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84f11a9e6d
VictoriaMetrics/app/vmselect/promql/transform.go
luckyxiaoqiang 8ce82c5400
app/vmselect/promql: add day_of_year() function (#5368) 
Co-authored-by: dingxiaoqiang <dingxiaoqiang@bytedance.com>
Co-authored-by: Roman Khavronenko <roman@victoriametrics.com>
(cherry picked from commit d7897e0d70)
2023-11-28 12:49:48 +01:00

2843 lines
68 KiB
Go
Raw Blame History

package promql
import (
"bytes"
"fmt"
"math"
"math/rand"
"regexp"
"sort"
"strconv"
"strings"
"time"
"github.com/VictoriaMetrics/metricsql"
"github.com/VictoriaMetrics/VictoriaMetrics/app/vmselect/searchutils"
"github.com/VictoriaMetrics/VictoriaMetrics/lib/bytesutil"
"github.com/VictoriaMetrics/VictoriaMetrics/lib/decimal"
"github.com/VictoriaMetrics/VictoriaMetrics/lib/logger"
"github.com/VictoriaMetrics/VictoriaMetrics/lib/storage"
)
var transformFuncs = map[string]transformFunc{
"": transformUnion, // empty func is a synonym to union
"abs": newTransformFuncOneArg(transformAbs),
"absent": transformAbsent,
"acos": newTransformFuncOneArg(transformAcos),
"acosh": newTransformFuncOneArg(transformAcosh),
"asin": newTransformFuncOneArg(transformAsin),
"asinh": newTransformFuncOneArg(transformAsinh),
"atan": newTransformFuncOneArg(transformAtan),
"atanh": newTransformFuncOneArg(transformAtanh),
"bitmap_and": newTransformBitmap(bitmapAnd),
"bitmap_or": newTransformBitmap(bitmapOr),
"bitmap_xor": newTransformBitmap(bitmapXor),
"buckets_limit": transformBucketsLimit,
"ceil": newTransformFuncOneArg(transformCeil),
"clamp": transformClamp,
"clamp_max": transformClampMax,
"clamp_min": transformClampMin,
"cos": newTransformFuncOneArg(transformCos),
"cosh": newTransformFuncOneArg(transformCosh),
"day_of_month": newTransformFuncDateTime(transformDayOfMonth),
"day_of_week": newTransformFuncDateTime(transformDayOfWeek),
"day_of_year": newTransformFuncDateTime(transformDayOfYear),
"days_in_month": newTransformFuncDateTime(transformDaysInMonth),
"deg": newTransformFuncOneArg(transformDeg),
"drop_common_labels": transformDropCommonLabels,
"drop_empty_series": transformDropEmptySeries,
"end": newTransformFuncZeroArgs(transformEnd),
"exp": newTransformFuncOneArg(transformExp),
"floor": newTransformFuncOneArg(transformFloor),
"histogram_avg": transformHistogramAvg,
"histogram_quantile": transformHistogramQuantile,
"histogram_quantiles": transformHistogramQuantiles,
"histogram_share": transformHistogramShare,
"histogram_stddev": transformHistogramStddev,
"histogram_stdvar": transformHistogramStdvar,
"hour": newTransformFuncDateTime(transformHour),
"interpolate": transformInterpolate,
"keep_last_value": transformKeepLastValue,
"keep_next_value": transformKeepNextValue,
"label_copy": transformLabelCopy,
"label_del": transformLabelDel,
"label_graphite_group": transformLabelGraphiteGroup,
"label_join": transformLabelJoin,
"label_keep": transformLabelKeep,
"label_lowercase": transformLabelLowercase,
"label_map": transformLabelMap,
"label_match": transformLabelMatch,
"label_mismatch": transformLabelMismatch,
"label_move": transformLabelMove,
"label_replace": transformLabelReplace,
"label_set": transformLabelSet,
"label_transform": transformLabelTransform,
"label_uppercase": transformLabelUppercase,
"label_value": transformLabelValue,
"limit_offset": transformLimitOffset,
"labels_equal": transformLabelsEqual,
"ln": newTransformFuncOneArg(transformLn),
"log2": newTransformFuncOneArg(transformLog2),
"log10": newTransformFuncOneArg(transformLog10),
"minute": newTransformFuncDateTime(transformMinute),
"month": newTransformFuncDateTime(transformMonth),
"now": transformNow,
"pi": transformPi,
"prometheus_buckets": transformPrometheusBuckets,
"rad": newTransformFuncOneArg(transformRad),
"rand": newTransformRand(newRandFloat64),
"rand_exponential": newTransformRand(newRandExpFloat64),
"rand_normal": newTransformRand(newRandNormFloat64),
"range_avg": newTransformFuncRange(runningAvg),
"range_first": transformRangeFirst,
"range_last": transformRangeLast,
"range_linear_regression": transformRangeLinearRegression,
"range_mad": transformRangeMAD,
"range_max": newTransformFuncRange(runningMax),
"range_min": newTransformFuncRange(runningMin),
"range_normalize": transformRangeNormalize,
"range_quantile": transformRangeQuantile,
"range_stddev": transformRangeStddev,
"range_stdvar": transformRangeStdvar,
"range_sum": newTransformFuncRange(runningSum),
"range_trim_outliers": transformRangeTrimOutliers,
"range_trim_spikes": transformRangeTrimSpikes,
"range_trim_zscore": transformRangeTrimZscore,
"range_zscore": transformRangeZscore,
"remove_resets": transformRemoveResets,
"round": transformRound,
"running_avg": newTransformFuncRunning(runningAvg),
"running_max": newTransformFuncRunning(runningMax),
"running_min": newTransformFuncRunning(runningMin),
"running_sum": newTransformFuncRunning(runningSum),
"scalar": transformScalar,
"sgn": transformSgn,
"sin": newTransformFuncOneArg(transformSin),
"sinh": newTransformFuncOneArg(transformSinh),
"smooth_exponential": transformSmoothExponential,
"sort": newTransformFuncSort(false),
"sort_by_label": newTransformFuncSortByLabel(false),
"sort_by_label_desc": newTransformFuncSortByLabel(true),
"sort_by_label_numeric": newTransformFuncNumericSort(false),
"sort_by_label_numeric_desc": newTransformFuncNumericSort(true),
"sort_desc": newTransformFuncSort(true),
"sqrt": newTransformFuncOneArg(transformSqrt),
"start": newTransformFuncZeroArgs(transformStart),
"step": newTransformFuncZeroArgs(transformStep),
"tan": newTransformFuncOneArg(transformTan),
"tanh": newTransformFuncOneArg(transformTanh),
"time": transformTime,
// "timestamp" has been moved to rollup funcs. See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/415
"timezone_offset": transformTimezoneOffset,
"union": transformUnion,
"vector": transformVector,
"year": newTransformFuncDateTime(transformYear),
}
// These functions don't change physical meaning of input time series,
// so they don't drop metric name
var transformFuncsKeepMetricName = map[string]bool{
"ceil": true,
"clamp": true,
"clamp_max": true,
"clamp_min": true,
"floor": true,
"interpolate": true,
"keep_last_value": true,
"keep_next_value": true,
"range_avg": true,
"range_first": true,
"range_last": true,
"range_linear_regression": true,
"range_max": true,
"range_min": true,
"range_normalize": true,
"range_quantile": true,
"range_stdvar": true,
"range_sddev": true,
"round": true,
"running_avg": true,
"running_max": true,
"running_min": true,
"smooth_exponential": true,
}
func getTransformFunc(s string) transformFunc {
s = strings.ToLower(s)
return transformFuncs[s]
}
type transformFuncArg struct {
ec *EvalConfig
fe *metricsql.FuncExpr
args [][]*timeseries
}
type transformFunc func(tfa *transformFuncArg) ([]*timeseries, error)
func newTransformFuncOneArg(tf func(v float64) float64) transformFunc {
tfe := func(values []float64) {
for i, v := range values {
values[i] = tf(v)
}
}
return func(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
return doTransformValues(args[0], tfe, tfa.fe)
}
}
func doTransformValues(arg []*timeseries, tf func(values []float64), fe *metricsql.FuncExpr) ([]*timeseries, error) {
name := strings.ToLower(fe.Name)
keepMetricNames := fe.KeepMetricNames
if transformFuncsKeepMetricName[name] {
keepMetricNames = true
}
for _, ts := range arg {
if !keepMetricNames {
ts.MetricName.ResetMetricGroup()
}
tf(ts.Values)
}
return arg, nil
}
func transformAbs(v float64) float64 {
return math.Abs(v)
}
func transformAbsent(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
