[postgis-users] Improve 3D BB query on large set of lines
Rémi Cura
remi.cura at gmail.com
Thu Jan 19 06:40:47 PST 2017
Hey,
This is an interesting problem for sure.
AS always in the "slow query" topic,
improvement can come from a number of places
- hardware : are your tables on SSD, does your index fit in RAM
- postgres tuning : you have customised postgresql.conf to fit your
hardware
- query writing : your query is optimally written (looks fine here)
- data model (detailed after)
IF you want fast results, you need a good indexing, and this is possible if
you exploit the sparsness of your data.
So where is the sparsness?
* Scenario A.1: your usual slices affects a majority of trees
- you don't really need an index on X,Y, but mainly on Z.
The idea is to separate your index into index(X,Y) + index(Z)
Try adding an index on GIST(numrange(ST_Zmin,ST_ZMax)) (you could add
it anyway)
and an index on GIST(geom) (aka only 2D).
* Scenario A.2 : your usual slices affect few trees
- you could easily perform a 2 part query, where the first part use an
index on tree bounding box, and the second perform the query on edge. This
require to create and maintain a tree_bounding_box table with appropriate
indexes, which is quite easy with triggers Ex (not optimised):
WITH filtering_by_tree AS (
SELECT distinct tree_id
FROM my_sync_table_of_tree_bounding_boxes as tree
WHERE tree.bbox &&& your_slice
) SELECT edge_id
FROM treenode_edge AS te
WHERE te.geom &&& your_slice
AND EXISTS (SELECT 1 FROM filtering_by_tree As ft WHERE ft.tree_id =
te.tree_id)
- you could use pgpointcloud to similar effect by construction a patch
per tree.
Now something is very ambiguous in your data, and it could have major
consequences :
* Are the edges between nodes that represent neuron __only logical__ (they
are straight lines, always the same Zmax-Zmin, and represent a conceptual
graph), or are they polylines that represent the __actual__ neurone path?
* Scenario B.1.1 : edges are only logical and have a constant Zmax-Zmin,
i.e. nodes have quantified Z :
- you don't need edges at all for indexing, you should work only on nodes
(i.e. index nodes IN Z + (X,Y), find nodes, then find relevant edges). The
great advantage in this case is that you can use the full power of
pgpointcloud to create meaningfull patches (of variables size for instance,
see this
<http://www.sciencedirect.com/science/article/pii/S092427161630123X>, 3.1,
Adapt patch grouping rules ), which means scalling in the range of billions
of points possible.
- you could try to think of using the dual of your current graph (edges
become nodes, nodes become edges).
* Scenario B.1.2 : edges are only logical, but nodes don't have a constant
Zmax-Zmin. : you could insert (virtual) nodes so you are again in the same
case as scenario B.1.1
* Scenario B.2 : edges are actual neurone path.
- you could construct intermediary objects (groups of neurone) of
variable size (those within cubes), so as to have again a 2 steps query :
first look for groups of neuron, then for neurons inside
* Scenario C : your data don't have obvious sparsity.
- use the same strategy you currently use, but scale by partitionning
your data into numerous tables.
You can see the partitionning entry of the postgres manual.
<https://www.postgresql.org/docs/current/static/ddl-partitioning.html>
This would be much easier to create and maintain if you could partition
on only Z dimension.
Note that you will need to duplicate (and then de-duplicate) some edges.
It's hard to recommend anything without further information on your
data/your requirement/your ressources.
Cheers,
Rémi-C
2017-01-17 22:43 GMT+01:00 Tom Kazimiers <tom at voodoo-arts.net>:
> Hi all,
>
> I am using Postgres 9.6 with PostGIS 2.3 and want to improve the
> performance of a bounding box intersection query on a large set of lines. I
> asked the same questions two month ago on gis.stackexchange.com, but
> didn't get any reply. Therefore, please excuse me for cross-posting, but I
> figured this list would be a good place to ask. This is the original post:
>
> https://gis.stackexchange.com/questions/215762
>
> Problem: We store about 6 million lines/edges in a 3D space and use an axis
> aligned bounding box to find all intersecting (and included) lines. This
> works
> well, but we want to improve the query time for larger point sets.
>
> Context: Tree-like 3D structures represent neurons, each node has a parent
> node
> or it is the root. At the moment we deal with about 15 million nodes,
> grouped
> into 150000 trees (many > 10000 nodes). I want to improve existing
> performance
> bottlenecks with bigger result sets plus I plan to scale this setup to
> 10-100x
> the nodes. Typical query bounding boxes are rather slices than boxes, i.e.
> they expand any range in XY, but only a short distance in Z, but it could
> in principal any dimension that is "flat".
>
> Setup: The table storing edges looks like this:
>
> =>\d+ treenode_edge
> Tabelle »public.treenode_edge«
> Spalte | Typ | Attribute
> ------------+-----------------------+-----------
> id | bigint | not null
> project_id | integer | not null
> edge | geometry(LineStringZ) | Indexe:
> "treenode_edge_pkey" PRIMARY KEY, btree (id)
> "treenode_edge_gix" gist (edge gist_geometry_ops_nd)
> "treenode_edge_project_id_index" btree (project_id)
>
> Note that there is a 3D index for the edge column in place
> (treenode_edge_gix).
