Dear Brent,
I must confess that my attempts to do this are so far proving very unsuccessful
If I run the following query:
SELECT (ST_SummaryStats(ST_Union(rast))).sum AS sum
FROM (SELECT ST_MapAlgebra(deposition.rast, concentrated.rast, 'CASE WHEN
[rast2] > 0.0 THEN [rast1] ELSE NULL END' ) As rast
FROM mymodel.deposition, mymodel.concentrated
WHERE ST_Intersects(deposition.rast, concentrated.rast) AND
deposition.rid=1 ) foo ;
It takes around 30 seconds to complete as I assume it is only looking at one
tile(they are 256x256 pixels) i.e. rid 1. It is not easy to check the sum – for
that I need one complete raster.
For the record this was marginally faster than
SELECT (ST_SummaryStats(ST_Union(rast))).sum AS sum
FROM (SELECT ST_MapAlgebra(deposition.rast, concentrated.rast, 'CASE WHEN
[rast2] > 0.0 THEN [rast1] ELSE NULL END' ) As rast
FROM mymodel.deposition, mymodel.concentrated
WHERE mymodel.deposition.filename='10_depo.tif' AND
ST_UpperleftX(mymodel.deposition.rast) =
ST_UpperleftX(mymodel.concentrated.rast) AND
ST_UpperleftY(mymodel.deposition.rast) =
ST_UpperleftY(mymodel.deposition.rast) ) foo ;
Even after I built indexes for the clauses after the WHERE.
Now there are 144 tiles in each of the rasters I want to perform this operation
on. Logic would therefore suggest this should take ~4500s
However when I perform the following query
SELECT (ST_SummaryStats(ST_Union(rast))).sum AS sum
FROM (SELECT ST_MapAlgebra(deposition.rast, concentrated.rast, 'CASE WHEN
[rast2] > 0.0 THEN [rast1] ELSE NULL END' ) As rast
FROM mymodel.deposition, mymodel.concentrated
WHERE ST_Intersects(deposition.rast, concentrated.rast) AND
deposition.filename='10_depo.tif' ) foo ;
The query is still running after 18000s! I must therefore assume I have done
something wrong but as you may have guessed the answer eludes me.
Any further suggestions would be welcome but I will continue to try and find a
solution as I have 135 rasters to perform this operations on now and
potentially many thousands more in the future.
Darrel
.
I
From: postgis-users [mailto:postgis-users-boun...@lists.osgeo.org] On Behalf Of
Darrel Maddy
Sent: 24 November 2015 19:52
To: Brent Wood <pcr...@yahoo.com>; postgis-users@lists.osgeo.org
Subject: Re: [postgis-users] Help with SQL query?
Dear Brent,
Many thanks. The data are tiled (256x256) hence the large number of rows from
the original 135 tifs. I did not build any indexes however, so I will do some
reading and see how best to approach that (the threads you listed look useful
so thanks for that).
I will run some additional mini queries limited to just one comparison and
check using QGIS as you suggest – I probably should have done that first!
My workstation has 64GB Ram and I would be surprised if it was significantly
caching to disk. I also have a hexacore intel extreme processor so I would not
expect this to be hardware limited. I must confess I expected it to finish
within a couple of hours.
Anyhow very many thanks. I will continue to explore and report back hopefully
with positive news.
Darrel
From: Brent Wood [mailto:pcr...@yahoo.com]
Sent: 24 November, 2015 7:36 PM
To: Darrel Maddy
<darrel.ma...@newcastle.ac.uk<mailto:darrel.ma...@newcastle.ac.uk>>;
postgis-users@lists.osgeo.org<mailto:postgis-users@lists.osgeo.org>
Subject: Re: [postgis-users] Help with SQL query?
Indexing can improve performance by 100s of x, without them things can be slow.
Also, did you tile the images when you imported them? If not, then each
iteration is working through all the pixels in the image, rather than a small
subset. Essentially with tiles, you have a deep (long) table rather than a wide
one. RDBMSs work better with lots of small records than a few wide ones,
especially when indexes are used.
This might help:
http://gis.stackexchange.com/questions/43053/how-to-speed-up-queries-for-raster-databases
and see the raster tutorial they mention for the SRTM data, as to how that is
loaded into Postgis:
https://trac.osgeo.org/postgis/wiki/WKTRasterTutorial01
To test the logic (the syntax is correct or it wouldn't be working) you could
add to the "where" clause an extra filter so that only a small subset of the
entire dataset is included (like just one QGIS operation) then compare this
with the QGIS result.
That would be much faster that testing on the entire dataset. Once you know it
is correct for the test case(s), then you can run it on the complete set.
Note that some queries can build up large in-memory objects, so make sure your
system is not swapping to disk, as that will also slow things down (hugely).
Cheers
Brent
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