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I wonder if you're confusing time series (which is a sequence of numbers which lends itself to stuff like regressions, etc, for predictions and other insights) with quite simply _logging_ so that you can go back a year later and figure out "what the hell happened then".

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While metrics are often evenly spaced, that's not always the case.

Good resolution on the timestamps helps avoid graph artifacts and other weirdness, even if you're only collecting them once a minute. Millisecond accuracy seems to be sufficient in my experience, there's other races of that magnitude anyway (network jitter, kernel scheduling etc.).

https://en.wikipedia.org/wiki/Unevenly_spaced_time_series You can call that _logging_ if you want, but people will ask for it anyway.

Interesting, but I'm not convinced. Everything is evenly spaced given correct resolution. If your timestamp is at nanosecond precision, then you simply have a 1ns resolution series which is very sparse, I suppose another name for it might be "unevenly spaced".

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