mirror of
https://github.com/VictoriaMetrics/VictoriaMetrics.git
synced 2024-12-01 14:47:38 +00:00
c780c6a280
Substitute sync.Map with an ordinary slice indexed by workerID. This should reduce the overhead when updating the incremental aggregate state
525 lines
12 KiB
Go
525 lines
12 KiB
Go
package promql
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import (
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"math"
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"strings"
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"unsafe"
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"github.com/VictoriaMetrics/VictoriaMetrics/app/vmselect/netstorage"
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"github.com/VictoriaMetrics/VictoriaMetrics/lib/bytesutil"
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"github.com/VictoriaMetrics/metricsql"
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)
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// callbacks for optimized incremental calculations for aggregate functions
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// over rollups over metricsql.MetricExpr.
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//
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// These calculations save RAM for aggregates over big number of time series.
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var incrementalAggrFuncCallbacksMap = map[string]*incrementalAggrFuncCallbacks{
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"sum": {
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updateAggrFunc: updateAggrSum,
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mergeAggrFunc: mergeAggrSum,
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finalizeAggrFunc: finalizeAggrCommon,
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},
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"min": {
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updateAggrFunc: updateAggrMin,
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mergeAggrFunc: mergeAggrMin,
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finalizeAggrFunc: finalizeAggrCommon,
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},
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"max": {
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updateAggrFunc: updateAggrMax,
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mergeAggrFunc: mergeAggrMax,
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finalizeAggrFunc: finalizeAggrCommon,
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},
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"avg": {
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updateAggrFunc: updateAggrAvg,
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mergeAggrFunc: mergeAggrAvg,
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finalizeAggrFunc: finalizeAggrAvg,
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},
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"count": {
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updateAggrFunc: updateAggrCount,
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mergeAggrFunc: mergeAggrCount,
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finalizeAggrFunc: finalizeAggrCount,
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},
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"sum2": {
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updateAggrFunc: updateAggrSum2,
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mergeAggrFunc: mergeAggrSum2,
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finalizeAggrFunc: finalizeAggrCommon,
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},
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"geomean": {
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updateAggrFunc: updateAggrGeomean,
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mergeAggrFunc: mergeAggrGeomean,
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finalizeAggrFunc: finalizeAggrGeomean,
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},
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"any": {
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updateAggrFunc: updateAggrAny,
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mergeAggrFunc: mergeAggrAny,
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finalizeAggrFunc: finalizeAggrCommon,
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keepOriginal: true,
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},
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"group": {
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updateAggrFunc: updateAggrCount,
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mergeAggrFunc: mergeAggrCount,
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finalizeAggrFunc: finalizeAggrGroup,
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},
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}
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type incrementalAggrContextMap struct {
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m map[string]*incrementalAggrContext
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// The padding prevents false sharing on widespread platforms with
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// 128 mod (cache line size) = 0 .
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_ [128 - unsafe.Sizeof(map[string]*incrementalAggrContext{})%128]byte
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}
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type incrementalAggrFuncContext struct {
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ae *metricsql.AggrFuncExpr
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byWorkerID []incrementalAggrContextMap
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callbacks *incrementalAggrFuncCallbacks
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}
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func newIncrementalAggrFuncContext(ae *metricsql.AggrFuncExpr, callbacks *incrementalAggrFuncCallbacks) *incrementalAggrFuncContext {
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return &incrementalAggrFuncContext{
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ae: ae,
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byWorkerID: make([]incrementalAggrContextMap, netstorage.MaxWorkers()),
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callbacks: callbacks,
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}
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}
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func (iafc *incrementalAggrFuncContext) updateTimeseries(tsOrig *timeseries, workerID uint) {
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v := &iafc.byWorkerID[workerID]
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if v.m == nil {
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v.m = make(map[string]*incrementalAggrContext, 1)
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}
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m := v.m
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ts := tsOrig
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keepOriginal := iafc.callbacks.keepOriginal
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if keepOriginal {
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var dst timeseries
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dst.CopyFromMetricNames(tsOrig)
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ts = &dst
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}
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removeGroupTags(&ts.MetricName, &iafc.ae.Modifier)
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bb := bbPool.Get()
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bb.B = marshalMetricNameSorted(bb.B[:0], &ts.MetricName)
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k := bytesutil.InternBytes(bb.B)
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iac := m[k]
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if iac == nil {
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if iafc.ae.Limit > 0 && len(m) >= iafc.ae.Limit {
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// Skip this time series, since the limit on the number of output time series has been already reached.
