VictoriaMetrics/app/vmselect/promql/eval.go
Aliaksandr Valialkin c86286ec1d app/vmselect/promql: do not ajdust offset value provided in the query
Previously it could be modified in order to improve response cache hit ratio.
This is unneeded, since cache hit ratio should remain good because the query time range
should be already aligned to multiple of `step` values.

See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/976
2020-12-27 14:09:25 +02:00

867 lines
24 KiB
Go

package promql
import (
"flag"
"fmt"
"math"
"sync"
"github.com/VictoriaMetrics/VictoriaMetrics/app/vmselect/netstorage"
"github.com/VictoriaMetrics/VictoriaMetrics/app/vmselect/searchutils"
"github.com/VictoriaMetrics/VictoriaMetrics/lib/bytesutil"
"github.com/VictoriaMetrics/VictoriaMetrics/lib/cgroup"
"github.com/VictoriaMetrics/VictoriaMetrics/lib/logger"
"github.com/VictoriaMetrics/VictoriaMetrics/lib/memory"
"github.com/VictoriaMetrics/VictoriaMetrics/lib/storage"
"github.com/VictoriaMetrics/metrics"
"github.com/VictoriaMetrics/metricsql"
)
var (
disableCache = flag.Bool("search.disableCache", false, "Whether to disable response caching. This may be useful during data backfilling")
maxPointsPerTimeseries = flag.Int("search.maxPointsPerTimeseries", 30e3, "The maximum points per a single timeseries returned from the search")
)
// The minimum number of points per timeseries for enabling time rounding.
// This improves cache hit ratio for frequently requested queries over
// big time ranges.
const minTimeseriesPointsForTimeRounding = 50
// ValidateMaxPointsPerTimeseries checks the maximum number of points that
// may be returned per each time series.
//
// The number mustn't exceed -search.maxPointsPerTimeseries.
func ValidateMaxPointsPerTimeseries(start, end, step int64) error {
points := (end-start)/step + 1
if uint64(points) > uint64(*maxPointsPerTimeseries) {
return fmt.Errorf(`too many points for the given step=%d, start=%d and end=%d: %d; cannot exceed -search.maxPointsPerTimeseries=%d`,
step, start, end, uint64(points), *maxPointsPerTimeseries)
}
return nil
}
// AdjustStartEnd adjusts start and end values, so response caching may be enabled.
//
// See EvalConfig.mayCache for details.
func AdjustStartEnd(start, end, step int64) (int64, int64) {
if *disableCache {
// Do not adjust start and end values when cache is disabled.
// See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/563
return start, end
}
points := (end-start)/step + 1
if points < minTimeseriesPointsForTimeRounding {
// Too small number of points for rounding.
return start, end
}
// Round start and end to values divisible by step in order
// to enable response caching (see EvalConfig.mayCache).
start, end = alignStartEnd(start, end, step)
// Make sure that the new number of points is the same as the initial number of points.
newPoints := (end-start)/step + 1
for newPoints > points {
end -= step
newPoints--
}
return start, end
}
func alignStartEnd(start, end, step int64) (int64, int64) {
// Round start to the nearest smaller value divisible by step.
start -= start % step
// Round end to the nearest bigger value divisible by step.
adjust := end % step
if adjust > 0 {
end += step - adjust
}
return start, end
}
// EvalConfig is the configuration required for query evaluation via Exec
type EvalConfig struct {
Start int64
End int64
Step int64
// QuotedRemoteAddr contains quoted remote address.
QuotedRemoteAddr string
Deadline searchutils.Deadline
MayCache bool
// LookbackDelta is analog to `-query.lookback-delta` from Prometheus.
LookbackDelta int64
timestamps []int64
timestampsOnce sync.Once
}
// newEvalConfig returns new EvalConfig copy from src.
func newEvalConfig(src *EvalConfig) *EvalConfig {
var ec EvalConfig
ec.Start = src.Start
ec.End = src.End
ec.Step = src.Step
ec.Deadline = src.Deadline
ec.MayCache = src.MayCache
ec.LookbackDelta = src.LookbackDelta
// do not copy src.timestamps - they must be generated again.
