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Distribution-to-Histogram conversion yields invalid data #25
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cboggs
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Duration-to-Histogram conversion yields invalid data
Distribution-to-Histogram conversion yields invalid data
Apr 12, 2018
It's not just Related: #116 (comment) |
SuperQ
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Nov 22, 2022
This PR introduces support for aggregating DELTA metrics in-memory. This implementation is somewhat based on #167 but with extra functionality which is intended to solve the problem identified here, #167 (comment). I chose to inject interfaces for the delta and distribution stores at the Collector level to try to keep library friendliness intact, #157 I did as much as I could to explain the solution and the tradeoffs with aggregating deltas in this manner in the README.md. The bulk of the changes are in `monitoring_metrics.go` where I tried to obfuscate as much of the delta logic as possible. Should hopefully resolve #116 and #25 Signed-off-by: Kyle Eckhart <[email protected]>
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Setup
Run stackdriver_exporter with
--monitoring.metrics-type-prefixes=bigtable.googleapis.com
.Expected
Metrics like
stackdriver_bigtable_table_bigtable_googleapis_com_server_latencies_count
should present as monotonically increasing counters, as would normal Prometheus histogram counters.Actual
Metrics
stackdriver_bigtable_table_bigtable_googleapis_com_server_latencies_count
frequently decrement and oscillate in ways they shouldn't.Screenshot:
This inherently throws off quantile calculations on the respective histogram buckets, as the individual bucket counts oscillate in the same manner. This yields per-bucket rates that provide errant quantiles (ex: 47-day-long latency measurements on p50).
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