fingerprint represents the
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// unique hash of label set, we always group by fingerprint regardless of the GroupBy
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// This sub result is then aggregated on dimensions using the provided GroupBy clause keys
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testCases := []struct {
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name string
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builderQuery *v3.BuilderQuery
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start int64
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end int64
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expectedQueryContains string
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}{
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{
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name: "test time aggregation = avg, temporality = cumulative",
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builderQuery: &v3.BuilderQuery{
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QueryName: "A",
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StepInterval: 60,
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DataSource: v3.DataSourceMetrics,
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AggregateAttribute: v3.AttributeKey{
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Key: "http_requests",
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DataType: v3.AttributeKeyDataTypeFloat64,
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Type: v3.AttributeKeyTypeUnspecified,
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IsColumn: true,
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IsJSON: false,
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},
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Temporality: v3.Cumulative,
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Filters: &v3.FilterSet{
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Operator: "AND",
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Items: []v3.FilterItem{
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{
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Key: v3.AttributeKey{
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Key: "service_name",
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Type: v3.AttributeKeyTypeTag,
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DataType: v3.AttributeKeyDataTypeString,
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},
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Operator: v3.FilterOperatorNotEqual,
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Value: "payment_service",
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},
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{
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Key: v3.AttributeKey{
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Key: "endpoint",
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Type: v3.AttributeKeyTypeTag,
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DataType: v3.AttributeKeyDataTypeString,
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},
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Operator: v3.FilterOperatorIn,
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Value: []interface{}{"/paycallback", "/payme", "/paypal"},
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},
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},
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},
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GroupBy: []v3.AttributeKey{{
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Key: "service_name",
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DataType: v3.AttributeKeyDataTypeString,
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Type: v3.AttributeKeyTypeTag,
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}},
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Expression: "A",
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Disabled: false,
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TimeAggregation: v3.TimeAggregationAvg,
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},
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start: 1701794980000,
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end: 1701796780000,