return &ec
}
func (ec *EvalConfig) validate() {
if ec.Start > ec.End {
logger.Panicf("BUG: start cannot exceed end; got %d vs %d", ec.Start, ec.End)
}
if ec.Step <= 0 {
logger.Panicf("BUG: step must be greater than 0; got %d", ec.Step)
}
}
func (ec *EvalConfig) mayCache() bool {
if *disableCache {
return false
}
if !ec.MayCache {
return false
}
if ec.Start%ec.Step != 0 {
return false
}
if ec.End%ec.Step != 0 {
return false
}
return true
}
func (ec *EvalConfig) getSharedTimestamps() []int64 {
ec.timestampsOnce.Do(ec.timestampsInit)
return ec.timestamps
}
func (ec *EvalConfig) timestampsInit() {
ec.timestamps = getTimestamps(ec.Start, ec.End, ec.Step)
}
func getTimestamps(start, end, step int64) []int64 {
// Sanity checks.
if step <= 0 {
logger.Panicf("BUG: Step must be bigger than 0; got %d", step)
}
if start > end {
logger.Panicf("BUG: Start cannot exceed End; got %d vs %d", start, end)
}
if err := ValidateMaxPointsPerTimeseries(start, end, step); err != nil {
logger.Panicf("BUG: %s; this must be validated before the call to getTimestamps", err)
}
// Prepare timestamps.
points := 1 + (end-start)/step
timestamps := make([]int64, points)
for i := range timestamps {
timestamps[i] = start
start += step
}
return timestamps
}
func evalExpr(ec *EvalConfig, e metricsql.Expr) ([]*timeseries, error) {
if me, ok := e.(*metricsql.MetricExpr); ok {
re := &metricsql.RollupExpr{
Expr: me,
}
rv, err := evalRollupFunc(ec, "default_rollup", rollupDefault, e, re, nil)
if err != nil {
return nil, fmt.Errorf(`cannot evaluate %q: %w`, me.AppendString(nil), err)
}
return rv, nil
}
if re, ok := e.(*metricsql.RollupExpr); ok {
rv, err := evalRollupFunc(ec, "default_rollup", rollupDefault, e, re, nil)
if err != nil {
return nil, fmt.Errorf(`cannot evaluate %q: %w`, re.AppendString(nil), err)
}
return rv, nil
}
if fe, ok := e.(*metricsql.FuncExpr); ok {
nrf := getRollupFunc(fe.Name)
if nrf == nil {
args, err := evalExprs(ec, fe.Args)
if err != nil {
return nil, err
}
tf := getTransformFunc(fe.Name)
if tf == nil {
return nil, fmt.Errorf(`unknown func %q`, fe.Name)
}
tfa := &transformFuncArg{
ec: ec,
fe: fe,
args: args,
}
rv, err := tf(tfa)
if err != nil {
return nil, fmt.Errorf(`cannot evaluate %q: %w`, fe.AppendString(nil), err)
}
return rv, nil
}
args, re, err := evalRollupFuncArgs(ec, fe)
if err != nil {
return nil, err
}
rf, err := nrf(args)
if err != nil {
return nil, err
}
rv, err := evalRollupFunc(ec, fe.Name, rf, e, re, nil)
if err != nil {
return nil, fmt.Errorf(`cannot evaluate %q: %w`, fe.AppendString(nil), err)
}
return rv, nil
}
if ae, ok := e.(*metricsql.AggrFuncExpr); ok {
if callbacks := getIncrementalAggrFuncCallbacks(ae.Name); callbacks != nil {
fe, nrf := tryGetArgRollupFuncWithMetricExpr(ae)
if fe != nil {
// There is an optimized path for calculating metricsql.AggrFuncExpr over rollupFunc over metricsql.MetricExpr.
// The optimized path saves RAM for aggregates over big number of time series.
