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range_vector.go
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range_vector.go
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package logql
import (
"fmt"
"math"
"sort"
"sync"
"time"
"github.com/prometheus/prometheus/model/labels"
"github.com/prometheus/prometheus/promql"
promql_parser "github.com/prometheus/prometheus/promql/parser"
"github.com/grafana/loki/v3/pkg/iter"
"github.com/grafana/loki/v3/pkg/logql/syntax"
"github.com/grafana/loki/v3/pkg/logql/vector"
)
// BatchRangeVectorAggregator aggregates samples for a given range of samples.
// It receives the current milliseconds timestamp and the list of point within
// the range.
type BatchRangeVectorAggregator func([]promql.FPoint) float64
// RangeStreamingAgg streaming aggregates sample for each sample
type RangeStreamingAgg interface {
// agg func works inside the Next func of RangeVectorIterator, agg used to agg each sample.
// agg will calculate the intermediate result after streaming agg each sample and try to save an aggregate value instead of keeping all samples.
agg(sample promql.FPoint)
// at func works inside the At func of RangeVectorIterator, get the intermediate result of agg func to provide the final value for At func of RangeVectorIterator
at() float64
}
// RangeVectorIterator iterates through a range of samples.
// To fetch the current vector use `At` with a `BatchRangeVectorAggregator` or `RangeStreamingAgg`.
type RangeVectorIterator interface {
Next() bool
At() (int64, StepResult)
Close() error
Error() error
}
func newRangeVectorIterator(
it iter.PeekingSampleIterator,
expr *syntax.RangeAggregationExpr,
selRange, step, start, end, offset int64) (RangeVectorIterator, error) {
// forces at least one step.
if step == 0 {
step = 1
}
if offset != 0 {
start = start - offset
end = end - offset
}
var overlap bool
if selRange >= step && start != end {
overlap = true
}
if !overlap {
_, err := streamingAggregator(expr)
if err != nil {
return nil, err
}
return &streamRangeVectorIterator{
iter: it,
step: step,
end: end,
selRange: selRange,
metrics: map[string]labels.Labels{},
r: expr,
current: start - step, // first loop iteration will set it to start
offset: offset,
}, nil
}
vectorAggregator, err := aggregator(expr)
if err != nil {
return nil, err
}
return &batchRangeVectorIterator{
iter: it,
step: step,
end: end,
selRange: selRange,
metrics: map[string]labels.Labels{},
window: map[string]*promql.Series{},
agg: vectorAggregator,
current: start - step, // first loop iteration will set it to start
offset: offset,
}, nil
}
//batch
type batchRangeVectorIterator struct {
iter iter.PeekingSampleIterator
selRange, step, end, current, offset int64
window map[string]*promql.Series
metrics map[string]labels.Labels
at []promql.Sample
agg BatchRangeVectorAggregator
}
func (r *batchRangeVectorIterator) Next() bool {
// slides the range window to the next position
r.current = r.current + r.step
if r.current > r.end {
return false
}
rangeEnd := r.current
rangeStart := rangeEnd - r.selRange
// load samples
r.popBack(rangeStart)
r.load(rangeStart, rangeEnd)
return true
}
func (r *batchRangeVectorIterator) Close() error {
return r.iter.Close()
}
func (r *batchRangeVectorIterator) Error() error {
return r.iter.Err()
}
// popBack removes all entries out of the current window from the back.
func (r *batchRangeVectorIterator) popBack(newStart int64) {
// possible improvement: if there is no overlap we can just remove all.
for fp := range r.window {
lastPoint := 0
remove := false
for i, p := range r.window[fp].Floats {
if p.T <= newStart {
lastPoint = i
remove = true
continue
}
break
}
if remove {
r.window[fp].Floats = r.window[fp].Floats[lastPoint+1:]
}
if len(r.window[fp].Floats) == 0 {
s := r.window[fp]
delete(r.window, fp)
putSeries(s)
}
}
}
// load the next sample range window.
