executor.go 3.2 KB

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127
  1. package alerting
  2. import (
  3. "fmt"
  4. "math"
  5. "github.com/grafana/grafana/pkg/log"
  6. m "github.com/grafana/grafana/pkg/models"
  7. b "github.com/grafana/grafana/pkg/services/alerting/datasources"
  8. )
  9. type Executor interface {
  10. Execute(rule *m.AlertJob, responseQueue chan *m.AlertResult)
  11. }
  12. var (
  13. resultLogFmt = "%s executor: %s %1.2f %s %1.2f : %v"
  14. descriptionFmt = "Actual value: %1.2f for %s"
  15. )
  16. type ExecutorImpl struct{}
  17. type compareFn func(float64, float64) bool
  18. type aggregationFn func(*m.TimeSeries) float64
  19. var operators = map[string]compareFn{
  20. ">": func(num1, num2 float64) bool { return num1 > num2 },
  21. ">=": func(num1, num2 float64) bool { return num1 >= num2 },
  22. "<": func(num1, num2 float64) bool { return num1 < num2 },
  23. "<=": func(num1, num2 float64) bool { return num1 <= num2 },
  24. "": func(num1, num2 float64) bool { return false },
  25. }
  26. var aggregator = map[string]aggregationFn{
  27. "avg": func(series *m.TimeSeries) float64 {
  28. sum := float64(0)
  29. for _, v := range series.Points {
  30. sum += v[0]
  31. }
  32. return sum / float64(len(series.Points))
  33. },
  34. "sum": func(series *m.TimeSeries) float64 {
  35. sum := float64(0)
  36. for _, v := range series.Points {
  37. sum += v[0]
  38. }
  39. return sum
  40. },
  41. "min": func(series *m.TimeSeries) float64 {
  42. min := series.Points[0][0]
  43. for _, v := range series.Points {
  44. if v[0] < min {
  45. min = v[0]
  46. }
  47. }
  48. return min
  49. },
  50. "max": func(series *m.TimeSeries) float64 {
  51. max := series.Points[0][0]
  52. for _, v := range series.Points {
  53. if v[0] > max {
  54. max = v[0]
  55. }
  56. }
  57. return max
  58. },
  59. "mean": func(series *m.TimeSeries) float64 {
  60. midPosition := int64(math.Floor(float64(len(series.Points)) / float64(2)))
  61. return series.Points[midPosition][0]
  62. },
  63. }
  64. func (executor *ExecutorImpl) Execute(job *m.AlertJob, responseQueue chan *m.AlertResult) {
  65. response, err := b.GetSeries(job)
  66. if err != nil {
  67. responseQueue <- &m.AlertResult{State: m.AlertStatePending, Id: job.Rule.Id, AlertJob: job}
  68. }
  69. result := executor.validateRule(job.Rule, response)
  70. result.AlertJob = job
  71. responseQueue <- result
  72. }
  73. func (executor *ExecutorImpl) validateRule(rule m.AlertRule, series m.TimeSeriesSlice) *m.AlertResult {
  74. for _, serie := range series {
  75. if aggregator[rule.Aggregator] == nil {
  76. continue
  77. }
  78. var aggValue = aggregator[rule.Aggregator](serie)
  79. var critOperartor = operators[rule.CritOperator]
  80. var critResult = critOperartor(aggValue, rule.CritLevel)
  81. log.Trace(resultLogFmt, "Crit", serie.Name, aggValue, rule.CritOperator, rule.CritLevel, critResult)
  82. if critResult {
  83. return &m.AlertResult{
  84. State: m.AlertStateCritical,
  85. Id: rule.Id,
  86. ActualValue: aggValue,
  87. Description: fmt.Sprintf(descriptionFmt, aggValue, serie.Name),
  88. }
  89. }
  90. var warnOperartor = operators[rule.CritOperator]
  91. var warnResult = warnOperartor(aggValue, rule.CritLevel)
  92. log.Trace(resultLogFmt, "Warn", serie.Name, aggValue, rule.WarnOperator, rule.WarnLevel, warnResult)
  93. if warnResult {
  94. return &m.AlertResult{
  95. State: m.AlertStateWarn,
  96. Id: rule.Id,
  97. Description: fmt.Sprintf(descriptionFmt, aggValue, serie.Name),
  98. ActualValue: aggValue,
  99. }
  100. }
  101. }
  102. return &m.AlertResult{State: m.AlertStateOk, Id: rule.Id, Description: "Alert is OK!"}
  103. }