Hi,
I depends on what you mean by "transparently substitute".
I f you want to be able to execute standard SQL queries using columnar
store, then it seems to be impossible without rewriting of executor.
I provided another approach based on calling standard functions which
perform manipulations not with scalar types but with timeseries.
For example instead of standard SQL
select sum(ClosePrice) from Quote;
I will have to write:
select cs_sum(ClosePrice) from Quote_get();
It looks similar but not quite the same.
And for more complex queries difference is larger.
For example the query
select sum(score*volenquired)/sum(volenquired) from DbItem group by
(trader,desk,office);
can be written as
select agg_val,cs_cut(group_by,'c22c30c10') from
(select (cs_project_agg(ss1.*)).* from
(select (s1).sum/(s2).sum,(s1).groups from DbItem_get() q,
cs_hash_sum(q.score*q.volenquired,
q.trader||q.desk||q.office) s1,
cs_hash_sum(q.volenquired, q.trader||q.desk||q.office)
s2) ss1) ss2;
Looks too complex, doesn't it?
But first two lines are responsible to perform reverse mapping: from
vertical data representation to normal horisontal tuples.
The good thing is that this query is executed more than 1000 times
faster (with default PostgreSQL configuration parameters except shared
shared_buffers
which was set large enough to fit all data in memory).
On 12/11/2013 07:14 PM, k...@rice.edu wrote:
On Mon, Dec 09, 2013 at 11:40:41PM +0400, knizhnik wrote:
Hello!
I want to annouce my implementation of In-Memory Columnar Store
extension for PostgreSQL:
Documentation: http://www.garret.ru/imcs/user_guide.html
Sources: http://www.garret.ru/imcs-1.01.tar.gz
Any feedbacks, bug reports and suggestions are welcome.
Vertical representation of data is stored in PostgreSQL shared memory.
This is why it is important to be able to utilize all available
physical memory.
Hi,
This is very neat! The question I have, which applies to the matview
support as well, is "How can we transparently substitute usage of the
in-memory columnar store/matview in a SQL query?".
Regards,
Ken
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