func evalMatchesBasedOnState() []*EvalMatch {
matches := make([]*EvalMatch, 0)
matches = append(matches, &EvalMatch{
Metric: "High value",
Value: null.FloatFrom(100),
})
matches = append(matches, &EvalMatch{
Metric: "Higher Value",
Value: null.FloatFrom(200),
})
return matches
}
func evalutorScenario(json string, reducedValue float64, datapoints ...float64) bool {
jsonModel, err := simplejson.NewJson([]byte(json))
So(err, ShouldBeNil)
evaluator, err := NewAlertEvaluator(jsonModel)
So(err, ShouldBeNil)
return evaluator.Eval(null.FloatFrom(reducedValue))
}
func (rp *ResponseParser) parseValue(value interface{}) null.Float {
number, ok := value.(json.Number)
if !ok {
return null.FloatFromPtr(nil)
}
fvalue, err := number.Float64()
if err == nil {
return null.FloatFrom(fvalue)
}
ivalue, err := number.Int64()
if err == nil {
return null.FloatFrom(float64(ivalue))
}
return null.FloatFromPtr(nil)
}
func NewTimeSeriesPointsFromArgs(values ...float64) TimeSeriesPoints {
points := make(TimeSeriesPoints, 0)
for i := 0; i < len(values); i += 2 {
points = append(points, NewTimePoint(null.FloatFrom(values[i]), values[i+1]))
}
return points
}
func testReducer(typ string, datapoints ...float64) float64 {
reducer := NewSimpleReducer(typ)
series := &tsdb.TimeSeries{
Name: "test time serie",
}
for idx := range datapoints {
series.Points = append(series.Points, tsdb.NewTimePoint(null.FloatFrom(datapoints[idx]), 1234134))
}
return reducer.Reduce(series).Float64
}
func (e *OpenTsdbExecutor) parseResponse(query OpenTsdbQuery, res *http.Response) (map[string]*tsdb.QueryResult, error) {
queryResults := make(map[string]*tsdb.QueryResult)
queryRes := tsdb.NewQueryResult()
body, err := ioutil.ReadAll(res.Body)
defer res.Body.Close()
if err != nil {
return nil, err
}
if res.StatusCode/100 != 2 {
plog.Info("Request failed", "status", res.Status, "body", string(body))
return nil, fmt.Errorf("Request failed status: %v", res.Status)
}
var data []OpenTsdbResponse
err = json.Unmarshal(body, &data)
if err != nil {
plog.Info("Failed to unmarshal opentsdb response", "error", err, "status", res.Status, "body", string(body))
return nil, err
}
for _, val := range data {
series := tsdb.TimeSeries{
Name: val.Metric,
}
for timeString, value := range val.DataPoints {
timestamp, err := strconv.ParseFloat(timeString, 64)
if err != nil {
plog.Info("Failed to unmarshal opentsdb timestamp", "timestamp", timeString)
return nil, err
}
series.Points = append(series.Points, tsdb.NewTimePoint(null.FloatFrom(value), timestamp))
}
queryRes.Series = append(queryRes.Series, &series)
}
queryResults["A"] = queryRes
return queryResults, nil
}
func parseResponse(value pmodel.Value, query *PrometheusQuery) (map[string]*tsdb.QueryResult, error) {
queryResults := make(map[string]*tsdb.QueryResult)
queryRes := tsdb.NewQueryResult()
data, ok := value.(pmodel.Matrix)
if !ok {
return queryResults, fmt.Errorf("Unsupported result format: %s", value.Type().String())
}
for _, v := range data {
series := tsdb.TimeSeries{
Name: formatLegend(v.Metric, query),
}
for _, k := range v.Values {
series.Points = append(series.Points, tsdb.NewTimePoint(null.FloatFrom(float64(k.Value)), float64(k.Timestamp.Unix()*1000)))
}