func (r *batchRangeVectorIterator) load(start, end int64) {
for lbs, sample, hasNext := r.iter.Peek(); hasNext; lbs, sample, hasNext = r.iter.Peek() {
if sample.Timestamp > end {
// not consuming the iterator as this belong to another range.
return
}
// the lower bound of the range is not inclusive
if sample.Timestamp <= start {
_ = r.iter.Next()
continue
}
// adds the sample.
var series *promql.Series
var ok bool
series, ok = r.window[lbs]
if !ok {
var metric labels.Labels
if metric, ok = r.metrics[lbs]; !ok {
var err error
metric, err = promql_parser.ParseMetric(lbs)
if err != nil {
_ = r.iter.Next()
continue
}
r.metrics[lbs] = metric
}
series = getSeries()
series.Metric = metric
r.window[lbs] = series
}
p := promql.FPoint{
T: sample.Timestamp,
F: sample.Value,
}
series.Floats = append(series.Floats, p)
_ = r.iter.Next()
}
}
func (r *batchRangeVectorIterator) At() (int64, StepResult) {
if r.at == nil {
r.at = make([]promql.Sample, 0, len(r.window))
}
r.at = r.at[:0]
// convert ts from nano to milli seconds as the iterator work with nanoseconds
ts := r.current/1e+6 + r.offset/1e+6
for _, series := range r.window {
r.at = append(r.at, promql.Sample{
F: r.agg(series.Floats),
T: ts,
Metric: series.Metric,
})
}
return ts, SampleVector(r.at)
}
var seriesPool sync.Pool
func getSeries() *promql.Series {
if r := seriesPool.Get(); r != nil {
s := r.(*promql.Series)
s.Floats = s.Floats[:0]
return s
}
return &promql.Series{
Floats: make([]promql.FPoint, 0, 1024),
}
}
func putSeries(s *promql.Series) {
seriesPool.Put(s)
}
func aggregator(r *syntax.RangeAggregationExpr) (BatchRangeVectorAggregator, error) {
switch r.Operation {
case syntax.OpRangeTypeRate:
return rateLogs(r.Left.Interval, r.Left.Unwrap != nil), nil
case syntax.OpRangeTypeRateCounter:
return rateCounter(r.Left.Interval), nil
case syntax.OpRangeTypeCount:
return countOverTime, nil
case syntax.OpRangeTypeBytesRate:
return rateLogBytes(r.Left.Interval), nil
case syntax.OpRangeTypeBytes, syntax.OpRangeTypeSum:
return sumOverTime, nil
case syntax.OpRangeTypeAvg:
return avgOverTime, nil
case syntax.OpRangeTypeMax:
return maxOverTime, nil
case syntax.OpRangeTypeMin:
return minOverTime, nil
case syntax.OpRangeTypeStddev:
return stddevOverTime, nil
case syntax.OpRangeTypeStdvar:
return stdvarOverTime, nil
case syntax.OpRangeTypeQuantile:
return quantileOverTime(*r.Params), nil
case syntax.OpRangeTypeFirst:
return first, nil
case syntax.OpRangeTypeLast:
return last, nil
case syntax.OpRangeTypeAbsent:
return one, nil
default:
return nil, fmt.Errorf(syntax.UnsupportedErr, r.Operation)
}
}
// rateLogs calculates the per-second rate of log lines or values extracted
// from log lines
func rateLogs(selRange time.Duration, computeValues bool) func(samples []promql.FPoint) float64 {
return func(samples []promql.FPoint) float64 {
if !computeValues {
return float64(len(samples)) / selRange.Seconds()
}
var result float64
for _, sample := range samples {
result += sample.F
}
return result / selRange.Seconds()
}
}
// rateCounter calculates the per-second rate of values extracted from log lines
// and treat them like a "counter" metric.
func rateCounter(selRange time.Duration) func(samples []promql.FPoint) float64 {
return func(samples []promql.FPoint) float64 {
return extrapolatedRate(samples, selRange, true, true)
}
}
// extrapolatedRate function is taken from prometheus code promql/functions.go:59
// extrapolatedRate is a utility function for rate/increase/delta.
