app/vmselect/promql: add range_over_time(m[d]) function for calculating value range for m over d

app/vmselect/promql/exec_test.go

@@ -3248,6 +3248,17 @@ func TestExecSuccess(t *testing.T) {
 		resultExpected := []netstorage.Result{r}
 		f(q, resultExpected)
 	})
+	t.Run(`range_over_time(time)`, func(t *testing.T) {
+		t.Parallel()
+		q := `range_over_time(alias(time()/100, "foobar")[3i])`
+		r := netstorage.Result{
+			MetricName: metricNameExpected,
+			Values:     []float64{4, 4, 4, 4, 4, 4},
+			Timestamps: timestampsExpected,
+		}
+		resultExpected := []netstorage.Result{r}
+		f(q, resultExpected)
+	})
 	t.Run(`sum(multi-vector)`, func(t *testing.T) {
 		t.Parallel()
 		q := `sum(label_set(10, "foo", "bar") or label_set(time()/100, "baz", "sss"))`

app/vmselect/promql/rollup.go

@@ -15,8 +15,6 @@ import (
 )
 
 var rollupFuncs = map[string]newRollupFunc{
-	"default_rollup": newRollupFuncOneArg(rollupDefault), // default rollup func
-
 	// Standard rollup funcs from PromQL.
 	// See funcs accepting range-vector on https://prometheus.io/docs/prometheus/latest/querying/functions/ .
 	"changes":            newRollupFuncOneArg(rollupChanges),
@@ -41,6 +39,8 @@ var rollupFuncs = map[string]newRollupFunc{
 	"absent_over_time":   newRollupFuncOneArg(rollupAbsent),
 
 	// Additional rollup funcs.
+	"default_rollup":        newRollupFuncOneArg(rollupDefault), // default rollup func
+	"range_over_time":       newRollupFuncOneArg(rollupRange),
 	"sum2_over_time":        newRollupFuncOneArg(rollupSum2),
 	"geomean_over_time":     newRollupFuncOneArg(rollupGeomean),
 	"first_over_time":       newRollupFuncOneArg(rollupFirst),
@@ -91,6 +91,7 @@ var rollupAggrFuncs = map[string]rollupFunc{
 	"absent_over_time": rollupAbsent,
 
 	// Additional rollup funcs.
+	"range_over_time":     rollupRange,
 	"sum2_over_time":      rollupSum2,
 	"geomean_over_time":   rollupGeomean,
 	"first_over_time":     rollupFirst,
@@ -1052,6 +1053,12 @@ func rollupSum(rfa *rollupFuncArg) float64 {
 	return sum
 }
 
+func rollupRange(rfa *rollupFuncArg) float64 {
+	max := rollupMax(rfa)
+	min := rollupMin(rfa)
+	return max - min
+}
+
 func rollupSum2(rfa *rollupFuncArg) float64 {
 	// There is no need in handling NaNs here, since they must be cleaned up
 	// before calling rollup funcs.

app/vmselect/promql/rollup_test.go

@@ -370,6 +370,7 @@ func TestRollupNewRollupFuncSuccess(t *testing.T) {
 	f("irate", 0)
 	f("rate", 2200)
 	f("resets", 5)
+	f("range_over_time", 111)
 	f("avg_over_time", 47.083333333333336)
 	f("min_over_time", 12)
 	f("max_over_time", 123)

docs/ExtendedPromQL.md

@@ -66,6 +66,7 @@ This functionality can be tried at [an editable Grafana dashboard](http://play-g
 - `lifetime(q[d])` - returns lifetime of `q` over `d` in seconds. It is expected that `d` exceeds the lifetime of `q`.
 - `scrape_interval(q[d])` - returns the average interval in seconds between data points of `q` over `d` aka `scrape interval`.
 - Trigonometric functions - `sin(q)`, `cos(q)`, `asin(q)`, `acos(q)` and `pi()`.
+- `range_over_time(m[d])` - returns value range for `m` over `d` time window, i.e. `max_over_time(m[d])-min_over_time(m[d])`.
 - `median_over_time(m[d])` - calculates median values for `m` over `d` time window. Shorthand to `quantile_over_time(0.5, m[d])`.
 - `median(q)` - median aggregate. Shorthand to `quantile(0.5, q)`.
 - `limitk(k, q)` - limits the number of time series returned from `q` to `k`.

lib/metricsql/rollup.go

@@ -30,6 +30,7 @@ var rollupFuncs = map[string]bool{
 
 	// Additional rollup funcs.
 	"default_rollup":        true,
+	"range_over_time":       true,
 	"sum2_over_time":        true,
 	"geomean_over_time":     true,
 	"first_over_time":       true,