args, re, err := evalRollupFuncArgs(ec, fe)
if err != nil {
return nil, err
}
rf, err := nrf(args)
if err != nil {
return nil, err
}
iafc := newIncrementalAggrFuncContext(ae, callbacks)
return evalRollupFunc(ec, fe.Name, rf, e, re, iafc)
}
}
args, err := evalExprs(ec, ae.Args)
if err != nil {
return nil, err
}
af := getAggrFunc(ae.Name)
if af == nil {
return nil, fmt.Errorf(`unknown func %q`, ae.Name)
}
afa := &aggrFuncArg{
ae: ae,
args: args,
ec: ec,
}
rv, err := af(afa)
if err != nil {
return nil, fmt.Errorf(`cannot evaluate %q: %w`, ae.AppendString(nil), err)
}
return rv, nil
}
if be, ok := e.(*metricsql.BinaryOpExpr); ok {
left, err := evalExpr(ec, be.Left)
if err != nil {
return nil, err
}
right, err := evalExpr(ec, be.Right)
if err != nil {
return nil, err
}
bf := getBinaryOpFunc(be.Op)
if bf == nil {
return nil, fmt.Errorf(`unknown binary op %q`, be.Op)
}
bfa := &binaryOpFuncArg{
be: be,
left: left,
right: right,
}
rv, err := bf(bfa)
if err != nil {
return nil, fmt.Errorf(`cannot evaluate %q: %w`, be.AppendString(nil), err)
}
return rv, nil
}
if ne, ok := e.(*metricsql.NumberExpr); ok {
rv := evalNumber(ec, ne.N)
return rv, nil
}
if se, ok := e.(*metricsql.StringExpr); ok {
rv := evalString(ec, se.S)
return rv, nil
}
return nil, fmt.Errorf("unexpected expression %q", e.AppendString(nil))
}
func tryGetArgRollupFuncWithMetricExpr(ae *metricsql.AggrFuncExpr) (*metricsql.FuncExpr, newRollupFunc) {
if len(ae.Args) != 1 {
return nil, nil
}
e := ae.Args[0]
// Make sure e contains one of the following:
// - metricExpr
// - metricExpr[d]
// - rollupFunc(metricExpr)
// - rollupFunc(metricExpr[d])
if me, ok := e.(*metricsql.MetricExpr); ok {
// e = metricExpr
if me.IsEmpty() {
return nil, nil
}
fe := &metricsql.FuncExpr{
Name: "default_rollup",
Args: []metricsql.Expr{me},
}
nrf := getRollupFunc(fe.Name)
return fe, nrf
}
if re, ok := e.(*metricsql.RollupExpr); ok {
if me, ok := re.Expr.(*metricsql.MetricExpr); !ok || me.IsEmpty() || re.ForSubquery() {
return nil, nil
}
// e = metricExpr[d]
fe := &metricsql.FuncExpr{
Name: "default_rollup",
Args: []metricsql.Expr{re},
}
nrf := getRollupFunc(fe.Name)
return fe, nrf
}
fe, ok := e.(*metricsql.FuncExpr)
if !ok {
return nil, nil
}
nrf := getRollupFunc(fe.Name)
if nrf == nil {
return nil, nil
}
rollupArgIdx := getRollupArgIdx(fe.Name)
if rollupArgIdx >= len(fe.Args) {
// Incorrect number of args for rollup func.
return nil, nil
}
arg := fe.Args[rollupArgIdx]
if me, ok := arg.(*metricsql.MetricExpr); ok {
if me.IsEmpty() {
return nil, nil
}
// e = rollupFunc(metricExpr)
return &metricsql.FuncExpr{
Name: fe.Name,
Args: []metricsql.Expr{me},
}, nrf
}
if re, ok := arg.(*metricsql.RollupExpr); ok {
if me, ok := re.Expr.(*metricsql.MetricExpr); !ok || me.IsEmpty() || re.ForSubquery() {
return nil, nil
}
// e = rollupFunc(metricExpr[d])
return fe, nrf
}
return nil, nil
}
func evalExprs(ec *EvalConfig, es []metricsql.Expr) ([][]*timeseries, error) {
var rvs [][]*timeseries
for _, e := range es {
rv, err := evalExpr(ec, e)
if err != nil {
return nil, err
}
rvs = append(rvs, rv)
}
return rvs, nil
}
func evalRollupFuncArgs(ec *EvalConfig, fe *metricsql.FuncExpr) ([]interface{}, *metricsql.RollupExpr, error) {
var re *metricsql.RollupExpr
rollupArgIdx := getRollupArgIdx(fe.Name)
if len(fe.Args) <= rollupArgIdx {
return nil, nil, fmt.Errorf("expecting at least %d args to %q; got %d args; expr: %q", rollupArgIdx+1, fe.Name, len(fe.Args), fe.AppendString(nil))
}
args := make([]interface{}, len(fe.Args))
for i, arg := range fe.Args {
if i == rollupArgIdx {
re = getRollupExprArg(arg)
args[i] = re
continue
}
ts, err := evalExpr(ec, arg)
if err != nil {
return nil, nil, fmt.Errorf("cannot evaluate arg #%d for %q: %w", i+1, fe.AppendString(nil), err)
}
args[i] = ts
}
return args, re, nil
}
func getRollupExprArg(arg metricsql.Expr) *metricsql.RollupExpr {
re, ok := arg.(*metricsql.RollupExpr)
if !ok {
// Wrap non-rollup arg into metricsql.RollupExpr.
