Re: [GENERAL] Improve PostGIS performance with 62 million rows?

[Israel Brewster]

> On Jan 9, 2017, at 1:54 PM, Kevin Grittner  wrote:
> 
> On Mon, Jan 9, 2017 at 11:49 AM, Israel Brewster  
> wrote:
> 
>> [load of new data]
> 
>> Limit  (cost=354643835.82..354643835.83 rows=1 width=9) (actual
>> time=225998.319..225998.320 rows=1 loops=1)
> 
>> [...] I ran the query again [...]
> 
>> Limit  (cost=354643835.82..354643835.83 rows=1 width=9) (actual
>> time=9636.165..9636.166 rows=1 loops=1)
> 
>> So from four minutes on the first run to around 9 1/2 seconds on the second.
>> Presumably this difference is due to caching?
> 
> It is likely to be, at least in part.  Did you run VACUUM on the
> data before the first run?  If not, hint bits may be another part
> of it.  The first access to each page after the bulk load would
> require some extra work for visibility checking and would cause a
> page rewrite for the hint bits.

That could be - I had planned to run a VACUUM ANALYZE after creating the 
indexes, but forgot. By the time I got around to running the second query, 
autovacuum should have kicked in and done it for me.

> 
> --
> Kevin Grittner
> EDB: http://www.enterprisedb.com
> The Enterprise PostgreSQL Company



-- 
Sent via pgsql-general mailing list (pgsql-general@postgresql.org)
To make changes to your subscription:
http://www.postgresql.org/mailpref/pgsql-general

Re: [GENERAL] Improve PostGIS performance with 62 million rows?

[Kevin Grittner]

On Mon, Jan 9, 2017 at 11:49 AM, Israel Brewster  wrote:

> [load of new data]

>  Limit  (cost=354643835.82..354643835.83 rows=1 width=9) (actual
> time=225998.319..225998.320 rows=1 loops=1)

> [...] I ran the query again [...]

>  Limit  (cost=354643835.82..354643835.83 rows=1 width=9) (actual
> time=9636.165..9636.166 rows=1 loops=1)

> So from four minutes on the first run to around 9 1/2 seconds on the second.
> Presumably this difference is due to caching?

It is likely to be, at least in part.  Did you run VACUUM on the
data before the first run?  If not, hint bits may be another part
of it.  The first access to each page after the bulk load would
require some extra work for visibility checking and would cause a
page rewrite for the hint bits.

--
Kevin Grittner
EDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company


-- 
Sent via pgsql-general mailing list (pgsql-general@postgresql.org)
To make changes to your subscription:
http://www.postgresql.org/mailpref/pgsql-general

Re: [GENERAL] Improve PostGIS performance with 62 million rows?

[Rémi Cura]

Hey,
I like your curiosity !

At the billion range, you __have__ to use pgpointcloud,
pyramid raster solution (actually the more common way to perform this task)
or another database (hello monetdb).
Cheers,
Rémi-C

2017-01-09 20:11 GMT+01:00 Jonathan Vanasco :

>
> On Jan 9, 2017, at 12:49 PM, Israel Brewster wrote:
>
> >  Planning time: 4.554 ms
> >  Execution time: 225998.839 ms
> > (20 rows)
> >
> > So a little less than four minutes. Not bad (given the size of the
> database), or so I thought.
> >
> > This morning (so a couple of days later) I ran the query again without
> the explain analyze to check the results, and noticed that it didn't take
> anywhere near four minutes to execute. So I ran the explain analyze again,
> and got this:
>
> ...
>
> >  Planning time: 0.941 ms
> >  Execution time: 9636.285 ms
> > (20 rows)
> >
> > So from four minutes on the first run to around 9 1/2 seconds on the
> second. Presumably this difference is due to caching? I would have expected
> any caches to have expired by the time I made the second run, but the data
> *is* static, so I guess not. Otherwise, I don't know how to explain the
> improvement on the second run - the query plans appear identical (at least
> to me). *IS* there something else (for example, auto vacuum running over
> the weekend) that could explain the performance difference?
>
>
> This may sound crazy, but I suggest running each of these scenarios 3+
> times:
>
> # cold explain
> stop postgres
> start postgres
> explain analyze SELECT
>
> # cold select
> stop postgres
> start postgres
> enable \t for query timing
> SELECT
>
> # cold explain to select
> stop postgres
> start postgres
> explain analyze SELECT
> enable \t for query timing
> SELECT
>
> # cold select to explain
> stop postgres
> start postgres
> enable \t for query timing
> SELECT
> explain analyze SELECT
>
> # cold select to select
> stop postgres
> start postgres
> enable \t for query timing
> SELECT
> SELECT
>
> I've found the timing for "Explain Analyze" to be incredibly different
> from an actual SELECT on complex/large dataset queries... and the
> differences don't seem to correlate to possible speedups from index/table
> caching.
>
>
>
>
> --
> Sent via pgsql-general mailing list (pgsql-general@postgresql.org)
> To make changes to your subscription:
> http://www.postgresql.org/mailpref/pgsql-general
>

Re: [GENERAL] Improve PostGIS performance with 62 million rows?

[Jonathan Vanasco]

On Jan 9, 2017, at 12:49 PM, Israel Brewster wrote:

>  Planning time: 4.554 ms
>  Execution time: 225998.839 ms
> (20 rows)
> 
> So a little less than four minutes. Not bad (given the size of the database), 
> or so I thought.
> 
> This morning (so a couple of days later) I ran the query again without the 
> explain analyze to check the results, and noticed that it didn't take 
> anywhere near four minutes to execute. So I ran the explain analyze again, 
> and got this:

...

>  Planning time: 0.941 ms
>  Execution time: 9636.285 ms
> (20 rows)
> 
> So from four minutes on the first run to around 9 1/2 seconds on the second. 
> Presumably this difference is due to caching? I would have expected any 
> caches to have expired by the time I made the second run, but the data *is* 
> static, so I guess not. Otherwise, I don't know how to explain the 
> improvement on the second run - the query plans appear identical (at least to 
> me). *IS* there something else (for example, auto vacuum running over the 
> weekend) that could explain the performance difference?


This may sound crazy, but I suggest running each of these scenarios 3+ times:

# cold explain
stop postgres
start postgres
explain analyze SELECT

# cold select
stop postgres
start postgres
enable \t for query timing
SELECT

# cold explain to select
stop postgres
start postgres
explain analyze SELECT
enable \t for query timing
SELECT

# cold select to explain
stop postgres
start postgres
enable \t for query timing
SELECT
explain analyze SELECT

# cold select to select
stop postgres
start postgres
enable \t for query timing
SELECT
SELECT

I've found the timing for "Explain Analyze" to be incredibly different from an 
actual SELECT on complex/large dataset queries... and the differences don't 
seem to correlate to possible speedups from index/table caching.




-- 
Sent via pgsql-general mailing list (pgsql-general@postgresql.org)
To make changes to your subscription:
http://www.postgresql.org/mailpref/pgsql-general

Re: [GENERAL] Improve PostGIS performance with 62 million rows?

