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promql: refactor: split topk/bottomk from sum/avg/etc 

They aggregate results in different ways.
topk/bottomk don't consider histograms so can simplify data collection.

Signed-off-by: Bryan Boreham <bjboreham@gmail.com>
pull/13744/head
Bryan Boreham 8 months ago
parent
74eed67ef6
commit
2f03acbafc
1 changed files with 159 additions and 105 deletions
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  1. 258
      promql/engine.go

258
promql/engine.go
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@ -1299,6 +1299,7 @@ func (ev *evaluator) rangeEvalAgg(aggExpr *parser.AggregateExpr, sortedGrouping
groupToResultIndex := make(map[uint64]int)
seriesToResult := make([]int, len(inputMatrix))
orderedResult := make([]*groupedAggregation, 0, 16)
var result Matrix
for si, series := range inputMatrix {
var groupingKey uint64
@ -1306,8 +1307,11 @@ func (ev *evaluator) rangeEvalAgg(aggExpr *parser.AggregateExpr, sortedGrouping
index, ok := groupToResultIndex[groupingKey]
// Add a new group if it doesn't exist.
if !ok {
if aggExpr.Op != parser.TOPK && aggExpr.Op != parser.BOTTOMK {
m := generateGroupingLabels(enh, series.Metric, aggExpr.Without, sortedGrouping)
newAgg := &groupedAggregation{labels: m}
result = append(result, Series{Metric: m})
}
newAgg := &groupedAggregation{}
index = len(orderedResult)
groupToResultIndex[groupingKey] = index
orderedResult = append(orderedResult, newAgg)
@ -1315,7 +1319,11 @@ func (ev *evaluator) rangeEvalAgg(aggExpr *parser.AggregateExpr, sortedGrouping
seriesToResult[si] = index
}
seriess := make(map[uint64]Series, len(inputMatrix)) // Output series by series hash.
var seriess map[uint64]Series
switch aggExpr.Op {
case parser.TOPK, parser.BOTTOMK:
seriess = make(map[uint64]Series, len(inputMatrix)) // Output series by series hash.
}
for ts := ev.startTimestamp; ts <= ev.endTimestamp; ts += ev.interval {
if err := contextDone(ev.ctx, "expression evaluation"); err != nil {
@ -1326,25 +1334,44 @@ func (ev *evaluator) rangeEvalAgg(aggExpr *parser.AggregateExpr, sortedGrouping
// Make the function call.
enh.Ts = ts
result, ws := ev.aggregation(aggExpr, param, inputMatrix, seriesToResult, orderedResult, enh, seriess)
warnings.Merge(ws)
var ws annotations.Annotations
switch aggExpr.Op {
case parser.TOPK, parser.BOTTOMK:
result, ws = ev.aggregationK(aggExpr, param, inputMatrix, seriesToResult, orderedResult, enh, seriess)
// If this could be an instant query, shortcut so as not to change sort order.
if ev.endTimestamp == ev.startTimestamp {
return result, warnings
return result, ws
}
default:
ws = ev.aggregation(aggExpr, param, inputMatrix, result, seriesToResult, orderedResult, enh)
}
warnings.Merge(ws)
if ev.currentSamples > ev.maxSamples {
ev.error(ErrTooManySamples(env))
}
}
// Assemble the output matrix. By the time we get here we know we don't have too many samples.
mat := make(Matrix, 0, len(seriess))
switch aggExpr.Op {
case parser.TOPK, parser.BOTTOMK:
result = make(Matrix, 0, len(seriess))
for _, ss := range seriess {
mat = append(mat, ss)
result = append(result, ss)
}
return mat, warnings
default:
// Remove empty result rows.
