mirror of
https://github.com/VictoriaMetrics/VictoriaMetrics.git
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app/vmselect/promql: optimize instant queries with min_over_time() and max_over_time() rollup functions
This is a follow-up for 41a0fdaf39
This commit is contained in:
Aliaksandr Valialkin
parent
7bbdecb79a
commit
c916294b61
@ -42,7 +42,7 @@ var (
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"See also -search.logSlowQueryDuration and -search.maxMemoryPerQuery")
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noStaleMarkers = flag.Bool("search.noStaleMarkers", false, "Set this flag to true if the database doesn't contain Prometheus stale markers, "+
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"so there is no need in spending additional CPU time on its handling. Staleness markers may exist only in data obtained from Prometheus scrape targets")
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minWindowForInstantRollupOptimization = flagutil.NewDuration("search.minWindowForInstantRollupOptimization", "6h", "Enable cache-based optimization for repeated queries "+
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minWindowForInstantRollupOptimization = flagutil.NewDuration("search.minWindowForInstantRollupOptimization", "3h", "Enable cache-based optimization for repeated queries "+
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"to /api/v1/query (aka instant queries), which contain rollup functions with lookbehind window exceeding the given value")
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)
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@ -1106,6 +1106,59 @@ func evalInstantRollup(qt *querytracer.Tracer, ec *EvalConfig, funcName string,
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}
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return offset >= maxOffset
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}
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deleteCachedSeries := func(qt *querytracer.Tracer) {
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rollupResultCacheV.DeleteInstantValues(qt, ec.AuthToken, expr, window, ec.Step, ec.EnforcedTagFilterss)
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}
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getCachedSeries := func(qt *querytracer.Tracer) ([]*timeseries, int64, error) {
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again:
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offset := int64(0)
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tssCached := rollupResultCacheV.GetInstantValues(qt, ec.AuthToken, expr, window, ec.Step, ec.EnforcedTagFilterss)
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ec.QueryStats.addSeriesFetched(len(tssCached))
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if len(tssCached) == 0 {
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// Cache miss. Re-populate the missing data.
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start := int64(fasttime.UnixTimestamp()*1000) - cacheTimestampOffset.Milliseconds()
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offset = timestamp - start
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if offset < 0 {
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start = timestamp
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offset = 0
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}
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if tooBigOffset(offset) {
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qt.Printf("cannot apply instant rollup optimization because the -search.cacheTimestampOffset=%s is too big "+
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"for the requested time=%s and window=%d", cacheTimestampOffset, storage.TimestampToHumanReadableFormat(timestamp), window)
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tss, err := evalAt(qt, timestamp, window)
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return tss, 0, err
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}
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qt.Printf("calculating the rollup at time=%s, because it is missing in the cache", storage.TimestampToHumanReadableFormat(start))
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tss, err := evalAt(qt, start, window)
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if err != nil {
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return nil, 0, err
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}
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if hasDuplicateSeries(tss) {
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qt.Printf("cannot apply instant rollup optimization because the result contains duplicate series")
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tss, err := evalAt(qt, timestamp, window)
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return tss, 0, err
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}
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rollupResultCacheV.PutInstantValues(qt, ec.AuthToken, expr, window, ec.Step, ec.EnforcedTagFilterss, tss)
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return tss, offset, nil
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}
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// Cache hit. Verify whether it is OK to use the cached data.
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offset = timestamp - tssCached[0].Timestamps[0]
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if offset < 0 {
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qt.Printf("do not apply instant rollup optimization because the cached values have bigger timestamp=%s than the requested one=%s",
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storage.TimestampToHumanReadableFormat(tssCached[0].Timestamps[0]), storage.TimestampToHumanReadableFormat(timestamp))
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// Delete the outdated cached values, so the cache could be re-populated with newer values.
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deleteCachedSeries(qt)
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goto again
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}
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if tooBigOffset(offset) {
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qt.Printf("do not apply instant rollup optimization because the offset=%d between the requested timestamp "+
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"and the cached values is too big comparing to window=%d", offset, window)
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// Delete the outdated cached values, so the cache could be re-populated with newer values.