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return
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}
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tsAggr := ×eries{
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Values: make([]float64, len(ts.Values)),
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Timestamps: ts.Timestamps,
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denyReuse: true,
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}
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if keepOriginal {
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ts = tsOrig
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}
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tsAggr.MetricName.CopyFrom(&ts.MetricName)
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iac = &incrementalAggrContext{
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ts: tsAggr,
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values: make([]float64, len(ts.Values)),
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}
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m[k] = iac
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}
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bbPool.Put(bb)
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iafc.callbacks.updateAggrFunc(iac, ts.Values)
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}
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func (iafc *incrementalAggrFuncContext) finalizeTimeseries() []*timeseries {
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mGlobal := make(map[string]*incrementalAggrContext)
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mergeAggrFunc := iafc.callbacks.mergeAggrFunc
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byWorkerID := iafc.byWorkerID
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for i := range byWorkerID {
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for k, iac := range byWorkerID[i].m {
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iacGlobal := mGlobal[k]
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if iacGlobal == nil {
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if iafc.ae.Limit > 0 && len(mGlobal) >= iafc.ae.Limit {
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// Skip this time series, since the limit on the number of output time series has been already reached.
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continue
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}
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mGlobal[k] = iac
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continue
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}
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mergeAggrFunc(iacGlobal, iac)
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}
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}
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tss := make([]*timeseries, 0, len(mGlobal))
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finalizeAggrFunc := iafc.callbacks.finalizeAggrFunc
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for _, iac := range mGlobal {
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finalizeAggrFunc(iac)
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tss = append(tss, iac.ts)
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}
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return tss
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}
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type incrementalAggrFuncCallbacks struct {
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updateAggrFunc func(iac *incrementalAggrContext, values []float64)
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mergeAggrFunc func(dst, src *incrementalAggrContext)
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finalizeAggrFunc func(iac *incrementalAggrContext)
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// Whether to keep the original MetricName for every time series during aggregation
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keepOriginal bool
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}
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func getIncrementalAggrFuncCallbacks(name string) *incrementalAggrFuncCallbacks {
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name = strings.ToLower(name)
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return incrementalAggrFuncCallbacksMap[name]
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}
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type incrementalAggrContext struct {
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ts *timeseries
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values []float64
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}
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func finalizeAggrCommon(iac *incrementalAggrContext) {
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counts := iac.values
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dstValues := iac.ts.Values
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_ = dstValues[len(counts)-1]
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for i, v := range counts {
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if v == 0 {
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dstValues[i] = nan
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}
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}
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}
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func updateAggrSum(iac *incrementalAggrContext, values []float64) {
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dstValues := iac.ts.Values
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dstCounts := iac.values
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_ = dstValues[len(values)-1]
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_ = dstCounts[len(values)-1]
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for i, v := range values {
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if math.IsNaN(v) {
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continue
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}
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if dstCounts[i] == 0 {
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dstValues[i] = v
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dstCounts[i] = 1
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continue
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}
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dstValues[i] += v
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}
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}
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func mergeAggrSum(dst, src *incrementalAggrContext) {
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srcValues := src.ts.Values
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dstValues := dst.ts.Values
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srcCounts := src.values
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dstCounts := dst.values
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_ = srcCounts[len(srcValues)-1]
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_ = dstCounts[len(srcValues)-1]
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_ = dstValues[len(srcValues)-1]
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for i, v := range srcValues {
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if srcCounts[i] == 0 {
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continue
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}
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if dstCounts[i] == 0 {
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dstValues[i] = v
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dstCounts[i] = 1
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continue
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}
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dstValues[i] += v
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}
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}
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func updateAggrMin(iac *incrementalAggrContext, values []float64) {
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dstValues := iac.ts.Values
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dstCounts := iac.values
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_ = dstValues[len(values)-1]
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_ = dstCounts[len(values)-1]
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for i, v := range values {
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if math.IsNaN(v) {
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continue
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}
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if dstCounts[i] == 0 {
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dstValues[i] = v
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dstCounts[i] = 1
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continue
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}
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if v < dstValues[i] {
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dstValues[i] = v
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}
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}
