Giving monetdb a fair chance

Martin Kersten martin at monetdb.org
Thu Jul 11 11:20:59 CEST 2013


Hi

Without insight into your benchmark queries and database schema for the row stores, little can be said.
A typical pitfall could be to use row-based queries such as "SELECT * FROM tableexpr", which would call
for expensive tuple reconstruction of all columns. Another explanation can come from a multidimensional
index maintained in a rowstore to speedup grouping. Last but not least, it could be grouping order or
skewed data distribution.

MonetDB provides some tools to see where time goes, which may give a hint.
http://www.monetdb.org/Documentation/Manuals/MonetDB/Profiler/Stethoscope

Your table sizes reported is not extreme at all.

regards, Martin

On 7/11/13 10:47 AM, Franck Routier wrote:
> Hi,
>
> I am benchmarking different DB alternatives for our BI application.
> We are using Mondrian/JPivot on a dataset with ~300 millions row, and several parent/child dimensions.
>
> We are running some 50 real life scenario (ie 50 differents cubes using parts of this dataset) and measuring user response time on a server with several stripped Sata3 SSD, 32 GB ram and a i7-3820 CPU @ 3.60GHz (6 cores).
>
> During the tests, Monetdb performed by far the worst of Postgresql, VectorWise and (obvioulsy) MonetDB.
> Things that take 10 seconds with Postgresql take 450 second with monetdb...
> This was not expected at all, so looking at the server, I saw that MonetDB was consuming all memory (why not), including swap : bad idea I think. I was expecting MonetDB to memory mapped files when needed, real memory, but not use swap as it it was real
> memory...
> So here is my question : with a (quite) big dataset, 32GB ram, is there anything I can do to help MonetDB behave according to expectancies ?
>
> (maybe I should use ulimit ?).
>
> Thanks for your input,
>
> Franck
>
>
>
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