4 ms·
And this is exactly the misconception. In my use cases I want to have the original data for at least a couple of weeks, maybe even a year. I also need milliseco
by CptMauli 10y ago
And this is exactly the misconception. In my use cases I want to have the original data for at least a couple of weeks, maybe even a year. I also need millisecond precision because some of the sources produce several values per second (others might not change for years). I also need to store the information if the value had an alarm, or an error or if it is an replacement value.
So a simple round robin scheme just doesn't work.
- gtrubetskoy 10y agoI 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".
- chaotic-good 10y agohttps://en.wikipedia.org/wiki/Unevenly_spaced_time_series https://en.wikipedia.org/wiki/Unevenly_spaced_time_series You can call that _logging_ if you want, but people will ask for it anyway.
- gtrubetskoy 10y agoInteresting, 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".
- bbrazil 10y agoWhile 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.).