If it is 20 mill. coordinates, it could be faster than 80 minutes, I
guess in the region of 10 minutes.
I would:
1 Combine 3+4 to something like (I assume ST_Multi is not needed) - this
avoids data duplication and one unnecessary transaction commit
SELECT randfield, (st_dump(ST_Union(f.geom)
I might suggest going with debian testing to get reasonably new dependent
libs (if that matters to you). I've been using it for 6 months for my
desktop and it has worked well for me.
On Mon, Jul 16, 2018 at 2:07 PM, Even Rouault
wrote:
> >
> > I made a very rough proof of concept out of the tru
>
> I made a very rough proof of concept out of the trusty_clang travis
> scripts...
> Thoughts? Comments?
Good initiative. As far as I'm concerned, the scripts/setdevenv.sh on my
native Linux env does the job, but that indeed requires to have all the
dependencies right.
>
> Without other st
I took the advice of Andreas and converted my code to using PostGIS.
And the speed difference is enormous.
The commands I've used:
// Import shapefile into PostGIS:
ogr2ogr -f PostgreSQL PG:"host=localhost user=..." fishnet.shp -gt
unlimited -lco GEOMETRY_NAME=geom -a_srs "EPSG:28992"
// Add rando
On 7/16/18 8:51 AM, Robert Coup wrote:
> Suggestion:
>
> 1. docker images with all the build/test/library dependencies already
> installed — publish them so getting the right dependencies & environment is
> only a download.
>
>
> Thoughts? Comments?
PDAL uses Alpine+Docker for its TravisCI builds
Hi All,
For me at least, maintaining working dev environments for GDAL can be
frustrating... there are a lot of dependencies, platforms, and a huge
number of config options. Let alone switching branches for backports/etc. I
typically use OSX as a desktop and linux environments running under that
v
On Mon, 16 Jul 2018, Paul Meems wrote:
Thanks, Jon for your suggestion of GeoPandas.
Unfortunately, I'm not allowed to use new external dependencies.
Some timing:
1,677 shapes --> 0.3s
4,810 shapes --> 1.8s
18,415 shapes --> 21.4s
72,288 shapes --> 5min, 54s
285,927 shapes --> 25m
1,139,424 s
ST_Union in PostGIS should scale better than SQLite.
ST_Dump gives you singlepart geometries.
Best Regards
Andreas Oxenstierna
> 16 juli 2018 kl. 10:53 skrev Paul Meems :
>
> Thanks, Jon for your suggestion of GeoPandas.
> Unfortunately, I'm not allowed to use new external dependencies.
>
Thanks, Jon for your suggestion of GeoPandas.
Unfortunately, I'm not allowed to use new external dependencies.
I tried doing all steps in an SQLite file instead of using several
intermediate shapefiles. And I had some good results, so I created a script
dissolving an increasingly higher number of