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}
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func mergeAggrMin(dst, src *incrementalAggrContext) {
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srcValues := src.ts.Values
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dstValues := dst.ts.Values
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srcCounts := src.values
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dstCounts := dst.values
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_ = srcCounts[len(srcValues)-1]
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_ = dstCounts[len(srcValues)-1]
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_ = dstValues[len(srcValues)-1]
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for i, v := range srcValues {
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if srcCounts[i] == 0 {
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continue
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}
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if dstCounts[i] == 0 {
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dstValues[i] = v
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dstCounts[i] = 1
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continue
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}
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if v < dstValues[i] {
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dstValues[i] = v
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}
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}
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}
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func updateAggrMax(iac *incrementalAggrContext, values []float64) {
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dstValues := iac.ts.Values
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dstCounts := iac.values
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_ = dstValues[len(values)-1]
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_ = dstCounts[len(values)-1]
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for i, v := range values {
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if math.IsNaN(v) {
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continue
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}
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if dstCounts[i] == 0 {
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dstValues[i] = v
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dstCounts[i] = 1
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continue
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}
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if v > dstValues[i] {
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dstValues[i] = v
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}
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}
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}
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func mergeAggrMax(dst, src *incrementalAggrContext) {
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srcValues := src.ts.Values
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dstValues := dst.ts.Values
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srcCounts := src.values
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dstCounts := dst.values
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_ = srcCounts[len(srcValues)-1]
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_ = dstCounts[len(srcValues)-1]
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_ = dstValues[len(srcValues)-1]
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for i, v := range srcValues {
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if srcCounts[i] == 0 {
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continue
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}
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if dstCounts[i] == 0 {
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dstValues[i] = v
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dstCounts[i] = 1
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continue
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}
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if v > dstValues[i] {
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dstValues[i] = v
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}
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}
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}
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func updateAggrAvg(iac *incrementalAggrContext, values []float64) {
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// Do not use `Rapid calculation methods` at https://en.wikipedia.org/wiki/Standard_deviation,
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// since it is slower and has no obvious benefits in increased precision.
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dstValues := iac.ts.Values
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dstCounts := iac.values
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_ = dstValues[len(values)-1]
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_ = dstCounts[len(values)-1]
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for i, v := range values {
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if math.IsNaN(v) {
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continue
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}
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if dstCounts[i] == 0 {
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dstValues[i] = v
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dstCounts[i] = 1
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continue
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}
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dstValues[i] += v
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dstCounts[i]++
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}
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}
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func mergeAggrAvg(dst, src *incrementalAggrContext) {
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srcValues := src.ts.Values
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dstValues := dst.ts.Values
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srcCounts := src.values
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dstCounts := dst.values
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_ = srcCounts[len(srcValues)-1]
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_ = dstCounts[len(srcValues)-1]
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_ = dstValues[len(srcValues)-1]
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for i, v := range srcValues {
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if srcCounts[i] == 0 {
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continue
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}
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if dstCounts[i] == 0 {
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dstValues[i] = v
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dstCounts[i] = srcCounts[i]
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continue
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}
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dstValues[i] += v
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dstCounts[i] += srcCounts[i]
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}
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}
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func finalizeAggrAvg(iac *incrementalAggrContext) {
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dstValues := iac.ts.Values
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counts := iac.values
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_ = dstValues[len(counts)-1]
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for i, v := range counts {
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if v == 0 {
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dstValues[i] = nan
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continue
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}
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dstValues[i] /= v
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}
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}
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func updateAggrCount(iac *incrementalAggrContext, values []float64) {
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dstValues := iac.ts.Values
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_ = dstValues[len(values)-1]
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for i, v := range values {
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if math.IsNaN(v) {
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continue
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}
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dstValues[i]++
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}
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}
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func mergeAggrCount(dst, src *incrementalAggrContext) {
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srcValues := src.ts.Values
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dstValues := dst.ts.Values
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_ = dstValues[len(srcValues)-1]
