Thanks a lot for the help, it literally instantly solved my problem!
There was a small mistake in the order of functions in your example, for
the record it should be:
EXP(AVG(LOG(event_total))) AS geometric_mean
And conveniently the geometric standard deviation can be calculated the
same way:
EXP(STDDEV(LOG(event_total))) AS geometric_stddev
I put it to the test on a specific set of data where we had a huge outlier,
and for that data it seems equivalent to excluding the lower and upper 10
percentiles, which is exactly what I was after.
On Wed, Apr 16, 2014 at 4:24 PM, Aaron Halfaker <ahalfaker(a)wikimedia.org>wrote;wrote:
Hi Gilles,
I think I know just the thing you're looking for.
It turns out that much of this performance data is log-normally
distributed[1]. Log-normal distributions tend to have a hockey stick
shape where most of the values are close to zero, but occasionally very
large values appear[3]. Taking the mean of a log-normal distributions tend
to be sensitive to outliers like the ones you describe.
A solution to this problem is to generate a geometric mean[2] instead.
One convenient thing about log-normal data is that if you log() it, it
becomes normal[4] -- and not sensitive to outliers in the usual way. Also
convenient, geometric means are super easy to generate. All you need to do
is this: (1) pass all of the data through log() (2) pass the same data
through mean() (or avg() -- whatever) (3) pass the result through exp().
The best thing about this is that you can do it in MySQL.
For example:
SELECT
country,
mean(timings) AS regular_mean,
exp(log(mean(timings)) AS geomteric_mean
FROM log.WhateverSchemaYouveGot
GROUP BY country
1.
https://en.wikipedia.org/wiki/Log-normal_distribution
2.
https://en.wikipedia.org/wiki/Geometric_mean
3. See distribution.log_normal.svg
(
24K)<https://mail.google.com/mail/u/0/?ui=2&ik=1aecb4a505&view=a…
4. See distribution.log_normal.logged.svg
(
33K)<https://mail.google.com/mail/u/0/?ui=2&ik=1aecb4a505&view=a…
-Aaron
On Wed, Apr 16, 2014 at 8:42 AM, Dan Andreescu <dandreescu(a)wikimedia.org>wrote;wrote:
So, my latest idea for a solution is to write a
python script that will
import
the section (last X days) of data from the EventLogging tables that
we're interested in into a temporary sqlite database, then proceed with
removing the upper and lower percentiles of the data, according to any
column grouping that might be necessary. And finally, once the data
preprocessing is done in sqlite, run similar queries as before to export
the mean, standard deviation, etc. for given metrics to tsvs. I think using
sqlite is cleaner than doing the preprocessing on db1047 anyway.
It's quite an undertaking, it basically means rewriting all our current
SQL => TSV conversion. The ability to use more steps in the conversion
means that we'd be able to have simpler, more readable SQL queries. It
would also be a good opportunity to clean up the giant performance query
with a bazillion JOINS:
https://gitorious.org/analytics/multimedia/source/a949b1c8723c4c41700cedf6e…
can actually be divided into several data sources all used in the
same graph.
Does that sound like a good idea, or is there a simpler solution out
there that someone can think of?
Well, I think this sounds like we need to seriously evaluate how people
are using EventLogging data and provide this sort of analysis as a feature.
We'd have to hear from more people but I bet it's the right thing to do
long term.
Meanwhile, "simple" is highly subjective here. If it was me, I'd clean
up the indentation of that giant SQL query you have, then maybe figure out
some ways to make it faster, then be happy as a clam. So if sql-lite is
the tool you feel happy as a clam with, then that sounds like a great
solution. Alternatives would be python, php, etc. I forgot if pandas was
allowed where you're working but that's a great python library that would
make what you're talking about fairly easy.
Another thing for us to seriously consider is PostgreSQL. This has
proper f-ing temporary tables and supports actual people doing actual work
with databases. We could dump data, especially really simple schemas like
EventLogging, into PostgreSQL for analysis.
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