return &metricsql.RollupExpr{
Expr: arg,
}
}
if !re.ForSubquery() {
// Return standard rollup if it doesn't contain subquery.
return re
}
me, ok := re.Expr.(*metricsql.MetricExpr)
if !ok {
// arg contains subquery.
return re
}
// Convert me[w:step] -> default_rollup(me)[w:step]
reNew := *re
reNew.Expr = &metricsql.FuncExpr{
Name: "default_rollup",
Args: []metricsql.Expr{
&metricsql.RollupExpr{Expr: me},
},
}
return &reNew
}
func evalRollupFunc(ec *EvalConfig, name string, rf rollupFunc, expr metricsql.Expr, re *metricsql.RollupExpr, iafc *incrementalAggrFuncContext) ([]*timeseries, error) {
ecNew := ec
var offset int64
if len(re.Offset) > 0 {
var err error
offset, err = metricsql.DurationValue(re.Offset, ec.Step)
if err != nil {
return nil, err
}
ecNew = newEvalConfig(ecNew)
ecNew.Start -= offset
ecNew.End -= offset
// There is no need in calling AdjustStartEnd() on ecNew if ecNew.MayCache is set to true,
// since the time range alignment has been already performed by the caller,
// so cache hit rate should be quite good.
// See also https://github.com/VictoriaMetrics/VictoriaMetrics/issues/976
}
if name == "rollup_candlestick" {
// Automatically apply `offset -step` to `rollup_candlestick` function
// in order to obtain expected OHLC results.
// See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/309#issuecomment-582113462
step := ecNew.Step
ecNew = newEvalConfig(ecNew)
ecNew.Start += step
ecNew.End += step
offset -= step
}
var rvs []*timeseries
var err error
if me, ok := re.Expr.(*metricsql.MetricExpr); ok {
rvs, err = evalRollupFuncWithMetricExpr(ecNew, name, rf, expr, me, iafc, re.Window)
} else {
if iafc != nil {
logger.Panicf("BUG: iafc must be nil for rollup %q over subquery %q", name, re.AppendString(nil))
}
rvs, err = evalRollupFuncWithSubquery(ecNew, name, rf, expr, re)
}
if err != nil {
return nil, err
}
if offset != 0 && len(rvs) > 0 {
// Make a copy of timestamps, since they may be used in other values.
srcTimestamps := rvs[0].Timestamps
dstTimestamps := append([]int64{}, srcTimestamps...)
for i := range dstTimestamps {
dstTimestamps[i] += offset
}
for _, ts := range rvs {
ts.Timestamps = dstTimestamps
}
}
return rvs, nil
}
func evalRollupFuncWithSubquery(ec *EvalConfig, name string, rf rollupFunc, expr metricsql.Expr, re *metricsql.RollupExpr) ([]*timeseries, error) {
// TODO: determine whether to use rollupResultCacheV here.
var step int64
if len(re.Step) > 0 {
var err error
step, err = metricsql.PositiveDurationValue(re.Step, ec.Step)
if err != nil {
return nil, err
}
} else {
step = ec.Step
}
var window int64
if len(re.Window) > 0 {
var err error
window, err = metricsql.PositiveDurationValue(re.Window, ec.Step)
if err != nil {
return nil, err
}
}
ecSQ := newEvalConfig(ec)
ecSQ.Start -= window + maxSilenceInterval + step
ecSQ.End += step
ecSQ.Step = step
if err := ValidateMaxPointsPerTimeseries(ecSQ.Start, ecSQ.End, ecSQ.Step); err != nil {
return nil, err
}
// unconditionally align start and end args to step for subquery as Prometheus does.