// It calculates the rate (allowing for counter resets if isCounter is true),
// extrapolates if the first/last sample is close to the boundary, and returns
// the result as either per-second (if isRate is true) or overall.
func extrapolatedRate(samples []promql.FPoint, selRange time.Duration, isCounter, isRate bool) float64 {
// No sense in trying to compute a rate without at least two points. Drop
// this Vector element.
if len(samples) < 2 {
return 0
}
var (
rangeStart = samples[0].T - durationMilliseconds(selRange)
rangeEnd = samples[len(samples)-1].T
)
resultValue := samples[len(samples)-1].F - samples[0].F
if isCounter {
var lastValue float64
for _, sample := range samples {
if sample.F < lastValue {
resultValue += lastValue
}
lastValue = sample.F
}
}
// Duration between first/last samples and boundary of range.
durationToStart := float64(samples[0].T-rangeStart) / 1000
durationToEnd := float64(rangeEnd-samples[len(samples)-1].T) / 1000
sampledInterval := float64(samples[len(samples)-1].T-samples[0].T) / 1000
averageDurationBetweenSamples := sampledInterval / float64(len(samples)-1)
if isCounter && resultValue > 0 && samples[0].F >= 0 {
// Counters cannot be negative. If we have any slope at
// all (i.e. resultValue went up), we can extrapolate
// the zero point of the counter. If the duration to the
// zero point is shorter than the durationToStart, we
// take the zero point as the start of the series,
// thereby avoiding extrapolation to negative counter
// values.
durationToZero := sampledInterval * (samples[0].F / resultValue)
if durationToZero < durationToStart {
durationToStart = durationToZero
}
}
// If the first/last samples are close to the boundaries of the range,
// extrapolate the result. This is as we expect that another sample
// will exist given the spacing between samples we've seen thus far,
// with an allowance for noise.
extrapolationThreshold := averageDurationBetweenSamples * 1.1
extrapolateToInterval := sampledInterval
if durationToStart < extrapolationThreshold {
extrapolateToInterval += durationToStart
} else {
extrapolateToInterval += averageDurationBetweenSamples / 2
}
if durationToEnd < extrapolationThreshold {
extrapolateToInterval += durationToEnd
} else {
extrapolateToInterval += averageDurationBetweenSamples / 2
}
resultValue = resultValue * (extrapolateToInterval / sampledInterval)
if isRate {
seconds := selRange.Seconds()
resultValue = resultValue / seconds
}
return resultValue
}
func durationMilliseconds(d time.Duration) int64 {
return int64(d / (time.Millisecond / time.Nanosecond))
}
// rateLogBytes calculates the per-second rate of log bytes.
func rateLogBytes(selRange time.Duration) func(samples []promql.FPoint) float64 {
return func(samples []promql.FPoint) float64 {
return sumOverTime(samples) / selRange.Seconds()
}
}
// countOverTime counts the amount of log lines.
func countOverTime(samples []promql.FPoint) float64 {
return float64(len(samples))
}
func sumOverTime(samples []promql.FPoint) float64 {
var sum float64
for _, v := range samples {
sum += v.F
}
return sum
}
func avgOverTime(samples []promql.FPoint) float64 {
var mean, count float64
for _, v := range samples {
count++
if math.IsInf(mean, 0) {
if math.IsInf(v.F, 0) && (mean > 0) == (v.F > 0) {
// The `mean` and `v.V` values are `Inf` of the same sign. They
// can't be subtracted, but the value of `mean` is correct
// already.
continue
}
if !math.IsInf(v.F, 0) && !math.IsNaN(v.F) {
// At this stage, the mean is an infinite. If the added
// value is neither an Inf or a Nan, we can keep that mean
// value.