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deleteCachedSeries(qt)
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goto again
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}
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return tssCached, offset, nil
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}
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if !ec.mayCache() {
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qt.Printf("do not apply instant rollup optimization because of disabled cache")
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@ -1181,6 +1234,136 @@ func evalInstantRollup(qt *querytracer.Tracer, ec *EvalConfig, funcName string,
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},
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}
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return evalExpr(qt, ec, be)
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case "max_over_time":
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if iafc != nil {
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if strings.ToLower(iafc.ae.Name) != "max" {
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qt.Printf("do not apply instant rollup optimization for non-max incremental aggregate %s()", iafc.ae.Name)
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return evalAt(qt, timestamp, window)
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}
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}
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// Calculate
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//
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// max_over_time(m[window] @ timestamp)
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//
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// as the maximum of
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//
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// - max_over_time(m[window] @ (timestamp-offset))
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// - max_over_time(m[offset] @ timestamp)
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//
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// if max_over_time(m[offset] @ (timestamp-window)) < max_over_time(m[window] @ (timestamp-offset))
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// otherwise do not apply the optimization
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//
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// where
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//
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// - max_over_time(m[window] @ (timestamp-offset)) is obtained from cache
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// - max_over_time(m[offset] @ timestamp) and max_over_time(m[offset] @ (timestamp-window)) are calculated from the storage
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// These rollups are calculated faster than max_over_time(m[window]) because offset is smaller than window.
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qtChild := qt.NewChild("optimized calculation for instant rollup %s at time=%s with lookbehind window=%d",
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expr.AppendString(nil), storage.TimestampToHumanReadableFormat(timestamp), window)
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defer qtChild.Done()
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tssCached, offset, err := getCachedSeries(qtChild)
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if err != nil {
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return nil, err
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}
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if offset == 0 {
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return tssCached, nil
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}
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// Calculate max_over_time(m[offset] @ (timestamp - window))
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tssEnd, err := evalAt(qtChild, timestamp-window, offset)
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if err != nil {
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return nil, err
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}
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if hasDuplicateSeries(tssEnd) {
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qtChild.Printf("cannot apply instant rollup optimization, since tssEnd contains duplicate series")
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return evalAt(qtChild, timestamp, window)
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}
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// Verify whether tssCached values are bigger than tssEnd values.
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// If this isn't the case, then the optimization cannot be applied.
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if !isLowerInstantValues(tssEnd, tssCached) {
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qtChild.Printf("cannot apply instant rollup optimization, since tssEnd contains bigger values than tssCached")
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deleteCachedSeries(qtChild)
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return evalAt(qt, timestamp, window)
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}
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// Calculate max_over_time(m[offset] @ timestamp)
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tssStart, err := evalAt(qtChild, timestamp, offset)
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if err != nil {
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return nil, err
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}
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if hasDuplicateSeries(tssStart) {
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qtChild.Printf("cannot apply instant rollup optimization, since tssStart contains duplicate series")
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return evalAt(qtChild, timestamp, window)
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}
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// Calculate the result
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tss := getMaxInstantValues(qtChild, tssCached, tssStart)
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return tss, nil
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case "min_over_time":
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if iafc != nil {
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if strings.ToLower(iafc.ae.Name) != "min" {
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qt.Printf("do not apply instant rollup optimization for non-min incremental aggregate %s()", iafc.ae.Name)
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return evalAt(qt, timestamp, window)
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}
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}
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// Calculate
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//
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// min_over_time(m[window] @ timestamp)
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//
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// as the minimum of
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//
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// - min_over_time(m[window] @ (timestamp-offset))
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// - min_over_time(m[offset] @ timestamp)
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//
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// if min_over_time(m[offset] @ (timestamp-window)) > min_over_time(m[window] @ (timestamp-offset))
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// otherwise do not apply the optimization
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//
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// where
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//
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// - min_over_time(m[window] @ (timestamp-offset)) is obtained from cache
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// - min_over_time(m[offset] @ timestamp) and min_over_time(m[offset] @ (timestamp-window)) are calculated from the storage
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// These rollups are calculated faster than min_over_time(m[window]) because offset is smaller than window.
