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This is a great post that also illustrates the tradeoffs inherent in telemetry collection (traces, logs, metrics) for analysis. It's a capital-H Hard space to operate in that a lot of developers either don't know about, or take for granted.

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Something I've considered writing about in the past is how sampling affects the shape of lines on graphs. Render the same underlying data with different sampling strategies and show how the resulting graph can look extremely different depending on the strategy used. I think it's an underappreciated thing a lot of people don't think about when looking at their observability tools.

Yeah it’s challenging. I work for such a tool and we re-weight counts which is generally the right move, but comes with its own subtleties like when you are looking for exact counts specifically to tune sampling, or your MoE is bad for the particular calculation and granularity of data.

Observability: easily one of the more underestimated fields in computing.

Sampling theorem.

It's interesting that people seem to think that sampling mathematics somehow applies to modems or RF but not to the data they are looking at. Things like aliasing absolutely matter for observability/telemetry.

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