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expectedQueryContains: "SELECT fingerprint, any(service_name) as service_name, toStartOfInterval(toDateTime(intDiv(timestamp_ms, 1000)), INTERVAL 60 SECOND) as ts, avg(value) as per_series_value FROM signoz_metrics.distributed_samples_v2 INNER JOIN (SELECT DISTINCT JSONExtractString(labels, 'service_name') as service_name, fingerprint FROM signoz_metrics.time_series_v2 WHERE metric_name = 'http_requests' AND temporality = 'Cumulative' AND JSONExtractString(labels, 'service_name') != 'payment_service' AND JSONExtractString(labels, 'endpoint') IN ['/paycallback','/payme','/paypal']) as filtered_time_series USING fingerprint WHERE metric_name = 'http_requests' AND timestamp_ms >= 1701794980000 AND timestamp_ms <= 1701796780000 GROUP BY fingerprint, ts ORDER BY fingerprint, ts",
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},
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{
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name: "test time aggregation = rate, temporality = cumulative",
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builderQuery: &v3.BuilderQuery{
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QueryName: "A",
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StepInterval: 60,
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DataSource: v3.DataSourceMetrics,
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AggregateAttribute: v3.AttributeKey{
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Key: "http_requests",
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DataType: v3.AttributeKeyDataTypeFloat64,
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Type: v3.AttributeKeyTypeUnspecified,
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IsColumn: true,
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IsJSON: false,
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},
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Temporality: v3.Cumulative,
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Filters: &v3.FilterSet{
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Operator: "AND",
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Items: []v3.FilterItem{
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{
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Key: v3.AttributeKey{
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Key: "service_name",
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Type: v3.AttributeKeyTypeTag,
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DataType: v3.AttributeKeyDataTypeString,
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},
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Operator: v3.FilterOperatorContains,
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Value: "payment_service",
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},
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},
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},
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GroupBy: []v3.AttributeKey{{
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Key: "service_name",
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DataType: v3.AttributeKeyDataTypeString,
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Type: v3.AttributeKeyTypeTag,
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}},
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Expression: "A",
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Disabled: false,
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TimeAggregation: v3.TimeAggregationRate,
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},
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start: 1701794980000,
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end: 1701796780000,
|
||||
expectedQueryContains: "SELECT service_name, ts, 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))) as per_series_value FROM (SELECT fingerprint, any(service_name) as service_name, toStartOfInterval(toDateTime(intDiv(timestamp_ms, 1000)), INTERVAL 60 SECOND) as ts, max(value) as per_series_value FROM signoz_metrics.distributed_samples_v2 INNER JOIN (SELECT DISTINCT JSONExtractString(labels, 'service_name') as service_name, fingerprint FROM signoz_metrics.time_series_v2 WHERE metric_name = 'http_requests' AND temporality = 'Cumulative' AND like(JSONExtractString(labels, 'service_name'), '%payment_service%')) as filtered_time_series USING fingerprint WHERE metric_name = 'http_requests' AND timestamp_ms >= 1701794980000 AND timestamp_ms <= 1701796780000 GROUP BY fingerprint, ts ORDER BY fingerprint, ts) WINDOW rate_window as (PARTITION BY fingerprint ORDER BY fingerprint, ts)",
|
||||
},
|
||||
}
|
||||
|
||||
for _, testCase := range testCases {
|
||||
t.Run(testCase.name, func(t *testing.T) {
|
||||
query, err := prepareTimeAggregationSubQueryTimeSeries(
|
||||
testCase.start,
|
||||
testCase.end,
|
||||
testCase.builderQuery.StepInterval,
|
||||
testCase.builderQuery,
|
||||
)
|
||||
assert.Nil(t, err)
|
||||
assert.Contains(t, query, testCase.expectedQueryContains)
|
||||
})
|
||||
}
|
||||
}
|
||||
func TestPrepareTimeseriesQuery(t *testing.T) {