[Paul Ramsey]

At BILLIONS, you're getting to a point where the point index is probably
(a) very large and (b) very deep, so you might want to do something
different with your data storage, like loading the data in spatially
compact patches of several 10s of points. Then the index will float more
nicely in memory, and be faster to traverse. Something like pgpointcloud
may start to look like it has some advantages.

WRT your time differences, make sure to try the same query but with
*different routes*. I find that often a slow query gets fast if I run it
twice identically, but if I run it twice with different parameterizations I
see slower execution. Basically the second time you're seeing some caching
of the immediately important blocks, but not necessarily every block you
might need for every case.

P.


On Mon, Jan 9, 2017 at 9:49 AM, Israel Brewster 
wrote:

> So just for interests sake, to kick things up a notch (and out of sheer
> morbid curiosity), I loaded a higher-resolution dataset (Elevation data for
> the state of Alaska, 2 arc second resolution, as opposed to 100 meter
> resolution before). Same structure/indexes and everything, just higher
> resolution. So the new database has 1,642,700,002 rows, and is somewhere
> around 300GB in size (including index). Due to the larger data size, I
> moved the database to a different table space which resides on a mirrored
> 2TB spinning platter disk (i.e. slower both because of the RAID and lack of
> SSD). Friday evening I ran the following query:
>
> EXPLAIN ANALYZE WITH segments AS (
> SELECT ST_MakeLine( lag((pt).geom , 1, NULL) OVER (ORDER BY (pt).path)
>   ,(pt).geom)::GEOGRAPHY AS short_line
> FROM ST_DumpPoints(
>   ST_Segmentize(
> ST_GeographyFromText('SRID=4326;LINESTRING(-150.008056
> 61.179167,-156.77 71.285833)'),
> 5000
> )::geometry
> ) as pt
> )
> SELECT elevation
> FROM data ,segments
> WHERE segments.short_line IS NOT NULL
>   AND  ST_DWithin(location, segments.short_line, 100) = TRUE
> ORDER BY elevation DESC
> limit 1;
>
> Which is the same query that took around 300 ms on the smaller dataset.
> The result was this (https://explain.depesz.com/s/mKFN):
>
>
>QUERY PLAN
>
> 
> 
> 
>  Limit  (cost=354643835.82..354643835.83 rows=1 width=9) (actual
> time=225998.319..225998.320 rows=1 loops=1)
>CTE segments
>  ->  WindowAgg  (cost=60.08..82.58 rows=1000 width=64) (actual
> time=0.488..4.032 rows=234 loops=1)
>->  Sort  (cost=60.08..62.58 rows=1000 width=64) (actual
> time=0.460..0.875 rows=234 loops=1)
>  Sort Key: pt.path
>  Sort Method: quicksort  Memory: 57kB
>  ->  Function Scan on st_dumppoints pt  (cost=0.25..10.25
> rows=1000 width=64) (actual time=0.354..0.387 rows=234 loops=1)
>->  Sort  (cost=354643753.25..354645115.32 rows=544829 width=9)
> (actual time=225998.319..225998.319 rows=1 loops=1)
>  Sort Key: data.elevation DESC
>  Sort Method: top-N heapsort  Memory: 25kB
>  ->  Nested Loop  (cost=0.68..354641029.10 rows=544829 width=9)
> (actual time=349.784..225883.557 rows=159654 loops=1)
>->  CTE Scan on segments  (cost=0.00..20.00 rows=995
> width=32) (actual time=0.500..4.823 rows=233 loops=1)
>  Filter: (short_line IS NOT NULL)
>  Rows Removed by Filter: 1
>->  Index Scan using location_gist_idx on
> data  (cost=0.68..356423.07 rows=5 width=41) (actual time=71.416..969.196
> rows=685 loops=233)
>  Index Cond: (location && _st_expand(segments.short_line,
> '100'::double precision))
>  Filter: ((segments.short_line && _st_expand(location,
> '100'::double precision)) AND _st_dwithin(location, segments.short_line,
> '100'::double precision, true))
>  Rows Removed by Filter: 8011
>  Planning time: 4.554 ms
>  Execution time: 225998.839 ms
> (20 rows)
>
> So a little less than four minutes. Not bad (given the size of the
> database), or so I thought.
>
> This morning (so a couple of days later) I ran the query again without the
> explain analyze to check the results, and noticed that it didn't take
> anywhere near four minutes to execute. So I ran the explain analyze again,
> and got this:
>
>
>QUERY PLAN
>
> 
> 
> 
>  Limit  (cost=354643835.82..354643835.83 rows=1 width=9) (actual
> time=9636.165..9636.166 rows=1 loops=1)
>CTE segments
>  ->  WindowAgg  (cost=60.08..82.58 rows=1000 width=64) (actual
> time=0.345..1.137 rows=234 loops=1)
>

Re: [GENERAL] Improve PostGIS performance with 62 million rows?

[Israel Brewster]

So just for interests sake, to kick things up a notch (and out of sheer morbid curiosity), I loaded a higher-resolution dataset (Elevation data for the state of Alaska, 2 arc second resolution, as opposed to 100 meter resolution before). Same structure/indexes and everything, just higher resolution. So the new database has 1,642,700,002 rows, and is somewhere around 300GB in size (including index). Due to the larger data size, I moved the database to a different table space which resides on a mirrored 2TB spinning platter disk (i.e. slower both because of the RAID and lack of SSD). Friday evening I ran the following query:EXPLAIN ANALYZE WITH segments AS (    SELECT ST_MakeLine( lag((pt).geom , 1, NULL) OVER (ORDER BY (pt).path)                          ,(pt).geom)::GEOGRAPHY AS short_line    FROM ST_DumpPoints(          ST_Segmentize(            ST_GeographyFromText('SRID=4326;LINESTRING(-150.008056 61.179167,-156.77 71.285833)'),            5000        )::geometry    ) as pt)SELECT elevationFROM data ,segmentsWHERE segments.short_line IS NOT NULL  AND  ST_DWithin(location, segments.short_line, 100) = TRUEORDER BY elevation DESClimit 1;Which is the same query that took around 300 ms on the smaller dataset. The result was this (https://explain.depesz.com/s/mKFN):                                                                                     QUERY PLAN                                                                                      Limit  (cost=354643835.82..354643835.83 rows=1 width=9) (actual time=225998.319..225998.320 rows=1 loops=1)   CTE segments     ->  WindowAgg  (cost=60.08..82.58 rows=1000 width=64) (actual time=0.488..4.032 rows=234 loops=1)           ->  Sort  (cost=60.08..62.58 rows=1000 width=64) (actual time=0.460..0.875 rows=234 loops=1)                 Sort Key: pt.path                 Sort Method: quicksort  Memory: 57kB                 ->  Function Scan on st_dumppoints pt  (cost=0.25..10.25 rows=1000 width=64) (actual time=0.354..0.387 rows=234 loops=1)   ->  Sort  (cost=354643753.25..354645115.32 rows=544829 width=9) (actual time=225998.319..225998.319 rows=1 loops=1)         Sort Key: data.elevation DESC         Sort Method: top-N heapsort  Memory: 25kB         ->  Nested Loop  (cost=0.68..354641029.10 rows=544829 width=9) (actual time=349.784..225883.557 rows=159654 loops=1)               ->  CTE Scan on segments  (cost=0.00..20.00 rows=995 width=32) (actual time=0.500..4.823 rows=233 loops=1)                     Filter: (short_line IS NOT NULL)                     Rows Removed by Filter: 1               ->  Index Scan using location_gist_idx on data  (cost=0.68..356423.07 rows=5 width=41) (actual time=71.416..969.196 rows=685 loops=233)                     Index Cond: (location && _st_expand(segments.short_line, '100'::double precision))                     Filter: ((segments.short_line && _st_expand(location, '100'::double precision)) AND _st_dwithin(location, segments.short_line, '100'::double precision, true))                     Rows Removed by Filter: 8011 Planning time: 4.554 ms Execution time: 225998.839 ms(20 rows)So a little less than four minutes. Not bad (given the size of the database), or so I thought.This morning (so a couple of days later) I ran the query again without the explain analyze to check the results, and noticed that it didn't take anywhere near four minutes to execute. So I ran the explain analyze again, and got this:                                                                                     QUERY PLAN                                                                                      Limit  (cost=354643835.82..354643835.83 rows=1 width=9) (actual time=9636.165..9636.166 rows=1 loops=1)   CTE segments     ->  WindowAgg  (cost=60.08..82.58 rows=1000 width=64) (actual time=0.345..1.137 rows=234 loops=1)           ->  Sort  (cost=60.08..62.58 rows=1000 width=64) (actual time=0.335..0.428 rows=234 loops=1)                 Sort Key: pt.path                 Sort Method: quicksort  Memory: 57kB                 ->  Function Scan on st_dumppoints pt  (cost=0.25..10.25 rows=1000 width=64) (actual time=0.198..0.230 rows=234 loops=1)   ->  Sort  (cost=354643753.25..354645115.32 rows=544829 width=9) (actual time=9636.165..9636.165 rows=1 loops=1)         Sort Key: data.elevation DESC         Sort Method: top-N heapsort  Memory: 25kB         ->  Nested Loop  (cost=0.68..354641029.10 rows=544829 width=9) (actual time=1.190..9602.606 rows=159654 loops=1)               ->  CTE Scan on segments  (cost=0.00..20.00 rows=995 width=32) (actual time=0.361..1.318 rows=233 loops=1)                     Filter: 

