bergquist
108 lines
3.2 KiB
Go
108 lines
3.2 KiB
Go
package alerting
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import (
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m "github.com/grafana/grafana/pkg/models"
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. "github.com/smartystreets/goconvey/convey"
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"testing"
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)
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func TestAlertingExecutor(t *testing.T) {
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Convey("Test alert execution", t, func() {
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executor := &ExecutorImpl{}
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Convey("single time serie", func() {
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Convey("Show return ok since avg is above 2", func() {
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rule := m.AlertRule{CritLevel: 10, CritOperator: ">", Aggregator: "sum"}
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timeSeries := []*m.TimeSeries{
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m.NewTimeSeries("test1", [][2]float64{{2, 0}}),
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}
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result := executor.ValidateRule(rule, timeSeries)
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So(result.State, ShouldEqual, m.AlertStateOk)
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})
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Convey("Show return critical since below 2", func() {
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rule := m.AlertRule{CritLevel: 10, CritOperator: "<", Aggregator: "sum"}
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timeSeries := []*m.TimeSeries{
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m.NewTimeSeries("test1", [][2]float64{{2, 0}}),
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}
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result := executor.ValidateRule(rule, timeSeries)
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So(result.State, ShouldEqual, m.AlertStateCritical)
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})
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Convey("Show return critical since sum is above 10", func() {
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rule := m.AlertRule{CritLevel: 10, CritOperator: ">", Aggregator: "sum"}
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timeSeries := []*m.TimeSeries{
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m.NewTimeSeries("test1", [][2]float64{{9, 0}, {9, 0}}),
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}
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result := executor.ValidateRule(rule, timeSeries)
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So(result.State, ShouldEqual, m.AlertStateCritical)
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})
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Convey("Show return ok since avg is below 10", func() {
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rule := m.AlertRule{CritLevel: 10, CritOperator: ">", Aggregator: "avg"}
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timeSeries := []*m.TimeSeries{
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m.NewTimeSeries("test1", [][2]float64{{9, 0}, {9, 0}}),
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}
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result := executor.ValidateRule(rule, timeSeries)
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So(result.State, ShouldEqual, m.AlertStateOk)
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})
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Convey("Show return ok since min is below 10", func() {
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rule := m.AlertRule{CritLevel: 10, CritOperator: ">", Aggregator: "min"}
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timeSeries := []*m.TimeSeries{
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m.NewTimeSeries("test1", [][2]float64{{11, 0}, {9, 0}}),
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}
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result := executor.ValidateRule(rule, timeSeries)
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So(result.State, ShouldEqual, m.AlertStateOk)
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})
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Convey("Show return ok since max is above 10", func() {
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rule := m.AlertRule{CritLevel: 10, CritOperator: ">", Aggregator: "max"}
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timeSeries := []*m.TimeSeries{
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m.NewTimeSeries("test1", [][2]float64{{1, 0}, {11, 0}}),
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}
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result := executor.ValidateRule(rule, timeSeries)
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So(result.State, ShouldEqual, m.AlertStateCritical)
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})
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})
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Convey("muliple time series", func() {
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Convey("both are ok", func() {
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rule := m.AlertRule{CritLevel: 10, CritOperator: ">", Aggregator: "sum"}
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timeSeries := []*m.TimeSeries{
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m.NewTimeSeries("test1", [][2]float64{{2, 0}}),
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m.NewTimeSeries("test1", [][2]float64{{2, 0}}),
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}
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result := executor.ValidateRule(rule, timeSeries)
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So(result.State, ShouldEqual, m.AlertStateOk)
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})
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Convey("first serie is good, second is critical", func() {
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rule := m.AlertRule{CritLevel: 10, CritOperator: ">", Aggregator: "sum"}
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timeSeries := []*m.TimeSeries{
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m.NewTimeSeries("test1", [][2]float64{{2, 0}}),
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m.NewTimeSeries("test1", [][2]float64{{11, 0}}),
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}
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result := executor.ValidateRule(rule, timeSeries)
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So(result.State, ShouldEqual, m.AlertStateCritical)
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})
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})
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})
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}
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