// === predict_linear(node model.ValMatrix, k model.ValScalar) Vector ===
func funcPredictLinear(ev *evaluator, args Expressions) model.Value {
vec := funcDeriv(ev, args[0:1]).(vector)
duration := model.SampleValue(model.SampleValue(ev.evalFloat(args[1])))
excludedLabels := map[model.LabelName]struct{}{
model.MetricNameLabel: {},
}
// Calculate predicted delta over the duration.
signatureToDelta := map[uint64]model.SampleValue{}
for _, el := range vec {
signature := model.SignatureWithoutLabels(el.Metric.Metric, excludedLabels)
signatureToDelta[signature] = el.Value * duration
}
// add predicted delta to last value.
matrixBounds := ev.evalMatrixBounds(args[0])
outVec := make(vector, 0, len(signatureToDelta))
for _, samples := range matrixBounds {
if len(samples.Values) < 2 {
continue
}
signature := model.SignatureWithoutLabels(samples.Metric.Metric, excludedLabels)
delta, ok := signatureToDelta[signature]
if ok {
samples.Metric.Del(model.MetricNameLabel)
outVec = append(outVec, &sample{
Metric: samples.Metric,
Value: delta + samples.Values[1].Value,
Timestamp: ev.Timestamp,
})
}
}
return outVec
}
// === histogram_quantile(k model.ValScalar, vector model.ValVector) Vector ===
func funcHistogramQuantile(ev *evaluator, args Expressions) model.Value {
q := model.SampleValue(ev.evalFloat(args[0]))
inVec := ev.evalVector(args[1])
outVec := vector{}
signatureToMetricWithBuckets := map[uint64]*metricWithBuckets{}
for _, el := range inVec {
upperBound, err := strconv.ParseFloat(
string(el.Metric.Metric[model.BucketLabel]), 64,
)
if err != nil {
// Oops, no bucket label or malformed label value. Skip.
// TODO(beorn7): Issue a warning somehow.
continue
}
signature := model.SignatureWithoutLabels(el.Metric.Metric, excludedLabels)
mb, ok := signatureToMetricWithBuckets[signature]
if !ok {
el.Metric.Del(model.BucketLabel)
el.Metric.Del(model.MetricNameLabel)
mb = &metricWithBuckets{el.Metric, nil}
signatureToMetricWithBuckets[signature] = mb
}
mb.buckets = append(mb.buckets, bucket{upperBound, el.Value})
}
for _, mb := range signatureToMetricWithBuckets {
outVec = append(outVec, &sample{
Metric: mb.metric,
Value: model.SampleValue(bucketQuantile(q, mb.buckets)),
Timestamp: ev.Timestamp,
})
}
return outVec
}
