func bucketsFromMetricPoints(metricPoints []metricPoint, resultField func(metricPoint) float64, timerange api.Timerange) [][]float64 {
buckets := make([][]float64, timerange.Slots())
for _, point := range metricPoints {
addMetricPoint(point, resultField, timerange, buckets)
}
return buckets
}
func (value ScalarValue) ToSeriesList(timerange api.Timerange) (api.SeriesList, error) {
series := make([]float64, timerange.Slots())
for i := range series {
series[i] = float64(value)
}
return api.SeriesList{
Series: []api.Timeseries{api.Timeseries{Values: series, TagSet: api.NewTagSet()}},
Timerange: timerange,
}, nil
}
func processResult(parsedResult queryResponse, timerange api.Timerange, sampler sampler) []float64 {
// buckets are each filled with from the points stored in result.Values, according to their timestamps.
buckets := bucketsFromMetricPoints(parsedResult.Values, sampler.fieldSelector, timerange)
// values will hold the final values to be returned as the series.
values := make([]float64, timerange.Slots())
for i, bucket := range buckets {
if len(bucket) == 0 {
values[i] = math.NaN()
continue
}
values[i] = sampler.bucketSampler(bucket)
}
return values
}
// Blueflood will use the finest-grained resolution which doesn't exceed the slot limit.
// Thus, if you request too many points, it will automatically reduce the resolution.
func (b *Blueflood) ChooseResolution(requested api.Timerange, smallestResolution time.Duration) time.Duration {
// In some cases, coarser-resolution data may have a shorter TTL.
// To accomodate these cases, it must be verified that the requested timerange will
// actually be present for the chosen resolution.
// TODO: figure out how to make this work with moving averages and timeshifts
requiredAge := b.timeSource().Sub(requested.StartTime())
for _, resolution := range Resolutions {
survivesFor := b.config.oldestViableDataForResolution(resolution)
if survivesFor < requiredAge {
// The data probably won't be around for the earliest part of the timerange,
// so don't use this resolution
continue
}
if resolution.duration < requested.Resolution() {
// Skip this timerange, it is finer than the one requested.
continue
}
// Check that the timerange is large enough
if resolution.duration >= smallestResolution {
return resolution.duration
}
}
// Leave it alone, since a better one can't be found
return requested.Resolution()
}
func processResult(
points []metricPoint,
timerange api.Timerange,
sampler sampler,
queryResolution Resolution) []float64 {
// buckets are each filled with from the points stored in `points`, according to their timestamps.
buckets := bucketsFromMetricPoints(points, sampler.fieldSelector, timerange)
// values will hold the final values to be returned as the series.
values := make([]float64, timerange.Slots())
for i, bucket := range buckets {
if len(bucket) == 0 {
values[i] = math.NaN()
continue
}
values[i] = sampler.bucketSampler(bucket)
}
// interpolate.
return values
}
func addMetricPoint(metricPoint metricPoint, field func(metricPoint) float64, timerange api.Timerange, buckets [][]float64) bool {
value := field(metricPoint)
// The index to assign within the array is computed using the timestamp.
// It floors to the nearest index.
index := (metricPoint.Timestamp - timerange.Start()) / timerange.ResolutionMillis()
if index < 0 || index >= int64(timerange.Slots()) {
return false
}
buckets[index] = append(buckets[index], value)
return true
}
func (f FakeTimeseriesStorageAPI) ChooseResolution(requested api.Timerange, smallestResolution time.Duration) time.Duration {
return requested.Resolution()
}