Re: Improve PostGIS performance with 62 million rows?

> On Jan 5, 2017, at 10:38 AM, Paul Ramsey <pramsey(at)cleverelephant(dot)ca> 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 <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 <https://explain.depesz.com/s/ukwc>):

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 <israel(at)ravnalaska(dot)net <mailto:israel(at)ravnalaska(dot)net>> wrote:
> On Jan 5, 2017, at 8:50 AM, Paul Ramsey <pramsey(at)cleverelephant(dot)ca <mailto:pramsey(at)cleverelephant(dot)ca>> 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 <tel:(907)%20450-7293>
> -----------------------------------------------
>
>>
>> On Thu, Jan 5, 2017 at 9:45 AM, Israel Brewster <israel(at)ravnalaska(dot)net <mailto:israel(at)ravnalaska(dot)net>> 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 <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 && '0102000020E6100000020000002C11A8FE41C062C0DFC2BAF1EE964E40713D0AD7A39863C086C77E164BD25140'::geography)
>> Filter: (('0102000020E6100000020000002C11A8FE41C062C0DFC2BAF1EE964E40713D0AD7A39863C086C77E164BD25140'::geography && _st_expand(location, '600'::double precision)) AND _st_dwithin(location, '0102000020E6100000020000002C11A8FE41C062C0DFC2BAF1EE964E40713D0AD7A39863C086C77E164BD25140'::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 <tel:(907)%20450-7293>
>> -----------------------------------------------
>>
>>
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