PostGIS : improving the speed of query over complex geometry

Question

I am new to PostgreSQL and PostGIS but the question is not trivial. I am using PostgreSQL 9.5 with PostGIS 2.2.

I need to run some queries that take a horrible amount of time.

First, let me explain the problem in non-GIS terms :
Basically, I have a set of several hundreds of thousands of points spread over a territory of about half a million square kilometres a (country). Over this territory, I have about a dozen sets of areas coming from various databases. In each set, I have between a few hundreds and a few thousands of areas. I want to find which points are in any of these areas.

Now, how I am currently working out the problem in GIS terms :

Each set of areas is a Postgresql table with a geometry column of the type multipolygon and with, as explained before a few hundreds to a few thousand records.
All these tables are contained in a schema donnees but I am using a different schema for these operations, called traitements.

So the process is a/ merging all the geometries into a single geometry, and then b/ finding which points are contained in this geometry.

The problem is that, if step a/ took a reasonable amount of time (several minutes), step b/ takes forever. I am currently working with only a sample of the points I must process (about 1% of them, i.e. about 7000) and it is not finished after several hours (the database connection eventually times out). I am making tests running the query by limiting the number of return rows to 10 or 50 and it still takes about half an hour for that.
I am using a Linux Mint 18 machine with 4 CPU and 8 Gb of RAM if you wonder.

I have created indexes on the geometry columns. All geometry columns use the same SRID.

Creating the tables :

CREATE TABLE traitements.sites_candidats (
    pkid serial PRIMARY KEY,
    statut varchar(255) NOT NULL,
    geom geometry(Point, 2154)
); 

CREATE UNIQUE INDEX ON traitements.sites_candidats (origine, origine_id ) ;
CREATE INDEX ON traitements.sites_candidats (statut);
CREATE INDEX sites_candidats_geométrie ON traitements.sites_candidats USING GIST ( geom );

CREATE TABLE  traitements.zones_traitements (
    pkid serial PRIMARY KEY,
    définition varchar(255) NOT NULL,
    geom geometry (MultiPolygon, 2154)
);
CREATE UNIQUE INDEX ON traitements.zones_traitements (définition) ;
CREATE INDEX zones_traitements_geométrie ON traitements.zones_traitements USING GIST ( geom );

Please note that I specified the geometry type of the geom column in table traitements only because I wanted to specify the SRID but I was not sure what is the correct syntax for any type of Geometry. Maybe "geom geometry (Geometry, 2154)" ?

Merging all the geometries of the various sets of areas :
As said before, all the tables hold geometries of the type multipolygon. This is the code I am using to merge all the geometries from one of the tables :

INSERT INTO traitements.zones_traitements
    ( définition,  , geom )
    VALUES
    (
        'first-level merge', 
        (
            SELECT ST_Multi(ST_Collect(dumpedGeometries)) AS singleMultiGeometry 
            FROM
            (
                SELECT ST_Force2D((ST_Dump(geom)).geom) AS dumpedGeometries
                FROM donnees.one_table
            ) AS dumpingGeometries
        )
    ) ;

I found that some of the geometries in some of the records are in 3D, so that's why I am using _ST_Force2D_.

I do this for all the tables and then merge the geometries again using :

INSERT INTO traitements.zones_traitements
    ( définition, geom )
    VALUES
    (
        'second-level merge',
        (
            SELECT ST_Multi(ST_Collect(dumpedGeometries)) AS singleMultiGeometry 
            FROM
            (
                SELECT (ST_Dump(geom)).geom AS dumpedGeometries 
                FROM traitements.zones_traitements
                WHERE définition != 'second-level merge'
            ) AS dumpingGeometries
        )
    ) ;

As said before, these queries take several minutes but that's fine.

Not the query that takes forever :

SELECT pkid
    FROM traitements.sites_candidats AS sites
    JOIN (
        SELECT geom FROM traitements.zones_traitements 
        WHERE définition = 'zones_rédhibitoires' ) AS zones
    ON ST_Contains(zones.geom , sites.geom)
    LIMIT 50;

Analysing the problem :
Obviously, it is the subquery selecting the points that takes a lot of time, not the update. So I have run an EXPLAIN (ANALYZE, BUFFERS) on the query :

EXPLAIN (ANALYZE, BUFFERS)
    SELECT pkid
    FROM traitements.sites_candidats AS sites
    JOIN (
        SELECT geom FROM traitements.zones_traitements 
        WHERE définition = 'second_level_merge' ) AS zones
    ON ST_Contains(zones.geom , sites.geom)
    LIMIT 10;