tss := args[0]
rvs := getAbsentTimeseries(tfa.ec, tfa.fe.Args[0])
if len(tss) == 0 {
return rvs, nil
}
for i := range tss[0].Values {
isAbsent := true
for _, ts := range tss {
if !math.IsNaN(ts.Values[i]) {
isAbsent = false
break
}
}
if !isAbsent {
rvs[0].Values[i] = nan
}
}
return rvs, nil
}
func getAbsentTimeseries(ec *EvalConfig, arg metricsql.Expr) []*timeseries {
// Copy tags from arg
rvs := evalNumber(ec, 1)
rv := rvs[0]
me, ok := arg.(*metricsql.MetricExpr)
if !ok {
return rvs
}
tfss := searchutils.ToTagFilterss(me.LabelFilterss)
if len(tfss) != 1 {
return rvs
}
tfs := tfss[0]
for i := range tfs {
tf := &tfs[i]
if len(tf.Key) == 0 {
continue
}
if tf.IsRegexp || tf.IsNegative {
continue
}
rv.MetricName.AddTagBytes(tf.Key, tf.Value)
}
return rvs
}
func transformCeil(v float64) float64 {
return math.Ceil(v)
}
func transformClamp(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 3); err != nil {
return nil, err
}
mins, err := getScalar(args[1], 1)
if err != nil {
return nil, err
}
maxs, err := getScalar(args[2], 2)
if err != nil {
return nil, err
}
tf := func(values []float64) {
for i, v := range values {
if v > maxs[i] {
values[i] = maxs[i]
} else if v < mins[i] {
values[i] = mins[i]
}
}
}
return doTransformValues(args[0], tf, tfa.fe)
}
func transformClampMax(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 2); err != nil {
return nil, err
}
maxs, err := getScalar(args[1], 1)
if err != nil {
return nil, err
}
tf := func(values []float64) {
for i, v := range values {
if v > maxs[i] {
values[i] = maxs[i]
}
}
}
return doTransformValues(args[0], tf, tfa.fe)
}
func transformClampMin(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 2); err != nil {
return nil, err
}
mins, err := getScalar(args[1], 1)
if err != nil {
return nil, err
}
tf := func(values []float64) {
for i, v := range values {
if v < mins[i] {
values[i] = mins[i]
}
}
}
return doTransformValues(args[0], tf, tfa.fe)
}
func newTransformFuncDateTime(f func(t time.Time) int) transformFunc {
return func(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if len(args) > 1 {
return nil, fmt.Errorf(`too many args; got %d; want up to %d`, len(args), 1)
}
var arg []*timeseries
if len(args) == 0 {
arg = evalTime(tfa.ec)
} else {
arg = args[0]
}
tf := func(values []float64) {
for i, v := range values {
if math.IsNaN(v) {
continue
}
t := time.Unix(int64(v), 0).UTC()
values[i] = float64(f(t))
}
}
return doTransformValues(arg, tf, tfa.fe)
}
}
func transformDayOfYear(t time.Time) int {
return t.YearDay()
}
func transformDayOfMonth(t time.Time) int {
return t.Day()
}
func transformDayOfWeek(t time.Time) int {
return int(t.Weekday())
}
func transformDaysInMonth(t time.Time) int {
m := t.Month()
if m == 2 && isLeapYear(uint32(t.Year())) {
return 29
}
return daysInMonth[m]
}
func transformExp(v float64) float64 {
return math.Exp(v)
}
func transformFloor(v float64) float64 {
return math.Floor(v)
}
func transformBucketsLimit(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 2); err != nil {
return nil, err
}
limit, err := getIntNumber(args[0], 0)
if err != nil {
return nil, err
}
if limit <= 0 {
return nil, nil
}
if limit < 3 {
// Preserve the first and the last bucket for better accuracy for min and max values.
limit = 3
}
tss := vmrangeBucketsToLE(args[1])
if len(tss) == 0 {
return nil, nil
}
pointsCount := len(tss[0].Values)
// Group timeseries by all MetricGroup+tags excluding `le` tag.
type x struct {
le float64
hits float64
ts *timeseries
}
m := make(map[string][]x)
var b []byte
var mn storage.MetricName
for _, ts := range tss {
leStr := ts.MetricName.GetTagValue("le")
if len(leStr) == 0 {
// Skip time series without `le` tag.
continue
}
le, err := strconv.ParseFloat(string(leStr), 64)
if err != nil {
// Skip time series with invalid `le` tag.
continue
}
mn.CopyFrom(&ts.MetricName)
mn.RemoveTag("le")
b = marshalMetricNameSorted(b[:0], &mn)
k := string(b)
m[k] = append(m[k], x{
le: le,
ts: ts,
})
}
// Remove buckets with the smallest counters.
rvs := make([]*timeseries, 0, len(tss))
for _, leGroup := range m {
if len(leGroup) <= limit {
// Fast path - the number of buckets doesn't exceed the given limit.
// Keep all the buckets as is.
for _, xx := range leGroup {
rvs = append(rvs, xx.ts)
}
continue
}
// Slow path - remove buckets with the smallest number of hits until their count reaches the limit.
// Calculate per-bucket hits.
sort.Slice(leGroup, func(i, j int) bool {
return leGroup[i].le < leGroup[j].le
})
for n := 0; n < pointsCount; n++ {
prevValue := float64(0)
for i := range leGroup {
xx := &leGroup[i]
value := xx.ts.Values[n]
xx.hits += value - prevValue
prevValue = value
}
}
for len(leGroup) > limit {
// Preserve the first and the last bucket for better accuracy for min and max values
xxMinIdx := 1
minMergeHits := leGroup[1].hits + leGroup[2].hits
for i := range leGroup[1 : len(leGroup)-2] {
mergeHits := leGroup[i+1].hits + leGroup[i+2].hits
if mergeHits < minMergeHits {
xxMinIdx = i + 1
minMergeHits = mergeHits
}
}
leGroup[xxMinIdx+1].hits += leGroup[xxMinIdx].hits
leGroup = append(leGroup[:xxMinIdx], leGroup[xxMinIdx+1:]...)
}
for _, xx := range leGroup {
rvs = append(rvs, xx.ts)
}
}
return rvs, nil
}
func transformPrometheusBuckets(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
rvs := vmrangeBucketsToLE(args[0])
return rvs, nil
}
func vmrangeBucketsToLE(tss []*timeseries) []*timeseries {
rvs := make([]*timeseries, 0, len(tss))
// Group timeseries by MetricGroup+tags excluding `vmrange` tag.
type x struct {
startStr string
endStr string
start float64
end float64
ts *timeseries
}
m := make(map[string][]x)
bb := bbPool.Get()
defer bbPool.Put(bb)
for _, ts := range tss {
vmrange := ts.MetricName.GetTagValue("vmrange")
if len(vmrange) == 0 {
if le := ts.MetricName.GetTagValue("le"); len(le) > 0 {
// Keep Prometheus-compatible buckets.
rvs = append(rvs, ts)
}
continue
}
n := strings.Index(bytesutil.ToUnsafeString(vmrange), "...")
if n < 0 {
continue
}
startStr := string(vmrange[:n])
start, err := strconv.ParseFloat(startStr, 64)
if err != nil {
continue
}
endStr := string(vmrange[n+len("..."):])
end, err := strconv.ParseFloat(endStr, 64)
if err != nil {
continue
}
ts.MetricName.RemoveTag("le")
ts.MetricName.RemoveTag("vmrange")
bb.B = marshalMetricNameSorted(bb.B[:0], &ts.MetricName)
k := string(bb.B)
m[k] = append(m[k], x{
startStr: startStr,
endStr: endStr,
start: start,
end: end,
ts: ts,
})
}
// Convert `vmrange` label in each group of time series to `le` label.
copyTS := func(src *timeseries, leStr string) *timeseries {
var ts timeseries
ts.CopyFromShallowTimestamps(src)
values := ts.Values
for i := range values {
values[i] = 0
}
ts.MetricName.RemoveTag("le")
ts.MetricName.AddTag("le", leStr)
return &ts
}
isZeroTS := func(ts *timeseries) bool {
for _, v := range ts.Values {
if v > 0 {
return false
}
}
return true
}
for _, xss := range m {
sort.Slice(xss, func(i, j int) bool { return xss[i].end < xss[j].end })
xssNew := make([]x, 0, len(xss)+2)
var xsPrev x
uniqTs := make(map[string]*timeseries, len(xss))
for _, xs := range xss {
ts := xs.ts
if isZeroTS(ts) {
// Skip buckets with zero values - they will be merged into a single bucket
// when the next non-zero bucket appears.
// Do not store xs in xsPrev in order to properly create `le` time series
// for zero buckets.
// See https://github.com/VictoriaMetrics/VictoriaMetrics/pull/4021
continue
}
if xs.start != xsPrev.end {
// There is a gap between the previous bucket and the current bucket
// or the previous bucket is skipped because it was zero.