ecSQ.Start, ecSQ.End = alignStartEnd(ecSQ.Start, ecSQ.End, ecSQ.Step)
tssSQ, err := evalExpr(ecSQ, re.Expr)
if err != nil {
return nil, err
}
if len(tssSQ) == 0 {
if name == "absent_over_time" {
tss := evalNumber(ec, 1)
return tss, nil
}
return nil, nil
}
sharedTimestamps := getTimestamps(ec.Start, ec.End, ec.Step)
preFunc, rcs, err := getRollupConfigs(name, rf, expr, ec.Start, ec.End, ec.Step, window, ec.LookbackDelta, sharedTimestamps)
if err != nil {
return nil, err
}
tss := make([]*timeseries, 0, len(tssSQ)*len(rcs))
var tssLock sync.Mutex
removeMetricGroup := !rollupFuncsKeepMetricGroup[name]
doParallel(tssSQ, func(tsSQ *timeseries, values []float64, timestamps []int64) ([]float64, []int64) {
values, timestamps = removeNanValues(values[:0], timestamps[:0], tsSQ.Values, tsSQ.Timestamps)
preFunc(values, timestamps)
for _, rc := range rcs {
if tsm := newTimeseriesMap(name, sharedTimestamps, &tsSQ.MetricName); tsm != nil {
rc.DoTimeseriesMap(tsm, values, timestamps)
tssLock.Lock()
tss = tsm.AppendTimeseriesTo(tss)
tssLock.Unlock()
continue
}
var ts timeseries
doRollupForTimeseries(rc, &ts, &tsSQ.MetricName, values, timestamps, sharedTimestamps, removeMetricGroup)
tssLock.Lock()
tss = append(tss, &ts)
tssLock.Unlock()
}
return values, timestamps
})
return tss, nil
}
func doParallel(tss []*timeseries, f func(ts *timeseries, values []float64, timestamps []int64) ([]float64, []int64)) {
concurrency := cgroup.AvailableCPUs()
if concurrency > len(tss) {
concurrency = len(tss)
}
workCh := make(chan *timeseries, concurrency)
var wg sync.WaitGroup
wg.Add(concurrency)
for i := 0; i < concurrency; i++ {
go func() {
defer wg.Done()
var tmpValues []float64
var tmpTimestamps []int64
for ts := range workCh {
tmpValues, tmpTimestamps = f(ts, tmpValues, tmpTimestamps)
}
}()
}
for _, ts := range tss {
workCh <- ts
}
close(workCh)
wg.Wait()
}
func removeNanValues(dstValues []float64, dstTimestamps []int64, values []float64, timestamps []int64) ([]float64, []int64) {
hasNan := false
for _, v := range values {
if math.IsNaN(v) {
hasNan = true
}
}
if !hasNan {
// Fast path - no NaNs.
dstValues = append(dstValues, values...)
dstTimestamps = append(dstTimestamps, timestamps...)
return dstValues, dstTimestamps
}
// Slow path - remove NaNs.
for i, v := range values {
if math.IsNaN(v) {
continue
}
dstValues = append(dstValues, v)
dstTimestamps = append(dstTimestamps, timestamps[i])
}
return dstValues, dstTimestamps
}
var (
rollupResultCacheFullHits = metrics.NewCounter(`vm_rollup_result_cache_full_hits_total`)
rollupResultCachePartialHits = metrics.NewCounter(`vm_rollup_result_cache_partial_hits_total`)
rollupResultCacheMiss = metrics.NewCounter(`vm_rollup_result_cache_miss_total`)
)
func evalRollupFuncWithMetricExpr(ec *EvalConfig, name string, rf rollupFunc,
expr metricsql.Expr, me *metricsql.MetricExpr, iafc *incrementalAggrFuncContext, windowStr string) ([]*timeseries, error) {
if me.IsEmpty() {
return evalNumber(ec, nan), nil
}
var window int64
if len(windowStr) > 0 {
var err error
window, err = metricsql.PositiveDurationValue(windowStr, ec.Step)
if err != nil {
return nil, err
}
}
// Search for partial results in cache.
tssCached, start := rollupResultCacheV.Get(ec, expr, window)
if start > ec.End {
// The result is fully cached.
rollupResultCacheFullHits.Inc()
return tssCached, nil
}
if start > ec.Start {
rollupResultCachePartialHits.Inc()
} else {
rollupResultCacheMiss.Inc()
}
// Obtain rollup configs before fetching data from db,
// so type errors can be caught earlier.