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qtChild := qt.NewChild("optimized calculation for instant rollup %s at time=%s with lookbehind window=%d",
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expr.AppendString(nil), storage.TimestampToHumanReadableFormat(timestamp), window)
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defer qtChild.Done()
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tssCached, offset, err := getCachedSeries(qtChild)
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if err != nil {
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return nil, err
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}
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if offset == 0 {
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return tssCached, nil
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}
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// Calculate min_over_time(m[offset] @ (timestamp - window))
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tssEnd, err := evalAt(qtChild, timestamp-window, offset)
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if err != nil {
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return nil, err
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}
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if hasDuplicateSeries(tssEnd) {
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qtChild.Printf("cannot apply instant rollup optimization, since tssEnd contains duplicate series")
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return evalAt(qtChild, timestamp, window)
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}
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// Verify whether tssCached values are smaller than tssEnd values.
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// If this isn't the case, then the optimization cannot be applied.
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if !isLowerInstantValues(tssCached, tssEnd) {
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qtChild.Printf("cannot apply instant rollup optimization, since tssEnd contains smaller values than tssCached")
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deleteCachedSeries(qtChild)
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return evalAt(qt, timestamp, window)
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}
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// Calculate min_over_time(m[offset] @ timestamp)
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tssStart, err := evalAt(qtChild, timestamp, offset)
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if err != nil {
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return nil, err
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}
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if hasDuplicateSeries(tssStart) {
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qtChild.Printf("cannot apply instant rollup optimization, since tssStart contains duplicate series")
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return evalAt(qtChild, timestamp, window)
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}
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// Calculate the result
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tss := getMinInstantValues(qtChild, tssCached, tssStart)
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return tss, nil
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case
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"count_eq_over_time",
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"count_gt_over_time",
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@ -1213,67 +1396,33 @@ func evalInstantRollup(qt *querytracer.Tracer, ec *EvalConfig, funcName string,
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expr.AppendString(nil), storage.TimestampToHumanReadableFormat(timestamp), window)
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defer qtChild.Done()
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again:
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offset := int64(0)
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tssCached := rollupResultCacheV.GetInstantValues(qtChild, ec.AuthToken, expr, window, ec.Step, ec.EnforcedTagFilterss)
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ec.QueryStats.addSeriesFetched(len(tssCached))
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if len(tssCached) == 0 {
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// Cache miss. Re-populate it
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start := int64(fasttime.UnixTimestamp()*1000) - cacheTimestampOffset.Milliseconds()
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offset = timestamp - start
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if offset < 0 {
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start = timestamp
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offset = 0
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}
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if tooBigOffset(offset) {
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qtChild.Printf("cannot apply instant rollup optimization because the -search.cacheTimestampOffset=%s is too big "+
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"for the requested time=%s and window=%d", cacheTimestampOffset, storage.TimestampToHumanReadableFormat(timestamp), window)
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return evalAt(qtChild, timestamp, window)
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}
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qtChild.Printf("calculating the rollup at time=%s, because it is missing in the cache", storage.TimestampToHumanReadableFormat(start))
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tss, err := evalAt(qtChild, start, window)
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tssCached, offset, err := getCachedSeries(qtChild)
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if err != nil {
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return nil, err
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}
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if !ec.IsPartialResponse.Load() {
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rollupResultCacheV.PutInstantValues(qtChild, ec.AuthToken, expr, window, ec.Step, ec.EnforcedTagFilterss, tss)
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}
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tssCached = tss
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} else {
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offset = timestamp - tssCached[0].Timestamps[0]
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if offset < 0 {
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qtChild.Printf("do not apply instant rollup optimization because the cached values have bigger timestamp=%s than the requested one=%s",
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storage.TimestampToHumanReadableFormat(tssCached[0].Timestamps[0]), storage.TimestampToHumanReadableFormat(timestamp))
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// Delete the outdated cached values, so the cache could be re-populated with newer values.