Re: [GENERAL] Improve PostGIS performance with 62 million rows?

[Israel Brewster]

On Jan 5, 2017, at 1:38 PM, Rémi Cura  wrote:
> 
> Hey,
> 1 sec seems really good in this case,
> and I'm assuming you tuned postgres so the main index fits into ram (work_mem 
> and all other stuff).
> 
> You could avoid a CTE by mixing both cte.
> 
> WITH pts AS (
> SELECT (pt).geom, (pt).path[1] as vert
> FROM 
> ST_DumpPoints(
> ST_Segmentize(
> ST_GeographyFromText('SRID=4326;LINESTRING(-150.008056 
> 61.179167,-156.77 71.285833)'),
> 600
> )::geometry
> ) as pt
> ) 
> SELECT elevation 
> FROM data 
> INNER JOIN (SELECT 
> ST_MakeLine(ARRAY[a.geom, b.geom]) as short_line
> FROM pts a 
> INNER JOIN pts b 
> ON a.vert=b.vert-1 AND b.vert>1) segments
> ON  ST_DWithin(location, segments.short_line, 600)
> ORDER BY elevation DESC limit 1;
> 
> 
> Then you could remove the useless and (potentially explosive if you have 
> large number of dump points) inner join on points : 
> "FROM pts a 
> INNER JOIN pts b " 
> 
> You could simply use a window function to generate the segments, like in here 
> .
> The idea is to dump points, order them by path, and then link each point with 
> the previous one (function lag).
> Assuming you don't want to use the available function,
> this would be something like : 
> 
>  
> 
> WITH segments AS (
> SELECT ST_MakeLine( lag((pt).geom , 1, NULL) OVER (ORDER BY (pt).path)
>   ,(pt).geom) AS short_line
> FROM ST_DumpPoints(
>   ST_Segmentize(
> ST_GeographyFromText('SRID=4326;LINESTRING(-150.008056 
> 61.179167,-156.77 71.285833)'),
> 600
> )::geometry
> ) as pt
> ) 
> SELECT elevation 
> FROM data ,segments
> WHERE segments.short_line IS NOT NULL --the first segment is null by design 
> (lag function)
>   AND  ST_DWithin(location, segments.short_line, 600) = TRUE
> ORDER BY elevation DESC 
> limit 1;
> 
> 
> I don't know if you can further improve this query after that,
> but I'll guess it would reduce your time and be more secure regarding scaling.
> 
> 
> if you want to further improve your result, 
> you'll have to reduce the number of row in your index, 
> that is partition your table into several tables !
> 
> This is not easy to do with current postgres partitionning methods as far as 
> I know
> (partitionning is easy, automatic efficient query is hard).
> 
> Another way would be to reduce you requirement, and consider that in some 
> case you may want less details in the altimetry, which would allow you to use 
> a Level Of Detail approach.
> 
> Congrats for the well explained query/problem anyway !
> Cheers,
> Rémi-C


Ooooh, nice use of a window function - that change right there cut the 
execution time in half! I was able to shave off a few hundreds of a second more 
but tweaking the ST_Segmentize length parameter up to 5,000 (still have to play 
with that number some), so execution time is now down to the sub-300ms range. 
If I reduce the radius I am looking around the line, I can additionally improve 
the time to around 200 ms, but I'm not sure that will be an option. Regardless, 
300ms is rather impressive, I think. Thanks!

> 
> 2017-01-05 23:09 GMT+01:00 Paul Ramsey  >:
> Varying the segment length upwards might have a salutary effect for a while, 
> as the efficiency improvement of fewer inner loops battles with the 
> inefficiency of having more points selected by the index filter. Worth an 
> experiment.
> 
> P
> 
> On Thu, Jan 5, 2017 at 1:00 PM, Israel Brewster  > wrote:
> 
>> On Jan 5, 2017, at 10:38 AM, Paul Ramsey > > wrote:
>> 
>> Yes, you did. You want a query that spits out a tupleset of goemetries (one 
>> each for each wee segment), and then you can join that set to your main 
>> table using st_dwithin() as the join clause.
>> So start by ditching the main table and just work on a query that generates 
>> a pile of wee segments.
> 
> Ahhh, I see you've done this sort of thing before 
> (http://blog.cleverelephant.ca/2015/02/breaking-linestring-into-segments.html 
> )
>  :-)
> 
> So following that advice I came up with the following query:
> 
> WITH dump AS (SELECT
> ST_DumpPoints(
> ST_Segmentize(
> ST_GeographyFromText('SRID=4326;LINESTRING(-150.008056 
> 61.179167,-156.77 71.285833)'),
> 600
> )::geometry
> ) as pt
> ),
> pts AS (
> SELECT (pt).geom, (pt).path[1] as vert FROM dump
> )
> SELECT elevation 
> FROM data 
> INNER JOIN (SELECT 
> ST_MakeLine(ARRAY[a.geom, b.geom]) as short_line
> FROM pts a 
> INNER JOIN pts b 
> ON a.vert=b.vert-1 AND b.vert>1) segments
> ON  

Re: [GENERAL] Improve PostGIS performance with 62 million rows?

