diff --git a/app/vmselect/promql/exec_test.go b/app/vmselect/promql/exec_test.go index 15317a698..a5819aa64 100644 --- a/app/vmselect/promql/exec_test.go +++ b/app/vmselect/promql/exec_test.go @@ -6503,6 +6503,17 @@ func TestExecSuccess(t *testing.T) { resultExpected := []netstorage.Result{r1, r2} f(q, resultExpected) }) + t.Run(`range_trim_outliers()`, func(t *testing.T) { + t.Parallel() + q := `range_trim_outliers(0.5, time())` + r := netstorage.Result{ + MetricName: metricNameExpected, + Values: []float64{nan, nan, 1400, 1600, nan, nan}, + Timestamps: timestampsExpected, + } + resultExpected := []netstorage.Result{r} + f(q, resultExpected) + }) t.Run(`range_trim_spikes()`, func(t *testing.T) { t.Parallel() q := `range_trim_spikes(0.2, time())` @@ -7059,6 +7070,17 @@ func TestExecSuccess(t *testing.T) { resultExpected := []netstorage.Result{r} f(q, resultExpected) }) + t.Run(`range_mad(time())`, func(t *testing.T) { + t.Parallel() + q := `range_mad(time())` + r := netstorage.Result{ + MetricName: metricNameExpected, + Values: []float64{300, 300, 300, 300, 300, 300}, + Timestamps: timestampsExpected, + } + resultExpected := []netstorage.Result{r} + f(q, resultExpected) + }) t.Run(`range_max(time())`, func(t *testing.T) { t.Parallel() q := `range_max(time())` @@ -8317,8 +8339,10 @@ func TestExecError(t *testing.T) { f(`end(1)`) f(`step(1)`) f(`running_sum(1, 2)`) + f(`range_mad()`) f(`range_sum(1, 2)`) f(`range_trim_spikes()`) + f(`range_trim_outliers()`) f(`range_first(1, 2)`) f(`range_last(1, 2)`) f(`range_linear_regression(1, 2)`) diff --git a/app/vmselect/promql/rollup.go b/app/vmselect/promql/rollup.go index 2b6dfe6b4..d8ec2e468 100644 --- a/app/vmselect/promql/rollup.go +++ b/app/vmselect/promql/rollup.go @@ -1219,18 +1219,21 @@ func rollupMAD(rfa *rollupFuncArg) float64 { // There is no need in handling NaNs here, since they must be cleaned up // before calling rollup funcs. + return mad(rfa.values) +} + +func mad(values []float64) float64 { // See https://en.wikipedia.org/wiki/Median_absolute_deviation - values := rfa.values median := quantile(0.5, values) a := getFloat64s() ds := a.A[:0] for _, v := range values { ds = append(ds, math.Abs(v-median)) } - mad := quantile(0.5, ds) + v := quantile(0.5, ds) a.A = ds putFloat64s(a) - return mad + return v } func rollupHistogram(rfa *rollupFuncArg) float64 { diff --git a/app/vmselect/promql/transform.go b/app/vmselect/promql/transform.go index 62e5cd92e..5d71d2f08 100644 --- a/app/vmselect/promql/transform.go +++ b/app/vmselect/promql/transform.go @@ -89,6 +89,7 @@ var transformFuncs = map[string]transformFunc{ "range_first": transformRangeFirst, "range_last": transformRangeLast, "range_linear_regression": transformRangeLinearRegression, + "range_mad": transformRangeMAD, "range_max": newTransformFuncRange(runningMax), "range_min": newTransformFuncRange(runningMin), "range_normalize": transformRangeNormalize, @@ -96,6 +97,7 @@ var transformFuncs = map[string]transformFunc{ "range_stddev": transformRangeStddev, "range_stdvar": transformRangeStdvar, "range_sum": newTransformFuncRange(runningSum), + "range_trim_outliers": transformRangeTrimOutliers, "range_trim_spikes": transformRangeTrimSpikes, "remove_resets": transformRemoveResets, "round": transformRound, @@ -1275,6 +1277,34 @@ func transformRangeNormalize(tfa *transformFuncArg) ([]*timeseries, error) { return rvs, nil } +func transformRangeTrimOutliers(tfa *transformFuncArg) ([]*timeseries, error) { + args := tfa.args + if err := expectTransformArgsNum(args, 2); err != nil { + return nil, err + } + ks, err := getScalar(args[0], 0) + if err != nil { + return nil, err + } + k := float64(0) + if len(ks) > 0 { + k = ks[0] + } + // Trim samples v satisfying the `abs(v - range_median(q)) > k*range_mad(q)` + rvs := args[1] + for _, ts := range rvs { + values := ts.Values + dMax := k * mad(values) + qMedian := quantile(0.5, values) + for i, v := range values { + if math.Abs(v-qMedian) > dMax { + values[i] = nan + } + } + } + return rvs, nil +} + func transformRangeTrimSpikes(tfa *transformFuncArg) ([]*timeseries, error) { args := tfa.args if err := expectTransformArgsNum(args, 2); err != nil { @@ -1344,6 +1374,22 @@ func transformRangeLinearRegression(tfa *transformFuncArg) ([]*timeseries, error return rvs, nil } +func transformRangeMAD(tfa *transformFuncArg) ([]*timeseries, error) { + args := tfa.args + if err := expectTransformArgsNum(args, 1); err != nil { + return nil, err + } + rvs := args[0] + for _, ts := range rvs { + values := ts.Values + v := mad(values) + for i := range values { + values[i] = v + } + } + return rvs, nil +} + func transformRangeStddev(tfa *transformFuncArg) ([]*timeseries, error) { args := tfa.args if err := expectTransformArgsNum(args, 1); err != nil { diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md index e2a7e5bbf..e48bf02e5 100644 --- a/docs/CHANGELOG.md +++ b/docs/CHANGELOG.md @@ -23,6 +23,8 @@ The following tip changes can be tested by building VictoriaMetrics components f * FEATURE: [vmauth](https://docs.victoriametrics.com/vmauth.html): choose the backend with the minimum number of concurrently executed requests [among the configured backends](https://docs.victoriametrics.com/vmauth.html#load-balancing) in a round-robin manner for serving the incoming requests. This allows spreading the load among backends more evenly, while improving the response time. * FEATURE: [vmalert enterprise](https://docs.victoriametrics.com/vmalert.html): add ability to read alerting and recording rules from S3, GCS or S3-compatible object storage. See [these docs](https://docs.victoriametrics.com/vmalert.html#reading-rules-from-object-storage). * FEATURE: [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html): add `mad_over_time(m[d])` function for calculating the [median absolute deviation](https://en.wikipedia.org/wiki/Median_absolute_deviation) over raw samples on the lookbehind window `d`. See [this feature request](https://github.com/prometheus/prometheus/issues/5514). +* FEATURE: [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html): add `range_mad(q)` function for calculating the [median absolute deviation](https://en.wikipedia.org/wiki/Median_absolute_deviation) over points per each time series returned by `q`. +* FEATURE: [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html): add `range_trim_outliers(k, q)` function for dropping outliers farther than `k*range_mad(q)` from the `range_median(q)`. This should removing outliers at query time at [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3759). * FEATURE: [vmui](https://docs.victoriametrics.com/#vmui): show `median` instead of `avg` in graph tooltip and line legend, since `median` is more tolerant against spikes. See [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3706). * BUGFIX: prevent from possible data ingestion slowdown and query performance slowdown during [background merges of big parts](https://docs.victoriametrics.com/#storage) on systems with small number of CPU cores (1 or 2 CPU cores). The issue has been introduced in [v1.85.0](https://docs.victoriametrics.com/CHANGELOG.html#v1850) when implementing [this feature](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3337). See also [this issue](https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3790). diff --git a/docs/MetricsQL.md b/docs/MetricsQL.md index caf714274..95860e83d 100644 --- a/docs/MetricsQL.md +++ b/docs/MetricsQL.md @@ -508,7 +508,7 @@ See also [duration_over_time](#duration_over_time) and [lag](#lag). `mad_over_time(series_selector[d])` is a [rollup function](#rollup-functions), which calculates [median absolute deviation](https://en.wikipedia.org/wiki/Median_absolute_deviation) over raw samples on the given lookbehind window `d` per each time series returned from the given [series_selector](https://docs.victoriametrics.com/keyConcepts.html#filtering). -See also [mad](#mad). +See also [mad](#mad) and [range_mad](#range_mad). #### max_over_time @@ -1221,6 +1221,13 @@ See also [rand](#rand) and [rand_exponential](#rand_exponential). `range_linear_regression(q)` is a [transform function](#transform-functions), which calculates [simple linear regression](https://en.wikipedia.org/wiki/Simple_linear_regression) over the selected time range per each time series returned by `q`. This function is useful for capacity planning and predictions. +#### range_mad + +`range_mad(q)` is a [transform function](#transform-functions), which calculates the [median absolute deviation](https://en.wikipedia.org/wiki/Median_absolute_deviation) +across points per each time series returned by `q`. + +See also [mad](#mad) and [mad_over_time](#mad_over_time). + #### range_max `range_max(q)` is a [transform function](#transform-functions), which calculates the max value across points per each time series returned by `q`. @@ -1257,11 +1264,22 @@ per each time series returned by `q` on the selected time range. `range_sum(q)` is a [transform function](#transform-functions), which calculates the sum of points per each time series returned by `q`. +#### range_trim_outliers + +`range_trim_outliers(k, q)` is a [transform function](#transform-functions), which drops points located farther than `k*range_mad(q)` +from the `range_median(q)`. E.g., it is equivalent to the following query: `q ifnot (abs(q - range_median(q)) > k*range_mad(q))`. + +The `phi` must be in the range `[0..1]`, where `0` means `0%` and `1` means `100%`. + +See also [range_trim_outliers](#range_trim_outliers). + #### range_trim_spikes `range_trim_spikes(phi, q)` is a [transform function](#transform-functions), which drops `phi` percent of biggest spikes from time series returned by `q`. The `phi` must be in the range `[0..1]`, where `0` means `0%` and `1` means `100%`. +See also [range_trim_outliers](#range_trim_outliers). + #### remove_resets `remove_resets(q)` is a [transform function](#transform-functions), which removes counter resets from time series returned by `q`. @@ -1731,7 +1749,7 @@ See also [limit_offset](#limit_offset). `mad(q) by (group_labels)` is [aggregate function](#aggregate-functions), which returns the [Median absolute deviation](https://en.wikipedia.org/wiki/Median_absolute_deviation) per each `group_labels` for all the time series returned by `q`. The aggregate is calculated individually per each group of points with the same timestamp. -See also [outliers_mad](#outliers_mad) and [stddev](#stddev). +See also [range_mad](#range_mad), [mad_over_time](#mad_over_time), [outliers_mad](#outliers_mad) and [stddev](#stddev). #### max diff --git a/go.mod b/go.mod index 45a7157be..7fbabe85f 