Actually after some more thought I think this situation is slightly different (although the technical solution might end up being the same). Here's the two cases as I understand them:
1) In my original case the need was to run a query against an Oracle spatial table and return a geometry value computed from the base geometry. So the SDO_FILTER clause for the query bounding box was still required, it just needed to be run against the base geometry, not the geometry returned by the query. 2) In the cluster case (if I understand it) no SDO_FILTER clause is required, but instead the bounding box values need to be passed to a custom Oracle function. It seems like #1 might be simpler to implement than #2. For instance, in #1 GeoServer can still run an "empty query" to determine the geometry column(s), whereas that doesn't seem feasible in #2. On Wed, Nov 18, 2015 at 11:22 PM, Andrea Aime <andrea.a...@geo-solutions.it> wrote: > Hi Martin, > it certainly looks similar, most likely same. > > No new development, still not possible, people still welcome to chip in > with resources/funds: > > https://github.com/geoserver/geoserver/wiki/Successfully-requesting-and-integrating-new-features-and-improvements-in-GeoServer > > Cheers > Andrea > > On Thu, Nov 19, 2015 at 6:32 AM, Martin Davis <mtncl...@gmail.com> wrote: > >> I think this is the same issue discussed in this (lengthy) thread: >> >> >> http://osgeo-org.1560.x6.nabble.com/Reducing-query-data-size-in-Oracle-td5192620.html >> >> TLDR: not possible right now, apparently requires clever development to >> make it work. >> >> If there has been improvement on this front it would be great to know >> about it. Or would be great to have someone implement a workaround! >> >> On Wed, Nov 18, 2015 at 12:08 PM, cheesybiscuits < >> thomaschrist...@gmail.com> wrote: >> >>> I've created a function within Oracle for on-the-fly clustering that I >>> hoped >>> to query via GeoServer using an SQL view layer. >>> >>> The function takes as its arguments the bounding box and the number of >>> grid >>> cells to use in a basic gridded clustering process (i.e. find the centre >>> of >>> mass for points within each grid cell). Please note I've tried the Point >>> Stacker extension and it isn't adequate for my needs. Executing the >>> following query within Oracle gives the list of sdo_geometry objects >>> that I >>> expect: >>> >>> select * from table(package_name.function_name(-180, -90, 180, 90, 3); >>> >>> My intention is to make WMS requests to GeoServer with the viewparams >>> parameter included that provides all required arguments. Because of the >>> way >>> the function works the returned sdo_geometry objects will only ever be >>> within the provided bounding box. >>> >>> I can successfully create an SQL view layer within GeoServer using this >>> syntax and everything seems OK while publishing. However when I access >>> the >>> layer everything falls apart. GeoServer takes my query and wraps it as >>> follows: >>> >>> SELECT GEOMETRY as GEOMETRY FROM (select * from >>> table(package_name.function_name(-180, -90, 180, 90, 3))) VTABLE WHERE >>> SDO_FILTER(GEOMETRY, ?, 'mask=anyinteract querytype=WINDOW') = 'TRUE' >>> >>> Oracle then errors because the function's table return type doesn't have >>> a >>> spatial index to use with SDO_FILTER. Although I did find a somewhat >>> similar problem on GIS.SE >>> < >>> http://gis.stackexchange.com/questions/92554/can-i-create-an-oracle-spatial-view-from-a-non-spatial-table >>> > >>> the same solution does not apply as my function returns a table, not a >>> single object, and Oracle doesn't permit indexes on named table types. >>> >>> As things currently stand I don't think I can make this work, and the >>> only >>> thing that could potentially help is to prevent GeoServer from appending >>> its >>> SDO_FILTER mask. However I'm guessing this just isn't possible without >>> digging into the source and creating a custom build (totally not an >>> option). >>> >>> Can anyone tell me if there's something I've missed here, or suggest a >>> different approach for solving the same problem? Any thoughts would be >>> very >>> much appreciated. >>> >>>
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