[Israel Brewster]

Ah, yes indeed. Upping the segment length to 1,000 brings the execution time down to 642 ms, and further upping it to 10,000 brings the execution time down again to 442.104 ms. I'll have to play around with it and see where the minimum is. Would that be likely to vary depending on initial path length?
---Israel BrewsterSystems Analyst IIRavn Alaska5245 Airport Industrial RdFairbanks, AK 99709(907) 450-7293---BEGIN:VCARD
VERSION:3.0
N:Brewster;Israel;;;
FN:Israel Brewster
ORG:Frontier Flying Service;MIS
TITLE:PC Support Tech II
EMAIL;type=INTERNET;type=WORK;type=pref:isr...@frontierflying.com
TEL;type=WORK;type=pref:907-450-7293
item1.ADR;type=WORK;type=pref:;;5245 Airport Industrial Wy;Fairbanks;AK;99701;
item1.X-ABADR:us
CATEGORIES:General
X-ABUID:36305438-95EA-4410-91AB-45D16CABCDDC\:ABPerson
END:VCARD


On Jan 5, 2017, at 1:09 PM, Paul Ramsey  wrote:Varying the segment length upwards might have a salutary effect for a while, as the efficiency improvement of fewer inner loops battles with the inefficiency of having more points selected by the index filter. Worth an experiment.POn Thu, Jan 5, 2017 at 1:00 PM, Israel Brewster  wrote:On Jan 5, 2017, at 10:38 AM, Paul Ramsey  wrote:Yes, you did. You want a query that spits out a tupleset of goemetries (one each for each wee segment), and then you can join that set to your main table using st_dwithin() as the join clause.So start by ditching the main table and just work on a query that generates a pile of wee segments.Ahhh, I see you've done this sort of thing before (http://blog.cleverelephant.ca/2015/02/breaking-linestring-into-segments.html) :-)So following that advice I came up with the following query:WITH dump AS (SELECT    ST_DumpPoints(        ST_Segmentize(            ST_GeographyFromText('SRID=4326;LINESTRING(-150.008056 61.179167,-156.77 71.285833)'),            600        )::geometry    ) as pt),pts AS (    SELECT (pt).geom, (pt).path[1] as vert FROM dump)SELECT elevation FROM data INNER JOIN (SELECT     ST_MakeLine(ARRAY[a.geom, b.geom]) as short_line    FROM pts a     INNER JOIN pts b     ON a.vert=b.vert-1 AND b.vert>1) segmentsON  ST_DWithin(location, segments.short_line, 600)ORDER BY elevation DESC limit 1;Which yields the following EXPLAIN ANALYZE (https://explain.depesz.com/s/RsTD):                                                                                                                 QUERY PLAN                                                                                                                  Limit  (cost=11611706.90..11611706.91 rows=1 width=4) (actual time=1171.814..1171.814 rows=1 loops=1)   CTE dump     ->  Result  (cost=0.00..5.25 rows=1000 width=32) (actual time=0.024..1.989 rows=1939 loops=1)   CTE pts     ->  CTE Scan on dump  (cost=0.00..20.00 rows=1000 width=36) (actual time=0.032..4.071 rows=1939 loops=1)   ->  Sort  (cost=11611681.65..11611768.65 rows=34800 width=4) (actual time=1171.813..1171.813 rows=1 loops=1)         Sort Key: data.elevation DESC         Sort Method: top-N heapsort  Memory: 25kB         ->  Nested Loop  (cost=0.55..11611507.65 rows=34800 width=4) (actual time=0.590..1167.615 rows=28408 loops=1)               ->  Nested Loop  (cost=0.00..8357.50 rows=1665 width=64) (actual time=0.046..663.475 rows=1938 loops=1)                     Join Filter: (a.vert = (b.vert - 1))                     Rows Removed by Join Filter: 3755844                     ->  CTE Scan on pts b  (cost=0.00..22.50 rows=333 width=36) (actual time=0.042..0.433 rows=1938 loops=1)                           Filter: (vert > 1)                           Rows Removed by Filter: 1                     ->  CTE Scan on pts a  (cost=0.00..20.00 rows=1000 width=36) (actual time=0.000..0.149 rows=1939 loops=1938)               ->  Index Scan using location_gix on data  (cost=0.55..6968.85 rows=1 width=36) (actual time=0.085..0.256 rows=15 loops=1938)                     Index Cond: (location && _st_expand((st_makeline(ARRAY[a.geom, b.geom]))::geography, '600'::double precision))                     Filter: (((st_makeline(ARRAY[a.geom, b.geom]))::geography && _st_expand(location, '600'::double precision)) AND _st_dwithin(location, (st_makeline(ARRAY[a.geom, b.geom]))::geography, '600'::double precision, true))                     Rows Removed by Filter: 7 Planning time: 4.318 ms Execution time: 1171.994 ms(22 rows)So not bad. Went from 20+ seconds to a little over 1 second. Still noticeable for a end user, but defiantly usable - and like mentioned, that's a worst-case scenario query. Thanks!Of course, if you have any suggestions for further 

Re: [GENERAL] Improve PostGIS performance with 62 million rows?

[Rémi Cura]

Hey,
1 sec seems really good in this case,
and I'm assuming you tuned postgres so the main index fits into ram
(work_mem and all other stuff).

You could avoid a CTE by mixing both cte.

WITH pts AS (
SELECT (pt).geom, (pt).path[1] as vert
FROM
ST_DumpPoints(
ST_Segmentize(
ST_GeographyFromText('SRID=4326;LINESTRING(-150.008056
61.179167,-156.77 71.285833)'),
600
)::geometry
) as pt
)
SELECT elevation
FROM data
INNER JOIN (SELECT
ST_MakeLine(ARRAY[a.geom, b.geom]) as short_line
FROM pts a
INNER JOIN pts b
ON a.vert=b.vert-1 AND b.vert>1) segments
ON  ST_DWithin(location, segments.short_line, 600)
ORDER BY elevation DESC limit 1;


Then you could remove the useless and (potentially explosive if you have
large number of dump points) inner join on points :
"FROM pts a
INNER JOIN pts b "

You could simply use a window function to generate the segments, like in
here

.
The idea is to dump points, order them by path, and then link each point
with the previous one (function lag).
Assuming you don't want to use the available function,
this would be something like :



WITH segments AS (
SELECT ST_MakeLine( lag((pt).geom , 1, NULL) OVER (ORDER BY (pt).path)
  ,(pt).geom) AS short_line
FROM ST_DumpPoints(
  ST_Segmentize(
ST_GeographyFromText('SRID=4326;LINESTRING(-150.008056
61.179167,-156.77 71.285833)'),
600
)::geometry
) as pt
)
SELECT elevation
FROM data ,segments
WHERE segments.short_line IS NOT NULL --the first segment is null by design
(lag function)
  AND  ST_DWithin(location, segments.short_line, 600) = TRUE
ORDER BY elevation DESC
limit 1;


I don't know if you can further improve this query after that,
but I'll guess it would reduce your time and be more secure regarding
scaling.


if you want to further improve your result,
you'll have to reduce the number of row in your index,
that is partition your table into several tables !

