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feat(new transform): Aggregate transform to reduce metric volume maintaining data #7846
feat(new transform): Aggregate transform to reduce metric volume maintaining data #7846
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Should there be a warning about loss of precision for any metric types?
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Not sure I follow. I would expect counters to be the only numerical values that sees actual mathematical addition. and normal floating point behaviors should apply. I wouldn't expect that to have much impact beyond what floating point details have on individual numbers in the real world, but I guess it's possible. I don't have a deep enough understanding for the distribution, histogram, and summary value types to say there.
If sinks are already doing implicit
add
s then I don't think this would add any new precision issues that aren't already present there.There was a problem hiding this comment.
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Just noting that this says 15 seconds but the
interval_ms
below is 5000.There was a problem hiding this comment.
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We need a
how_it_works
section that describes:log_to_metric
transform since this will commonly be paired?There was a problem hiding this comment.
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With Counters/Incremental no meaningful data should be lost in the bigger sense it'll just shift the granularity at which it appears.
With Gauges/Absolute it'll reduce granularity/resolution, potentially losing short term spikes/dips.
That and overhead. Network transfers, processing time in the metrics storage system etc. It can push the "work" of aggregating data out to the "edge" where it'll better scale in many cases.
Not aware of why this would be the case so would need a snippet to include it.
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Done. With the exception of 4.