// Fill it with a time series with le=xs.start.
if uniqTs[xs.startStr] == nil {
uniqTs[xs.startStr] = xs.ts
xssNew = append(xssNew, x{
endStr: xs.startStr,
end: xs.start,
ts: copyTS(ts, xs.startStr),
})
}
}
// Convert the current time series to a time series with le=xs.end
ts.MetricName.AddTag("le", xs.endStr)
prevTs := uniqTs[xs.endStr]
if prevTs != nil {
// the end of the current bucket is not unique, need to merge it with the existing bucket.
_ = mergeNonOverlappingTimeseries(prevTs, xs.ts)
} else {
xssNew = append(xssNew, xs)
uniqTs[xs.endStr] = xs.ts
}
xsPrev = xs
}
if xsPrev.ts != nil && !math.IsInf(xsPrev.end, 1) && !isZeroTS(xsPrev.ts) {
xssNew = append(xssNew, x{
endStr: "+Inf",
end: math.Inf(1),
ts: copyTS(xsPrev.ts, "+Inf"),
})
}
xss = xssNew
if len(xss) == 0 {
continue
}
for i := range xss[0].ts.Values {
count := float64(0)
for _, xs := range xss {
ts := xs.ts
v := ts.Values[i]
if !math.IsNaN(v) && v > 0 {
count += v
}
ts.Values[i] = count
}
}
for _, xs := range xss {
rvs = append(rvs, xs.ts)
}
}
return rvs
}
func transformHistogramShare(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if len(args) < 2 || len(args) > 3 {
return nil, fmt.Errorf("unexpected number of args; got %d; want 2...3", len(args))
}
les, err := getScalar(args[0], 0)
if err != nil {
return nil, fmt.Errorf("cannot parse le: %w", err)
}
// Convert buckets with `vmrange` labels to buckets with `le` labels.
tss := vmrangeBucketsToLE(args[1])
// Parse boundsLabel. See https://github.com/prometheus/prometheus/issues/5706 for details.
var boundsLabel string
if len(args) > 2 {
s, err := getString(args[2], 2)
if err != nil {
return nil, fmt.Errorf("cannot parse boundsLabel (arg #3): %w", err)
}
boundsLabel = s
}
// Group metrics by all tags excluding "le"
m := groupLeTimeseries(tss)
// Calculate share for les
share := func(i int, les []float64, xss []leTimeseries) (q, lower, upper float64) {
leReq := les[i]
if math.IsNaN(leReq) || len(xss) == 0 {
return nan, nan, nan
}
fixBrokenBuckets(i, xss)
if leReq < 0 {
return 0, 0, 0
}
if math.IsInf(leReq, 1) {
return 1, 1, 1
}
var vPrev, lePrev float64
for _, xs := range xss {
v := xs.ts.Values[i]
le := xs.le
if leReq >= le {
vPrev = v
lePrev = le
continue
}
// precondition: lePrev <= leReq < le
vLast := xss[len(xss)-1].ts.Values[i]
lower = vPrev / vLast
if math.IsInf(le, 1) {
return lower, lower, 1
}
if lePrev == leReq {
return lower, lower, lower
}
upper = v / vLast
q = lower + (v-vPrev)/vLast*(leReq-lePrev)/(le-lePrev)
return q, lower, upper
}
// precondition: leReq > leLast
return 1, 1, 1
}
rvs := make([]*timeseries, 0, len(m))
for _, xss := range m {
sort.Slice(xss, func(i, j int) bool {
return xss[i].le < xss[j].le
})
xss = mergeSameLE(xss)
dst := xss[0].ts
var tsLower, tsUpper *timeseries
if len(boundsLabel) > 0 {
tsLower = &timeseries{}
tsLower.CopyFromShallowTimestamps(dst)
tsLower.MetricName.RemoveTag(boundsLabel)
tsLower.MetricName.AddTag(boundsLabel, "lower")
tsUpper = &timeseries{}
tsUpper.CopyFromShallowTimestamps(dst)
tsUpper.MetricName.RemoveTag(boundsLabel)
tsUpper.MetricName.AddTag(boundsLabel, "upper")
}
for i := range dst.Values {
q, lower, upper := share(i, les, xss)
dst.Values[i] = q
if len(boundsLabel) > 0 {
tsLower.Values[i] = lower
tsUpper.Values[i] = upper
}
}
rvs = append(rvs, dst)
if len(boundsLabel) > 0 {
rvs = append(rvs, tsLower)
rvs = append(rvs, tsUpper)
}
}
return rvs, nil
}
func transformHistogramAvg(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
tss := vmrangeBucketsToLE(args[0])
m := groupLeTimeseries(tss)
rvs := make([]*timeseries, 0, len(m))
for _, xss := range m {
sort.Slice(xss, func(i, j int) bool {
return xss[i].le < xss[j].le
})
dst := xss[0].ts
for i := range dst.Values {
dst.Values[i] = avgForLeTimeseries(i, xss)
}
rvs = append(rvs, dst)
}
return rvs, nil
}
func transformHistogramStddev(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
tss := vmrangeBucketsToLE(args[0])
m := groupLeTimeseries(tss)
rvs := make([]*timeseries, 0, len(m))
for _, xss := range m {
sort.Slice(xss, func(i, j int) bool {
return xss[i].le < xss[j].le
})
dst := xss[0].ts
for i := range dst.Values {
v := stdvarForLeTimeseries(i, xss)
dst.Values[i] = math.Sqrt(v)
}
rvs = append(rvs, dst)
}
return rvs, nil
}
func transformHistogramStdvar(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
tss := vmrangeBucketsToLE(args[0])
m := groupLeTimeseries(tss)
rvs := make([]*timeseries, 0, len(m))
for _, xss := range m {
sort.Slice(xss, func(i, j int) bool {
return xss[i].le < xss[j].le
})
dst := xss[0].ts
for i := range dst.Values {
dst.Values[i] = stdvarForLeTimeseries(i, xss)
}
rvs = append(rvs, dst)
}
return rvs, nil
}
func avgForLeTimeseries(i int, xss []leTimeseries) float64 {
lePrev := float64(0)
vPrev := float64(0)
sum := float64(0)
weightTotal := float64(0)
for _, xs := range xss {
if math.IsInf(xs.le, 0) {
continue
}
le := xs.le
n := (le + lePrev) / 2
v := xs.ts.Values[i]
weight := v - vPrev
sum += n * weight
weightTotal += weight
lePrev = le
vPrev = v
}
if weightTotal == 0 {
return nan
}
return sum / weightTotal
}
func stdvarForLeTimeseries(i int, xss []leTimeseries) float64 {
lePrev := float64(0)
vPrev := float64(0)
sum := float64(0)
sum2 := float64(0)
weightTotal := float64(0)
for _, xs := range xss {
if math.IsInf(xs.le, 0) {
continue
}
le := xs.le
n := (le + lePrev) / 2
v := xs.ts.Values[i]
weight := v - vPrev
sum += n * weight
sum2 += n * n * weight
weightTotal += weight
lePrev = le
vPrev = v
}
if weightTotal == 0 {
return nan
}
avg := sum / weightTotal
avg2 := sum2 / weightTotal
stdvar := avg2 - avg*avg
if stdvar < 0 {
// Correct possible calculation error.
stdvar = 0
}
return stdvar
}
func transformHistogramQuantiles(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if len(args) < 3 {
return nil, fmt.Errorf("unexpected number of args: %d; expecting at least 3 args", len(args))
}
dstLabel, err := getString(args[0], 0)
if err != nil {
return nil, fmt.Errorf("cannot obtain dstLabel: %w", err)
}
phiArgs := args[1 : len(args)-1]
tssOrig := args[len(args)-1]
// Calculate quantile individually per each phi.
var rvs []*timeseries
for i, phiArg := range phiArgs {
phis, err := getScalar(phiArg, i)
if err != nil {
return nil, fmt.Errorf("cannot parse phi: %w", err)
}
phiStr := fmt.Sprintf("%g", phis[0])
tss := copyTimeseries(tssOrig)
tfaTmp := &transformFuncArg{
ec: tfa.ec,
fe: tfa.fe,
args: [][]*timeseries{
phiArg,
tss,
},
}
tssTmp, err := transformHistogramQuantile(tfaTmp)
if err != nil {
return nil, fmt.Errorf("cannot calculate quantile %s: %w", phiStr, err)
}
for _, ts := range tssTmp {
ts.MetricName.RemoveTag(dstLabel)
ts.MetricName.AddTag(dstLabel, phiStr)
}
rvs = append(rvs, tssTmp...)