sharedTimestamps := getTimestamps(start, ec.End, ec.Step)
preFunc, rcs, err := getRollupConfigs(name, rf, expr, start, ec.End, ec.Step, window, ec.LookbackDelta, sharedTimestamps)
if err != nil {
return nil, err
}
// Fetch the remaining part of the result.
tfs := toTagFilters(me.LabelFilters)
minTimestamp := start - maxSilenceInterval
if window > ec.Step {
minTimestamp -= window
} else {
minTimestamp -= ec.Step
}
sq := storage.NewSearchQuery(minTimestamp, ec.End, [][]storage.TagFilter{tfs})
rss, err := netstorage.ProcessSearchQuery(sq, true, ec.Deadline)
if err != nil {
return nil, err
}
rssLen := rss.Len()
if rssLen == 0 {
rss.Cancel()
var tss []*timeseries
if name == "absent_over_time" {
tss = getAbsentTimeseries(ec, me)
}
// Add missing points until ec.End.
// Do not cache the result, since missing points
// may be backfilled in the future.
tss = mergeTimeseries(tssCached, tss, start, ec)
return tss, nil
}
// Verify timeseries fit available memory after the rollup.
// Take into account points from tssCached.
pointsPerTimeseries := 1 + (ec.End-ec.Start)/ec.Step
timeseriesLen := rssLen
if iafc != nil {
// Incremental aggregates require holding only GOMAXPROCS timeseries in memory.
timeseriesLen = cgroup.AvailableCPUs()
if iafc.ae.Modifier.Op != "" {
if iafc.ae.Limit > 0 {
// There is an explicit limit on the number of output time series.
timeseriesLen *= iafc.ae.Limit
} else {
// Increase the number of timeseries for non-empty group list: `aggr() by (something)`,
// since each group can have own set of time series in memory.
timeseriesLen *= 1000
}
}
// The maximum number of output time series is limited by rssLen.
if timeseriesLen > rssLen {
timeseriesLen = rssLen
}
}
rollupPoints := mulNoOverflow(pointsPerTimeseries, int64(timeseriesLen*len(rcs)))
rollupMemorySize := mulNoOverflow(rollupPoints, 16)
rml := getRollupMemoryLimiter()
if !rml.Get(uint64(rollupMemorySize)) {
rss.Cancel()
return nil, fmt.Errorf("not enough memory for processing %d data points across %d time series with %d points in each time series; "+
"total available memory for concurrent requests: %d bytes; "+
"possible solutions are: reducing the number of matching time series; switching to node with more RAM; "+
"increasing -memory.allowedPercent; increasing `step` query arg (%gs)",
rollupPoints, timeseriesLen*len(rcs), pointsPerTimeseries, rml.MaxSize, float64(ec.Step)/1e3)
}
defer rml.Put(uint64(rollupMemorySize))
// Evaluate rollup
removeMetricGroup := !rollupFuncsKeepMetricGroup[name]
var tss []*timeseries
if iafc != nil {
tss, err = evalRollupWithIncrementalAggregate(name, iafc, rss, rcs, preFunc, sharedTimestamps, removeMetricGroup)
} else {
tss, err = evalRollupNoIncrementalAggregate(name, rss, rcs, preFunc, sharedTimestamps, removeMetricGroup)
}
if err != nil {
return nil, err
}
tss = mergeTimeseries(tssCached, tss, start, ec)
rollupResultCacheV.Put(ec, expr, window, tss)
return tss, nil
}
var (
rollupMemoryLimiter memoryLimiter
rollupMemoryLimiterOnce sync.Once
)
func getRollupMemoryLimiter() *memoryLimiter {
rollupMemoryLimiterOnce.Do(func() {
rollupMemoryLimiter.MaxSize = uint64(memory.Allowed()) / 4
})
return &rollupMemoryLimiter
}
func evalRollupWithIncrementalAggregate(name string, iafc *incrementalAggrFuncContext, rss *netstorage.Results, rcs []*rollupConfig,
preFunc func(values []float64, timestamps []int64), sharedTimestamps []int64, removeMetricGroup bool) ([]*timeseries, error) {
err := rss.RunParallel(func(rs *netstorage.Result, workerID uint) error {
preFunc(rs.Values, rs.Timestamps)
ts := getTimeseries()
defer putTimeseries(ts)
for _, rc := range rcs {
if tsm := newTimeseriesMap(name, sharedTimestamps, &rs.MetricName); tsm != nil {
rc.DoTimeseriesMap(tsm, rs.Values, rs.Timestamps)
for _, ts := range tsm.m {
iafc.updateTimeseries(ts, workerID)
}
continue
}
ts.Reset()
doRollupForTimeseries(rc, ts, &rs.MetricName, rs.Values, rs.Timestamps, sharedTimestamps, removeMetricGroup)
iafc.updateTimeseries(ts, workerID)
// ts.Timestamps points to sharedTimestamps. Zero it, so it can be re-used.
