On Tue, Sep 13, 2016 at 2:54 PM, Istvan Soos <istvan.soos@gmail.com> wrote:
> On Tue, Sep 13, 2016 at 2:06 AM, Vinicius Segalin <vinisegalin@gmail.com> wrote:
>> 2016-09-12 18:22 GMT-03:00 Istvan Soos <istvan.soos@gmail.com>:
>>> At Heap we have non-trivial complexity in our analytical queries, and
>>> some of them can take a long time to complete. We did analyze features
>>> like the query planner's output, our query properties (type,
>>> parameters, complexity) and tried to automatically identify factors
>>> that contribute the most into the total query time. It turns out that
>>> you don't need to use machine learning for the basics, but at this
>>> point we were not aiming for predictions yet.
>>
>> And how did you do that? Manually analyzing some queries?
>
> In this case, it was automatic analysis and feature discovery. We were
> generating features out of our query parameters, out of the SQL
> string, and also out of the explain analyze output. For each of these
> features, we have examined the P(query is slow | feature is present),
> and measured its statistical properties (precision, recall,
> correlations...).
>
> With these we have built a decision tree-based partitioning, where our
> feature-predicates divided the queries into subsets. Such a tree could
> be used for predictions, or if we would like to be fancy, we could use
> the feature vectors to train a neural network.
FYI, please check https://pgconf.ru/2016/89977
>
> Hope this helps for now,
> Istvan
>
>
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