queryRes.Series = append(queryRes.Series, &series)
}
queryResults["A"] = queryRes
return queryResults, nil
}
func TestQueryCondition(t *testing.T) {
Convey("when evaluating query condition", t, func() {
queryConditionScenario("Given avg() and > 100", func(ctx *queryConditionTestContext) {
ctx.reducer = `{"type": "avg"}`
ctx.evaluator = `{"type": "gt", "params": [100]}`
Convey("Can read query condition from json model", func() {
ctx.exec()
So(ctx.condition.Query.From, ShouldEqual, "5m")
So(ctx.condition.Query.To, ShouldEqual, "now")
So(ctx.condition.Query.DatasourceId, ShouldEqual, 1)
Convey("Can read query reducer", func() {
reducer, ok := ctx.condition.Reducer.(*SimpleReducer)
So(ok, ShouldBeTrue)
So(reducer.Type, ShouldEqual, "avg")
})
Convey("Can read evaluator", func() {
evaluator, ok := ctx.condition.Evaluator.(*ThresholdEvaluator)
So(ok, ShouldBeTrue)
So(evaluator.Type, ShouldEqual, "gt")
})
})
Convey("should fire when avg is above 100", func() {
points := tsdb.NewTimeSeriesPointsFromArgs(120, 0)
ctx.series = tsdb.TimeSeriesSlice{tsdb.NewTimeSeries("test1", points)}
cr, err := ctx.exec()
So(err, ShouldBeNil)
So(cr.Firing, ShouldBeTrue)
})
Convey("Should not fire when avg is below 100", func() {
points := tsdb.NewTimeSeriesPointsFromArgs(90, 0)
ctx.series = tsdb.TimeSeriesSlice{tsdb.NewTimeSeries("test1", points)}
cr, err := ctx.exec()
So(err, ShouldBeNil)
So(cr.Firing, ShouldBeFalse)
})
Convey("Should fire if only first serie matches", func() {
ctx.series = tsdb.TimeSeriesSlice{
tsdb.NewTimeSeries("test1", tsdb.NewTimeSeriesPointsFromArgs(120, 0)),
tsdb.NewTimeSeries("test2", tsdb.NewTimeSeriesPointsFromArgs(0, 0)),
}
cr, err := ctx.exec()
So(err, ShouldBeNil)
So(cr.Firing, ShouldBeTrue)
})
Convey("No series", func() {
Convey("Should set NoDataFound when condition is gt", func() {
ctx.series = tsdb.TimeSeriesSlice{}
cr, err := ctx.exec()
So(err, ShouldBeNil)
So(cr.Firing, ShouldBeFalse)
So(cr.NoDataFound, ShouldBeTrue)
})
Convey("Should be firing when condition is no_value", func() {
ctx.evaluator = `{"type": "no_value", "params": []}`
ctx.series = tsdb.TimeSeriesSlice{}
cr, err := ctx.exec()
So(err, ShouldBeNil)
So(cr.Firing, ShouldBeTrue)
})
})
Convey("Empty series", func() {
Convey("Should set Firing if eval match", func() {
ctx.evaluator = `{"type": "no_value", "params": []}`
ctx.series = tsdb.TimeSeriesSlice{
tsdb.NewTimeSeries("test1", tsdb.NewTimeSeriesPointsFromArgs()),
}
cr, err := ctx.exec()
So(err, ShouldBeNil)
So(cr.Firing, ShouldBeTrue)
})
Convey("Should set NoDataFound both series are empty", func() {
ctx.series = tsdb.TimeSeriesSlice{
tsdb.NewTimeSeries("test1", tsdb.NewTimeSeriesPointsFromArgs()),
tsdb.NewTimeSeries("test2", tsdb.NewTimeSeriesPointsFromArgs()),
}
cr, err := ctx.exec()
So(err, ShouldBeNil)
So(cr.NoDataFound, ShouldBeTrue)
})
Convey("Should set NoDataFound both series contains null", func() {
ctx.series = tsdb.TimeSeriesSlice{
tsdb.NewTimeSeries("test1", tsdb.TimeSeriesPoints{tsdb.TimePoint{null.FloatFromPtr(nil), null.FloatFrom(0)}}),
tsdb.NewTimeSeries("test2", tsdb.TimeSeriesPoints{tsdb.TimePoint{null.FloatFromPtr(nil), null.FloatFrom(0)}}),
}
cr, err := ctx.exec()
So(err, ShouldBeNil)
So(cr.NoDataFound, ShouldBeTrue)
})
