bosun-monitor · captncraig · Jul 20, 2015 · Jul 13, 2015
diff --git a/cmd/bosun/expr/expr.go b/cmd/bosun/expr/expr.go
@@ -580,7 +580,7 @@ func (e *State) walkFunc(node *parse.FuncNode, T miniprofiler.Timer) *Results {
 	var res *Results
 	T.Step("func: "+node.Name, func(T miniprofiler.Timer) {
 		var in []reflect.Value
-		for _, a := range node.Args {
+		for i, a := range node.Args {
 			var v interface{}
 			switch t := a.(type) {
 			case *parse.StringNode:
@@ -596,6 +596,9 @@ func (e *State) walkFunc(node *parse.FuncNode, T miniprofiler.Timer) *Results {
 			default:
 				panic(fmt.Errorf("expr: unknown func arg type"))
 			}
+			if f, ok := v.(float64); ok && node.F.Args[i] == parse.TypeNumberSet {
+				v = fromScalar(f)
+			}
 			in = append(in, reflect.ValueOf(v))
 		}
 		f := reflect.ValueOf(node.F.F)

diff --git a/cmd/bosun/expr/expr_test.go b/cmd/bosun/expr/expr_test.go
@@ -143,31 +143,32 @@ func TestQueryExpr(t *testing.T) {
 		},
 	}
 	d := time.Date(1970, time.January, 1, 0, 0, 0, 0, time.UTC)
-	tests := map[string]map[string]map[time.Time]float64{
+	tests := map[string]map[string]Value{
 		`window("avg:m{a=*}", "5m", "1h", 2, "max")`: {
-			"a=b": {
+			"a=b": Series{
 				d: 2,
 				d.Add(time.Second * 2): 6,
 			},
-			"a=c": {
+			"a=c": Series{
 				d.Add(time.Second * 1): 8,
 			},
-			"a=d": {
+			"a=d": Series{
 				d.Add(time.Second * 8): 9,
 			},
 		},
 		`window("avg:m{a=*}", "5m", "1h", 2, "avg")`: {
-			"a=b": {
+			"a=b": Series{
 				d: 1.5,
 				d.Add(time.Second * 2): 5,
 			},
-			"a=c": {
+			"a=c": Series{
 				d.Add(time.Second * 1): 7.5,
 			},
-			"a=d": {
+			"a=d": Series{
 				d.Add(time.Second * 8): 8.5,
 			},
 		},
+		`abs(-1)`: {"": Number(1)},
 	}
 
 	ts := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
@@ -214,15 +215,34 @@ func TestQueryExpr(t *testing.T) {
 				t.Errorf("missing tag %v", tag)
 				continue
 			}
-			val := r.Value.(Series)
-			if len(val) != len(ex) {
-				t.Errorf("unmatched values in %v", tag)
-			}
-			for k, v := range ex {
-				got := val[k]
-				if got != v {
-					t.Errorf("%v, %v: got %v, expected %v", tag, k, got, v)
+			switch val := r.Value.(type) {
+			case Series:
+				ex, ok := ex.(Series)
+				if !ok {
+					t.Errorf("%v: bad type %T", exprText, ex)
+					continue
+				}
+				if len(val) != len(ex) {
+					t.Errorf("unmatched values in %v", tag)
+					continue
+				}
+				for k, v := range ex {
+					got := val[k]
+					if got != v {
+						t.Errorf("%v, %v: got %v, expected %v", tag, k, got, v)
+					}
+				}
+			case Number:
+				ex, ok := ex.(Number)
+				if !ok {
+					t.Errorf("%v: bad type %T", exprText, ex)
+					continue
+				}
+				if ex != val {
+					t.Errorf("%v: got %v, expected %v", exprText, r.Value, ex)
 				}
+			default:
+				t.Errorf("%v: unknown type %T", exprText, r.Value)
 			}
 		}
 	}