This is not easy to do with current postgres partitionning methods as far
as I know
(partitionning is easy, automatic efficient query is hard).

Another way would be to reduce you requirement, and consider that in some
case you may want less details in the altimetry, which would allow you to
use a Level Of Detail approach.

Congrats for the well explained query/problem anyway !
Cheers,
Rémi-C

2017-01-05 23:09 GMT+01:00 Paul Ramsey :

> Varying the segment length upwards might have a salutary effect for a
> while, as the efficiency improvement of fewer inner loops battles with the
> inefficiency of having more points selected by the index filter. Worth an
> experiment.
>
> P
>
> On Thu, Jan 5, 2017 at 1:00 PM, Israel Brewster 
> wrote:
>
>>
>> On Jan 5, 2017, at 10:38 AM, Paul Ramsey 
>> wrote:
>>
>> Yes, you did. You want a query that spits out a tupleset of goemetries
>> (one each for each wee segment), and then you can join that set to your
>> main table using st_dwithin() as the join clause.
>> So start by ditching the main table and just work on a query that
>> generates a pile of wee segments.
>>
>>
>> Ahhh, I see you've done this sort of thing before (
>> http://blog.cleverelephant.ca/2015/02/breaking-linestring-
>> into-segments.html) :-)
>>
>> So following that advice I came up with the following query:
>>
>> WITH dump AS (SELECT
>> ST_DumpPoints(
>> ST_Segmentize(
>> ST_GeographyFromText('SRID=4326;LINESTRING(-150.008056
>> 61.179167,-156.77 71.285833)'),
>> 600
>> )::geometry
>> ) as pt
>> ),
>> pts AS (
>> SELECT (pt).geom, (pt).path[1] as vert FROM dump
>> )
>> SELECT elevation
>> FROM data
>> INNER JOIN (SELECT
>> ST_MakeLine(ARRAY[a.geom, b.geom]) as short_line
>> FROM pts a
>> INNER JOIN pts b
>> ON a.vert=b.vert-1 AND b.vert>1) segments
>> ON  ST_DWithin(location, segments.short_line, 600)
>> ORDER BY elevation DESC limit 1;
>>
>> Which yields the following EXPLAIN ANALYZE (https://explain.depesz.com/s/
>> RsTD ):
>>
>>
>>QUERY PLAN
>>
>>
>> 
>> 
>> 
>> 
>>  Limit  (cost=11611706.90..11611706.91 rows=1 width=4) (actual
>> time=1171.814..1171.814 rows=1 loops=1)
>>CTE dump
>>  ->  Result  (cost=0.00..5.25 rows=1000 width=32) (actual
>> time=0.024..1.989 rows=1939 loops=1)
>>CTE pts
>>  ->  CTE Scan on dump  (cost=0.00..20.00 rows=1000 width=36) (actual
>> time=0.032..4.071 rows=1939 loops=1)
>>->  Sort  (cost=11611681.65..11611768.65 rows=34800 width=4) (actual

Re: [GENERAL] Improve PostGIS performance with 62 million rows?

[Paul Ramsey]

Varying the segment length upwards might have a salutary effect for a
while, as the efficiency improvement of fewer inner loops battles with the
inefficiency of having more points selected by the index filter. Worth an
experiment.

P

On Thu, Jan 5, 2017 at 1:00 PM, Israel Brewster 
wrote:

>
> On Jan 5, 2017, at 10:38 AM, Paul Ramsey 
> wrote:
>
> Yes, you did. You want a query that spits out a tupleset of goemetries
> (one each for each wee segment), and then you can join that set to your
> main table using st_dwithin() as the join clause.
> So start by ditching the main table and just work on a query that
> generates a pile of wee segments.
>
>
> Ahhh, I see you've done this sort of thing before (
> http://blog.cleverelephant.ca/2015/02/breaking-
> linestring-into-segments.html) :-)
>
> So following that advice I came up with the following query:
>
> WITH dump AS (SELECT
> ST_DumpPoints(
> ST_Segmentize(
> ST_GeographyFromText('SRID=4326;LINESTRING(-150.008056
> 61.179167,-156.77 71.285833)'),
> 600
> )::geometry
> ) as pt
> ),
> pts AS (
> SELECT (pt).geom, (pt).path[1] as vert FROM dump
> )
> SELECT elevation
> FROM data
> INNER JOIN (SELECT
> ST_MakeLine(ARRAY[a.geom, b.geom]) as short_line
> FROM pts a
> INNER JOIN pts b
> ON a.vert=b.vert-1 AND b.vert>1) segments
> ON  ST_DWithin(location, segments.short_line, 600)
> ORDER BY elevation DESC limit 1;
>
> Which yields the following EXPLAIN ANALYZE (https://explain.depesz.com/s/
> RsTD ):
>
>
>QUERY PLAN
>
>
> 
> 
> 
> 
>  Limit  (cost=11611706.90..11611706.91 rows=1 width=4) (actual
> time=1171.814..1171.814 rows=1 loops=1)
>CTE dump
>  ->  Result  (cost=0.00..5.25 rows=1000 width=32) (actual
> time=0.024..1.989 rows=1939 loops=1)
>CTE pts
>  ->  CTE Scan on dump  (cost=0.00..20.00 rows=1000 width=36) (actual
> time=0.032..4.071 rows=1939 loops=1)
>->  Sort  (cost=11611681.65..11611768.65 rows=34800 width=4) (actual
> time=1171.813..1171.813 rows=1 loops=1)
>  Sort Key: data.elevation DESC
>  Sort Method: top-N heapsort  Memory: 25kB
>  ->  Nested Loop  (cost=0.55..11611507.65 rows=34800 width=4)
> (actual time=0.590..1167.615 rows=28408 loops=1)
>->  Nested Loop  (cost=0.00..8357.50 rows=1665 width=64)
> (actual time=0.046..663.475 rows=1938 loops=1)
>  Join Filter: (a.vert = (b.vert - 1))
>  Rows Removed by Join Filter: 3755844
>  ->  CTE Scan on pts b  (cost=0.00..22.50 rows=333
> width=36) (actual time=0.042..0.433 rows=1938 loops=1)
>Filter: (vert > 1)
>Rows Removed by Filter: 1
>  ->  CTE Scan on pts a  (cost=0.00..20.00 rows=1000
> width=36) (actual time=0.000..0.149 rows=1939 loops=1938)
>->  Index Scan using location_gix on
> data  (cost=0.55..6968.85 rows=1 width=36) (actual time=0.085..0.256
> rows=15 loops=1938)
>  Index Cond: (location &&
> _st_expand((st_makeline(ARRAY[a.geom, b.geom]))::geography, '600'::double
> precision))
>  Filter: (((st_makeline(ARRAY[a.geom,
> b.geom]))::geography && _st_expand(location, '600'::double precision)) AND
> _st_dwithin(location, (st_makeline(ARRAY[a.geom,
> b.geom]))::geography, '600'::double precision, true))
>  Rows Removed by Filter: 7
>  Planning time: 4.318 ms
>  Execution time: 1171.994 ms
> (22 rows)
>
> So not bad. Went from 20+ seconds to a little over 1 second. Still
> noticeable for a end user, but defiantly usable - and like mentioned,
> that's a worst-case scenario query. Thanks!
>
> Of course, if you have any suggestions for further improvement, I'm all
> ears :-)
> ---
> Israel Brewster
> Systems Analyst II
> Ravn Alaska
> 5245 Airport Industrial Rd
> Fairbanks, AK 99709
> (907) 450-7293
> ---
>
>
> On Thu, Jan 5, 2017 at 11:36 AM, Israel Brewster 
> wrote:
>
>> On Jan 5, 2017, at 8:50 AM, Paul Ramsey 
>> wrote:
>>
>>
>> The index filters using bounding boxes.  A long, diagonal route will have
>> a large bounding box, relative to the area you actually care about (within
>> a narrow strip of the route). Use ST_Segmentize() to add points to your
>> route, ST_DumpPoints() to dump those out as point and ST_MakeLine to
>> generate new lines from those points, each line very short. The maximum
>> index effectiveness will come when your line length is close to your 

Re: [GENERAL] Improve PostGIS performance with 62 million rows?

