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app/vmselect/promql: add hoeffding_bound_upper(phi, m[d])
and hoeffding_bound_lower(phi, m[d])
functions
These functions can be used for calculating Hoeffding bounds for `m` over `d` time range and for the given `phi` in the range `[0..1]`. Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/283
This commit is contained in:
parent
8eaced8cae
commit
8b14572f70
6 changed files with 205 additions and 63 deletions
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@ -605,6 +605,14 @@ func evalRollupFuncWithMetricExpr(ec *EvalConfig, name string, rf rollupFunc,
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rollupResultCacheMiss.Inc()
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}
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// Obtain rollup configs before fetching data from db,
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// so type errors can be caught earlier.
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sharedTimestamps := getTimestamps(start, ec.End, ec.Step)
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preFunc, rcs, err := getRollupConfigs(name, rf, expr, start, ec.End, ec.Step, window, ec.LookbackDelta, sharedTimestamps)
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if err != nil {
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return nil, err
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}
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// Fetch the remaining part of the result.
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tfs := toTagFilters(me.LabelFilters)
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sq := &storage.SearchQuery{
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@ -629,12 +637,6 @@ func evalRollupFuncWithMetricExpr(ec *EvalConfig, name string, rf rollupFunc,
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tss = mergeTimeseries(tssCached, tss, start, ec)
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return tss, nil
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}
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sharedTimestamps := getTimestamps(start, ec.End, ec.Step)
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preFunc, rcs, err := getRollupConfigs(name, rf, expr, start, ec.End, ec.Step, window, ec.LookbackDelta, sharedTimestamps)
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if err != nil {
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rss.Cancel()
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return nil, err
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}
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// Verify timeseries fit available memory after the rollup.
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// Take into account points from tssCached.
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@ -4480,6 +4480,29 @@ func TestExecSuccess(t *testing.T) {
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resultExpected := []netstorage.Result{r}
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f(q, resultExpected)
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})
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t.Run(`hoeffding_bound_lower()`, func(t *testing.T) {
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t.Parallel()
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q := `hoeffding_bound_lower(0.9, rand(0)[:10s])`
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r := netstorage.Result{
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MetricName: metricNameExpected,
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Values: []float64{0.2516770508510652, 0.2830570387745462, 0.27716232108436645, 0.3679356319931767, 0.3168460474120903, 0.23156726248243734},
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Timestamps: timestampsExpected,
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}
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resultExpected := []netstorage.Result{r}
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f(q, resultExpected)
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})
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t.Run(`hoeffding_bound_upper()`, func(t *testing.T) {
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t.Parallel()
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q := `hoeffding_bound_upper(0.9, alias(rand(0), "foobar")[:10s])`
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r := netstorage.Result{
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MetricName: metricNameExpected,
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Values: []float64{0.6510581320042821, 0.7261021731890429, 0.7245290097397009, 0.8113950442584258, 0.7736122275568004, 0.6658564048254882},
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Timestamps: timestampsExpected,
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}
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r.MetricName.MetricGroup = []byte("foobar")
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resultExpected := []netstorage.Result{r}
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f(q, resultExpected)
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})
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t.Run(`aggr_over_time(single-func)`, func(t *testing.T) {
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t.Parallel()
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q := `aggr_over_time("increase", rand(0)[:10s])`
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@ -5267,6 +5290,12 @@ func TestExecError(t *testing.T) {
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f(`aggr_over_time()`)
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f(`aggr_over_time(foo)`)
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f(`aggr_over_time("foo", bar, 1)`)
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f(`hoeffding_bound_lower()`)
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f(`hoeffding_bound_lower(1)`)
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f(`hoeffding_bound_lower(0.99, foo, 1)`)
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f(`hoeffding_bound_upper()`)
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f(`hoeffding_bound_upper(1)`)
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f(`hoeffding_bound_upper(0.99, foo, 1)`)
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// Invalid argument type
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f(`median_over_time({}, 2)`)
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@ -65,6 +65,8 @@ var rollupFuncs = map[string]newRollupFunc{
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"rollup_increase": newRollupFuncOneArg(rollupFake), // + rollupFuncsRemoveCounterResets
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"rollup_candlestick": newRollupFuncOneArg(rollupFake),
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"aggr_over_time": newRollupFuncTwoArgs(rollupFake),
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"hoeffding_bound_upper": newRollupHoeffdingBoundUpper,
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"hoeffding_bound_lower": newRollupHoeffdingBoundLower,
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}
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// rollupAggrFuncs are functions that can be passed to `aggr_over_time()`
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@ -143,6 +145,8 @@ var rollupFuncsKeepMetricGroup = map[string]bool{
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"quantile_over_time": true,
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"rollup": true,
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"geomean_over_time": true,
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"hoeffding_bound_lower": true,
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"hoeffding_bound_upper": true,
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}
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func getRollupAggrFuncNames(expr metricsql.Expr) ([]string, error) {
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@ -200,7 +204,8 @@ func getRollupArgIdx(funcName string) int {
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logger.Panicf("BUG: getRollupArgIdx is called for non-rollup func %q", funcName)
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}
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switch funcName {
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case "quantile_over_time", "aggr_over_time":
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case "quantile_over_time", "aggr_over_time",
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"hoeffding_bound_lower", "hoeffding_bound_upper":
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return 1
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default:
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return 0
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@ -828,6 +833,66 @@ func newRollupShareFilter(args []interface{}, countFilter func(values []float64,
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return rf, nil
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}
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func newRollupHoeffdingBoundLower(args []interface{}) (rollupFunc, error) {
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if err := expectRollupArgsNum(args, 2); err != nil {
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return nil, err
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}
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phis, err := getScalar(args[0], 0)
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if err != nil {
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return nil, err
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}
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rf := func(rfa *rollupFuncArg) float64 {
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bound, avg := rollupHoeffdingBoundInternal(rfa, phis)
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return avg - bound
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}
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return rf, nil
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}
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func newRollupHoeffdingBoundUpper(args []interface{}) (rollupFunc, error) {
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if err := expectRollupArgsNum(args, 2); err != nil {
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return nil, err
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}
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phis, err := getScalar(args[0], 0)
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if err != nil {
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return nil, err
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}
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rf := func(rfa *rollupFuncArg) float64 {
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bound, avg := rollupHoeffdingBoundInternal(rfa, phis)
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return avg + bound
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}
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return rf, nil
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}
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func rollupHoeffdingBoundInternal(rfa *rollupFuncArg, phis []float64) (float64, float64) {
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// There is no need in handling NaNs here, since they must be cleaned up
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// before calling rollup funcs.