100644 --- a/go.mod +++ b/go.mod @@ -12,7 +12,7 @@ require ( // like https://github.com/valyala/fasthttp/commit/996610f021ff45fdc98c2ce7884d5fa4e7f9199b github.com/VictoriaMetrics/fasthttp v1.1.0 github.com/VictoriaMetrics/metrics v1.23.1 - github.com/VictoriaMetrics/metricsql v0.53.0 + github.com/VictoriaMetrics/metricsql v0.54.0 github.com/aws/aws-sdk-go-v2 v1.17.4 github.com/aws/aws-sdk-go-v2/config v1.18.12 github.com/aws/aws-sdk-go-v2/feature/s3/manager v1.11.51 diff --git a/go.sum b/go.sum index a763c684d..3f0c9b87c 100644 --- a/go.sum +++ b/go.sum @@ -69,8 +69,8 @@ github.com/VictoriaMetrics/fasthttp v1.1.0/go.mod h1:/7DMcogqd+aaD3G3Hg5kFgoFwlR github.com/VictoriaMetrics/metrics v1.18.1/go.mod h1:ArjwVz7WpgpegX/JpB0zpNF2h2232kErkEnzH1sxMmA= github.com/VictoriaMetrics/metrics v1.23.1 h1:/j8DzeJBxSpL2qSIdqnRFLvQQhbJyJbbEi22yMm7oL0= github.com/VictoriaMetrics/metrics v1.23.1/go.mod h1:rAr/llLpEnAdTehiNlUxKgnjcOuROSzpw0GvjpEbvFc= -github.com/VictoriaMetrics/metricsql v0.53.0 h1:R//oEGo+G0DtmNxF111ClM2e2pjC4sG14geyZzXfbjU= -github.com/VictoriaMetrics/metricsql v0.53.0/go.mod h1:6pP1ZeLVJHqJrHlF6Ij3gmpQIznSsgktEcZgsAWYel0= +github.com/VictoriaMetrics/metricsql v0.54.0 h1:dKAIJtWcSPKnMNhRY5MYpqC77ZyHtA1xuDRr1pJuN5Q= +github.com/VictoriaMetrics/metricsql v0.54.0/go.mod h1:6pP1ZeLVJHqJrHlF6Ij3gmpQIznSsgktEcZgsAWYel0= github.com/VividCortex/ewma v1.1.1/go.mod h1:2Tkkvm3sRDVXaiyucHiACn4cqf7DpdyLvmxzcbUokwA= github.com/VividCortex/ewma v1.2.0 h1:f58SaIzcDXrSy3kWaHNvuJgJ3Nmz59Zji6XoJR/q1ow= github.com/VividCortex/ewma v1.2.0/go.mod h1:nz4BbCtbLyFDeC9SUHbtcT5644juEuWfUAUnGx7j5l4= diff --git a/vendor/github.com/VictoriaMetrics/metricsql/optimizer.go b/vendor/github.com/VictoriaMetrics/metricsql/optimizer.go index 341528586..1ae86f027 100644 --- a/vendor/github.com/VictoriaMetrics/metricsql/optimizer.go +++ b/vendor/github.com/VictoriaMetrics/metricsql/optimizer.go @@ -392,7 +392,8 @@ func getTransformArgIdxForOptimization(funcName string, args []Expr) int { return -1 case "limit_offset": return 2 - case "buckets_limit", "histogram_quantile", "histogram_share", "range_quantile", "range_trim_spikes": + case "buckets_limit", "histogram_quantile", "histogram_share", "range_quantile", + "range_trim_outliers", "range_trim_spikes": return 1 case "histogram_quantiles": return len(args) - 1 diff --git a/vendor/github.com/VictoriaMetrics/metricsql/transform.go b/vendor/github.com/VictoriaMetrics/metricsql/transform.go index 31029f2c3..d80cefeb6 100644 --- a/vendor/github.com/VictoriaMetrics/metricsql/transform.go +++ b/vendor/github.com/VictoriaMetrics/metricsql/transform.go @@ -74,6 +74,7 @@ var transformFuncs = map[string]bool{ "range_first": true, "range_last": true, "range_linear_regression": true, + "range_mad": true, "range_max": true, "range_min": true, "range_normalize": true, @@ -81,6 +82,7 @@ var transformFuncs = map[string]bool{ "range_stddev": true, "range_stdvar": true, "range_sum": true, + "range_trim_outliers": true, "range_trim_spikes": true, "remove_resets": true, "round": true, diff --git a/vendor/modules.txt b/vendor/modules.txt index 8e61e07fc..c7bfb3153 100644 --- a/vendor/modules.txt +++ b/vendor/modules.txt @@ -71,7 +71,7 @@ github.com/VictoriaMetrics/fasthttp/stackless # github.com/VictoriaMetrics/metrics v1.23.1 ## explicit; go 1.15 github.com/VictoriaMetrics/metrics -# github.com/VictoriaMetrics/metricsql v0.53.0 +# github.com/VictoriaMetrics/metricsql v0.54.0 ## explicit; go 1.13 github.com/VictoriaMetrics/metricsql github.com/VictoriaMetrics/metricsql/binaryop