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Be more specific when identifying a sparse histogram
It's a prefectly valid use case to have a sparse histogram with a zero threshold of zero (i.e. only observations of exactly zero go into the zero bucket). Even if the current PoC implementation of client_golang doesn't allow that, such a case should be ingested properly. However, there is now the edge case af a sparse histogram with a zero threshold of zero and no observations yet. Such a histogram would look the same if it was meant to be a conventional histogram. For now, we ingest this case as a conventional histogram, but the final format should have means to unambiguously express if a histogram is meant to be ingested as a sparse histogram or as a conventional histogram. Signed-off-by: beorn7 <beorn@grafana.com>
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@ -309,7 +309,7 @@ func (p *ProtobufParser) Next() (Entry, error) {
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p.state = EntryType
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p.state = EntryType
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case EntryType:
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case EntryType:
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if p.mf.GetType() == dto.MetricType_HISTOGRAM &&
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if p.mf.GetType() == dto.MetricType_HISTOGRAM &&
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p.mf.GetMetric()[0].GetHistogram().GetSbZeroThreshold() != 0 {
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isSparseHistogram(p.mf.GetMetric()[0].GetHistogram()) {
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p.state = EntryHistogram
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p.state = EntryHistogram
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} else {
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} else {
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p.state = EntrySeries
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p.state = EntrySeries
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@ -459,3 +459,19 @@ func formatOpenMetricsFloat(f float64) string {
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}
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}
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return s + ".0"
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return s + ".0"
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}
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}
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// isSparseHistogram returns false iff the provided histograms has no
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// SparseBuckets and a zero threshold of 0 and a zero count of 0. In principle,
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// this could still be meant to be a sparse histgram (with a zero threshold of 0
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// and no observations yet), but for now, we'll treat this case as a conventional
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// histogram.
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//
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// TODO(beorn7): In the final format, there should be an unambiguous way of
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// deciding if a histogram should be ingested as a conventional one or a sparse
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// one.
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func isSparseHistogram(h *dto.Histogram) bool {
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return len(h.GetSbNegative().GetDelta()) > 0 ||
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len(h.GetSbPositive().GetDelta()) > 0 ||
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h.GetSbZeroCount() > 0 ||
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h.GetSbZeroThreshold() > 0
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}
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