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rollupResultCacheV.DeleteInstantValues(qtChild, ec.AuthToken, expr, window, ec.Step, ec.EnforcedTagFilterss)
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goto again
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}
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if tooBigOffset(offset) {
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qtChild.Printf("do not apply instant rollup optimization because the offset=%d between the requested timestamp "+
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"and the cached values is too big comparing to window=%d", offset, window)
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// Delete the outdated cached values, so the cache could be re-populated with newer values.
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rollupResultCacheV.DeleteInstantValues(qtChild, ec.AuthToken, expr, window, ec.Step, ec.EnforcedTagFilterss)
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goto again
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}
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}
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if offset == 0 {
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qtChild.Printf("return cached values, since they have the requested timestamp=%s", storage.TimestampToHumanReadableFormat(timestamp))
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return tssCached, nil
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}
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// Calculate count_over_time(m[offset] @ timestamp)
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// Calculate rf(m[offset] @ timestamp)
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tssStart, err := evalAt(qtChild, timestamp, offset)
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if err != nil {
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return nil, err
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}
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// Calculate count_over_time(m[offset] @ (timestamp - window))
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if hasDuplicateSeries(tssStart) {
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qtChild.Printf("cannot apply instant rollup optimization, since tssStart contains duplicate series")
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return evalAt(qtChild, timestamp, window)
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}
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// Calculate rf(m[offset] @ (timestamp - window))
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tssEnd, err := evalAt(qtChild, timestamp-window, offset)
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if err != nil {
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return nil, err
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}
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tss, err := mergeInstantValues(qtChild, tssCached, tssStart, tssEnd)
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if err != nil {
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return nil, fmt.Errorf("cannot merge instant series: %w", err)
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if hasDuplicateSeries(tssEnd) {
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qtChild.Printf("cannot apply instant rollup optimization, since tssEnd contains duplicate series")
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return evalAt(qtChild, timestamp, window)
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}
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// Calculate the result
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tss := getSumInstantValues(qtChild, tssCached, tssStart, tssEnd)
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return tss, nil
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default:
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qt.Printf("instant rollup optimization isn't implemented for %s()", funcName)
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@ -1281,9 +1430,118 @@ func evalInstantRollup(qt *querytracer.Tracer, ec *EvalConfig, funcName string,
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}
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}
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// mergeInstantValues calculates tssCached + tssStart - tssEnd
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func mergeInstantValues(qt *querytracer.Tracer, tssCached, tssStart, tssEnd []*timeseries) ([]*timeseries, error) {
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qt = qt.NewChild("merge instant values across series; cached=%d, start=%d, end=%d", len(tssCached), len(tssStart), len(tssEnd))
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func hasDuplicateSeries(tss []*timeseries) bool {
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m := make(map[string]struct{}, len(tss))
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bb := bbPool.Get()
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defer bbPool.Put(bb)
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for _, ts := range tss {
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bb.B = marshalMetricNameSorted(bb.B[:0], &ts.MetricName)
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if _, ok := m[string(bb.B)]; ok {
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return true
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}
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m[string(bb.B)] = struct{}{}
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}
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return false
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}
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// isLowerInstantValues verifies that tssA contains lower values than tssB
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func isLowerInstantValues(tssA, tssB []*timeseries) bool {
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assertInstantValues(tssA)
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assertInstantValues(tssB)
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m := make(map[string]*timeseries, len(tssA))
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bb := bbPool.Get()
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defer bbPool.Put(bb)
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for _, ts := range tssA {
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bb.B = marshalMetricNameSorted(bb.B[:0], &ts.MetricName)
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if _, ok := m[string(bb.B)]; ok {
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logger.Panicf("BUG: duplicate series found: %s", &ts.MetricName)
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}
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m[string(bb.B)] = ts
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}
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for _, tsB := range tssB {
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bb.B = marshalMetricNameSorted(bb.B[:0], &tsB.MetricName)
