// Detect input metric with its matched rules via 3-sigma.
//
// 1. Get history values for this metric.
// 2. Get current index for this metric.
// 3. Calculate score via 3-sigma.
// 4. Get score trending via ewma.
// 5. Save the metric and index to db.
// 6. Test with its matched rules and output it.
//
func (d *Detector) detect(m *models.Metric, rules []*models.Rule) (*models.Event, error) {
// Get index.
idx, err := d.db.Index.Get(m.Name)
if err != nil {
if err == indexdb.ErrNotFound {
idx = nil
} else {
return nil, err // unexcepted
}
}
// Fill zero?
fz := idx != nil && d.shouldFz(m)
// History values.
vals, err := d.values(m, fz)
if err != nil {
return nil, err // unexcepted
}
// Apply 3-sigma.
d.div3Sigma(m, vals)
// New index.
idx = d.nextIdx(idx, m)
// Test with rules.
d.test(m, idx, rules)
// Save
err = d.save(m, idx)
if len(m.TestedRules) > 0 {
// Test ok.
ev := models.NewEvent(m, idx)
return ev, err
}
return nil, err
}
// detect input metric with its matched rules.
// Details:
// 1. Get its index from db.
// 2. If the metric need to be analyzed, analyze it.
// 3. Save its index and the metricinto db.
// 4. Test the metric with matched rules.
// 5. Make events with its tested rules.
func (d *Detector) detect(m *models.Metric, rules []*models.Rule) (evs []*models.Event, err error) {
var idx *models.Index
if idx, err = d.db.Index.Get(m.Name); err != nil {
if err == indexdb.ErrNotFound {
idx = nil
} else {
return nil, err
}
}
idx = d.analyze(idx, m, rules)
if err = d.save(m, idx); err != nil {
return
}
for _, rule := range d.test(m, idx, rules) {
evs = append(evs, models.NewEvent(m, idx, rule))
}
return
}