}
return rvs, nil
}
func transformHistogramQuantile(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if len(args) < 2 || len(args) > 3 {
return nil, fmt.Errorf("unexpected number of args; got %d; want 2...3", len(args))
}
phis, err := getScalar(args[0], 0)
if err != nil {
return nil, fmt.Errorf("cannot parse phi: %w", err)
}
// Convert buckets with `vmrange` labels to buckets with `le` labels.
tss := vmrangeBucketsToLE(args[1])
// Parse boundsLabel. See https://github.com/prometheus/prometheus/issues/5706 for details.
var boundsLabel string
if len(args) > 2 {
s, err := getString(args[2], 2)
if err != nil {
return nil, fmt.Errorf("cannot parse boundsLabel (arg #3): %w", err)
}
boundsLabel = s
}
// Group metrics by all tags excluding "le"
m := groupLeTimeseries(tss)
// Calculate quantile for each group in m
lastNonInf := func(i int, xss []leTimeseries) float64 {
for len(xss) > 0 {
xsLast := xss[len(xss)-1]
if !math.IsInf(xsLast.le, 0) {
return xsLast.le
}
xss = xss[:len(xss)-1]
}
return nan
}
quantile := func(i int, phis []float64, xss []leTimeseries) (q, lower, upper float64) {
phi := phis[i]
if math.IsNaN(phi) {
return nan, nan, nan
}
fixBrokenBuckets(i, xss)
vLast := float64(0)
if len(xss) > 0 {
vLast = xss[len(xss)-1].ts.Values[i]
}
if vLast == 0 {
return nan, nan, nan
}
if phi < 0 {
return -inf, -inf, xss[0].ts.Values[i]
}
if phi > 1 {
return inf, vLast, inf
}
vReq := vLast * phi
vPrev := float64(0)
lePrev := float64(0)
for _, xs := range xss {
v := xs.ts.Values[i]
le := xs.le
if v <= 0 {
// Skip zero buckets.
lePrev = le
continue
}
if v < vReq {
vPrev = v
lePrev = le
continue
}
if math.IsInf(le, 0) {
break
}
if v == vPrev {
return lePrev, lePrev, v
}
vv := lePrev + (le-lePrev)*(vReq-vPrev)/(v-vPrev)
return vv, lePrev, le
}
vv := lastNonInf(i, xss)
return vv, vv, inf
}
rvs := make([]*timeseries, 0, len(m))
for _, xss := range m {
sort.Slice(xss, func(i, j int) bool {
return xss[i].le < xss[j].le
})
xss = mergeSameLE(xss)
dst := xss[0].ts
var tsLower, tsUpper *timeseries
if len(boundsLabel) > 0 {
tsLower = &timeseries{}
tsLower.CopyFromShallowTimestamps(dst)
tsLower.MetricName.RemoveTag(boundsLabel)
tsLower.MetricName.AddTag(boundsLabel, "lower")
tsUpper = &timeseries{}
tsUpper.CopyFromShallowTimestamps(dst)
tsUpper.MetricName.RemoveTag(boundsLabel)
tsUpper.MetricName.AddTag(boundsLabel, "upper")
}
for i := range dst.Values {
v, lower, upper := quantile(i, phis, xss)
dst.Values[i] = v
if len(boundsLabel) > 0 {
tsLower.Values[i] = lower
tsUpper.Values[i] = upper
}
}
rvs = append(rvs, dst)
if len(boundsLabel) > 0 {
rvs = append(rvs, tsLower)
rvs = append(rvs, tsUpper)
}
}
return rvs, nil
}
type leTimeseries struct {
le float64
ts *timeseries
}
func groupLeTimeseries(tss []*timeseries) map[string][]leTimeseries {
m := make(map[string][]leTimeseries)
bb := bbPool.Get()
for _, ts := range tss {
tagValue := ts.MetricName.GetTagValue("le")
if len(tagValue) == 0 {
continue
}
le, err := strconv.ParseFloat(bytesutil.ToUnsafeString(tagValue), 64)
if err != nil {
continue
}
ts.MetricName.ResetMetricGroup()
ts.MetricName.RemoveTag("le")
bb.B = marshalMetricTagsSorted(bb.B[:0], &ts.MetricName)
k := string(bb.B)
m[k] = append(m[k], leTimeseries{
le: le,
ts: ts,
})
}
bbPool.Put(bb)
return m
}
func fixBrokenBuckets(i int, xss []leTimeseries) {
// Buckets are already sorted by le, so their values must be in ascending order,
// since the next bucket includes all the previous buckets.
// If the next bucket has lower value than the current bucket,
// then the current bucket must be substituted with the next bucket value.
// See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2819
if len(xss) < 2 {
return
}
// Fill NaN in upper buckets with the first non-NaN value found in lower buckets.
for j := len(xss) - 1; j >= 0; j-- {
v := xss[j].ts.Values[i]
if !math.IsNaN(v) {
j++
for j < len(xss) {
xss[j].ts.Values[i] = v
j++
}
break
}
}
// Substitute lower bucket values with upper values if the lower values are NaN
// or are bigger than the upper bucket values.
vNext := xss[len(xss)-1].ts.Values[i]
for j := len(xss) - 2; j >= 0; j-- {
v := xss[j].ts.Values[i]
if math.IsNaN(v) || v > vNext {
xss[j].ts.Values[i] = vNext
} else {
vNext = v
}
}
}
func mergeSameLE(xss []leTimeseries) []leTimeseries {
// Merge buckets with identical le values.
// See https://github.com/VictoriaMetrics/VictoriaMetrics/pull/3225
xsDst := xss[0]
dst := xss[:1]
for j := 1; j < len(xss); j++ {
xs := xss[j]
if xs.le != xsDst.le {
dst = append(dst, xs)
xsDst = xs
continue
}
dstValues := xsDst.ts.Values
for k, v := range xs.ts.Values {
dstValues[k] += v
}
}
return dst
}
func transformHour(t time.Time) int {
return t.Hour()
}
func runningSum(a, b float64, _ int) float64 {
return a + b
}
func runningMax(a, b float64, _ int) float64 {
if a > b {
return a
}
return b
}
func runningMin(a, b float64, _ int) float64 {
if a < b {
return a
}
return b
}
func runningAvg(a, b float64, idx int) float64 {
// See `Rapid calculation methods` at https://en.wikipedia.org/wiki/Standard_deviation
return a + (b-a)/float64(idx+1)
}
func skipLeadingNaNs(values []float64) []float64 {
i := 0
for i < len(values) && math.IsNaN(values[i]) {
i++
}
return values[i:]
}
func skipTrailingNaNs(values []float64) []float64 {
i := len(values) - 1
for i >= 0 && math.IsNaN(values[i]) {
i--
}
return values[:i+1]
}
func transformKeepLastValue(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
rvs := args[0]
for _, ts := range rvs {
values := ts.Values
if len(values) == 0 {
continue
}
lastValue := values[0]
for i, v := range values {
if !math.IsNaN(v) {
lastValue = v
continue
}
values[i] = lastValue
}
}
return rvs, nil
}
func transformKeepNextValue(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
rvs := args[0]
for _, ts := range rvs {
values := ts.Values
if len(values) == 0 {
continue
}
nextValue := values[len(values)-1]
for i := len(values) - 1; i >= 0; i-- {
v := values[i]
if !math.IsNaN(v) {
nextValue = v
continue
}
values[i] = nextValue
}
}
return rvs, nil
}
func transformInterpolate(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
rvs := args[0]
for _, ts := range rvs {
values := skipLeadingNaNs(ts.Values)
values = skipTrailingNaNs(values)
if len(values) == 0 {
continue
}
prevValue := nan
var nextValue float64
for i := 0; i < len(values); i++ {
if !math.IsNaN(values[i]) {
continue
}
if i > 0 {
prevValue = values[i-1]
}
j := i + 1
for j < len(values) {
if !math.IsNaN(values[j]) {
break
}
j++
}
if j >= len(values) {
nextValue = prevValue
} else {
nextValue = values[j]
}
if math.IsNaN(prevValue) {
prevValue = nextValue
}
delta := (nextValue - prevValue) / float64(j-i+1)
for i < j {
prevValue += delta
values[i] = prevValue
i++
}
}
}
return rvs, nil
}
func newTransformFuncRunning(rf func(a, b float64, idx int) float64) transformFunc {
return func(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
rvs := args[0]
for _, ts := range rvs {
ts.MetricName.ResetMetricGroup()
values := skipLeadingNaNs(ts.Values)
if len(values) == 0 {
continue
}
prevValue := values[0]
values = values[1:]
for i, v := range values {
if !math.IsNaN(v) {
prevValue = rf(prevValue, v, i+1)
}
values[i] = prevValue
}
}
return rvs, nil
}
}
func newTransformFuncRange(rf func(a, b float64, idx int) float64) transformFunc {
tfr := newTransformFuncRunning(rf)
return func(tfa *transformFuncArg) ([]*timeseries, error) {
rvs, err := tfr(tfa)
if err != nil {
return nil, err
}
setLastValues(rvs)
return rvs, nil
}
}
func transformRangeNormalize(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
var rvs []*timeseries
for _, tss := range args {
for _, ts := range tss {
values := ts.Values
vMin := inf
vMax := -inf
for _, v := range values {
if math.IsNaN(v) {
continue
}
if v < vMin {
vMin = v
}
if v > vMax {
vMax = v
}
}
d := vMax - vMin
if math.IsInf(d, 0) {
continue
}
for i, v := range values {
values[i] = (v - vMin) / d
}
rvs = append(rvs, ts)
}
}
return rvs, nil
}
func transformRangeTrimZscore(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 2); err != nil {
return nil, err
}
zs, err := getScalar(args[0], 0)
if err != nil {
return nil, err
}
z := float64(0)
if len(zs) > 0 {
z = math.Abs(zs[0])
}
// Trim samples with z-score above z.
