On 24/10/13 10:34, Marc Mamin wrote:
Oscillating plan changes may fit multimodal but I don't feel that's
typical.  My experience has been it's either an extremely rare plan
difference or it's a shift from one plan to another over time.
After all, all of avg, min, max and stdev are only numerical value for 
predicting model. There aren't the robustness and strictness such as Write 
Ahead Logging. It resembles a weather forecast. They are still better than 
It is needed a human judgment to finally suppose a cause from the numerical 
values. By the way, we can guess probability of the value from stdev.
Therefore we can guess easily even if there is an extreme value in min/max 
whether it is normal or not.
What I've been gathering from my quick chat this morning is that
either you know how to characterize the distribution and then the min
max and average are useful on their own, or you need to keep track of
an histogram where all the bins are of the same size to be able to
learn what the distribution actually is.

We have an in house reporting application doing a lot of response times 
Our experience has shown that in many cases of interest (the one you want to 
dig in)
a logarithmic scale for histogram bins result in a better visualization.
attached an example from a problematic postgres query...

my 2 pences,

Marc Mamin
Looks definitely bimodal in the log version, very clear!

Yes, I feel that having a 32 log binary binned histogram (as Alvaro Herrera suggested) would be very useful. Especially if the size of the first bin can be set - as some people would like to be 100us and others might prefer 1ms or something else.


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