Wondering if anyone has a solution to this?
I primarily use influxDB to store software and hardware latencies. It's well understood that latencies are not normally distributed. This means that, if we want to measure the variability of latencies, then the well-known standard deviation is a bad metric to use. (see https://www.infoq.com/presentations/latency-pitfalls ). There are a number of alternative measures of scale that work well for data that isn't normally distributed (see https://en.wikipedia.org/wiki/Robust_measures_of_scale ). The median absolute deviation isn't the best, but it is relatively simple to compute and conceptualize. (See https://en.wikipedia.org/wiki/Median_absolute_deviation) . I can compute the deviation from the median but, in the absence of an abs() or sqrt() function, it's not obvious how to compute the absolute deviation from the median, which is a precursor to computing the MAD. Can anyone suggest how to compute MAD within influxDB? Peter -- Remember to include the version number! --- You received this message because you are subscribed to the Google Groups "InfluxData" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/influxdb. To view this discussion on the web visit https://groups.google.com/d/msgid/influxdb/bc0f5e3b-4f29-4d18-9bb1-c1f78ca87690%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