rvs := args[1]
for _, ts := range rvs {
values := ts.Values
qStddev := stddev(values)
avg := mean(values)
for i, v := range values {
zCurr := math.Abs(v-avg) / qStddev
if zCurr > z {
values[i] = nan
}
}
}
return rvs, nil
}
func transformRangeZscore(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
rvs := args[0]
for _, ts := range rvs {
values := ts.Values
qStddev := stddev(values)
avg := mean(values)
for i, v := range values {
values[i] = (v - avg) / qStddev
}
}
return rvs, nil
}
func mean(values []float64) float64 {
var sum float64
var n int
for _, v := range values {
if !math.IsNaN(v) {
sum += v
n++
}
}
return sum / float64(n)
}
func transformRangeTrimOutliers(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 2); err != nil {
return nil, err
}
ks, err := getScalar(args[0], 0)
if err != nil {
return nil, err
}
k := float64(0)
if len(ks) > 0 {
k = ks[0]
}
// Trim samples satisfying the `abs(v - range_median(q)) > k*range_mad(q)`
rvs := args[1]
for _, ts := range rvs {
values := ts.Values
dMax := k * mad(values)
qMedian := quantile(0.5, values)
for i, v := range values {
if math.Abs(v-qMedian) > dMax {
values[i] = nan
}
}
}
return rvs, nil
}
func transformRangeTrimSpikes(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 2); err != nil {
return nil, err
}
phis, err := getScalar(args[0], 0)
if err != nil {
return nil, err
}
phi := float64(0)
if len(phis) > 0 {
phi = phis[0]
}
// Trim 100% * (phi / 2) samples with the lowest / highest values per each time series
phi /= 2
phiUpper := 1 - phi
phiLower := phi
rvs := args[1]
a := getFloat64s()
values := a.A[:0]
for _, ts := range rvs {
values := values[:0]
originValues := ts.Values
for _, v := range originValues {
if math.IsNaN(v) {
continue
}
values = append(values, v)
}
sort.Float64s(values)
vMax := quantileSorted(phiUpper, values)
vMin := quantileSorted(phiLower, values)
for i, v := range originValues {
if math.IsNaN(v) {
continue
}
if v > vMax {
originValues[i] = nan
} else if v < vMin {
originValues[i] = nan
}
}
}
a.A = values
putFloat64s(a)
return rvs, nil
}
func transformRangeLinearRegression(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
rvs := args[0]
for _, ts := range rvs {
values := ts.Values
timestamps := ts.Timestamps
if len(timestamps) == 0 {
continue
}
interceptTimestamp := timestamps[0]
v, k := linearRegression(values, timestamps, interceptTimestamp)
for i, t := range timestamps {
values[i] = v + k*float64(t-interceptTimestamp)/1e3
}
}
return rvs, nil
}
func transformRangeMAD(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
rvs := args[0]
for _, ts := range rvs {
values := ts.Values
v := mad(values)
for i := range values {
values[i] = v
}
}
return rvs, nil
}
func transformRangeStddev(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
rvs := args[0]
for _, ts := range rvs {
values := ts.Values
v := stddev(values)
for i := range values {
values[i] = v
}
}
return rvs, nil
}
func transformRangeStdvar(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
rvs := args[0]
for _, ts := range rvs {
values := ts.Values
v := stdvar(values)
for i := range values {
values[i] = v
}
}
return rvs, nil
}
func transformRangeQuantile(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 2); err != nil {
return nil, err
}
phis, err := getScalar(args[0], 0)
if err != nil {
return nil, err
}
phi := float64(0)
if len(phis) > 0 {
phi = phis[0]
}
rvs := args[1]
a := getFloat64s()
values := a.A[:0]
for _, ts := range rvs {
lastIdx := -1
originValues := ts.Values
values = values[:0]
for i, v := range originValues {
if math.IsNaN(v) {
continue
}
values = append(values, v)
lastIdx = i
}
if lastIdx >= 0 {
sort.Float64s(values)
originValues[lastIdx] = quantileSorted(phi, values)
}
}
a.A = values
putFloat64s(a)
setLastValues(rvs)
return rvs, nil
}
func transformRangeFirst(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
rvs := args[0]
for _, ts := range rvs {
values := skipLeadingNaNs(ts.Values)
if len(values) == 0 {
continue
}
vFirst := values[0]
for i, v := range values {
if math.IsNaN(v) {
continue
}
values[i] = vFirst
}
}
return rvs, nil
}
func transformRangeLast(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
rvs := args[0]
setLastValues(rvs)
return rvs, nil
}
func setLastValues(tss []*timeseries) {
for _, ts := range tss {
values := skipTrailingNaNs(ts.Values)
if len(values) == 0 {
continue
}
vLast := values[len(values)-1]
for i, v := range values {
if math.IsNaN(v) {
continue
}
values[i] = vLast
}
}
}
func transformSmoothExponential(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 2); err != nil {
return nil, err
}
sfs, err := getScalar(args[1], 1)
if err != nil {
return nil, err
}
rvs := args[0]
for _, ts := range rvs {
values := skipLeadingNaNs(ts.Values)
for i, v := range values {
if !math.IsInf(v, 0) {
values = values[i:]
break
}
}
if len(values) == 0 {
continue
}
avg := values[0]
values = values[1:]
sfsX := sfs[len(ts.Values)-len(values):]
for i, v := range values {
if math.IsNaN(v) {
continue
}
if math.IsInf(v, 0) {
values[i] = avg
continue
}
sf := sfsX[i]
if math.IsNaN(sf) {
sf = 1
}
if sf < 0 {
sf = 0
}
if sf > 1 {
sf = 1
}
avg = avg*(1-sf) + v*sf
values[i] = avg
}
}
return rvs, nil
}
func transformRemoveResets(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
rvs := args[0]
for _, ts := range rvs {
removeCounterResetsMaybeNaNs(ts.Values)
}
return rvs, nil
}
func transformUnion(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if len(args) < 1 {
return evalNumber(tfa.ec, nan), nil
}
rvs := make([]*timeseries, 0, len(args[0]))
m := make(map[string]bool, len(args[0]))
bb := bbPool.Get()
for _, arg := range args {
for _, ts := range arg {
bb.B = marshalMetricNameSorted(bb.B[:0], &ts.MetricName)
k := string(bb.B)
if m[k] {
continue
}
m[k] = true
rvs = append(rvs, ts)
}
}
bbPool.Put(bb)
return rvs, nil
}
func transformLabelKeep(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if len(args) < 1 {
return nil, fmt.Errorf(`not enough args; got %d; want at least %d`, len(args), 1)
}
var keepLabels []string
for i := 1; i < len(args); i++ {
keepLabel, err := getString(args[i], i)
if err != nil {
return nil, err
}
keepLabels = append(keepLabels, keepLabel)
}
rvs := args[0]
for _, ts := range rvs {
ts.MetricName.RemoveTagsOn(keepLabels)
}
return rvs, nil
}
func transformLabelDel(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if len(args) < 1 {
return nil, fmt.Errorf(`not enough args; got %d; want at least %d`, len(args), 1)
}
var delLabels []string
for i := 1; i < len(args); i++ {
delLabel, err := getString(args[i], i)
if err != nil {
return nil, err
}
delLabels = append(delLabels, delLabel)
}
rvs := args[0]
for _, ts := range rvs {
ts.MetricName.RemoveTagsIgnoring(delLabels)
}
return rvs, nil
}
func transformLabelSet(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if len(args) < 1 {
return nil, fmt.Errorf(`not enough args; got %d; want at least %d`, len(args), 1)
}
dstLabels, dstValues, err := getStringPairs(args[1:])
if err != nil {
return nil, err
}
rvs := args[0]
for _, ts := range rvs {
mn := &ts.MetricName
for i, dstLabel := range dstLabels {
value := dstValues[i]
dstValue := getDstValue(mn, dstLabel)
*dstValue = append((*dstValue)[:0], value...)