// This is required because our calculation below removes
// the mean value, which would look like Inf += x - Inf and
// end up as a NaN.
continue
}
}
mean += v.F/count - mean/count
}
return mean
}
func maxOverTime(samples []promql.FPoint) float64 {
max := samples[0].F
for _, v := range samples {
if v.F > max || math.IsNaN(max) {
max = v.F
}
}
return max
}
func minOverTime(samples []promql.FPoint) float64 {
min := samples[0].F
for _, v := range samples {
if v.F < min || math.IsNaN(min) {
min = v.F
}
}
return min
}
// stdvarOverTime calculates the variance using Welford's online algorithm.
// See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Welford's_online_algorithm
func stdvarOverTime(samples []promql.FPoint) float64 {
var aux, count, mean float64
for _, v := range samples {
count++
delta := v.F - mean
mean += delta / count
aux += delta * (v.F - mean)
}
return aux / count
}
func stddevOverTime(samples []promql.FPoint) float64 {
var aux, count, mean float64
for _, v := range samples {
count++
delta := v.F - mean
mean += delta / count
aux += delta * (v.F - mean)
}
return math.Sqrt(aux / count)
}
func quantileOverTime(q float64) func(samples []promql.FPoint) float64 {
return func(samples []promql.FPoint) float64 {
values := make(vector.HeapByMaxValue, 0, len(samples))
for _, v := range samples {
values = append(values, promql.Sample{F: v.F})
}
return Quantile(q, values)
}
}
// Quantile calculates the given Quantile of a vector of samples.
//
// The Vector will be sorted.
// If 'values' has zero elements, NaN is returned.
// If q<0, -Inf is returned.
// If q>1, +Inf is returned.
func Quantile(q float64, values vector.HeapByMaxValue) float64 {
if len(values) == 0 {
return math.NaN()
}
if q < 0 {
return math.Inf(-1)
}
if q > 1 {
return math.Inf(+1)
}
sort.Sort(values)
n := float64(len(values))
// When the quantile lies between two samples,
// we use a weighted average of the two samples.
rank := q * (n - 1)
lowerIndex := math.Max(0, math.Floor(rank))
upperIndex := math.Min(n-1, lowerIndex+1)
weight := rank - math.Floor(rank)
return values[int(lowerIndex)].F*(1-weight) + values[int(upperIndex)].F*weight
}
func first(samples []promql.FPoint) float64 {
if len(samples) == 0 {
return math.NaN()
}
return samples[0].F
}
func last(samples []promql.FPoint) float64 {
if len(samples) == 0 {
return math.NaN()
}
return samples[len(samples)-1].F
}
func one(_ []promql.FPoint) float64 {
return 1.0
}
// streaming range agg
type streamRangeVectorIterator struct {
iter iter.PeekingSampleIterator
selRange, step, end, current, offset int64
windowRangeAgg map[string]RangeStreamingAgg
r *syntax.RangeAggregationExpr
metrics map[string]labels.Labels
at []promql.Sample
agg BatchRangeVectorAggregator
}
func (r *streamRangeVectorIterator) Next() bool {
// slides the range window to the next position
r.current = r.current + r.step
if r.current > r.end {
return false
}
rangeEnd := r.current
rangeStart := rangeEnd - r.selRange
// load samples
r.windowRangeAgg = make(map[string]RangeStreamingAgg, 0)
r.metrics = map[string]labels.Labels{}
r.load(rangeStart, rangeEnd)
return true
}
func (r *streamRangeVectorIterator) Close() error {
return r.iter.Close()
}
func (r *streamRangeVectorIterator) Error() error {
return r.iter.Err()
}
// load the next sample range window.
func (r *streamRangeVectorIterator) load(start, end int64) {
for lbs, sample, hasNext := r.iter.Peek(); hasNext; lbs, sample, hasNext = r.iter.Peek() {
if sample.Timestamp > end {
// not consuming the iterator as this belong to another range.
return
}
// the lower bound of the range is not inclusive
if sample.Timestamp <= start {
_ = r.iter.Next()
continue
}
// adds the sample.