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for i, v := range srcValues {
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dstValues[i] += v
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}
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}
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func finalizeAggrCount(iac *incrementalAggrContext) {
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dstValues := iac.ts.Values
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for i, v := range dstValues {
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if v == 0 {
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dstValues[i] = nan
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}
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}
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}
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func finalizeAggrGroup(iac *incrementalAggrContext) {
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dstValues := iac.ts.Values
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for i, v := range dstValues {
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if v == 0 {
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dstValues[i] = nan
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} else {
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dstValues[i] = 1
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}
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}
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}
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func updateAggrSum2(iac *incrementalAggrContext, values []float64) {
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dstValues := iac.ts.Values
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dstCounts := iac.values
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_ = dstValues[len(values)-1]
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_ = dstCounts[len(values)-1]
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for i, v := range values {
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if math.IsNaN(v) {
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continue
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}
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if dstCounts[i] == 0 {
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dstValues[i] = v * v
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dstCounts[i] = 1
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continue
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}
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dstValues[i] += v * v
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}
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}
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func mergeAggrSum2(dst, src *incrementalAggrContext) {
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srcValues := src.ts.Values
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dstValues := dst.ts.Values
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srcCounts := src.values
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dstCounts := dst.values
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_ = srcCounts[len(srcValues)-1]
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_ = dstCounts[len(srcValues)-1]
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_ = dstValues[len(srcValues)-1]
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for i, v := range srcValues {
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if srcCounts[i] == 0 {
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continue
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}
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if dstCounts[i] == 0 {
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dstValues[i] = v
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dstCounts[i] = 1
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continue
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}
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dstValues[i] += v
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}
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}
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func updateAggrGeomean(iac *incrementalAggrContext, values []float64) {
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dstValues := iac.ts.Values
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dstCounts := iac.values
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_ = dstValues[len(values)-1]
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_ = dstCounts[len(values)-1]
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for i, v := range values {
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if math.IsNaN(v) {
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continue
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}
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if dstCounts[i] == 0 {
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dstValues[i] = v
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dstCounts[i] = 1
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continue
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}
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dstValues[i] *= v
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dstCounts[i]++
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}
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}
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func mergeAggrGeomean(dst, src *incrementalAggrContext) {
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srcValues := src.ts.Values
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dstValues := dst.ts.Values
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srcCounts := src.values
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dstCounts := dst.values
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_ = srcCounts[len(srcValues)-1]
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_ = dstCounts[len(srcValues)-1]
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_ = dstValues[len(srcValues)-1]
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for i, v := range srcValues {
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if srcCounts[i] == 0 {
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continue
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}
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if dstCounts[i] == 0 {
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dstValues[i] = v
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dstCounts[i] = srcCounts[i]
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continue
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}
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dstValues[i] *= v
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dstCounts[i] += srcCounts[i]
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}
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}
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func finalizeAggrGeomean(iac *incrementalAggrContext) {
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dstValues := iac.ts.Values
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counts := iac.values
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_ = dstValues[len(counts)-1]
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for i, v := range counts {
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if v == 0 {
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dstValues[i] = nan
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continue
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}
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dstValues[i] = math.Pow(dstValues[i], 1/v)
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}
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}
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func updateAggrAny(iac *incrementalAggrContext, values []float64) {
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dstCounts := iac.values
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if dstCounts[0] > 0 {
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return
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}
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for i := range values {
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dstCounts[i] = 1
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}
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iac.ts.Values = append(iac.ts.Values[:0], values...)
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}
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func mergeAggrAny(dst, src *incrementalAggrContext) {
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srcValues := src.ts.Values
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srcCounts := src.values
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dstCounts := dst.values
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if dstCounts[0] > 0 {
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return
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}
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dstCounts[0] = srcCounts[0]
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dst.ts.Values = append(dst.ts.Values[:0], srcValues...)
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}
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