>
> Current timing: To request 2000 nodes with an typical bounding box size I
> use
> the following query:
>
> SELECT te.id
> FROM treenode_edge te
> -- left
> bottom z2, right top z1 WHERE te.edge &&& 'LINESTRINGZ( -537284.0
> 699984.0 84770.0,
>
> 1456444.0 -128432.0 84735.0)'
> -- left top halfz, right top halfz,
> -- right bottom halfz, left bottom
> halfz,
> -- left top halfz; halfz (distance)
> AND ST_3DDWithin(te.edge, ST_MakePolygon(ST_GeomFromText(
> 'LINESTRING( -537284.0 -128432.0 84752.5, 1456444.0 -128432.0
> 84752.5,
> 1456444.0 699984.0 84752.5, -537284.0
> 699984.0 84752.5,
> -537284.0 -128432.0 84752.5)')), 17.5)
> AND te.project_id = 1
> LIMIT 2000;
>
> This takes about 900ms, statistics are up-to-date. This is not so bad, but
> I look for strategies to make this much faster still. The &&& operator
> filters already most of the existing edges by bounding box test (using
> index) so that ST_3DWithin only needs to check the edges that are most
> likely part of the result.
>
> The query plan looks like this:
>
> Limit (cost=48.26..4311.24 rows=70 width=8) (actual
> time=856.261..864.208 rows=2000 loops=1)
> Buffers: shared hit=20470
> -> Bitmap Heap Scan on treenode_edge te (cost=48.26..4311.24
> rows=70 width=8) (actual time=856.257..863.974 rows=2000 loops=1)
> Recheck Cond: (edge &&& '01020000800200000000000000886
> 520C100000000A05C25410000000020B2F440000000003C3936410000000
> 0005BFFC000000000F0AFF440'::geometry)
> Filter: ((edge && '01030000800100000005000000000
> 00000AB6520C100000000185CFFC000000000F0AFF44000000000AB6520C
> 100000000C35C254100000000F0AFF440000000804D39364100000000C35
> C25410000000020B2F440000000804D39364100000000185CFFC00000000
> 020B2F44000000000AB6520C100000000185CFFC000000000F0AFF440'::geometry) AND
> (project_id = 1) AND ('0103000080010000000500000000
> 000000886520C100000000005BFFC00000000008B1F440000000003C3936
> 4100000000005BFFC00000000008B1F440000000003C39364100000000A0
> 5C25410000000008B1F44000000000886520C100000000A05C2541000000
> 0008B1F44000000000886520C100000000005BFFC00000000008B1F440'::geometry &&
> st_expand(edge, '17.5'::double precision)) AND _st_3ddwithin(edge,
> '0103000080010000000500000000000000886520C100000000005BFFC00
> 000000008B1F440000000003C39364100000000005BFFC00000000008B1F
> 440000000003C39364100000000A05C25410000000008B1F440000000008
> 86520C100000000A05C25410000000008B1F44000000000886520C100000
> 000005BFFC00000000008B1F440'::geometry, '17.5'::double precision))
> Heap Blocks: exact=1816
> Buffers: shared hit=20470
> -> Bitmap Index Scan on treenode_edge_gix
> (cost=0.00..48.25 rows=1044 width=0) (actual time=855.795..855.795
> rows=2856 loops=1)
> Index Cond: (edge &&&
> '01020000800200000000000000886520C100000000A05C2541000000002
> 0B2F440000000003C39364100000000005BFFC000000000F0AFF440'::geometry)
> Buffers: shared hit=18654
> Planning time: 3.467 ms
> Execution time: 864.614 ms
>
> This shows clearly that the big bounding box test on all existing edges is
> the
> problem, though it already makes use of the GiST index. Are there maybe
> other indices available that can discard many far-away bounding boxes
> without looking at them, i.e. that useses a hierarchy like an octree? So
> far I didn't find any, but it looked like the SP-GiST interface might be
> useful if one were to create a new one.
>
> Alternatives: There is one approach that I currently implement to improve
> this,
> but I'd like to hear if someone has alternative, possibly better or other
> suggestions?
>
> This is what I tried: create a new table that would partition the space
> into many cubes and store for each all intersecting edges. New, updated or
> deleted nodes would then lead to an update of the relevant cubes, something
> that would probably have to run asynchronously (didn't implement the
> update yet). Queries then pre-filtered the space by only looking at
> relevant cubes instead of using the &&& operator with the bounding box of
> each edge. For a range of cubse sizes this improved query time quite a bit,
> the above query would run in about a third of thet time above. However, I
> realize cell sizes will behave differently for different dataset, but I
> didn't try to implement a dynamic cube size update, yet. Thinking about
> it, it makes me wonder if it wouldn't be best if I would implement a
> octree---table based or as an extension.
>
> Now I also saw the pointcloud extension and its indexing looked very
> interesting (octree), but I couldn't really see how it could work in my
> use case. But I would appreciate ideas if you see a way to make it work.
>
> Best,
> Tom
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