var rangeAgg RangeStreamingAgg
var ok bool
rangeAgg, ok = r.windowRangeAgg[lbs]
if !ok {
var metric labels.Labels
if _, ok = r.metrics[lbs]; !ok {
var err error
metric, err = promql_parser.ParseMetric(lbs)
if err != nil {
_ = r.iter.Next()
continue
}
r.metrics[lbs] = metric
}
// never err here ,we have check error at evaluator.go rangeAggEvaluator() func
rangeAgg, _ = streamingAggregator(r.r)
r.windowRangeAgg[lbs] = rangeAgg
}
p := promql.FPoint{
T: sample.Timestamp,
F: sample.Value,
}
rangeAgg.agg(p)
_ = r.iter.Next()
}
}
func (r *streamRangeVectorIterator) At() (int64, StepResult) {
if r.at == nil {
r.at = make([]promql.Sample, 0, len(r.windowRangeAgg))
}
r.at = r.at[:0]
// convert ts from nano to milli seconds as the iterator work with nanoseconds
ts := r.current/1e+6 + r.offset/1e+6
for lbs, rangeAgg := range r.windowRangeAgg {
r.at = append(r.at, promql.Sample{
F: rangeAgg.at(),
T: ts,
Metric: r.metrics[lbs],
})
}
return ts, SampleVector(r.at)
}
func streamingAggregator(r *syntax.RangeAggregationExpr) (RangeStreamingAgg, error) {
switch r.Operation {
case syntax.OpRangeTypeRate:
return newRateLogs(r.Left.Interval, r.Left.Unwrap != nil), nil
case syntax.OpRangeTypeRateCounter:
return &RateCounterOverTime{selRange: r.Left.Interval, samples: make([]promql.FPoint, 0)}, nil
case syntax.OpRangeTypeCount:
return &CountOverTime{}, nil
case syntax.OpRangeTypeBytesRate:
return &RateLogBytesOverTime{selRange: r.Left.Interval}, nil
case syntax.OpRangeTypeBytes, syntax.OpRangeTypeSum:
return &SumOverTime{}, nil
case syntax.OpRangeTypeAvg:
return &AvgOverTime{}, nil
case syntax.OpRangeTypeMax:
return &MaxOverTime{max: math.NaN()}, nil
case syntax.OpRangeTypeMin:
return &MinOverTime{min: math.NaN()}, nil
case syntax.OpRangeTypeStddev:
return &StddevOverTime{}, nil
case syntax.OpRangeTypeStdvar:
return &StdvarOverTime{}, nil
case syntax.OpRangeTypeQuantile:
return &QuantileOverTime{q: *r.Params, values: make(vector.HeapByMaxValue, 0)}, nil
case syntax.OpRangeTypeFirst:
return &FirstOverTime{}, nil
case syntax.OpRangeTypeLast:
return &LastOverTime{}, nil
case syntax.OpRangeTypeAbsent:
return &OneOverTime{}, nil
default:
return nil, fmt.Errorf(syntax.UnsupportedErr, r.Operation)
}
}
func newRateLogs(selRange time.Duration, computeValues bool) RangeStreamingAgg {
return &RateLogsOverTime{
selRange: selRange,
computeValues: computeValues,
}
}
// rateLogs calculates the per-second rate of log lines or values extracted
// from log lines
type RateLogsOverTime struct {
selRange time.Duration
val float64
count float64
computeValues bool
}
func (a *RateLogsOverTime) agg(sample promql.FPoint) {
a.count++
a.val += sample.F
}
func (a *RateLogsOverTime) at() float64 {
if !a.computeValues {
return a.count / a.selRange.Seconds()
}
return a.val / a.selRange.Seconds()
}
// rateCounter calculates the per-second rate of values extracted from log lines
// and treat them like a "counter" metric.