---------------------------------

"Limit  (cost=4.18..20.23 rows=1 width=22) (actual time=6052.069..4393634.244 rows=10 loops=1)"
"  Buffers: shared hit=1 read=688784"
"  ->  Nested Loop  (cost=4.18..20.23 rows=1 width=22) (actual time=6052.068..4391938.803 rows=10 loops=1)"
"        Buffers: shared hit=1 read=688784"
"        ->  Seq Scan on zones_traitements  (cost=0.00..1.23 rows=1 width=54939392) (actual time=0.016..0.016 rows=1 loops=1)"
"              Filter: (("définition")::text = 'zones_rédhibitoires'::text)"
"              Rows Removed by Filter: 17"
"              Buffers: shared hit=1"
"        ->  Bitmap Heap Scan on sites_candidats sites  (cost=4.18..19.00 rows=1 width=54) (actual time=6052.044..4391260.053 rows=10 loops=1)"
"              Recheck Cond: (zones_traitements.geom ~ geom)"
"              Filter: _st_contains(zones_traitements.geom, geom)"
"              Heap Blocks: exact=1"
"              Buffers: shared read=688784"
"              ->  Bitmap Index Scan on "sites_candidats_geométrie"  (cost=0.00..4.18 rows=4 width=0) (actual time=23.284..23.284 rows=3720 loops=1)"
"                    Index Cond: (zones_traitements.geom ~ geom)"
"                    Buffers: shared read=51"
"Planning time: 91.967 ms"
"Execution time: 4399271.394 ms"

I am not sure how to read this output.

Nevertheless, I suspect that the query is so slow because of the geometry obtained by merging all these multipolygons into a single one.

Questions :
Would that work better using a different type of geometry to merge the others, like a GeometryCollection ? How does the indexes work in this case ?

Is there more efficient than ST_Contains() ?

Answer 1

Let´s see. First off, you should ask GIS specific questions over at GIS Stackexchange. But I´ll try to help here:

• Technically, your geometry column definition is correct, and using 'primitives' (e.g. POINT, LINE, POLYGON and their MULTIs) is favorable over GEOMETRYCOLLECTIONs.
  However, it is almost always the better choice to run spatial relation functions on as small a geometry as possible; for most of those functions, PostGIS has to check each and every vertice of the input geometries against each other (so in this case, it has to traverse the polygon's millions of vertices once for each point to be checked in ST_Contains).
  PostGIS will in fact fire up a bbox comparison prior to the relation checks (if an index is present on both geometries) to limit the possible matches and effectively speeding up the check by several magnitudes; this is rendered useless here.
  (I would almost recommend to actually dump the MULTIs into simple POLYGONS, but not without knowing your data).
• Why are you dumping the MULTI geometries just to collect them back into MULTIs? If your source table's geometries are actually stored as MULTIPOLYGONS (and hopefully for good reason), simply copy them into the intermediate table, with ST_Force2D used on the MULTIs and ST_IsValid in the WHERE block (you can try ST_MakeValidon the geometries, but there's no guarantee it will work).
  If you have inserted all tables into the zones_traitements table, run VACUUM ANALYZE and REINDEX to actually make use of the index!
• In your 'second merge' query...are you simply adding the 'merged' geometries to the existing ones in the table? Don´t, that´s just wrong. It messes up table statistics and the index and is quite the unnecessary overhead. You should do these things within your query, but it´s not necessary here.
• Keep in mind that geometries of different types or extends created or derived by or within queries can neither have an index nor use the initial one. This applies to your 'merging' queries!

Then run

SELECT pkid
FROM traitements.sites_candidats AS sites
JOIN traitements.zones_traitements AS zones
  ON ST_Intersects(zones.geom, sites.geom)

to return one pkid for every intersection with a zone so that if one point intersects two MULTIOLYGONs, you´ll get two rows for that point. Use SELECT DISTINCT pkid ... to only get one row per pkid that is intersecting any zone.
(Note: I used ST_Intersection because that should imply on less check on the relation. If you absolutely need ST_Contains, just replace it)

Hope this helps. If not, say a word.

Answer 2

Again, thanks.

I had come to the same conclusion as your advice : that, instead of merging all the thousands of multipolygons into a single huge one, whose bbox is too huge, it would be more efficient to decompose all the multipolygons into simple polygons using ST_Dump and insert these into a dedicated table with an appropriate index. Nevertheless, to do this, I first had to correct geometries : certain multipolygons had indeed unvalid geometries. St_MakeValid would make valid 90% of them as multipolygons but the rest was transformed into either GeometryCollections or MultilineStrings. To correct these, I used ST_Buffer, with a buffer of 0.01 meter, the result of which being a correct multipolygon. Once this was done, all my multipolygons were valid and I could dump them into simple polygons.

Doing this, I reduced the search time by a factor of +/- 5000 !

:D