cmd/bosun: promote scalars to numberSets

fixes #1123

cmd/bosun/expr/expr.go

@@ -578,7 +578,7 @@ 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:
@@ -594,6 +594,15 @@ 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 = &Results{
+ Results: ResultSlice{
+ &Result{
+ Value: Number(f),
+ },
+ },
+ }
+ }
  in = append(in, reflect.ValueOf(v))
  }
  f := reflect.ValueOf(node.F.F)

cmd/bosun/expr/expr_test.go

@@ -224,3 +224,31 @@ func TestQueryExpr(t *testing.T) {
  }
  }
 }
+
+func TestScalarPromotion(t *testing.T) {
+ tests := map[string]map[string]Value{
+ `abs(-1)`: {"": Number(1)},
+ }
+
+ for exprText, expected := range tests {
+ e, err := New(exprText)
+ if err != nil {
+ t.Fatal(err)
+ }
+ results, _, err := e.Execute(nil, nil, nil, nil, nil, queryTime, 0, false, nil, nil, nil)
+ if err != nil {
+ t.Fatal(err)
+ }
+ for _, r := range results.Results {
+ tag := r.Group.Tags()
+ ex := expected[tag]
+ if ex == nil {
+ t.Errorf("missing tag %v", tag)
+ continue
+ }
+ if ex != r.Value {
+ t.Errorf("unmatched values in %v", tag)
+ }
+ }
+ }
+}

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.

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 {

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.