[Israel Brewster]

> On Jan 5, 2017, at 10:38 AM, Paul Ramsey  wrote:
> 
> Yes, you did. You want a query that spits out a tupleset of goemetries (one 
> each for each wee segment), and then you can join that set to your main table 
> using st_dwithin() as the join clause.
> So start by ditching the main table and just work on a query that generates a 
> pile of wee segments.

Ahhh, I see you've done this sort of thing before 
(http://blog.cleverelephant.ca/2015/02/breaking-linestring-into-segments.html 
) 
:-)

So following that advice I came up with the following query:

WITH dump AS (SELECT
ST_DumpPoints(
ST_Segmentize(
ST_GeographyFromText('SRID=4326;LINESTRING(-150.008056 
61.179167,-156.77 71.285833)'),
600
)::geometry
) as pt
),
pts AS (
SELECT (pt).geom, (pt).path[1] as vert FROM dump
)
SELECT elevation 
FROM data 
INNER JOIN (SELECT 
ST_MakeLine(ARRAY[a.geom, b.geom]) as short_line
FROM pts a 
INNER JOIN pts b 
ON a.vert=b.vert-1 AND b.vert>1) segments
ON  ST_DWithin(location, segments.short_line, 600)
ORDER BY elevation DESC limit 1;

Which yields the following EXPLAIN ANALYZE (https://explain.depesz.com/s/RsTD 
):


 QUERY PLAN 


 Limit  (cost=11611706.90..11611706.91 rows=1 width=4) (actual 
time=1171.814..1171.814 rows=1 loops=1)
   CTE dump
 ->  Result  (cost=0.00..5.25 rows=1000 width=32) (actual time=0.024..1.989 
rows=1939 loops=1)
   CTE pts
 ->  CTE Scan on dump  (cost=0.00..20.00 rows=1000 width=36) (actual 
time=0.032..4.071 rows=1939 loops=1)
   ->  Sort  (cost=11611681.65..11611768.65 rows=34800 width=4) (actual 
time=1171.813..1171.813 rows=1 loops=1)
 Sort Key: data.elevation DESC
 Sort Method: top-N heapsort  Memory: 25kB
 ->  Nested Loop  (cost=0.55..11611507.65 rows=34800 width=4) (actual 
time=0.590..1167.615 rows=28408 loops=1)
   ->  Nested Loop  (cost=0.00..8357.50 rows=1665 width=64) (actual 
time=0.046..663.475 rows=1938 loops=1)
 Join Filter: (a.vert = (b.vert - 1))
 Rows Removed by Join Filter: 3755844
 ->  CTE Scan on pts b  (cost=0.00..22.50 rows=333 
width=36) (actual time=0.042..0.433 rows=1938 loops=1)
   Filter: (vert > 1)
   Rows Removed by Filter: 1
 ->  CTE Scan on pts a  (cost=0.00..20.00 rows=1000 
width=36) (actual time=0.000..0.149 rows=1939 loops=1938)
   ->  Index Scan using location_gix on data  (cost=0.55..6968.85 
rows=1 width=36) (actual time=0.085..0.256 rows=15 loops=1938)
 Index Cond: (location && 
_st_expand((st_makeline(ARRAY[a.geom, b.geom]))::geography, '600'::double 
precision))
 Filter: (((st_makeline(ARRAY[a.geom, b.geom]))::geography 
&& _st_expand(location, '600'::double precision)) AND _st_dwithin(location, 
(st_makeline(ARRAY[a.geom, b.geom]))::geography, '600'::double precision, true))
 Rows Removed by Filter: 7
 Planning time: 4.318 ms
 Execution time: 1171.994 ms
(22 rows)

So not bad. Went from 20+ seconds to a little over 1 second. Still noticeable 
for a end user, but defiantly usable - and like mentioned, that's a worst-case 
scenario query. Thanks!

Of course, if you have any suggestions for further improvement, I'm all ears :-)
---
Israel Brewster
Systems Analyst II
Ravn Alaska
5245 Airport Industrial Rd
Fairbanks, AK 99709
(907) 450-7293
---

> 
> On Thu, Jan 5, 2017 at 11:36 AM, Israel Brewster  > wrote:
> On Jan 5, 2017, at 8:50 AM, Paul Ramsey  > wrote:
>> 
>> The index filters using bounding boxes.  A long, diagonal route will have a 
>> large bounding box, relative to the area you actually care about (within a 
>> narrow strip of the route). Use ST_Segmentize() to add points to your route, 
>> ST_DumpPoints() to dump those out as point and ST_MakeLine to generate new 
>> lines from those points, each line very short. The maximum index 
>> effectiveness will come when your line length is close to your buffer width.
>> 
>> P
> 
> Ok, I think I understand the concept. So attempting to follow your advice, I 
> modified the query to be:
> 
> SELECT 

Re: [GENERAL] Improve PostGIS performance with 62 million rows?

[Paul Ramsey]

Yes, you did. You want a query that spits out a tupleset of goemetries (one
each for each wee segment), and then you can join that set to your main
table using st_dwithin() as the join clause.
So start by ditching the main table and just work on a query that generates
a pile of wee segments.

On Thu, Jan 5, 2017 at 11:36 AM, Israel Brewster 
wrote:

