Re: [GENERAL] Fwd: Raster performance
082 rows=7677 loops=1) Total runtime: 866691.113 ms On Fri, May 29, 2015 at 6:40 AM, PT wrote: > On Thu, 28 May 2015 10:06:24 -0500 > David Haynes II wrote: > > > > The query run times are significantly slower on outdb as that using indb > > here are the run times on 2 queries. > > > > ST_Count/ST_Clip(select single band here)/Inner Join/ST_Transform (US > > Counties) > > OutDB: 873.564s (14 minutes 33s) InDB: 127.36s (2 minutes 7s) > > > > ST_Count(select single band here)/ST_Clip(on all bands)/Inner > > Join/ST_Transform (US Counties) > > OutDB: 9537.371s (2 hours 38minutes) InDB: 310s (5 minutes 10 seconds) > > > > In the query planner it shows a large change in the number of columns > > (width) that are picked up in the CTE_rast_select. > > These extra columns slow down the ability to process the data. > > As I mentioned before, that's not how that works. Those are estimates. If > those rows are actually different between the two tables, then your data > is different between the two databases and you can't expect the performance > to be the same. > > Additionally, the part you snip out below isn't the part that's different. > In particular, the difference is coming from the fact that one of the > plans uses modis and the other uses modis_noout. > > Does modis exist in indb? Does modis_noout exist on outdb? What is the > difference between these two tables? Because _that_ is where the time > difference is most likely happening (based on the explain output). > > Additionally, run EXPLAIN ANALYZE on these queries to get the actual > times in addition to the estimates. Furthermore, given that a lot of > the confusion in this question is due to a lack of iformation, it would > be a good idea to include the table definitions. > > > OUT DB > > CTE rast_select > > > -> Nested Loop (cost=0.28..76131.41 rows=62033 *width=1086)* > > > -> CTE Scan on poly s (cost=0.00..62.18 rows=3109 > width=250) > > > > > > In DB > > Nested Loop (cost=0.28..51767.41 rows=62033 *width=272*) > > > -> CTE Scan on poly s (cost=0.00..62.18 rows=3109 > width=250) > > > > On Wed, May 27, 2015 at 4:31 PM, PT wrote: > > > > > On Tue, 26 May 2015 12:52:24 -0500 > > > David Haynes II wrote: > > > > > > > Hello, > > > > > > > > I have a question about the query optimizer and its performance on > > > spatial > > > > datasets, specifically rasters. My use case is rather unique, the > > > > application that I am developing allows users to request > summarizations > > > of > > > > various geographic boundaries around the world. Therefore our raster > > > > datasets are global. We are in the process of conducting some > benchmarks > > > > for our system and we noticed something unexpected. > > > > > > > > The query is the same except the first is run on a raster (46gigs) > in out > > > > of database (outdb) and the second is the same raster (46gigs) > stored in > > > > database (indb). The raster is multibanded (13), with each band > > > > representing one entire MODIS global scene. A single year of MODIS is > > > > approximately 3.6 gigs. > > > > > > > > The outdb is being out performed by indb, because the query optimizer > > > gets > > > > smarter. But what is also interesting is all the extra pieces that > are > > > > brought in with outdb. > > > > > > > > with poly as > > > > ( SELECT gid, label as name, ST_Transform(geom, 6842) as geom FROM > > > > us_counties ) > > > > , rast_select as > > > > ( SELECT r.rid as id, s.name , ST_CLIP(r.rast, s.geom) as rast from > > > > rasters.glc2000 as r inner join poly as s on ST_Intersects(r.rast, > > > s.geom) ) > > > > select r.id, r.name, ST_Count(r.rast, 1, True) > > > > > > > > > > > >QUERY PLAN With Outdb > > > > > > > > -- > > > > Sort (cost=93911.29..93926.80 rows=6204 width=254) > > > > Sort Key: r.id, r.name > > > > CTE poly > > > > -> Seq Scan on us_counties (cost=0.00..112.86 rows=3109 > > > width=62247) > > > > CTE rast_select > > > > -> Nested Loop (cost=0.28..76131.41 rows=62033 width=1086) > > > > ->
Re: [GENERAL] Fwd: Raster performance
Sorry, The query run times are significantly slower on outdb as that using indb here are the run times on 2 queries. ST_Count/ST_Clip(select single band here)/Inner Join/ST_Transform (US Counties) OutDB: 873.564s (14 minutes 33s) InDB: 127.36s (2 minutes 7s) ST_Count(select single band here)/ST_Clip(on all bands)/Inner Join/ST_Transform (US Counties) OutDB: 9537.371s (2 hours 38minutes) InDB: 310s (5 minutes 10 seconds) In the query planner it shows a large change in the number of columns (width) that are picked up in the CTE_rast_select. These extra columns slow down the ability to process the data. OUT DB CTE rast_select > -> Nested Loop (cost=0.28..76131.41 rows=62033 *width=1086)* > -> CTE Scan on poly s (cost=0.00..62.18 rows=3109 width=250) In DB Nested Loop (cost=0.28..51767.41 rows=62033 *width=272*) > -> CTE Scan on poly s (cost=0.00..62.18 rows=3109 width=250) On Wed, May 27, 2015 at 4:31 PM, PT wrote: > On Tue, 26 May 2015 12:52:24 -0500 > David Haynes II wrote: > > > Hello, > > > > I have a question about the query optimizer and its performance on > spatial > > datasets, specifically rasters. My use case is rather unique, the > > application that I am developing allows users to request summarizations > of > > various geographic boundaries around the world. Therefore our raster > > datasets are global. We are in the process of conducting some benchmarks > > for our system and we noticed something unexpected. > > > > The query is the same except the first is run on a raster (46gigs) in out > > of database (outdb) and the second is the same raster (46gigs) stored in > > database (indb). The raster is multibanded (13), with each band > > representing one entire MODIS global scene. A single year of MODIS is > > approximately 3.6 gigs. > > > > The outdb is being out performed by indb, because the query optimizer > gets > > smarter. But what is also interesting is all the extra pieces that are > > brought in with outdb. > > > > with poly as > > ( SELECT gid, label as name, ST_Transform(geom, 6842) as geom FROM > > us_counties ) > > , rast_select as > > ( SELECT r.rid as id, s.name , ST_CLIP(r.rast, s.geom) as rast from > > rasters.glc2000 as r inner join poly as s on ST_Intersects(r.rast, > s.geom) ) > > select r.id, r.name, ST_Count(r.rast, 1, True) > > > > > >QUERY PLAN With Outdb > > > -- > > Sort (cost=93911.29..93926.80 rows=6204 width=254) > > Sort Key: r.id, r.name > > CTE poly > > -> Seq Scan on us_counties (cost=0.00..112.86 rows=3109 > width=62247) > > CTE rast_select > > -> Nested Loop (cost=0.28..76131.41 rows=62033 width=1086) > > -> CTE Scan on poly s (cost=0.00..62.18 rows=3109 width=250) > > -> Index Scan using modis_rast_gist on modis r_1 > > (cost=0.28..24.40 rows=2 width=836) > > Index Cond: ((rast)::geometry && s.geom) > > Filter: _st_intersects(s.geom, rast, NULL::integer) > > -> HashAggregate (cost=17214.16..17276.20 rows=6204 width=254) > > -> CTE Scan on rast_select r (cost=0.00..1240.66 rows=62033 > > width=254) > > > > QUERY PLAN With Indb > > > > > - > > Sort (cost=69547.29..69562.80 rows=6204 width=254) > > Sort Key: r.id, r.name > > CTE poly > > -> Seq Scan on us_counties (cost=0.00..112.86 rows=3109 > width=62247) > > CTE rast_select > > -> Nested Loop (cost=0.28..51767.41 rows=62033 width=272) > > -> CTE Scan on poly s (cost=0.00..62.18 rows=3109 width=250) > > -> Index Scan using modis_noout_rast_gist on modis_noout r_1 > > (cost=0.28..16.56 rows=2 width=22) > > Index Cond: ((rast)::geometry && s.geom) > > Filter: _st_intersects(s.geom, rast, NULL::integer) > > -> HashAggregate (cost=17214.16..17276.20 rows=6204 width=254) > > -> CTE Scan on rast_select r (cost=0.00..1240.66 rows=62033 > > width=254) > > I could be missing something here, but I don't see how long the queries > actually take to run. Have you actually run the queries and timed them? > Keep in mind that analyze does not actually run the query, it only plans > it, so the actual run time is unknown if all you do is analyze. > > The quer
[GENERAL] Fwd: Raster performance
Hello, I have a question about the query optimizer and its performance on spatial datasets, specifically rasters. My use case is rather unique, the application that I am developing allows users to request summarizations of various geographic boundaries around the world. Therefore our raster datasets are global. We are in the process of conducting some benchmarks for our system and we noticed something unexpected. The query is the same except the first is run on a raster (46gigs) in out of database (outdb) and the second is the same raster (46gigs) stored in database (indb). The raster is multibanded (13), with each band representing one entire MODIS global scene. A single year of MODIS is approximately 3.6 gigs. The outdb is being out performed by indb, because the query optimizer gets smarter. But what is also interesting is all the extra pieces that are brought in with outdb. with poly as ( SELECT gid, label as name, ST_Transform(geom, 6842) as geom FROM us_counties ) , rast_select as ( SELECT r.rid as id, s.name , ST_CLIP(r.rast, s.geom) as rast from rasters.glc2000 as r inner join poly as s on ST_Intersects(r.rast, s.geom) ) select r.id, r.name, ST_Count(r.rast, 1, True) QUERY PLAN With Outdb -- Sort (cost=93911.29..93926.80 rows=6204 width=254) Sort Key: r.id, r.name CTE poly -> Seq Scan on us_counties (cost=0.00..112.86 rows=3109 width=62247) CTE rast_select -> Nested Loop (cost=0.28..76131.41 rows=62033 width=1086) -> CTE Scan on poly s (cost=0.00..62.18 rows=3109 width=250) -> Index Scan using modis_rast_gist on modis r_1 (cost=0.28..24.40 rows=2 width=836) Index Cond: ((rast)::geometry && s.geom) Filter: _st_intersects(s.geom, rast, NULL::integer) -> HashAggregate (cost=17214.16..17276.20 rows=6204 width=254) -> CTE Scan on rast_select r (cost=0.00..1240.66 rows=62033 width=254) QUERY PLAN With Indb - Sort (cost=69547.29..69562.80 rows=6204 width=254) Sort Key: r.id, r.name CTE poly -> Seq Scan on us_counties (cost=0.00..112.86 rows=3109 width=62247) CTE rast_select -> Nested Loop (cost=0.28..51767.41 rows=62033 width=272) -> CTE Scan on poly s (cost=0.00..62.18 rows=3109 width=250) -> Index Scan using modis_noout_rast_gist on modis_noout r_1 (cost=0.28..16.56 rows=2 width=22) Index Cond: ((rast)::geometry && s.geom) Filter: _st_intersects(s.geom, rast, NULL::integer) -> HashAggregate (cost=17214.16..17276.20 rows=6204 width=254) -> CTE Scan on rast_select r (cost=0.00..1240.66 rows=62033 width=254) -- David Haynes, Ph.D. Research Associate Terra Populus Minnesota Population Center www.terrapop.org -- David Haynes, Ph.D. Research Associate Terra Populus Minnesota Population Center www.terrapop.org
