I am using the following Nearest Neighbor Query in PostGIS :
SELECT g1.gid g2.gid FROM points as g1, polygons g2
WHERE g1.gid <> g2.gid
ORDER BY g1.gid, ST_Distance(g1.the_geom,g2.the_geom)
LIMIT k;
Now, that I have created indexes on the_geom as well as gid column on both the tables, this query is taking much more time than other spatial queries involving spatial joins b/w two tables.
Is there any better way to find K-nearest neighbors? I am using PostGIS.
And, another query which is taking a unusually long time despite creating indexes on geometry column is:
select g1.gid , g2.gid from polygons as g1 , polygons as g2
where st_area(g1.the_geom) > st_area(g2.the_geom) ;
I believe, these queries arent benefited by gist indexes, but why?
Whereas this query:
select a.polyid , sum(length(b.the_geom)) from polygon as a , roads as b
where st_intersects(a.the_geom , b.the_geom);
returns result after some time despite involving "roads" table which is much bigger than polygons or points table and also involve more complex spatial operators.
Assuming you have p point and g polygons, your original query:
Is returning the k nearest neighbours in the p x g set. The query may be using indexes, but it still has to order the entire p x g set to find the k rows with the smallest distance. What you instead want is the following:
You can do it with KNN index and lateral join.
Just a few thoughts on your problem:
st_distance as well as st_area are not able to use indices. This is because both functions can not be reduced to questions like "Is a within b?" or "Do a and b overlap?". Even more concrete: GIST-indices can only operate on the bounding boxes of two objects.
For more information on this you just could look in the postgis manual, which states an example with st_distance and how the query could be improved to perform better.
However, this does not solve your k-nearest-neighbour-problem. For that, right now I do not have a good idea how to improve the performance of the query. The only chance I see would be assuming that the k nearest neighbors are always in a distance of below x meters. Then you could use a similar approach as done in the postgis manual.
Your second query could be speeded up a bit. Currently, you compute the area for each object in table 1 as often as table has rows - the strategy is first to join the data and then select based on that function. You could reduce the count of area computations significantly be precomputing the area:
Your third query can be significantly optimized using bounding boxes: When the bounding boxes of two objects do not overlap, there is no way the objects do. This allows the usage of a given index and thus a huge performance gain.
What you may need is the KNN index which is hopefully available soon in PostGIS 2.x and PostgreSQL 9.1: See http://blog.opengeo.org/tag/knn/
Since late September 2011, PostGIS has supported indexed nearest neighbor queries via a special operator(s) usable in the ORDER BY clause:
...will return the 10 objects whose
geom
is nearest-90,40
in a scalable way. A few more details (options and caveats) are in that announcement post and use of the <-> and the <#> operators is also now documented in the official PostGIS 2.0 reference. (The main difference between the two is that<->
compares the shape centroids and<#>
compares their boundaries — no difference for points, other shapes choose what is appropriate for your queries.)