Convey("Should not set NoDataFound if one serie is empty", func() {
ctx.series = tsdb.TimeSeriesSlice{
tsdb.NewTimeSeries("test1", tsdb.NewTimeSeriesPointsFromArgs()),
tsdb.NewTimeSeries("test2", tsdb.NewTimeSeriesPointsFromArgs(120, 0)),
}
cr, err := ctx.exec()
So(err, ShouldBeNil)
So(cr.NoDataFound, ShouldBeFalse)
})
})
})
})
}
func TestSimpleReducer(t *testing.T) {
Convey("Test simple reducer by calculating", t, func() {
Convey("sum", func() {
result := testReducer("sum", 1, 2, 3)
So(result, ShouldEqual, float64(6))
})
Convey("min", func() {
result := testReducer("min", 3, 2, 1)
So(result, ShouldEqual, float64(1))
})
Convey("max", func() {
result := testReducer("max", 1, 2, 3)
So(result, ShouldEqual, float64(3))
})
Convey("count", func() {
result := testReducer("count", 1, 2, 3000)
So(result, ShouldEqual, float64(3))
})
Convey("last", func() {
result := testReducer("last", 1, 2, 3000)
So(result, ShouldEqual, float64(3000))
})
Convey("median odd amount of numbers", func() {
result := testReducer("median", 1, 2, 3000)
So(result, ShouldEqual, float64(2))
})
Convey("median even amount of numbers", func() {
result := testReducer("median", 1, 2, 4, 3000)
So(result, ShouldEqual, float64(3))
})
Convey("median with one values", func() {
result := testReducer("median", 1)
So(result, ShouldEqual, float64(1))
})
Convey("avg", func() {
result := testReducer("avg", 1, 2, 3)
So(result, ShouldEqual, float64(2))
})
Convey("avg with only nulls", func() {
reducer := NewSimpleReducer("avg")
series := &tsdb.TimeSeries{
Name: "test time serie",
}
series.Points = append(series.Points, tsdb.NewTimePoint(null.FloatFromPtr(nil), 1))
So(reducer.Reduce(series).Valid, ShouldEqual, false)
})
Convey("avg of number values and null values should ignore nulls", func() {
reducer := NewSimpleReducer("avg")
series := &tsdb.TimeSeries{
Name: "test time serie",
}
series.Points = append(series.Points, tsdb.NewTimePoint(null.FloatFrom(3), 1))
series.Points = append(series.Points, tsdb.NewTimePoint(null.FloatFromPtr(nil), 2))
series.Points = append(series.Points, tsdb.NewTimePoint(null.FloatFromPtr(nil), 3))
series.Points = append(series.Points, tsdb.NewTimePoint(null.FloatFrom(3), 4))
So(reducer.Reduce(series).Float64, ShouldEqual, float64(3))
})
})
}
func (s *SimpleReducer) Reduce(series *tsdb.TimeSeries) null.Float {
if len(series.Points) == 0 {
return null.FloatFromPtr(nil)
}
value := float64(0)
allNull := true
switch s.Type {
case "avg":
validPointsCount := 0
for _, point := range series.Points {
if point[0].Valid {
value += point[0].Float64
validPointsCount += 1
allNull = false
}
}
if validPointsCount > 0 {
value = value / float64(validPointsCount)
}
case "sum":
for _, point := range series.Points {
if point[0].Valid {
value += point[0].Float64
allNull = false
}
}
case "min":
value = math.MaxFloat64
for _, point := range series.Points {
if point[0].Valid {
allNull = false
if value > point[0].Float64 {
value = point[0].Float64
}
}
}
case "max":
value = -math.MaxFloat64
for _, point := range series.Points {
if point[0].Valid {
allNull = false
if value < point[0].Float64 {
value = point[0].Float64
}
}
}
case "count":
value = float64(len(series.Points))
allNull = false
case "last":
points := series.Points
