Hi Vinicius, 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. As a spoiler: queries take long time because they do a lot of IO. Features like reachback depth and duration (e.g. what period is the analytical query about) can contribute a lot to the amount of IO, thus, the query time. I have a blog post in my queue about our analysis, would gladly bump its priority if there is interest in such details. I'm also curious: if you had a great way to predict the time/cost of the queries, how would you use it? Best regards, Istvan On Mon, Sep 12, 2016 at 4:03 PM, Vinicius Segalin <vinisegalin@xxxxxxxxx> wrote: > Hi everyone, > > I'm trying to find a way to predict query runtime (I don't need to be > extremely precise). I've been reading some papers about it, and people are > using machine learning to do so. For the feature vector, they use what the > DBMS's query planner provide, such as operators and their cost. The thing is > that I haven't found any work using PostgreSQL, so I'm struggling to adapt > it. > My question is if anyone is aware of a work that uses machine learning and > PostgreSQL to predict query runtime, or maybe some other method to perform > this. > > Thank you. > > Best regards, > > Vinicius Segalin -- Sent via pgsql-general mailing list (pgsql-general@xxxxxxxxxxxxxx) To make changes to your subscription: http://www.postgresql.org/mailpref/pgsql-general