ts.Timestamps = nil
ts.denyReuse = false
}
return nil
})
if err != nil {
return nil, err
}
tss := iafc.finalizeTimeseries()
return tss, nil
}
func evalRollupNoIncrementalAggregate(name string, rss *netstorage.Results, rcs []*rollupConfig,
preFunc func(values []float64, timestamps []int64), sharedTimestamps []int64, removeMetricGroup bool) ([]*timeseries, error) {
tss := make([]*timeseries, 0, rss.Len()*len(rcs))
var tssLock sync.Mutex
err := rss.RunParallel(func(rs *netstorage.Result, workerID uint) error {
preFunc(rs.Values, rs.Timestamps)
for _, rc := range rcs {
if tsm := newTimeseriesMap(name, sharedTimestamps, &rs.MetricName); tsm != nil {
rc.DoTimeseriesMap(tsm, rs.Values, rs.Timestamps)
tssLock.Lock()
tss = tsm.AppendTimeseriesTo(tss)
tssLock.Unlock()
continue
}
var ts timeseries
doRollupForTimeseries(rc, &ts, &rs.MetricName, rs.Values, rs.Timestamps, sharedTimestamps, removeMetricGroup)
tssLock.Lock()
tss = append(tss, &ts)
tssLock.Unlock()
}
return nil
})
if err != nil {
return nil, err
}
return tss, nil
}
func doRollupForTimeseries(rc *rollupConfig, tsDst *timeseries, mnSrc *storage.MetricName, valuesSrc []float64, timestampsSrc []int64,
sharedTimestamps []int64, removeMetricGroup bool) {
tsDst.MetricName.CopyFrom(mnSrc)
if len(rc.TagValue) > 0 {
tsDst.MetricName.AddTag("rollup", rc.TagValue)
}
if removeMetricGroup {
tsDst.MetricName.ResetMetricGroup()
}
tsDst.Values = rc.Do(tsDst.Values[:0], valuesSrc, timestampsSrc)
tsDst.Timestamps = sharedTimestamps
tsDst.denyReuse = true
}
var bbPool bytesutil.ByteBufferPool
func evalNumber(ec *EvalConfig, n float64) []*timeseries {
var ts timeseries
ts.denyReuse = true
timestamps := ec.getSharedTimestamps()
values := make([]float64, len(timestamps))
for i := range timestamps {
values[i] = n
}
ts.Values = values
ts.Timestamps = timestamps
return []*timeseries{&ts}
}
func evalString(ec *EvalConfig, s string) []*timeseries {
rv := evalNumber(ec, nan)
rv[0].MetricName.MetricGroup = append(rv[0].MetricName.MetricGroup[:0], s...)
return rv
}
func evalTime(ec *EvalConfig) []*timeseries {
rv := evalNumber(ec, nan)
timestamps := rv[0].Timestamps
values := rv[0].Values
for i, ts := range timestamps {
values[i] = float64(ts) / 1e3
}
return rv
}
func mulNoOverflow(a, b int64) int64 {
if math.MaxInt64/b < a {
// Overflow
return math.MaxInt64
}
return a * b
}
func toTagFilters(lfs []metricsql.LabelFilter) []storage.TagFilter {
tfs := make([]storage.TagFilter, len(lfs))
for i := range lfs {
toTagFilter(&tfs[i], &lfs[i])
}
return tfs
}
func toTagFilter(dst *storage.TagFilter, src *metricsql.LabelFilter) {
if src.Label != "__name__" {
dst.Key = []byte(src.Label)
} else {
// This is required for storage.Search.
dst.Key = nil
}
dst.Value = []byte(src.Value)
dst.IsRegexp = src.IsRegexp
dst.IsNegative = src.IsNegative
}