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tsA := m[string(bb.B)]
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if tsA != nil && !math.IsNaN(tsA.Values[0]) && !math.IsNaN(tsB.Values[0]) {
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if tsA.Values[0] >= tsB.Values[0] {
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return false
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}
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}
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}
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return true
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}
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func getMinInstantValues(qt *querytracer.Tracer, tssCached, tssStart []*timeseries) []*timeseries {
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qt = qt.NewChild("calculate the minimum for instant values across series; cached=%d, start=%d", len(tssCached), len(tssStart))
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defer qt.Done()
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getMin := func(a, b float64) float64 {
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if a < b {
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return a
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}
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return b
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}
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tss := getMinMaxInstantValues(tssCached, tssStart, getMin)
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qt.Printf("resulting series=%d", len(tss))
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return tss
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}
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func getMaxInstantValues(qt *querytracer.Tracer, tssCached, tssStart []*timeseries) []*timeseries {
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qt = qt.NewChild("calculate the maximum for instant values across series; cached=%d, start=%d", len(tssCached), len(tssStart))
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defer qt.Done()
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getMax := func(a, b float64) float64 {
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if a > b {
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return a
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}
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return b
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}
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tss := getMinMaxInstantValues(tssCached, tssStart, getMax)
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qt.Printf("resulting series=%d", len(tss))
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return tss
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}
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func getMinMaxInstantValues(tssCached, tssStart []*timeseries, f func(a, b float64) float64) []*timeseries {
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assertInstantValues(tssCached)
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assertInstantValues(tssStart)
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m := make(map[string]*timeseries, len(tssCached))
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bb := bbPool.Get()
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defer bbPool.Put(bb)
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for _, ts := range tssCached {
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bb.B = marshalMetricNameSorted(bb.B[:0], &ts.MetricName)
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if _, ok := m[string(bb.B)]; ok {
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logger.Panicf("BUG: duplicate series found: %s", &ts.MetricName)
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}
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m[string(bb.B)] = ts
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}
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for _, ts := range tssStart {
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bb.B = marshalMetricNameSorted(bb.B[:0], &ts.MetricName)
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tsCached := m[string(bb.B)]
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if tsCached != nil && !math.IsNaN(tsCached.Values[0]) {
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if !math.IsNaN(ts.Values[0]) {
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tsCached.Values[0] = f(ts.Values[0], tsCached.Values[0])
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}
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} else {
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m[string(bb.B)] = ts
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}
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}
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rvs := make([]*timeseries, 0, len(m))
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for _, ts := range m {
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rvs = append(rvs, ts)
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}
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return rvs
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}
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|
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// getSumInstantValues calculates tssCached + tssStart - tssEnd
|
||||
func getSumInstantValues(qt *querytracer.Tracer, tssCached, tssStart, tssEnd []*timeseries) []*timeseries {
|
||||
qt = qt.NewChild("calculate the sum for instant values across series; cached=%d, start=%d, end=%d", len(tssCached), len(tssStart), len(tssEnd))
|
||||
defer qt.Done()
|
||||
|
||||
assertInstantValues(tssCached)
|
||||
@ -1296,8 +1554,8 @@ func mergeInstantValues(qt *querytracer.Tracer, tssCached, tssStart, tssEnd []*t
|
||||
|
||||
for _, ts := range tssCached {
|
||||
bb.B = marshalMetricNameSorted(bb.B[:0], &ts.MetricName)
|
||||
if tsExisting := m[string(bb.B)]; tsExisting != nil {
|
||||
return nil, fmt.Errorf("duplicate series found: %s", &ts.MetricName)
|
||||
if _, ok := m[string(bb.B)]; ok {
|
||||
logger.Panicf("BUG: duplicate series found: %s", &ts.MetricName)
|
||||
}
|
||||
m[string(bb.B)] = ts
|
||||
}
|
||||
@ -1329,7 +1587,7 @@ func mergeInstantValues(qt *querytracer.Tracer, tssCached, tssStart, tssEnd []*t
|
||||
rvs = append(rvs, ts)
|
||||
}
|
||||
qt.Printf("resulting series=%d", len(rvs))
|
||||
return rvs, nil
|
||||
return rvs
|
||||
}
|
||||
|
||||
func assertInstantValues(tss []*timeseries) {
|
||||
|
||||
@ -34,15 +34,17 @@ The sandbox cluster installation is running under the constant load generated by
|
||||
* SECURITY: upgrade Go builder from Go1.21.1 to Go1.21.4. See [the list of issues addressed in Go1.21.2](https://github.com/golang/go/issues?q=milestone%3AGo1.21.2+label%3ACherryPickApproved), [the list of issues addressed in Go1.21.3](https://github.com/golang/go/issues?q=milestone%3AGo1.21.3+label%3ACherryPickApproved) and [the list of issues addressed in Go1.21.4](https://github.com/golang/go/issues?q=milestone%3AGo1.21.4+label%3ACherryPickApproved).