|
||||
testCases := []struct {
|
||||
name string
|
||||
builderQuery *v3.BuilderQuery
|
||||
start int64
|
||||
end int64
|
||||
expectedQueryContains string
|
||||
}{
|
||||
{
|
||||
name: "test time aggregation = avg, space aggregation = sum, temporality = unspecified",
|
||||
builderQuery: &v3.BuilderQuery{
|
||||
QueryName: "A",
|
||||
StepInterval: 60,
|
||||
DataSource: v3.DataSourceMetrics,
|
||||
AggregateAttribute: v3.AttributeKey{
|
||||
Key: "system_memory_usage",
|
||||
DataType: v3.AttributeKeyDataTypeFloat64,
|
||||
Type: v3.AttributeKeyTypeUnspecified,
|
||||
IsColumn: true,
|
||||
IsJSON: false,
|
||||
},
|
||||
Temporality: v3.Unspecified,
|
||||
Filters: &v3.FilterSet{
|
||||
Operator: "AND",
|
||||
Items: []v3.FilterItem{
|
||||
{
|
||||
Key: v3.AttributeKey{
|
||||
Key: "state",
|
||||
Type: v3.AttributeKeyTypeTag,
|
||||
DataType: v3.AttributeKeyDataTypeString,
|
||||
},
|
||||
Operator: v3.FilterOperatorNotEqual,
|
||||
Value: "idle",
|
||||
},
|
||||
},
|
||||
},
|
||||
GroupBy: []v3.AttributeKey{},
|
||||
Expression: "A",
|
||||
Disabled: false,
|
||||
TimeAggregation: v3.TimeAggregationAvg,
|
||||
SpaceAggregation: v3.SpaceAggregationSum,
|
||||
},
|
||||
start: 1701794980000,
|
||||
end: 1701796780000,
|
||||
expectedQueryContains: "SELECT ts, sum(per_series_value) as value FROM (SELECT fingerprint, toStartOfInterval(toDateTime(intDiv(timestamp_ms, 1000)), INTERVAL 60 SECOND) as ts, avg(value) as per_series_value FROM signoz_metrics.distributed_samples_v2 INNER JOIN (SELECT DISTINCT fingerprint FROM signoz_metrics.time_series_v2 WHERE metric_name = 'system_memory_usage' AND temporality = 'Unspecified' AND JSONExtractString(labels, 'state') != 'idle') as filtered_time_series USING fingerprint WHERE metric_name = 'system_memory_usage' AND timestamp_ms >= 1701794980000 AND timestamp_ms <= 1701796780000 GROUP BY fingerprint, ts ORDER BY fingerprint, ts) WHERE isNaN(per_series_value) = 0 GROUP BY GROUPING SETS ( (ts), () ) ORDER BY ts ASC",
|
||||
},
|
||||
{
|
||||
name: "test time aggregation = rate, space aggregation = sum, temporality = cumulative",
|
||||
builderQuery: &v3.BuilderQuery{
|
||||
QueryName: "A",
|
||||
StepInterval: 60,
|
||||
DataSource: v3.DataSourceMetrics,
|
||||
AggregateAttribute: v3.AttributeKey{
|
||||
Key: "http_requests",
|
||||
DataType: v3.AttributeKeyDataTypeFloat64,
|
||||
Type: v3.AttributeKeyTypeUnspecified,
|
||||
IsColumn: true,
|
||||
IsJSON: false,
|
||||
},
|
||||
Temporality: v3.Cumulative,
|
||||
Filters: &v3.FilterSet{
|
||||
Operator: "AND",
|
||||
Items: []v3.FilterItem{
|
||||
{
|
||||
Key: v3.AttributeKey{
|
||||
Key: "service_name",
|
||||
Type: v3.AttributeKeyTypeTag,
|
||||
DataType: v3.AttributeKeyDataTypeString,
|
||||
},
|
||||
Operator: v3.FilterOperatorContains,
|
||||
Value: "payment_service",
|
||||
},
|
||||
},
|
||||
},
|
||||
GroupBy: []v3.AttributeKey{{
|
||||
Key: "service_name",
|
||||
DataType: v3.AttributeKeyDataTypeString,
|
||||
Type: v3.AttributeKeyTypeTag,
|
||||
}},
|
||||
Expression: "A",
|
||||
Disabled: false,
|
||||
TimeAggregation: v3.TimeAggregationRate,
|
||||
SpaceAggregation: v3.SpaceAggregationSum,
|
||||
},
|
||||
start: 1701794980000,
|
||||
end: 1701796780000,
|
||||
expectedQueryContains: "SELECT service_name, ts, sum(per_series_value) as value FROM (SELECT service_name, ts, 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))) as per_series_value FROM (SELECT fingerprint, any(service_name) as service_name, toStartOfInterval(toDateTime(intDiv(timestamp_ms, 1000)), INTERVAL 60 SECOND) as ts, max(value) as per_series_value FROM signoz_metrics.distributed_samples_v2 INNER JOIN (SELECT DISTINCT JSONExtractString(labels, 'service_name') as service_name, fingerprint FROM signoz_metrics.time_series_v2 WHERE metric_name = 'http_requests' AND temporality = 'Cumulative' AND like(JSONExtractString(labels, 'service_name'), '%payment_service%')) as filtered_time_series USING fingerprint WHERE metric_name = 'http_requests' AND timestamp_ms >= 1701794980000 AND timestamp_ms <= 1701796780000 GROUP BY fingerprint, ts ORDER BY fingerprint, ts) WINDOW rate_window as (PARTITION BY fingerprint ORDER BY fingerprint, ts)) WHERE isNaN(per_series_value) = 0 GROUP BY GROUPING SETS ( (service_name, ts), (service_name) ) ORDER BY service_name ASC, ts ASC",
|
||||
},
|
||||
}
|
||||
|
||||
for _, testCase := range testCases {
|
||||
t.Run(testCase.name, func(t *testing.T) {
|
||||
query, err := prepareMetricQueryCumulativeTimeSeries(
|
||||
testCase.start,
|
||||
testCase.end,
|
||||
testCase.builderQuery.StepInterval,
|
||||
testCase.builderQuery,
|
||||
)
|
||||
assert.Nil(t, err)
|
||||
assert.Contains(t, query, testCase.expectedQueryContains)
|
||||
})
|
||||
}
|
||||
}
|
||||
Loading…
x
Reference in New Issue
Block a user