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promql: store labels per-group only for count_values

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This saves memory in other kinds of aggregation.

We don't need `orderedResult` in `aggregationCountValues`; the ordering
is not guaranteed.

Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
This commit is contained in:
Bryan Boreham 2024-04-05 14:39:29 +01:00
parent 185290a0d2
commit 2cf3c9de8f
1 changed files with 11 additions and 12 deletions
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23
promql/engine.go
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@ -2730,7 +2730,6 @@ func vectorElemBinop(op parser.ItemType, lhs, rhs float64, hlhs, hrhs *histogram
type groupedAggregation struct {
hasFloat bool // Has at least 1 float64 sample aggregated.
hasHistogram bool // Has at least 1 histogram sample aggregated.
labels labels.Labels
floatValue float64
histogramValue *histogram.FloatHistogram
floatMean float64
@ -3044,8 +3043,11 @@ func (ev *evaluator) aggregationK(e *parser.AggregateExpr, k int, inputMatrix Ma
// aggregationK evaluates count_values on vec.
// Outputs as many series per group as there are values in the input.
func (ev *evaluator) aggregationCountValues(e *parser.AggregateExpr, grouping []string, valueLabel string, vec Vector, enh *EvalNodeHelper) (Vector, annotations.Annotations) {
result := map[uint64]*groupedAggregation{}
orderedResult := []*groupedAggregation{}
type groupCount struct {
labels labels.Labels
count int
}
result := map[uint64]*groupCount{}
var buf []byte
for _, s := range vec {
@ -3062,24 +3064,21 @@ func (ev *evaluator) aggregationCountValues(e *parser.AggregateExpr, grouping []
group, ok := result[groupingKey]
// Add a new group if it doesn't exist.
if !ok {
newAgg := &groupedAggregation{
labels: generateGroupingLabels(enh, metric, e.Without, grouping),
groupCount: 1,
result[groupingKey] = &groupCount{
labels: generateGroupingLabels(enh, metric, e.Without, grouping),
count: 1,
}
result[groupingKey] = newAgg
orderedResult = append(orderedResult, newAgg)
continue
}
group.groupCount++
group.count++
}
// Construct the result Vector from the aggregated groups.
for _, aggr := range orderedResult {
for _, aggr := range result {
enh.Out = append(enh.Out, Sample{
Metric: aggr.labels,
F: float64(aggr.groupCount),
F: float64(aggr.count),
})
}
return enh.Out, nil