if len(value) == 0 {
mn.RemoveTag(dstLabel)
}
}
}
return rvs, nil
}
func transformLabelUppercase(tfa *transformFuncArg) ([]*timeseries, error) {
return transformLabelValueFunc(tfa, strings.ToUpper)
}
func transformLabelLowercase(tfa *transformFuncArg) ([]*timeseries, error) {
return transformLabelValueFunc(tfa, strings.ToLower)
}
func transformLabelValueFunc(tfa *transformFuncArg, f func(string) string) ([]*timeseries, error) {
args := tfa.args
if len(args) < 2 {
return nil, fmt.Errorf(`not enough args; got %d; want at least %d`, len(args), 2)
}
labels := make([]string, 0, len(args)-1)
for i := 1; i < len(args); i++ {
label, err := getString(args[i], i)
if err != nil {
return nil, err
}
labels = append(labels, label)
}
rvs := args[0]
for _, ts := range rvs {
mn := &ts.MetricName
for _, label := range labels {
dstValue := getDstValue(mn, label)
*dstValue = append((*dstValue)[:0], f(string(*dstValue))...)
if len(*dstValue) == 0 {
mn.RemoveTag(label)
}
}
}
return rvs, nil
}
func transformLabelMap(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if len(args) < 2 {
return nil, fmt.Errorf(`not enough args; got %d; want at least %d`, len(args), 2)
}
label, err := getString(args[1], 1)
if err != nil {
return nil, fmt.Errorf("cannot read label name: %w", err)
}
srcValues, dstValues, err := getStringPairs(args[2:])
if err != nil {
return nil, err
}
m := make(map[string]string, len(srcValues))
for i, srcValue := range srcValues {
m[srcValue] = dstValues[i]
}
rvs := args[0]
for _, ts := range rvs {
mn := &ts.MetricName
dstValue := getDstValue(mn, label)
value, ok := m[string(*dstValue)]
if ok {
*dstValue = append((*dstValue)[:0], value...)
}
if len(*dstValue) == 0 {
mn.RemoveTag(label)
}
}
return rvs, nil
}
func transformDropCommonLabels(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if len(args) < 1 {
return nil, fmt.Errorf(`not enough args; got %d; want at least %d`, len(args), 1)
}
rvs := args[0]
for _, tss := range args[1:] {
rvs = append(rvs, tss...)
}
m := make(map[string]map[string]int)
countLabel := func(name, value string) {
x := m[name]
if x == nil {
x = make(map[string]int)
m[name] = x
}
x[value]++
}
for _, ts := range rvs {
countLabel("__name__", string(ts.MetricName.MetricGroup))
for _, tag := range ts.MetricName.Tags {
countLabel(string(tag.Key), string(tag.Value))
}
}
for labelName, x := range m {
for _, count := range x {
if count != len(rvs) {
continue
}
for _, ts := range rvs {
ts.MetricName.RemoveTag(labelName)
}
}
}
return rvs, nil
}
func transformDropEmptySeries(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
rvs := removeEmptySeries(args[0])
return rvs, nil
}
func transformLabelCopy(tfa *transformFuncArg) ([]*timeseries, error) {
return transformLabelCopyExt(tfa, false)
}
func transformLabelMove(tfa *transformFuncArg) ([]*timeseries, error) {
return transformLabelCopyExt(tfa, true)
}
func transformLabelCopyExt(tfa *transformFuncArg, removeSrcLabels bool) ([]*timeseries, error) {
args := tfa.args
if len(args) < 1 {
return nil, fmt.Errorf(`not enough args; got %d; want at least %d`, len(args), 1)
}
srcLabels, dstLabels, err := getStringPairs(args[1:])
if err != nil {
return nil, err
}
rvs := args[0]
for _, ts := range rvs {
mn := &ts.MetricName
for i, srcLabel := range srcLabels {
dstLabel := dstLabels[i]
value := mn.GetTagValue(srcLabel)
if len(value) == 0 {
// Do not remove destination label if the source label doesn't exist.
continue
}
dstValue := getDstValue(mn, dstLabel)
*dstValue = append((*dstValue)[:0], value...)
if removeSrcLabels && srcLabel != dstLabel {
mn.RemoveTag(srcLabel)
}
}
}
return rvs, nil
}
func getStringPairs(args [][]*timeseries) ([]string, []string, error) {
if len(args)%2 != 0 {
return nil, nil, fmt.Errorf(`the number of string args must be even; got %d`, len(args))
}
var ks, vs []string
for i := 0; i < len(args); i += 2 {
k, err := getString(args[i], i)
if err != nil {
return nil, nil, err
}
ks = append(ks, k)
v, err := getString(args[i+1], i+1)
if err != nil {
return nil, nil, err
}
vs = append(vs, v)
}
return ks, vs, nil
}
func transformLabelJoin(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if len(args) < 3 {
return nil, fmt.Errorf(`not enough args; got %d; want at least %d`, len(args), 3)
}
dstLabel, err := getString(args[1], 1)
if err != nil {
return nil, err
}
separator, err := getString(args[2], 2)
if err != nil {
return nil, err
}
var srcLabels []string
for i := 3; i < len(args); i++ {
srcLabel, err := getString(args[i], i)
if err != nil {
return nil, err
}
srcLabels = append(srcLabels, srcLabel)
}
rvs := args[0]
for _, ts := range rvs {
mn := &ts.MetricName
dstValue := getDstValue(mn, dstLabel)
b := *dstValue
b = b[:0]
for j, srcLabel := range srcLabels {
srcValue := mn.GetTagValue(srcLabel)
b = append(b, srcValue...)
if j+1 < len(srcLabels) {
b = append(b, separator...)
}
}
*dstValue = b
if len(b) == 0 {
mn.RemoveTag(dstLabel)
}
}
return rvs, nil
}
func transformLabelTransform(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 4); err != nil {
return nil, err
}
label, err := getString(args[1], 1)
if err != nil {
return nil, err
}
regex, err := getString(args[2], 2)
if err != nil {
return nil, err
}
replacement, err := getString(args[3], 3)
if err != nil {
return nil, err
}
r, err := metricsql.CompileRegexp(regex)
if err != nil {
return nil, fmt.Errorf(`cannot compile regex %q: %w`, regex, err)
}
return labelReplace(args[0], label, r, label, replacement)
}
func transformLabelReplace(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 5); err != nil {
return nil, err
}
dstLabel, err := getString(args[1], 1)
if err != nil {
return nil, err
}
replacement, err := getString(args[2], 2)
if err != nil {
return nil, err
}
srcLabel, err := getString(args[3], 3)
if err != nil {
return nil, err
}
regex, err := getString(args[4], 4)
if err != nil {
return nil, err
}
r, err := metricsql.CompileRegexpAnchored(regex)
if err != nil {
return nil, fmt.Errorf(`cannot compile regex %q: %w`, regex, err)
}
return labelReplace(args[0], srcLabel, r, dstLabel, replacement)
}
func labelReplace(tss []*timeseries, srcLabel string, r *regexp.Regexp, dstLabel, replacement string) ([]*timeseries, error) {
replacementBytes := []byte(replacement)
for _, ts := range tss {
mn := &ts.MetricName
srcValue := mn.GetTagValue(srcLabel)
if !r.Match(srcValue) {
continue
}
b := r.ReplaceAll(srcValue, replacementBytes)
dstValue := getDstValue(mn, dstLabel)
*dstValue = append((*dstValue)[:0], b...)