dst := 0
for _, series := range result {
if len(series.Floats) > 0 || len(series.Histograms) > 0 {
result[dst] = series
dst++
}
}
result = result[:dst]
}
return result, warnings
}
// evalSubquery evaluates given SubqueryExpr and returns an equivalent
@ -2698,25 +2725,14 @@ type groupedAggregation struct {
reverseHeap vectorByReverseValueHeap
}
// aggregation evaluates an aggregation operation on a Vector. The provided grouping labels
// must be sorted.
func (ev *evaluator) aggregation(e *parser.AggregateExpr, q float64, inputMatrix Matrix, seriesToResult []int, orderedResult []*groupedAggregation, enh *EvalNodeHelper, seriess map[uint64]Series) (Matrix, annotations.Annotations) {
// aggregation evaluates sum, avg, count, stdvar, stddev or quantile at one timestep on inputMatrix.
// These functions produce one output series for each group specified in the expression, with just the labels from `by(...)`.
// outputMatrix should be already populated with grouping labels; groups is one-to-one with outputMatrix.
// seriesToResult maps inputMatrix indexes to outputMatrix indexes.
func (ev *evaluator) aggregation(e *parser.AggregateExpr, q float64, inputMatrix, outputMatrix Matrix, seriesToResult []int, orderedResult []*groupedAggregation, enh *EvalNodeHelper) annotations.Annotations {
op := e.Op
var annos annotations.Annotations
seen := make([]bool, len(orderedResult)) // Which output groups were seen in the input at this timestamp.
k := 1
if op == parser.TOPK || op == parser.BOTTOMK {
if !convertibleToInt64(q) {
ev.errorf("Scalar value %v overflows int64", q)
}
k = int(q)
if k > len(inputMatrix) {
k = len(inputMatrix)
}
if k < 1 {
return nil, annos
}
}
if op == parser.QUANTILE {
if math.IsNaN(q) || q < 0 || q > 1 {
annos.Add(annotations.NewInvalidQuantileWarning(q, e.Param.PositionRange()))
@ -2733,7 +2749,6 @@ func (ev *evaluator) aggregation(e *parser.AggregateExpr, q float64, inputMatrix
// Initialize this group if it's the first time we've seen it.
if !seen[seriesToResult[si]] {
*group = groupedAggregation{
labels: group.labels,
floatValue: s.F,
floatMean: s.F,
groupCount: 1,
@ -2754,18 +2769,12 @@ func (ev *evaluator) aggregation(e *parser.AggregateExpr, q float64, inputMatrix
switch op {
case parser.STDVAR, parser.STDDEV:
group.floatValue = 0
case parser.TOPK, parser.QUANTILE:
group.heap = make(vectorByValueHeap, 1, k)
case parser.QUANTILE:
group.heap = make(vectorByValueHeap, 1)
group.heap[0] = Sample{
F: s.F,
Metric: s.Metric,
}
case parser.BOTTOMK:
group.reverseHeap = make(vectorByReverseValueHeap, 1, k)
group.reverseHeap[0] = Sample{
F: s.F,
Metric: s.Metric,
}
case parser.GROUP:
group.floatValue = 1
}
@ -2848,20 +2857,118 @@ func (ev *evaluator) aggregation(e *parser.AggregateExpr, q float64, inputMatrix
group.floatValue += delta * (s.F - group.floatMean)
}
case parser.QUANTILE:
group.heap = append(group.heap, s)
default:
panic(fmt.Errorf("expected aggregation operator but got %q", op))
}
}
// Construct the output matrix from the aggregated groups.
numSteps := int((ev.endTimestamp-ev.startTimestamp)/ev.interval) + 1
for ri, aggr := range orderedResult {
if !seen[ri] {
continue
}
switch op {
case parser.AVG:
if aggr.hasFloat && aggr.hasHistogram {
// We cannot aggregate histogram sample with a float64 sample.
