Forwarding to user-list as this seems to have gotten lost
Actually, I'm running a few different versions of MonetDB on different environments. July2012SP2, Feb2013SP4 & SP5. July2012SP2 on Windows and Linux (Ubuntu); Feb2013SP5 on Windows and Linux, and Feb2013SP4 on Linux. All were installed using pre-built packages/installers. The trace files themselves are from a Linux Feb2013SP4 installation. The Feb2013SP5 install was an inplace install on top of July2012SP2 and wasn't sure if different "data" versions would cloud the trace.
To Martins questions (which I never got in my inbox...or junk) yes, date_hour_id is a unique key for the dim_date_hour table, but obviously duplicated within the fact_utilization_hourly table.
My question was more generalized, but if a specific solution can be found, fantastic. In general I am seeing joins drastically reduce performance overall.
All are running on EC2; m1.xlarge with High I/O
Thanx, Bryan
On Mon, Oct 28, 2013 at 9:10 AM, Bryan Senseman monetdb@openbi.com wrote:
Forwarding to user-list as this seems to have gotten lost
Actually, I'm running a few different versions of MonetDB on different environments. July2012SP2, Feb2013SP4 & SP5. July2012SP2 on Windows and Linux (Ubuntu); Feb2013SP5 on Windows and Linux, and Feb2013SP4 on Linux. All were installed using pre-built packages/installers. The trace files themselves are from a Linux Feb2013SP4 installation. The Feb2013SP5 install was an inplace install on top of July2012SP2 and wasn't sure if different "data" versions would cloud the trace.
To Martins questions (which I never got in my inbox...or junk) yes, date_hour_id is a unique key for the dim_date_hour table, but obviously duplicated within the fact_utilization_hourly table.
My question was more generalized, but if a specific solution can be found, fantastic. In general I am seeing joins drastically reduce performance overall.
All are running on EC2; m1.xlarge with High I/O
Thanx, Bryan
---------- Forwarded message ---------- From: Bryan Senseman monetdb@openbi.com Date: Sat, Oct 19, 2013 at 8:51 PM Subject: Re: Are joins really this expensive? To: Stefan Manegold Stefan.Manegold@cwi.nl
Ask, and ye shall receive, let me know if you need anything else; and I can't share the data, nor would you want a many GB data bomb.
Bryan
On Sat, Oct 19, 2013 at 3:17 PM, Stefan Manegold Stefan.Manegold@cwi.nlwrote:
Dear Bryan,
unless you could share your data such that we could reproduce the problem, could you please profile both queries using TRACE, stethoscope or tomograph; cf., http://www.monetdb.org/Documentation/Cookbooks/SQLrecipes/QueryTiming and share the respective output?
Then we might be able analyze what exactly causes the performance difference between both queries.
Thanks, Stefan
Bryan Senseman monetdb@openbi.com wrote:
I have created a traditional star-schema in MonetDB, using both Linux and Windows. We are using Mondrian OLAP on top so it generates the standard OLAP-y type of query using group-bys of dimensional values. What I’m finding is that doing joins in MonetDB is extremely expensive, for example:
The following query against a fact table with 261 million rows returns in 9-11seconds consistently
select date_hour_id/10000 as date_month_id, avg(seconds) from fact_utilization_hourly group by date_month_id;
returning 41 rows, the query includes a “Trick” to get to a month_year value because the time_ids are smart keys… yyyyMMddhh.
With the following query, using standard OLAP-y syntax, the query takes over 5 mins consistently.
select year_month_value, avg(seconds) from fact_utilization_hourly f join dim_date_hour d on f.date_hour_id = d.date_hour_id group by year_month_value;
returning the same 41 rows.
I’m at a loss to figure out why this join causes a 3200% percent increase in response time. And this seems to have gotten worse in SP5 from anecdotal evidence.
Bryan
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