Hi Dennis
If you compare the two traces you will notice that the fast one
exploits the fact that
the column is sorted on x. No further actions are required, so
query is done.
In the second query, although it is fast to access the
x-candidates, that is not
necessarily true for the corresponding y-candidates.
To make it fast, first question to answer is you are running
hot/cold queries.
MonetDB may decide to build a hash index on y, the first time you
use it
in your session.
So, what are the traces of a cold/hot run?
If you use a b-tree with compound (x,y) key in Postgres you
benefit
from the multidimensional sort.
regards, Martin
On 07/29/2014 11:43 AM, Dennis Pallett wrote:
Hi
all,
When I run the following query the results are computed extremely
fast (within 5 ms):
SELECT id_str, len,coordinates_x,coordinates_y FROM
uk_neogeo_sorted
WHERE coordinates_x >= 0.0 AND coordinates_x <=
22.499999996867977
LIMIT 100;
However if I add additional conditions to the query so that it
becomes the following:
SELECT id_str,len,coordinates_x,coordinates_y FROM
uk_neogeo_sorted
WHERE coordinates_x >= 0.0 AND coordinates_x <=
22.499999996867977
AND coordinates_y >= 0.0 AND coordinates_y <=
61.60639636617352
LIMIT 100;
The time it takes to compute the results is approximately 1000x
bigger (i.e. 5 seconds). Clearly the additional conditions on the
coordinates_y column is forcing MonetDB to take a different query
strategy but I don't know how I can solve this. In Postgres I
would make sure there is an index on the (coordinates_x,
coordinates_y) column but this doesn't seem to have any effect
with MonetDB.
I've attached traces of both queries. There are approximately 11
million rows in the table. Can anyone tell me why there is such a
huge difference in query execution time and how I can prevent it?
Best regards,
Dennis Pallett
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