annos.Add(annotations.NewMixedFloatsHistogramsAggWarning(e.Expr.PositionRange()))
continue
}
if aggr.hasHistogram {
aggr.histogramValue = aggr.histogramMean.Compact(0)
} else {
aggr.floatValue = aggr.floatMean
}
case parser.COUNT:
aggr.floatValue = float64(aggr.groupCount)
case parser.STDVAR:
aggr.floatValue /= float64(aggr.groupCount)
case parser.STDDEV:
aggr.floatValue = math.Sqrt(aggr.floatValue / float64(aggr.groupCount))
case parser.QUANTILE:
aggr.floatValue = quantile(q, aggr.heap)
case parser.SUM:
if aggr.hasFloat && aggr.hasHistogram {
// We cannot aggregate histogram sample with a float64 sample.
annos.Add(annotations.NewMixedFloatsHistogramsAggWarning(e.Expr.PositionRange()))
continue
}
if aggr.hasHistogram {
aggr.histogramValue.Compact(0)
}
default:
// For other aggregations, we already have the right value.
}
ss := &outputMatrix[ri]
addToSeries(ss, enh.Ts, aggr.floatValue, aggr.histogramValue, numSteps)
}
return annos
}
// aggregationK evaluates topk or bottomk at one timestep on inputMatrix.
// Output that has the same labels as the input, but just k of them per group.
// seriesToResult maps inputMatrix indexes to groups indexes.
// For an instant query, returns a Matrix in descending order for topk or ascending for bottomk.
// For a range query, aggregates output in the seriess map.
func (ev *evaluator) aggregationK(e *parser.AggregateExpr, q float64, inputMatrix Matrix, seriesToResult []int, orderedResult []*groupedAggregation, enh *EvalNodeHelper, seriess map[uint64]Series) (Matrix, annotations.Annotations) {
op := e.Op
var annos annotations.Annotations
seen := make([]bool, len(orderedResult)) // Which output groups were seen in the input at this timestamp.
if !convertibleToInt64(q) {
ev.errorf("Scalar value %v overflows int64", q)
}
k := int(q)
if k > len(inputMatrix) {
k = len(inputMatrix)
}
if k < 1 {
return nil, annos
}
for si := range inputMatrix {
s, ok := ev.nextSample(enh.Ts, inputMatrix, si)
if !ok {
continue
}
group := orderedResult[seriesToResult[si]]
// Initialize this group if it's the first time we've seen it.
if !seen[seriesToResult[si]] {
*group = groupedAggregation{}
switch op {
case parser.TOPK:
group.heap = make(vectorByValueHeap, 1, k)
group.heap[0] = s
case parser.BOTTOMK:
group.reverseHeap = make(vectorByReverseValueHeap, 1, k)
group.reverseHeap[0] = s
}
seen[seriesToResult[si]] = true
continue
}
switch op {
case parser.TOPK:
// We build a heap of up to k elements, with the smallest element at heap[0].
switch {
case len(group.heap) < k:
heap.Push(&group.heap, &Sample{
F: s.F,
Metric: s.Metric,
})
heap.Push(&group.heap, &s)
case group.heap[0].F < s.F || (math.IsNaN(group.heap[0].F) && !math.IsNaN(s.F)):
// This new element is bigger than the previous smallest element - overwrite that.
group.heap[0] = Sample{
F: s.F,
Metric: s.Metric,
}
group.heap[0] = s
if k > 1 {
heap.Fix(&group.heap, 0) // Maintain the heap invariant.
}
@ -2871,24 +2978,15 @@ func (ev *evaluator) aggregation(e *parser.AggregateExpr, q float64, inputMatrix
// We build a heap of up to k elements, with the biggest element at heap[0].
switch {
case len(group.reverseHeap) < k:
heap.Push(&group.reverseHeap, &Sample{
F: s.F,
Metric: s.Metric,
})
heap.Push(&group.reverseHeap, &s)
case group.reverseHeap[0].F > s.F || (math.IsNaN(group.reverseHeap[0].F) && !math.IsNaN(s.F)):
// This new element is smaller than the previous biggest element - overwrite that.
group.reverseHeap[0] = Sample{
F: s.F,
Metric: s.Metric,
}
group.reverseHeap[0] = s
if k > 1 {
heap.Fix(&group.reverseHeap, 0) // Maintain the heap invariant.