type RateCounterOverTime struct {
samples []promql.FPoint
selRange time.Duration
}
func (a *RateCounterOverTime) agg(sample promql.FPoint) {
a.samples = append(a.samples, sample)
}
func (a *RateCounterOverTime) at() float64 {
return extrapolatedRate(a.samples, a.selRange, true, true)
}
// rateLogBytes calculates the per-second rate of log bytes.
type RateLogBytesOverTime struct {
sum float64
selRange time.Duration
}
func (a *RateLogBytesOverTime) agg(sample promql.FPoint) {
a.sum += sample.F
}
func (a *RateLogBytesOverTime) at() float64 {
return a.sum / a.selRange.Seconds()
}
type CountOverTime struct {
count float64
}
func (a *CountOverTime) agg(_ promql.FPoint) {
a.count++
}
func (a *CountOverTime) at() float64 {
return a.count
}
type SumOverTime struct {
sum float64
}
func (a *SumOverTime) agg(sample promql.FPoint) {
a.sum += sample.F
}
func (a *SumOverTime) at() float64 {
return a.sum
}
type AvgOverTime struct {
mean, count float64
}
func (a *AvgOverTime) agg(sample promql.FPoint) {
a.count++
if math.IsInf(a.mean, 0) {
if math.IsInf(sample.F, 0) && (a.mean > 0) == (sample.F > 0) {
// The `mean` and `v.V` values are `Inf` of the same sign. They
// can't be subtracted, but the value of `mean` is correct
// already.
return
}
if !math.IsInf(sample.F, 0) && !math.IsNaN(sample.F) {
// At this stage, the mean is an infinite. If the added
// value is neither an Inf or a Nan, we can keep that mean
// value.
// This is required because our calculation below removes
// the mean value, which would look like Inf += x - Inf and
// end up as a NaN.
return
}
}
a.mean += sample.F/a.count - a.mean/a.count
}
func (a *AvgOverTime) at() float64 {
return a.mean
}
type MaxOverTime struct {
max float64
}
func (a *MaxOverTime) agg(sample promql.FPoint) {
if sample.F > a.max || math.IsNaN(a.max) {
a.max = sample.F
}
}
func (a *MaxOverTime) at() float64 {
return a.max
}
type MinOverTime struct {
min float64
}
func (a *MinOverTime) agg(sample promql.FPoint) {
if sample.F < a.min || math.IsNaN(a.min) {
a.min = sample.F
}
}
func (a *MinOverTime) at() float64 {
return a.min
}
type StdvarOverTime struct {
aux, count, mean float64
}
func (a *StdvarOverTime) agg(sample promql.FPoint) {
a.count++
delta := sample.F - a.mean
a.mean += delta / a.count
a.aux += delta * (sample.F - a.mean)
}
func (a *StdvarOverTime) at() float64 {
return a.aux / a.count
}
type StddevOverTime struct {
aux, count, mean float64
}
func (a *StddevOverTime) agg(sample promql.FPoint) {
a.count++
delta := sample.F - a.mean
a.mean += delta / a.count
a.aux += delta * (sample.F - a.mean)
}
func (a *StddevOverTime) at() float64 {
return math.Sqrt(a.aux / a.count)
}
type QuantileOverTime struct {
q float64
values vector.HeapByMaxValue
}
func (a *QuantileOverTime) agg(sample promql.FPoint) {
a.values = append(a.values, promql.Sample{F: sample.F})
}
func (a *QuantileOverTime) at() float64 {
return Quantile(a.q, a.values)
}
type FirstOverTime struct {
v float64
hasData bool
}
func (a *FirstOverTime) agg(sample promql.FPoint) {
if a.hasData {
return
}
a.v = sample.F
a.hasData = true
}
func (a *FirstOverTime) at() float64 {
return a.v
}
type LastOverTime struct {
v float64
}
func (a *LastOverTime) agg(sample promql.FPoint) {
a.v = sample.F
}
func (a *LastOverTime) at() float64 {
return a.v
}
type OneOverTime struct {
}
func (a *OneOverTime) agg(_ promql.FPoint) {
}
func (a *OneOverTime) at() float64 {
return 1.0
}