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No, ArcSDE is not capable of full-text search. but the databases through which it is used to connect are. Furthermore, the ArcSDE API exposes some of the database capability in functions to access XML column contents. I've never personally successfully configured a database to perform free-text search. I suspect there may be black magic involved, since I ...


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Given the nature of your data and how many attributes you have, you don't have any choice but to split it up into different tables. From the ArcGIS help: Most size limits in a database depend on the DBMS edition and hardware limitations. One exception is the number of fields (columns) supported in a table or feature class; the maximum number is 500. ...


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There are several tools you can use to achieve a format conversion like this, among them, in no particular order: Open Source Server solution: GeoServer, which has capabilities to import various files and store them in a PostGIS db. Info is available here: http://docs.geoserver.org/2.6.x/en/user/data/vector/shapefile.html Open Source Desktop Solution: ...


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I did a similar thing, converting 2D polygons to 3D buildings. I did it in OL3-Cesium though, without any tool, by coding only. I had the coordinates for the polygon, and the height attribute for the building present. (Used a default value in case height was not provided). What I did was adding the 2D polygon as it is to the map. In addition to that, I ...


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Try the below. Could do something with arcpy.ListFeatureClasses as well, but using arcpy.da.Walk will allow you to step into any feature datasets you might have in your geodatabase. import arcpy import os from collections import OrderedDict def find_smallest(gdb): d = {} walk = arcpy.da.Walk(gdb, datatype='FeatureClass') for p, dirnames, fcs ...


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this might help: import arcpy arcpy.env.workspace=r"C:\temp\data.gdb" fcs = arcpy.ListFeatureClasses() rc_old = int(99999999999999) export_fc = "" for fc in fcs: rc = int(arcpy.GetCount_management(fc)[0]) print fc print rc if rc < rc_old: export_fc = fc rc_old = rc arcpy.FeatureClassToShapefile_conversion(export_fc,r"C:\temp")


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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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Ok, I had a look at shapefile.py and was able to arrive at this solution: from struct import unpack points = [] msacur.execute("SELECT ID, Shape FROM site_locations") # Field Shape contains binary data in GeomSHP format - to be converted rows = msacur.fetchall() for row in rows: # Find out the shape type shapeType = unpack("<i", ...



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