for i := len(points) - 1; i >= 0; i-- {
if points[i][0].Valid {
value = points[i][0].Float64
allNull = false
break
}
}
case "median":
var values []float64
for _, v := range series.Points {
if v[0].Valid {
allNull = false
values = append(values, v[0].Float64)
}
}
if len(values) >= 1 {
sort.Float64s(values)
length := len(values)
if length%2 == 1 {
value = values[(length-1)/2]
} else {
value = (values[(length/2)-1] + values[length/2]) / 2
}
}
}
if allNull {
return null.FloatFromPtr(nil)
}
return null.FloatFrom(value)
}
func init() {
ScenarioRegistry = make(map[string]*Scenario)
logger := log.New("tsdb.testdata")
logger.Debug("Initializing TestData Scenario")
registerScenario(&Scenario{
Id: "random_walk",
Name: "Random Walk",
Handler: func(query *tsdb.Query, context *tsdb.QueryContext) *tsdb.QueryResult {
timeWalkerMs := context.TimeRange.GetFromAsMsEpoch()
to := context.TimeRange.GetToAsMsEpoch()
series := newSeriesForQuery(query)
points := make(tsdb.TimeSeriesPoints, 0)
walker := rand.Float64() * 100
for i := int64(0); i < 10000 && timeWalkerMs < to; i++ {
points = append(points, tsdb.NewTimePoint(null.FloatFrom(walker), float64(timeWalkerMs)))
walker += rand.Float64() - 0.5
timeWalkerMs += query.IntervalMs
}
series.Points = points
queryRes := tsdb.NewQueryResult()
queryRes.Series = append(queryRes.Series, series)
return queryRes
},
})
registerScenario(&Scenario{
Id: "no_data_points",
Name: "No Data Points",
Handler: func(query *tsdb.Query, context *tsdb.QueryContext) *tsdb.QueryResult {
return tsdb.NewQueryResult()
},
})
registerScenario(&Scenario{
Id: "datapoints_outside_range",
Name: "Datapoints Outside Range",
Handler: func(query *tsdb.Query, context *tsdb.QueryContext) *tsdb.QueryResult {
queryRes := tsdb.NewQueryResult()
series := newSeriesForQuery(query)
outsideTime := context.TimeRange.MustGetFrom().Add(-1*time.Hour).Unix() * 1000
series.Points = append(series.Points, tsdb.NewTimePoint(null.FloatFrom(10), float64(outsideTime)))
queryRes.Series = append(queryRes.Series, series)
return queryRes
},
})
registerScenario(&Scenario{
Id: "csv_metric_values",
Name: "CSV Metric Values",
StringInput: "1,20,90,30,5,0",
Handler: func(query *tsdb.Query, context *tsdb.QueryContext) *tsdb.QueryResult {
queryRes := tsdb.NewQueryResult()
stringInput := query.Model.Get("stringInput").MustString()
stringInput = strings.Replace(stringInput, " ", "", -1)
values := []null.Float{}
for _, strVal := range strings.Split(stringInput, ",") {
if strVal == "null" {
values = append(values, null.FloatFromPtr(nil))
}
if val, err := strconv.ParseFloat(strVal, 64); err == nil {
values = append(values, null.FloatFrom(val))
}
}
if len(values) == 0 {
return queryRes
}
series := newSeriesForQuery(query)
startTime := context.TimeRange.GetFromAsMsEpoch()
endTime := context.TimeRange.GetToAsMsEpoch()
step := (endTime - startTime) / int64(len(values)-1)
for _, val := range values {
series.Points = append(series.Points, tsdb.TimePoint{val, null.FloatFrom(float64(startTime))})
startTime += step
}
queryRes.Series = append(queryRes.Series, series)
return queryRes
},
})
}
func NewTimePoint(value null.Float, timestamp float64) TimePoint {
return TimePoint{value, null.FloatFrom(timestamp)}
}