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The time it spent for reading metadata of all available tables. By looking at the GUI of QGIS it does not offer a proper method for speeding it up. Checking the "Only look for user's tables" would work if you can change the ownership to QGIS user but if you work in an enterprise that is probably not possible and a bad idea anyways. There should be a ...


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Go with steps: -Create spatial indexes for your tables. -Create Buffers CREATE OR REPLACE VIEW b1 as SELECT ST_Buffer(the_geom,500) as the_geom, attributes, fid FROM stores; CREATE OR REPLACE VIEW b2 as SELECT ST_Buffer(the_geom,2000) as the_geom, attributes, fid FROM stores; (I am not sure, but maybe you should reduce here you 8 million point to ...


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Your query looks rather strange. What are you trying to achieve with two buffers around the same point? You can replace ST_Distance with operator, but still we have to overthink the buffer thing. Could you specify your goal a little? PS: Did you build spatial index? UPDATE: I would try SELECT * FROM ( SELECT a.gid, b.gid, ...


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The main bottle neck in my previous code was in reading from a GDB featureclass. Once I converted to a shapefile, my read time dropped to about 10% of the original. I should have remembered that I had the same problem just a few months ago #facepalm. Additionally, in a previous iteration of the current problem, I tried to copy my features to an in_memory ...


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The Solution Thx to AndreJ to show us the way. First you have to know that when adding a Layer from your database, the database parameters are burnt into the QGIS project file. This mean if you modify those parameters from QGIS source, it will not affect you project file. If you need to modify those parameters there is only 2 solutions : 1/ Remove the ...


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Thought to comment first, but then it got large.. Processing that much data in 20 min is reasonable time imo. I've also tried to speed up some of the arcpy operations - look here. If performance is an issue, you could read the file geodatabase with API, but I doubt it would be any faster than reading the data with arcpy into a pure Python data structure ...


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If you choose a geographic coordinate system as a projection, the tool will run faster. The reason why mine took so long is because my feature class was projected using an equidistant projection. I reprojected the feature class using a geographic coordinate system and now the conversion from feature class to raster takes seconds. Note that in my case I ...


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My current workaround is to build a side table with 2 columns, the objectid and geometry( as PostGIS geometry). And to maintain that table with triggers on the featureclass. Then perform spatial queries against the side table and join back to the featureclass. I have an open incident with Esri, but so far, no luck. I will update this answer if/when we make ...


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While it's true that you've implemented the worst possible case of feature dataset (mis)use, I've worked with customers with more feature datasets than that (2000), and Oracle upgrade took about five minutes, so I suspect you're looking in the wrong place for optimization. Some of the things you can do: Make sure your database is optimally configured, and ...



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