diff --git a/cmd/bosun/expr/funcs.go b/cmd/bosun/expr/funcs.go
@@ -160,7 +160,7 @@ var builtins = map[string]parse.Func{
 		F:      First,
 	},
 	"forecastlr": {
-		Args:   []parse.FuncType{parse.TypeSeriesSet, parse.TypeScalar},
+		Args:   []parse.FuncType{parse.TypeSeriesSet, parse.TypeNumberSet},
 		Return: parse.TypeNumberSet,
 		Tags:   tagFirst,
 		F:      Forecast_lr,
@@ -196,7 +196,7 @@ var builtins = map[string]parse.Func{
 		F:      Min,
 	},
 	"percentile": {
-		Args:   []parse.FuncType{parse.TypeSeriesSet, parse.TypeScalar},
+		Args:   []parse.FuncType{parse.TypeSeriesSet, parse.TypeNumberSet},
 		Return: parse.TypeNumberSet,
 		Tags:   tagFirst,
 		F:      Percentile,
@@ -260,25 +260,25 @@ var builtins = map[string]parse.Func{
 		F:      Des,
 	},
 	"dropge": {
-		Args:   []parse.FuncType{parse.TypeSeriesSet, parse.TypeScalar},
+		Args:   []parse.FuncType{parse.TypeSeriesSet, parse.TypeNumberSet},
 		Return: parse.TypeSeriesSet,
 		Tags:   tagFirst,
 		F:      DropGe,
 	},
 	"dropg": {
-		Args:   []parse.FuncType{parse.TypeSeriesSet, parse.TypeScalar},
+		Args:   []parse.FuncType{parse.TypeSeriesSet, parse.TypeNumberSet},
 		Return: parse.TypeSeriesSet,
 		Tags:   tagFirst,
 		F:      DropG,
 	},
 	"drople": {
-		Args:   []parse.FuncType{parse.TypeSeriesSet, parse.TypeScalar},
+		Args:   []parse.FuncType{parse.TypeSeriesSet, parse.TypeNumberSet},
 		Return: parse.TypeSeriesSet,
 		Tags:   tagFirst,
 		F:      DropLe,
 	},
 	"dropl": {
-		Args:   []parse.FuncType{parse.TypeSeriesSet, parse.TypeScalar},
+		Args:   []parse.FuncType{parse.TypeSeriesSet, parse.TypeNumberSet},
 		Return: parse.TypeSeriesSet,
 		Tags:   tagFirst,
 		F:      DropL,
@@ -380,39 +380,41 @@ func Duration(e *State, T miniprofiler.Timer, d string) (*Results, error) {
 	}, nil
 }
 
-func DropValues(e *State, T miniprofiler.Timer, series *Results, threshold float64, dropFunction func(float64, float64) bool) (*Results, error) {
-	for _, res := range series.Results {
+func DropValues(e *State, T miniprofiler.Timer, series *Results, threshold *Results, dropFunction func(float64, float64) bool) (*Results, error) {
+	f := func(res *Results, s *Result, floats []float64) error {
 		nv := make(Series)
-		for k, v := range res.Value.Value().(Series) {
-			if !dropFunction(float64(v), threshold) {
+		for k, v := range s.Value.Value().(Series) {
+			if !dropFunction(float64(v), floats[0]) {
 				//preserve values which should not be discarded
 				nv[k] = v
 			}
 		}
 		if len(nv) == 0 {
-			return nil, fmt.Errorf("series %s is empty", res.Group)
+			return fmt.Errorf("series %s is empty", s.Group)
 		}
-		res.Value = nv
+		s.Value = nv
+		res.Results = append(res.Results, s)
+		return nil
 	}
-	return series, nil
+	return match(f, series, threshold)
 }
 
-func DropGe(e *State, T miniprofiler.Timer, series *Results, threshold float64) (*Results, error) {
+func DropGe(e *State, T miniprofiler.Timer, series *Results, threshold *Results) (*Results, error) {
 	dropFunction := func(value float64, threshold float64) bool { return value >= threshold }
 	return DropValues(e, T, series, threshold, dropFunction)
 }
 
-func DropG(e *State, T miniprofiler.Timer, series *Results, threshold float64) (*Results, error) {
+func DropG(e *State, T miniprofiler.Timer, series *Results, threshold *Results) (*Results, error) {
 	dropFunction := func(value float64, threshold float64) bool { return value > threshold }
 	return DropValues(e, T, series, threshold, dropFunction)
 }
 
-func DropLe(e *State, T miniprofiler.Timer, series *Results, threshold float64) (*Results, error) {
+func DropLe(e *State, T miniprofiler.Timer, series *Results, threshold *Results) (*Results, error) {
 	dropFunction := func(value float64, threshold float64) bool { return value <= threshold }
 	return DropValues(e, T, series, threshold, dropFunction)
 }
 