}
}
case parser.QUANTILE:
group.heap = append(group.heap, s)
default:
panic(fmt.Errorf("expected aggregation operator but got %q", op))
}
@ -2901,14 +2999,10 @@ func (ev *evaluator) aggregation(e *parser.AggregateExpr, q float64, inputMatrix
mat = make(Matrix, 0, len(orderedResult))
}
add := func(lbls labels.Labels, f float64, h *histogram.FloatHistogram) {
add := func(lbls labels.Labels, f float64) {
// If this could be an instant query, add directly to the matrix so the result is in consistent order.
if ev.endTimestamp == ev.startTimestamp {
if h == nil {
mat = append(mat, Series{Metric: lbls, Floats: []FPoint{{T: enh.Ts, F: f}}})
} else {
mat = append(mat, Series{Metric: lbls, Histograms: []HPoint{{T: enh.Ts, H: h}}})
}
} else {
// Otherwise the results are added into seriess elements.
hash := lbls.Hash()
@ -2916,7 +3010,7 @@ func (ev *evaluator) aggregation(e *parser.AggregateExpr, q float64, inputMatrix
if !ok {
ss = Series{Metric: lbls}
}
addToSeries(&ss, enh.Ts, f, h, numSteps)
addToSeries(&ss, enh.Ts, f, nil, numSteps)
seriess[hash] = ss
}
}
@ -2925,36 +3019,14 @@ func (ev *evaluator) aggregation(e *parser.AggregateExpr, q float64, inputMatrix
continue
}
switch op {
case parser.AVG:
if aggr.hasFloat && aggr.hasHistogram {
// We cannot aggregate histogram sample with a float64 sample.
annos.Add(annotations.NewMixedFloatsHistogramsAggWarning(e.Expr.PositionRange()))
continue
}
if aggr.hasHistogram {
aggr.histogramValue = aggr.histogramMean.Compact(0)
} else {
aggr.floatValue = aggr.floatMean
}
case parser.COUNT:
aggr.floatValue = float64(aggr.groupCount)
case parser.STDVAR:
aggr.floatValue /= float64(aggr.groupCount)
case parser.STDDEV:
aggr.floatValue = math.Sqrt(aggr.floatValue / float64(aggr.groupCount))
case parser.TOPK:
// The heap keeps the lowest value on top, so reverse it.
if len(aggr.heap) > 1 {
sort.Sort(sort.Reverse(aggr.heap))
}
for _, v := range aggr.heap {
add(v.Metric, v.F, nil)
add(v.Metric, v.F)
}
continue // Bypass default append.
case parser.BOTTOMK:
// The heap keeps the highest value on top, so reverse it.
@ -2962,27 +3034,9 @@ func (ev *evaluator) aggregation(e *parser.AggregateExpr, q float64, inputMatrix
sort.Sort(sort.Reverse(aggr.reverseHeap))
}
for _, v := range aggr.reverseHeap {
add(v.Metric, v.F, nil)
}
continue // Bypass default append.
case parser.QUANTILE:
aggr.floatValue = quantile(q, aggr.heap)
case parser.SUM:
if aggr.hasFloat && aggr.hasHistogram {
// We cannot aggregate histogram sample with a float64 sample.
annos.Add(annotations.NewMixedFloatsHistogramsAggWarning(e.Expr.PositionRange()))
continue
add(v.Metric, v.F)
}
if aggr.hasHistogram {
aggr.histogramValue.Compact(0)
}
default:
// For other aggregations, we already have the right value.
}
add(aggr.labels, aggr.floatValue, aggr.histogramValue)
}
return mat, annos

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