if len(b) == 0 {
mn.RemoveTag(dstLabel)
}
}
return tss, nil
}
func transformLabelsEqual(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if len(args) < 3 {
return nil, fmt.Errorf("unexpected number of args; got %d; want at least 3", len(args))
}
tss := args[0]
var labelNames []string
for i, ts := range args[1:] {
labelName, err := getString(ts, i+1)
if err != nil {
return nil, fmt.Errorf("cannot get label name: %w", err)
}
labelNames = append(labelNames, labelName)
}
rvs := tss[:0]
for _, ts := range tss {
if hasIdenticalLabelValues(&ts.MetricName, labelNames) {
rvs = append(rvs, ts)
}
}
return rvs, nil
}
func hasIdenticalLabelValues(mn *storage.MetricName, labelNames []string) bool {
if len(labelNames) < 2 {
return true
}
labelValue := mn.GetTagValue(labelNames[0])
for _, labelName := range labelNames[1:] {
b := mn.GetTagValue(labelName)
if string(labelValue) != string(b) {
return false
}
}
return true
}
func transformLabelValue(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 2); err != nil {
return nil, err
}
labelName, err := getString(args[1], 1)
if err != nil {
return nil, fmt.Errorf("cannot get label name: %w", err)
}
rvs := args[0]
for _, ts := range rvs {
ts.MetricName.ResetMetricGroup()
labelValue := ts.MetricName.GetTagValue(labelName)
v, err := strconv.ParseFloat(string(labelValue), 64)
if err != nil {
v = nan
}
values := ts.Values
for i, vOrig := range values {
if !math.IsNaN(vOrig) {
values[i] = v
}
}
}
// Do not remove timeseries with only NaN values, so `default` could be applied to them:
// label_value(q, "label") default 123
return rvs, nil
}
func transformLabelMatch(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 3); err != nil {
return nil, err
}
labelName, err := getString(args[1], 1)
if err != nil {
return nil, fmt.Errorf("cannot get label name: %w", err)
}
labelRe, err := getString(args[2], 2)
if err != nil {
return nil, fmt.Errorf("cannot get regexp: %w", err)
}
r, err := metricsql.CompileRegexpAnchored(labelRe)
if err != nil {
return nil, fmt.Errorf(`cannot compile regexp %q: %w`, labelRe, err)
}
tss := args[0]
rvs := tss[:0]
for _, ts := range tss {
labelValue := ts.MetricName.GetTagValue(labelName)
if r.Match(labelValue) {
rvs = append(rvs, ts)
}
}
return rvs, nil
}
func transformLabelMismatch(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 3); err != nil {
return nil, err
}
labelName, err := getString(args[1], 1)
if err != nil {
return nil, fmt.Errorf("cannot get label name: %w", err)
}
labelRe, err := getString(args[2], 2)
if err != nil {
return nil, fmt.Errorf("cannot get regexp: %w", err)
}
r, err := metricsql.CompileRegexpAnchored(labelRe)
if err != nil {
return nil, fmt.Errorf(`cannot compile regexp %q: %w`, labelRe, err)
}
tss := args[0]
rvs := tss[:0]
for _, ts := range tss {
labelValue := ts.MetricName.GetTagValue(labelName)
if !r.Match(labelValue) {
rvs = append(rvs, ts)
}
}
return rvs, nil
}
func transformLabelGraphiteGroup(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if len(args) < 2 {
return nil, fmt.Errorf("unexpected number of args: %d; want at least 2 args", len(args))
}
tss := args[0]
groupArgs := args[1:]
groupIDs := make([]int, len(groupArgs))
for i, arg := range groupArgs {
groupID, err := getIntNumber(arg, i+1)
if err != nil {
return nil, fmt.Errorf("cannot get group name from arg #%d: %w", i+1, err)
}
groupIDs[i] = groupID
}
for _, ts := range tss {
groups := bytes.Split(ts.MetricName.MetricGroup, dotSeparator)
groupName := ts.MetricName.MetricGroup[:0]
for j, groupID := range groupIDs {
if groupID >= 0 && groupID < len(groups) {
groupName = append(groupName, groups[groupID]...)
}
if j < len(groupIDs)-1 {
groupName = append(groupName, '.')
}
}
ts.MetricName.MetricGroup = groupName
}
return tss, nil
}
var dotSeparator = []byte(".")
func transformLimitOffset(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 3); err != nil {
return nil, err
}
limit, err := getIntNumber(args[0], 0)
if err != nil {
return nil, fmt.Errorf("cannot obtain limit arg: %w", err)
}
offset, err := getIntNumber(args[1], 1)
if err != nil {
return nil, fmt.Errorf("cannot obtain offset arg: %w", err)
}
// removeEmptySeries so offset will be calculated after empty series
// were filtered out.
rvs := removeEmptySeries(args[2])
if len(rvs) >= offset {
rvs = rvs[offset:]
} else {
rvs = nil
}
if len(rvs) > limit {
rvs = rvs[:limit]
}
return rvs, nil
}
func transformLn(v float64) float64 {
return math.Log(v)
}
func transformLog2(v float64) float64 {
return math.Log2(v)
}
func transformLog10(v float64) float64 {
return math.Log10(v)
}
func transformMinute(t time.Time) int {
return t.Minute()
}
func transformMonth(t time.Time) int {
return int(t.Month())
}
func transformRound(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if len(args) != 1 && len(args) != 2 {
return nil, fmt.Errorf(`unexpected number of args: %d; want 1 or 2`, len(args))
}
var nearestArg []*timeseries
if len(args) == 1 {
nearestArg = evalNumber(tfa.ec, 1)
} else {
nearestArg = args[1]
}
nearest, err := getScalar(nearestArg, 1)
if err != nil {
return nil, err
}
tf := func(values []float64) {
var nPrev float64
var p10 float64
for i, v := range values {
n := nearest[i]
if n != nPrev {
nPrev = n
_, e := decimal.FromFloat(n)
p10 = math.Pow10(int(-e))
}
v += 0.5 * math.Copysign(n, v)
v -= math.Mod(v, n)
v, _ = math.Modf(v * p10)
values[i] = v / p10
}
}
return doTransformValues(args[0], tf, tfa.fe)
}
func transformSgn(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
tf := func(values []float64) {
for i, v := range values {
sign := float64(0)
if v < 0 {
sign = -1
} else if v > 0 {
sign = 1
}
values[i] = sign
}
}
return doTransformValues(args[0], tf, tfa.fe)
}
func transformScalar(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
// Verify whether the arg is a string.
// Then try converting the string to number.
if se, ok := tfa.fe.Args[0].(*metricsql.StringExpr); ok {
n, err := strconv.ParseFloat(se.S, 64)
if err != nil {
n = nan
}
return evalNumber(tfa.ec, n), nil
}
// The arg isn't a string. Extract scalar from it.
arg := args[0]
if len(arg) != 1 {
return evalNumber(tfa.ec, nan), nil
}
arg[0].MetricName.Reset()
return arg, nil
}
func newTransformFuncSortByLabel(isDesc bool) transformFunc {
return func(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if len(args) < 2 {
return nil, fmt.Errorf("expecting at least 2 args; got %d args", len(args))
}
var labels []string
for i, arg := range args[1:] {
label, err := getString(arg, 1)
if err != nil {
return nil, fmt.Errorf("cannot parse label #%d for sorting: %w", i+1, err)
}
labels = append(labels, label)
}
rvs := args[0]
sort.SliceStable(rvs, func(i, j int) bool {
for _, label := range labels {
a := rvs[i].MetricName.GetTagValue(label)
b := rvs[j].MetricName.GetTagValue(label)
if string(a) == string(b) {
continue
}
if isDesc {
return string(b) < string(a)
}
return string(a) < string(b)
}
return false
})
return rvs, nil
}
}
func newTransformFuncNumericSort(isDesc bool) transformFunc {
return func(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if len(args) < 2 {
return nil, fmt.Errorf("expecting at least 2 args; got %d args", len(args))
}
var labels []string
for i, arg := range args[1:] {
label, err := getString(arg, i+1)
if err != nil {
return nil, fmt.Errorf("cannot parse label #%d for sorting: %w", i+1, err)
}
labels = append(labels, label)
}
rvs := args[0]
sort.SliceStable(rvs, func(i, j int) bool {
for _, label := range labels {
a := rvs[i].MetricName.GetTagValue(label)
b := rvs[j].MetricName.GetTagValue(label)
if string(a) == string(b) {
continue
}
aStr := bytesutil.ToUnsafeString(a)
bStr := bytesutil.ToUnsafeString(b)
if isDesc {
return numericLess(bStr, aStr)
}
return numericLess(aStr, bStr)
}
return false
})
return rvs, nil
}
}
func numericLess(a, b string) bool {
for {
if len(b) == 0 {
return false
}
if len(a) == 0 {
return true
}
aPrefix := getNumPrefix(a)
bPrefix := getNumPrefix(b)
a = a[len(aPrefix):]
b = b[len(bPrefix):]
if len(aPrefix) > 0 || len(bPrefix) > 0 {
if len(aPrefix) == 0 {
return false
}
if len(bPrefix) == 0 {
return true
}
aNum := mustParseNum(aPrefix)
bNum := mustParseNum(bPrefix)
if aNum != bNum {
return aNum < bNum
}
}
aPrefix = getNonNumPrefix(a)
bPrefix = getNonNumPrefix(b)