-func DropL(e *State, T miniprofiler.Timer, series *Results, threshold float64) (*Results, error) {
+func DropL(e *State, T miniprofiler.Timer, series *Results, threshold *Results) (*Results, error) {
 	dropFunction := func(value float64, threshold float64) bool { return value < threshold }
 	return DropValues(e, T, series, threshold, dropFunction)
 }
@@ -421,7 +423,7 @@ func DropNA(e *State, T miniprofiler.Timer, series *Results) (*Results, error) {
 	dropFunction := func(value float64, threshold float64) bool {
 		return math.IsNaN(float64(value)) || math.IsInf(float64(value), 0)
 	}
-	return DropValues(e, T, series, 0, dropFunction)
+	return DropValues(e, T, series, fromScalar(0), dropFunction)
 }
 
 func parseGraphiteResponse(req *graphite.Request, s *graphite.Response, formatTags []string) ([]*Result, error) {
@@ -874,32 +876,63 @@ func Change(e *State, T miniprofiler.Timer, query, sduration, eduration string)
 	if err != nil {
 		return
 	}
-	r, err = reduce(e, T, r, change, (sd - ed).Seconds())
+	r, err = reduce(e, T, r, change, fromScalar((sd - ed).Seconds()))
 	return
 }
 
 func change(dps Series, args ...float64) float64 {
 	return avg(dps) * args[0]
 }
 
-func reduce(e *State, T miniprofiler.Timer, series *Results, F func(Series, ...float64) float64, args ...float64) (*Results, error) {
+func fromScalar(f float64) *Results {
+	return &Results{
+		Results: ResultSlice{
+			&Result{
+				Value: Number(f),
+			},
+		},
+	}
+}
+
+func match(f func(res *Results, series *Result, floats []float64) error, series *Results, numberSets ...*Results) (*Results, error) {
 	res := *series
 	res.Results = nil
 	for _, s := range series.Results {
-		switch t := s.Value.(type) {
-		case Series:
-			if len(t) == 0 {
-				continue
+		var floats []float64
+		for _, num := range numberSets {
+			for _, n := range num.Results {
+				if len(n.Group) == 0 || s.Group.Overlaps(n.Group) {
+					floats = append(floats, float64(n.Value.(Number)))
+					break
+				}
 			}
-			s.Value = Number(F(t, args...))
-			res.Results = append(res.Results, s)
-		default:
-			panic(fmt.Errorf("expr: expected a series"))
+		}
+		if len(floats) != len(numberSets) {
+			if !series.IgnoreUnjoined {
+				return nil, fmt.Errorf("unjoined groups for %s", s.Group)
+			}
+			continue
+		}
+		if err := f(&res, s, floats); err != nil {
+			return nil, err
 		}
 	}
 	return &res, nil
 }
 
+func reduce(e *State, T miniprofiler.Timer, series *Results, F func(Series, ...float64) float64, args ...*Results) (*Results, error) {
+	f := func(res *Results, s *Result, floats []float64) error {
+		t := s.Value.(Series)
+		if len(t) == 0 {
+			return nil
+		}
+		s.Value = Number(F(t, floats...))
+		res.Results = append(res.Results, s)
+		return nil
+	}
+	return match(f, series, args...)
+}
+
 func Abs(e *State, T miniprofiler.Timer, series *Results) *Results {
 	for _, s := range series.Results {
 		s.Value = Number(math.Abs(float64(s.Value.Value().(Number))))
@@ -1070,7 +1103,7 @@ func (e *State) since(dps Series, args ...float64) (a float64) {
 	return s.Seconds()
 }
 
-func Forecast_lr(e *State, T miniprofiler.Timer, series *Results, y float64) (r *Results, err error) {
+func Forecast_lr(e *State, T miniprofiler.Timer, series *Results, y *Results) (r *Results, err error) {
 	return reduce(e, T, series, e.forecast_lr, y)
 }
 
@@ -1107,20 +1140,20 @@ func (e *State) forecast_lr(dps Series, args ...float64) float64 {
 	return s.Seconds()
 }
 