|
||||
|
||||
* FEATURE: `vmselect`: improve performance for repeated [instant queries](https://docs.victoriametrics.com/keyConcepts.html#instant-query) if they contain one of the following [rollup functions](https://docs.victoriametrics.com/MetricsQL.html#rollup-functions):
|
||||
- [avg_over_time](https://docs.victoriametrics.com/MetricsQL.html#avg_over_time)
|
||||
- [sum_over_time](https://docs.victoriametrics.com/MetricsQL.html#sum_over_time)
|
||||
- [count_eq_over_time](https://docs.victoriametrics.com/MetricsQL.html#count_eq_over_time)
|
||||
- [count_gt_over_time](https://docs.victoriametrics.com/MetricsQL.html#count_gt_over_time)
|
||||
- [count_le_over_time](https://docs.victoriametrics.com/MetricsQL.html#count_le_over_time)
|
||||
- [count_ne_over_time](https://docs.victoriametrics.com/MetricsQL.html#count_ne_over_time)
|
||||
- [count_over_time](https://docs.victoriametrics.com/MetricsQL.html#count_over_time)
|
||||
- [increase](https://docs.victoriametrics.com/MetricsQL.html#increase)
|
||||
- [rate](https://docs.victoriametrics.com/MetricsQL.html#rate)
|
||||
- [`avg_over_time`](https://docs.victoriametrics.com/MetricsQL.html#avg_over_time)
|
||||
- [`sum_over_time`](https://docs.victoriametrics.com/MetricsQL.html#sum_over_time)
|
||||
- [`count_eq_over_time`](https://docs.victoriametrics.com/MetricsQL.html#count_eq_over_time)
|
||||
- [`count_gt_over_time`](https://docs.victoriametrics.com/MetricsQL.html#count_gt_over_time)
|
||||
- [`count_le_over_time`](https://docs.victoriametrics.com/MetricsQL.html#count_le_over_time)
|
||||
- [`count_ne_over_time`](https://docs.victoriametrics.com/MetricsQL.html#count_ne_over_time)
|
||||
- [`count_over_time`](https://docs.victoriametrics.com/MetricsQL.html#count_over_time)
|
||||
- [`increase`](https://docs.victoriametrics.com/MetricsQL.html#increase)
|
||||
- [`max_over_time`](https://docs.victoriametrics.com/MetricsQL.html#max_over_time)
|
||||
- [`min_over_time`](https://docs.victoriametrics.com/MetricsQL.html#min_over_time)
|
||||
- [`rate`](https://docs.victoriametrics.com/MetricsQL.html#rate)
|
||||
The optimization is enabled when these functions contain lookbehind window in square brackets bigger or equal to `6h` (the threshold can be changed via `-search.minWindowForInstantRollupOptimization` command-line flag). The optimization improves performance for SLO/SLI-like queries such as `avg_over_time(up[30d])` or `sum(rate(http_request_errors_total[3d])) / sum(rate(http_requests_total[3d]))`, which can be generated by [sloth](https://github.com/slok/sloth) or similar projects.
|
||||
* FEATURE: `vmselect`: improve query performance on systems with big number of CPU cores (`>=32`). Add `-search.maxWorkersPerQuery` command-line flag, which can be used for fine-tuning query performance on systems with big number of CPU cores. See [this pull request](https://github.com/VictoriaMetrics/VictoriaMetrics/pull/5195).
|
||||
* FEATURE: `vmselect`: expose `vm_memory_intensive_queries_total` counter metric which gets increased each time `-search.logQueryMemoryUsage` memory limit is exceeded by a query. This metric should help to identify expensive and heavy queries without inspecting the logs.
|
||||
|
||||
Loading…
Reference in New Issue
Block a user