That's an interesting idea. Monday, when the JRC is open again, will be the day to test that.
Thank you Stefan. Thank you also Markus for the tips. I am positive that eventually we will find a balanced approach for the workflow. Cheers, Nikos * Stefan Blumentrath <stefan.blumentr...@nina.no> [2018-05-08 18:31:49 +0000]:
Hei Nikos, What about using gdal_rasterize and then r.external output=MASK? Cheers Stefan ________________________________ From: grass-user <grass-user-boun...@lists.osgeo.org> on behalf of Nikos Alexandris <n...@nikosalexandris.net> Sent: Tuesday, May 8, 2018 4:01:14 PM To: GRASS-GIS user mailing list Cc: giacomo.de...@ext.ec.europa.eu Subject: [GRASS-user] To import from or to link to data stored in a PostgreSQL data base? Dears, the following concerns an update of an existing workflow, part of which is GRASS GIS, that makes use of a large PostgreSQL data base which does not reside locally. The original data set consists of tens of thousands of (overlapping) polygons. The data are required solely to build raster MASKs. So, importing the whole of it, is an overkill. Instead, options, already working, are to split all records in single tables or views. Then access these via GRASS to perform some analytics. First instructions of the workflow are: - read a (external) vector map - set the computational region - build a raster mask. Building a MASK using a pseudo vector map that links to an external table, stored in a PostgreSQL data base, is times slower than importing the vetor of interest in GRASS GIS and then building a MASK using the "native" GRASS GIS vector map. Giacomo timed different options, using `v.external` as well as importing the data using `v.in.ogr`. Specifically, - building a MASK using one pseudo vector map (without and with a spatial-index), takes about 9 minutes (real time). time r.mask vector=test_nogeoindex --o real 8m40.306s user 5m14.225s sys 0m56.378s and time r.mask vector=test_geoindex --o real 8m46.096s user 5m15.693s sys 0m56.346s - building a MASK using a native GRASS GIS vector map, imported via a table or a view, takes about 0.4 seconds. real 0m0.373s user 0m0.191s sys 0m0.111s and real 0m0.350s user 0m0.179s sys 0m0.115s For the latter, building a view is way faster than a table (half a minute for more than 20000 views, while it would take approximately an hour to build single tables). The trade-off appears to be space vs time. If data are imported, more disk space is required. If data are not imported, and `v.external` is used, then `r.mask` takes too much time to build a raster MASK. - Is it acceptable for `r.mask` to take so long in building a MASK based on an external vector map stored in a PostgreSQL data base? - Is network connection a limiting factor here, since the PG data base is not local? - Would anyone have any recommendations/considerations on this approach? Thank you, Nikos
-- Nikos Alexandris | Remote Sensing & GeomaticsGPG Key Fingerprint 6F9D4506F3CA28380974D31A9053534B693C4FB3
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