-func Percentile(e *State, T miniprofiler.Timer, series *Results, p float64) (r *Results, err error) {
+func Percentile(e *State, T miniprofiler.Timer, series *Results, p *Results) (r *Results, err error) {
 	return reduce(e, T, series, percentile, p)
 }
 
 func Min(e *State, T miniprofiler.Timer, series *Results) (r *Results, err error) {
-	return reduce(e, T, series, percentile, 0)
+	return reduce(e, T, series, percentile, fromScalar(0))
 }
 
 func Median(e *State, T miniprofiler.Timer, series *Results) (r *Results, err error) {
-	return reduce(e, T, series, percentile, .5)
+	return reduce(e, T, series, percentile, fromScalar(.5))
 }
 
 func Max(e *State, T miniprofiler.Timer, series *Results) (r *Results, err error) {
-	return reduce(e, T, series, percentile, 1)
+	return reduce(e, T, series, percentile, fromScalar(1))
 }
 
 // percentile returns the value at the corresponding percentile between 0 and 1.

diff --git a/cmd/bosun/expr/parse/node.go b/cmd/bosun/expr/parse/node.go
@@ -111,7 +111,9 @@ func (f *FuncNode) Check(t *Tree) error {
 	for i, a := range f.Args {
 		ft := f.F.Args[i]
 		at := a.Return()
-		if ft != at {
+		if ft == TypeNumberSet && at == TypeScalar {
+			// Scalars are promoted to NumberSets during execution.
+		} else if ft != at {
 			return fmt.Errorf("parse: expected %v, got %v", ft, at)
 		}
 		if err := a.Check(t); err != nil {

diff --git a/docs/expressions.md b/docs/expressions.md
@@ -25,7 +25,7 @@ This section documents Bosun's expression language, which is used to define the
 There are three data types in Bosun's expression language:
 
  1. **Scalar**: This is the simplest type, it is a single numeric value with no group associated with it. Keep in mind that an empty group, `{}` is still a group.
- 2. **NumberSet**: A number set is a group of tagged numeric values with one value per unique grouping.
+ 2. **NumberSet**: A number set is a group of tagged numeric values with one value per unique grouping. As a special case, a **scalar** may be used in place of a **numberSet** with a single member with an empty group.
  3. **SeriesSet**: A series is an array of timestamp-value pairs and an associated group.
 
 In the vast majority of your alerts you will getting ***seriesSets*** back from your time series database and ***reducing*** them into ***numberSets***.
@@ -205,7 +205,7 @@ Diff returns the last point of each series minus the first point.
 
 Returns the first (least recent) data point in each series.
 
-## forecastlr(seriesSet, y_val scalar) numberSet
+## forecastlr(seriesSet, y_val numberSet|scalar) numberSet
 
 Returns the number of seconds until a linear regression of each series will reach y_val.
 
@@ -229,7 +229,7 @@ Returns the median value of each series, same as calling percentile(series, .5).
 
 Returns the minimum value of each series, same as calling percentile(series, 0).
 
-## percentile(seriesSet, p scalar) numberSet
+## percentile(seriesSet, p numberSet|scalar) numberSet
 
 Returns the value from each series at the percentile p. Min and Max can be simulated using `p <= 0` and `p >= 1`, respectively.
 
@@ -344,23 +344,22 @@ Returns series smoothed using Holt-Winters double exponential smoothing. Alpha
 (scalar) is the data smoothing factor. Beta (scalar) is the trend smoothing
 factor.
 
-## dropg(seriesSet, scalar) seriesSet
+## dropg(seriesSet, threshold numberSet|scalar) seriesSet
 
 Remove any values greater than number from a series. Will error if this operation results in an empty series.
 
-## dropge(seriesSet, scalar) seriesSet
+## dropge(seriesSet, threshold numberSet|scalar) seriesSet
 
 Remove any values greater than or equal to number from a series. Will error if this operation results in an empty series.
 
-## dropl(seriesSet, scalar) seriesSet
+## dropl(seriesSet, threshold numberSet|scalar) seriesSet
 
 Remove any values lower than number from a series. Will error if this operation results in an empty series.
 
-## drople(seriesSet, scalar) seriesSet
+## drople(seriesSet, threshold numberSet|scalar) seriesSet
 
 Remove any values lower than or equal to number from a series. Will error if this operation results in an empty series.
 
-
 ## dropna(seriesSet) seriesSet
 
 Remove any NaN or Inf values from a series. Will error if this operation results in an empty series.