> On Jan 5, 2017, at 8:50 AM, Paul Ramsey  wrote:
>
>
> The index filters using bounding boxes.  A long, diagonal route will have
> a large bounding box, relative to the area you actually care about (within
> a narrow strip of the route). Use ST_Segmentize() to add points to your
> route, ST_DumpPoints() to dump those out as point and ST_MakeLine to
> generate new lines from those points, each line very short. The maximum
> index effectiveness will come when your line length is close to your buffer
> width.
>
> P
>
>
> Ok, I think I understand the concept. So attempting to follow your advice,
> I modified the query to be:
>
> SELECT elevation
> FROM data
> WHERE
> ST_DWithin(
> location,
> (SELECT ST_MakeLine(geom)::geography as split_line
>  FROM (SELECT
> (ST_DumpPoints(
> ST_Segmentize(
> ST_GeographyFromText('SRID=4326;LINESTRING(-150.008056
> 61.179167,-156.77 71.285833)'),
> 600
> )::geometry
> )).geom
> ) s1),
> 600
> )
> ORDER BY elevation DESC limit 1;
>
> It took some fiddling to find a syntax that Postgresql would accept, but
> eventually that's what I came up with. Unfortunately, far from improving
> performance, it killed it - in running the query, it went from 22 seconds
> to several minutes (EXPLAIn ANALYZE has yet to return a result). Looking at
> the query execution plan shows, at least partially, why:
>
>   QUERY PLAN
>
> 
> --
>  Limit  (cost=17119748.98..17119748.98 rows=1 width=4)
>InitPlan 1 (returns $0)
>  ->  Aggregate  (cost=17.76..17.77 rows=1 width=32)
>->  Result  (cost=0.00..5.25 rows=1000 width=32)
>->  Sort  (cost=17119731.21..17171983.43 rows=20900890 width=4)
>  Sort Key: data.elevation DESC
>  ->  Seq Scan on data  (cost=0.00..17015226.76 rows=20900890
> width=4)
>Filter: st_dwithin(location, $0, '600'::double precision)
> (8 rows)
>
> So apparently it is now doing a sequential scan on data rather than using
> the index. And, of course, sorting 20 million rows is not trivial either.
> Did I do something wrong with forming the query?
>
> ---
> Israel Brewster
> Systems Analyst II
> Ravn Alaska
> 5245 Airport Industrial Rd
> Fairbanks, AK 99709
> (907) 450-7293
> ---
>
>
> On Thu, Jan 5, 2017 at 9:45 AM, Israel Brewster 
> wrote:
>
>> I have a database (PostgreSQL 9.6.1) containing 62,702,675 rows of
>> latitude (numeric), longitude(numeric), elevation(integer) data, along with
>> a PostGIS (2.3.0) geometry column (location), running on a CentOS 6.8 box
>> with 64GB RAM and a RAID10 SSD data drive. I'm trying to get the maximum
>> elevation along a path, for which purpose I've come up with the following
>> query (for one particular path example):
>>
>> SELECT elevation FROM data
>>
>>
>>
>>
>> WHERE ST_DWithin(location, 
>> ST_GeographyFromText('SRID=4326;LINESTRING(-150.008056
>> 61.179167,-156.77 71.285833)'), 600)
>>
>>
>>
>>   ORDER BY elevation LIMIT 1;
>>
>> The EXPLAIN ANALYZE output of this particular query (
>> https://explain.depesz.com/s/heZ) shows:
>>
>>
>>
>> QUERY PLAN
>>
>>
>> 
>> 
>> 
>> 
>> 
>> --
>>  Limit  (cost=4.83..4.83 rows=1 width=4) (actual
>> time=22653.840..22653.842 rows=1 loops=1)
>>->  Sort  (cost=4.83..4.83 rows=1 width=4) (actual
>> time=22653.837..22653.837 rows=1 loops=1)
>>  Sort Key: elevation DESC
>>  Sort Method: top-N heapsort  Memory: 25kB
>>  ->  Index Scan using location_gix on data  (cost=0.42..4.82
>> rows=1 width=4) (actual time=15.786..22652.041 rows=11081 loops=1)
>>Index Cond: (location && '010220E61002002C1
>> 1A8FE41C062C0DFC2BAF1EE964E40713D0AD7A39863C086C77E164BD2514
>> 0'::geography)
>>Filter: (('010220E61002002
>> C11A8FE41C062C0DFC2BAF1EE964E40713D0AD7A39863C086C77E164BD25140'::geography
>> && _st_expand(location, '600'::double precision)) AND
>> _st_dwithin(location, 

Re: [GENERAL] Improve PostGIS performance with 62 million rows?

[Israel Brewster]

On Jan 5, 2017, at 8:50 AM, Paul Ramsey  wrote:
> 
> The index filters using bounding boxes.  A long, diagonal route will have a 
> large bounding box, relative to the area you actually care about (within a 
> narrow strip of the route). Use ST_Segmentize() to add points to your route, 
> ST_DumpPoints() to dump those out as point and ST_MakeLine to generate new 
> lines from those points, each line very short. The maximum index 
> effectiveness will come when your line length is close to your buffer width.
> 
> P

Ok, I think I understand the concept. So attempting to follow your advice, I 
modified the query to be:

SELECT elevation
FROM data
WHERE
ST_DWithin(
location,
(SELECT ST_MakeLine(geom)::geography as split_line
 FROM (SELECT
(ST_DumpPoints(
ST_Segmentize(
ST_GeographyFromText('SRID=4326;LINESTRING(-150.008056 
61.179167,-156.77 71.285833)'),
600
)::geometry
)).geom
) s1),
600
)
ORDER BY elevation DESC limit 1;

It took some fiddling to find a syntax that Postgresql would accept, but 
eventually that's what I came up with. Unfortunately, far from improving 
performance, it killed it - in running the query, it went from 22 seconds to 
several minutes (EXPLAIn ANALYZE has yet to return a result). Looking at the 
query execution plan shows, at least partially, why:

  QUERY PLAN  
--
 Limit  (cost=17119748.98..17119748.98 rows=1 width=4)
   InitPlan 1 (returns $0)
 ->  Aggregate  (cost=17.76..17.77 rows=1 width=32)
   ->  Result  (cost=0.00..5.25 rows=1000 width=32)
   ->  Sort  (cost=17119731.21..17171983.43 rows=20900890 width=4)
 Sort Key: data.elevation DESC
 ->  Seq Scan on data  (cost=0.00..17015226.76 rows=20900890 width=4)
   Filter: st_dwithin(location, $0, '600'::double precision)
(8 rows)

So apparently it is now doing a sequential scan on data rather than using the 
index. And, of course, sorting 20 million rows is not trivial either. Did I do 
something wrong with forming the query?

---
Israel Brewster
Systems Analyst II
Ravn Alaska
5245 Airport Industrial Rd
Fairbanks, AK 99709
(907) 450-7293
---

> 
> On Thu, Jan 5, 2017 at 9:45 AM, Israel Brewster  > wrote:
> I have a database (PostgreSQL 9.6.1) containing 62,702,675 rows of latitude 
> (numeric), longitude(numeric), elevation(integer) data, along with a PostGIS 
> (2.3.0) geometry column (location), running on a CentOS 6.8 box with 64GB RAM 
> and a RAID10 SSD data drive. I'm trying to get the maximum elevation along a 
> path, for which purpose I've come up with the following query (for one 
> particular path example):
> 
> SELECT elevation FROM data
>   
>   
>   
>   
> WHERE ST_DWithin(location, 
> ST_GeographyFromText('SRID=4326;LINESTRING(-150.008056 61.179167,-156.77 
> 71.285833)'), 600)
>   
>   
>   ORDER BY elevation 
> LIMIT 1;
> 
> The EXPLAIN ANALYZE output of this particular query 
> (https://explain.depesz.com/s/heZ ) shows:
> 
>   
>   
>   QUERY PLAN  
>   
>   
> --
>  Limit  (cost=4.83..4.83 rows=1 width=4) (actual time=22653.840..22653.842 
> rows=1 loops=1)
>->  Sort  (cost=4.83..4.83 rows=1 width=4) (actual 
> time=22653.837..22653.837 rows=1 loops=1)
>  Sort Key: elevation DESC
>  Sort Method: top-N heapsort  Memory: 25kB
>  ->  

Re: [GENERAL] Improve PostGIS performance with 62 million rows?