a = a[len(aPrefix):]
b = b[len(bPrefix):]
if aPrefix != bPrefix {
return aPrefix < bPrefix
}
}
}
func getNumPrefix(s string) string {
i := 0
if len(s) > 0 {
switch s[0] {
case '-', '+':
i++
}
}
hasNum := false
hasDot := false
for i < len(s) {
if !isDecimalChar(s[i]) {
if !hasDot && s[i] == '.' {
hasDot = true
i++
continue
}
if !hasNum {
return ""
}
return s[:i]
}
hasNum = true
i++
}
if !hasNum {
return ""
}
return s
}
func getNonNumPrefix(s string) string {
i := 0
for i < len(s) {
if isDecimalChar(s[i]) {
return s[:i]
}
i++
}
return s
}
func isDecimalChar(ch byte) bool {
return ch >= '0' && ch <= '9'
}
func mustParseNum(s string) float64 {
f, err := strconv.ParseFloat(s, 64)
if err != nil {
logger.Panicf("BUG: unexpected error when parsing the number %q: %s", s, err)
}
return f
}
func newTransformFuncSort(isDesc bool) transformFunc {
return func(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
rvs := args[0]
sort.Slice(rvs, func(i, j int) bool {
a := rvs[i].Values
b := rvs[j].Values
n := len(a) - 1
for n >= 0 {
if !math.IsNaN(a[n]) {
if math.IsNaN(b[n]) {
return false
}
if a[n] != b[n] {
break
}
} else if !math.IsNaN(b[n]) {
return true
}
n--
}
if n < 0 {
return false
}
if isDesc {
return b[n] < a[n]
}
return a[n] < b[n]
})
return rvs, nil
}
}
func transformSqrt(v float64) float64 {
return math.Sqrt(v)
}
func transformSin(v float64) float64 {
return math.Sin(v)
}
func transformSinh(v float64) float64 {
return math.Sinh(v)
}
func transformCos(v float64) float64 {
return math.Cos(v)
}
func transformCosh(v float64) float64 {
return math.Cosh(v)
}
func transformTan(v float64) float64 {
return math.Tan(v)
}
func transformTanh(v float64) float64 {
return math.Tanh(v)
}
func transformAsin(v float64) float64 {
return math.Asin(v)
}
func transformAsinh(v float64) float64 {
return math.Asinh(v)
}
func transformAtan(v float64) float64 {
return math.Atan(v)
}
func transformAtanh(v float64) float64 {
return math.Atanh(v)
}
func transformAcos(v float64) float64 {
return math.Acos(v)
}
func transformAcosh(v float64) float64 {
return math.Acosh(v)
}
func transformDeg(v float64) float64 {
return v * 180 / math.Pi
}
func transformRad(v float64) float64 {
return v * math.Pi / 180
}
func newTransformRand(newRandFunc func(r *rand.Rand) func() float64) transformFunc {
return func(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if len(args) > 1 {
return nil, fmt.Errorf(`unexpected number of args; got %d; want 0 or 1`, len(args))
}
var seed int64
if len(args) == 1 {
tmp, err := getScalar(args[0], 0)
if err != nil {
return nil, err
}
if len(tmp) > 0 {
seed = int64(tmp[0])
}
} else {
seed = time.Now().UnixNano()
}
source := rand.NewSource(seed)
r := rand.New(source)
randFunc := newRandFunc(r)
tss := evalNumber(tfa.ec, 0)
values := tss[0].Values
for i := range values {
values[i] = randFunc()
}
return tss, nil
}
}
func newRandFloat64(r *rand.Rand) func() float64 {
return r.Float64
}
func newRandNormFloat64(r *rand.Rand) func() float64 {
return r.NormFloat64
}
func newRandExpFloat64(r *rand.Rand) func() float64 {
return r.ExpFloat64
}
func transformPi(tfa *transformFuncArg) ([]*timeseries, error) {
if err := expectTransformArgsNum(tfa.args, 0); err != nil {
return nil, err
}
return evalNumber(tfa.ec, math.Pi), nil
}
func transformNow(tfa *transformFuncArg) ([]*timeseries, error) {
if err := expectTransformArgsNum(tfa.args, 0); err != nil {
return nil, err
}
now := float64(time.Now().UnixNano()) / 1e9
return evalNumber(tfa.ec, now), nil
}
func bitmapAnd(a, b uint64) uint64 {
return a & b
}
func bitmapOr(a, b uint64) uint64 {
return a | b
}
func bitmapXor(a, b uint64) uint64 {
return a ^ b
}
func newTransformBitmap(bitmapFunc func(a, b uint64) uint64) func(tfa *transformFuncArg) ([]*timeseries, error) {
return func(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 2); err != nil {
return nil, err
}
ns, err := getScalar(args[1], 1)
if err != nil {
return nil, err
}
tf := func(values []float64) {
for i, v := range values {
w := ns[i]
result := nan
if !math.IsNaN(v) && !math.IsNaN(w) {
result = float64(bitmapFunc(uint64(v), uint64(w)))
}
values[i] = result
}
}
return doTransformValues(args[0], tf, tfa.fe)
}
}
func transformTimezoneOffset(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
tzString, err := getString(args[0], 0)
if err != nil {
return nil, fmt.Errorf("cannot get timezone name: %w", err)
}
loc, err := time.LoadLocation(tzString)
if err != nil {
return nil, fmt.Errorf("cannot load timezone %q: %w", tzString, err)
}
tss := evalNumber(tfa.ec, nan)
ts := tss[0]
for i, timestamp := range ts.Timestamps {
_, offset := time.Unix(timestamp/1000, 0).In(loc).Zone()
ts.Values[i] = float64(offset)
}
return tss, nil
}
func transformTime(tfa *transformFuncArg) ([]*timeseries, error) {
if err := expectTransformArgsNum(tfa.args, 0); err != nil {
return nil, err
}
return evalTime(tfa.ec), nil
}
func transformVector(tfa *transformFuncArg) ([]*timeseries, error) {
args := tfa.args
if err := expectTransformArgsNum(args, 1); err != nil {
return nil, err
}
rvs := args[0]
return rvs, nil
}
func transformYear(t time.Time) int {
return t.Year()
}
func newTransformFuncZeroArgs(f func(tfa *transformFuncArg) float64) transformFunc {
return func(tfa *transformFuncArg) ([]*timeseries, error) {
if err := expectTransformArgsNum(tfa.args, 0); err != nil {
return nil, err
}
v := f(tfa)
return evalNumber(tfa.ec, v), nil
}
}
func transformStep(tfa *transformFuncArg) float64 {
return float64(tfa.ec.Step) / 1e3
}
func transformStart(tfa *transformFuncArg) float64 {
return float64(tfa.ec.Start) / 1e3
}
func transformEnd(tfa *transformFuncArg) float64 {
return float64(tfa.ec.End) / 1e3
}
// copyTimeseries returns a copy of tss.
func copyTimeseries(tss []*timeseries) []*timeseries {
rvs := make([]*timeseries, len(tss))
for i, src := range tss {
var dst timeseries
dst.CopyFromShallowTimestamps(src)
rvs[i] = &dst
}
return rvs
}
// copyTimeseriesMetricNames returns a copy of tss with real copy of MetricNames,
// but with shallow copy of Timestamps and Values if makeCopy is set.
//
// Otherwise tss is returned.
func copyTimeseriesMetricNames(tss []*timeseries, makeCopy bool) []*timeseries {
if !makeCopy {
return tss
}
rvs := make([]*timeseries, len(tss))
for i, src := range tss {
var dst timeseries
dst.CopyFromMetricNames(src)
rvs[i] = &dst
}
return rvs
}
// copyTimeseriesShallow returns a copy of src with shallow copies of MetricNames, Timestamps and Values.
func copyTimeseriesShallow(src []*timeseries) []*timeseries {
tss := make([]timeseries, len(src))
for i, src := range src {
tss[i].CopyShallow(src)
}
rvs := make([]*timeseries, len(tss))
for i := range tss {
rvs[i] = &tss[i]
}
return rvs
}
func getDstValue(mn *storage.MetricName, dstLabel string) *[]byte {
if dstLabel == "__name__" {
return &mn.MetricGroup
}
tags := mn.Tags
for i := range tags {
tag := &tags[i]
if string(tag.Key) == dstLabel {
return &tag.Value
}
}
if len(tags) < cap(tags) {
tags = tags[:len(tags)+1]
} else {
tags = append(tags, storage.Tag{})
}
mn.Tags = tags
tag := &tags[len(tags)-1]
tag.Key = append(tag.Key[:0], dstLabel...)
return &tag.Value
}
func isLeapYear(y uint32) bool {
if y%4 != 0 {
return false
}
if y%100 != 0 {
return true
}
return y%400 == 0
}
var daysInMonth = [...]int{
time.January: 31,
time.February: 28,
time.March: 31,
time.April: 30,
time.May: 31,
time.June: 30,
time.July: 31,
time.August: 31,
time.September: 30,
time.October: 31,
time.November: 30,
time.December: 31,
}
func expectTransformArgsNum(args [][]*timeseries, expectedNum int) error {
if len(args) == expectedNum {
return nil
}
return fmt.Errorf(`unexpected number of args; got %d; want %d`, len(args), expectedNum)
}
func removeCounterResetsMaybeNaNs(values []float64) {
values = skipLeadingNaNs(values)
if len(values) == 0 {
return
}
var correction float64
prevValue := values[0]
for i, v := range values {
if math.IsNaN(v) {
continue
}
d := v - prevValue
if d < 0 {
if (-d * 8) < prevValue {
// This is likely a partial counter reset.
// See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2787
correction += prevValue - v
} else {
correction += prevValue
}
}
prevValue = v
values[i] = v + correction
}
}
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