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values := rfa.values
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if len(values) == 0 {
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return nan, nan
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}
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if len(values) == 1 {
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return 0, values[0]
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}
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vMax := rollupMax(rfa)
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vMin := rollupMin(rfa)
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vAvg := rollupAvg(rfa)
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vRange := vMax - vMin
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if vRange <= 0 {
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return 0, vAvg
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}
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phi := phis[rfa.idx]
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if phi >= 1 {
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return inf, vAvg
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}
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if phi <= 0 {
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return 0, vAvg
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}
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// See https://en.wikipedia.org/wiki/Hoeffding%27s_inequality
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// and https://www.youtube.com/watch?v=6UwcqiNsZ8U&feature=youtu.be&t=1237
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bound := vRange * math.Sqrt(math.Log(1/(1-phi))/(2*float64(len(values))))
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return bound, vAvg
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}
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func newRollupQuantile(args []interface{}) (rollupFunc, error) {
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if err := expectRollupArgsNum(args, 2); err != nil {
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return nil, err
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@ -310,6 +310,48 @@ func TestRollupHoltWinters(t *testing.T) {
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f(0.9, 0.9, 33.99637566941818)
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}
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func TestRollupHoeffdingBoundLower(t *testing.T) {
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f := func(phi, vExpected float64) {
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t.Helper()
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phis := []*timeseries{{
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Values: []float64{phi},
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Timestamps: []int64{123},
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}}
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var me metricsql.MetricExpr
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args := []interface{}{phis, &metricsql.RollupExpr{Expr: &me}}
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testRollupFunc(t, "hoeffding_bound_lower", args, &me, vExpected)
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}
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f(0.5, 28.21949401521037)
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f(-1, 47.083333333333336)
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f(0, 47.083333333333336)
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f(1, -inf)
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f(2, -inf)
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f(0.1, 39.72878000047643)
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f(0.9, 12.701803086472331)
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}
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func TestRollupHoeffdingBoundUpper(t *testing.T) {
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f := func(phi, vExpected float64) {
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t.Helper()
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phis := []*timeseries{{
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Values: []float64{phi},
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Timestamps: []int64{123},
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}}
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var me metricsql.MetricExpr
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args := []interface{}{phis, &metricsql.RollupExpr{Expr: &me}}
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testRollupFunc(t, "hoeffding_bound_upper", args, &me, vExpected)
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}
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f(0.5, 65.9471726514563)
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f(-1, 47.083333333333336)
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f(0, 47.083333333333336)
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f(1, inf)
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f(2, inf)
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f(0.1, 54.43788666619024)
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f(0.9, 81.46486358019433)
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}
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func TestRollupNewRollupFuncSuccess(t *testing.T) {
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f := func(funcName string, vExpected float64) {
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t.Helper()
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@ -103,3 +103,5 @@ This functionality can be tried at [an editable Grafana dashboard](http://play-g
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- `aggr_over_time(("aggr_func1", "aggr_func2", ...), m[d])` - simultaneously calculates all the listed `aggr_func*` for `m` over `d` time range.
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`aggr_func*` can contain any functions that accept range vector. For instance, `aggr_over_time(("min_over_time", "max_over_time", "rate"), m[d])`
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would calculate `min_over_time`, `max_over_time` and `rate` for `m[d]`.
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- `hoeffding_bound_upper(phi, m[d])` and `hoeffding_bound_lower(phi, m[d])` - return upper and lower [Hoeffding bounds](https://en.wikipedia.org/wiki/Hoeffding%27s_inequality)
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for the given `phi` in the range `[0..1]`.
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@ -54,6 +54,8 @@ var rollupFuncs = map[string]bool{
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"rollup_increase": true,
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"rollup_candlestick": true,
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"aggr_over_time": true,
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"hoeffding_bound_upper": true,
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"hoeffding_bound_lower": true,
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}
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// IsRollupFunc returns whether funcName is known rollup function.
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