[Paul Ramsey]

The index filters using bounding boxes.  A long, diagonal route will have a
large bounding box, relative to the area you actually care about (within a
narrow strip of the route). Use ST_Segmentize() to add points to your
route, ST_DumpPoints() to dump those out as point and ST_MakeLine to
generate new lines from those points, each line very short. The maximum
index effectiveness will come when your line length is close to your buffer
width.

P

On Thu, Jan 5, 2017 at 9:45 AM, Israel Brewster 
wrote:

> I have a database (PostgreSQL 9.6.1) containing 62,702,675 rows of
> latitude (numeric), longitude(numeric), elevation(integer) data, along with
> a PostGIS (2.3.0) geometry column (location), running on a CentOS 6.8 box
> with 64GB RAM and a RAID10 SSD data drive. I'm trying to get the maximum
> elevation along a path, for which purpose I've come up with the following
> query (for one particular path example):
>
> SELECT elevation FROM data
>
>
>
>
> WHERE ST_DWithin(location, 
> ST_GeographyFromText('SRID=4326;LINESTRING(-150.008056
> 61.179167,-156.77 71.285833)'), 600)
>
>
>
>   ORDER BY elevation LIMIT 1;
>
> The EXPLAIN ANALYZE output of this particular query (
> https://explain.depesz.com/s/heZ) shows:
>
>
>
> QUERY PLAN
>
>
> 
> 
> 
> 
> 
> --
>  Limit  (cost=4.83..4.83 rows=1 width=4) (actual time=22653.840..22653.842
> rows=1 loops=1)
>->  Sort  (cost=4.83..4.83 rows=1 width=4) (actual
> time=22653.837..22653.837 rows=1 loops=1)
>  Sort Key: elevation DESC
>  Sort Method: top-N heapsort  Memory: 25kB
>  ->  Index Scan using location_gix on data  (cost=0.42..4.82
> rows=1 width=4) (actual time=15.786..22652.041 rows=11081 loops=1)
>Index Cond: (location && '010220E61002002C11
> A8FE41C062C0DFC2BAF1EE964E40713D0AD7A39863C086C77E164BD25140'::geography)
>Filter: (('010220E61002002C11
> A8FE41C062C0DFC2BAF1EE964E40713D0AD7A39863C086C77E164BD25140'::geography
> && _st_expand(location, '600'::double precision)) AND
> _st_dwithin(location, '010220E61002002C11
> A8FE41C062C0DFC2BAF1EE964E40713D0AD7A39863C086C77E164BD25140'::geography,
> '600'::double precision, true))
>Rows Removed by Filter: 4934534
>  Planning time: 0.741 ms
>  Execution time: 22653.906 ms
> (10 rows)
>
> So it is using the index properly, but still takes a good 22 seconds to
> run, most of which appears to be in the Index Scan.
>
> Is there any way to improve this, or is this going to be about as good as
> it gets with the number of rows being dealt with? I was planning to use
> this for a real-time display - punch in a couple of points, get some
> information about the route between, including maximum elevation - but with
> it taking 22 seconds for the longer routes at least, that doesn't make for
> the best user experience.
>
> It's perhaps worth noting that the example above is most likely a worst
> case scenario. I would expect the vast majority of routes to be
> significantly shorter, and I want to say the shorter routes query much
> faster [testing needed]. That said, the faster the better, even for short
> routes :-)
> ---
> Israel Brewster
> Systems Analyst II
> Ravn Alaska
> 5245 Airport Industrial Rd
> Fairbanks, AK 99709
> (907) 450-7293
> ---
>
>
>
>
>
>

[GENERAL] Improve PostGIS performance with 62 million rows?

[Israel Brewster]

I have a database (PostgreSQL 9.6.1) containing 62,702,675 rows of latitude (numeric), longitude(numeric), elevation(integer) data, along with a PostGIS (2.3.0) geometry column (location), running on a CentOS 6.8 box with 64GB RAM and a RAID10 SSD data drive. I'm trying to get the maximum elevation along a path, for which purpose I've come up with the following query (for one particular path example):SELECT elevation FROM data                                                                                                                                                                                                                                                                                                                                                                                WHERE ST_DWithin(location, ST_GeographyFromText('SRID=4326;LINESTRING(-150.008056 61.179167,-156.77 71.285833)'), 600)                                                                                                                                                                                                                                                                              ORDER BY elevation LIMIT 1;The EXPLAIN ANALYZE output of this particular query (https://explain.depesz.com/s/heZ) shows:                                                                                                                                                                      QUERY PLAN                                                                                                                                                                      -- Limit  (cost=4.83..4.83 rows=1 width=4) (actual time=22653.840..22653.842 rows=1 loops=1)   ->  Sort  (cost=4.83..4.83 rows=1 width=4) (actual time=22653.837..22653.837 rows=1 loops=1)         Sort Key: elevation DESC         Sort Method: top-N heapsort  Memory: 25kB         ->  Index Scan using location_gix on data  (cost=0.42..4.82 rows=1 width=4) (actual time=15.786..22652.041 rows=11081 loops=1)               Index Cond: (location && '010220E61002002C11A8FE41C062C0DFC2BAF1EE964E40713D0AD7A39863C086C77E164BD25140'::geography)               Filter: (('010220E61002002C11A8FE41C062C0DFC2BAF1EE964E40713D0AD7A39863C086C77E164BD25140'::geography && _st_expand(location, '600'::double precision)) AND _st_dwithin(location, '010220E61002002C11A8FE41C062C0DFC2BAF1EE964E40713D0AD7A39863C086C77E164BD25140'::geography, '600'::double precision, true))               Rows Removed by Filter: 4934534 Planning time: 0.741 ms Execution time: 22653.906 ms(10 rows)So it is using the index properly, but still takes a good 22 seconds to run, most of which appears to be in the Index Scan.Is there any way to improve this, or is this going to be about as good as it gets with the number of rows being dealt with? I was planning to use this for a real-time display - punch in a couple of points, get some information about the route between, including maximum elevation - but with it taking 22 seconds for the longer routes at least, that doesn't make for the best user experience.It's perhaps worth noting that the example above is most likely a worst case scenario. I would expect the vast majority of routes to be significantly shorter, and I want to say the shorter routes query much faster [testing needed]. That said, the faster the better, even for short routes :-)
---Israel BrewsterSystems Analyst IIRavn Alaska5245 Airport Industrial RdFairbanks, AK 99709(907) 450-7293---BEGIN:VCARD
VERSION:3.0
N:Brewster;Israel;;;
FN:Israel Brewster
ORG:Frontier Flying Service;MIS
TITLE:PC Support Tech II
EMAIL;type=INTERNET;type=WORK;type=pref:isr...@frontierflying.com
TEL;type=WORK;type=pref:907-450-7293
item1.ADR;type=WORK;type=pref:;;5245 Airport Industrial Wy;Fairbanks;AK;99701;
item1.X-ABADR:us
CATEGORIES:General
X-ABUID:36305438-95EA-4410-91AB-45D16CABCDDC\:ABPerson
END:VCARD