Hi,
How about tiling the features so that the processing would be divided into groups?
(1) Add a unique ID attribute to the candidates.
(2) Extract geometry as an attribute for each candidate (GeometryExtractor).
(3) Send both filters and candidates to a Tiler.
(4) Separate the tiled filters and candidates, send them to the SpatialFilter. Set tile column and row attributes to the "Group By" parameter.
(5) Select unique Passed features using the DuplicateRemover (Key Attribute = ID attribute of candidate).
(7) Restore the original geometry (GeometryReplacer).
The optimal number of tiles is unknown but I expect 10x10 - 20x20 would be effective.
Takashi
The tiling strategy has as perk that you can use the parallelprocessing option (because of the group by for one)
Should yield (albeit maybe slight) performence gain.
Hello, @takashi,
first of all, thank you a lot for all your good advices you gave in this share point.
have a question to your process, you wrote here.
First, to my process: I need to check: points objects with another point objects (if they are equal or not). So i made as in your list written until the step Nr. 4.
With step 5 and 7 i m not so sure if i need them to complete. Can you please explain me what for you do these steps? Are they needed in my process, u think?
Thanks,
Anna
Hi @annachukicheva, Regarding the original question, I assumed that the filter and candidates geometries are areas. In your case, both geometries are points, so I think that you can omit the step 1,2 and 5-7.
Hi @annachukicheva, Regarding the original question, I assumed that the filter and candidates geometries are areas. In your case, both geometries are points, so I think that you can omit the step 1,2 and 5-7.
Thank you @takashi a lot!
